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- data/airplane/gt_filtered.ply +0 -0
- data/airplane/inference.ipynb +151 -0
- data/airplane/noisy_filtered.ply +110 -0
- data/airplane/random_rotate.ipynb +218 -0
- data/airplane/source.ply +0 -0
- data/airplane/target.ply +0 -0
- data/bottle/filter_tea.ipynb +245 -0
- data/bottle/inference.ipynb +209 -0
- data/bottle/tea_gt_filtered.ply +0 -0
- data/bottle/tea_noisy_filtered.ply +0 -0
- data/bottle_2/RMSE.ipynb +197 -0
- data/bottle_2/all_infer.ipynb +0 -0
- data/bottle_2/all_infer.py +109 -0
- data/bottle_2/bottle.csv +25 -0
- data/bottle_2/bottle2.csv +5 -0
- data/bottle_2/bottle2_data_num.csv +6 -0
- data/bottle_2/cut_files.json +7 -0
- data/bottle_2/dataset_pandas.ipynb +606 -0
- data/bottle_2/filename.txt +1 -0
- data/bottle_2/filter_tea .ipynb +459 -0
- data/bottle_2/filter_tea.py +400 -0
- data/bottle_2/generategt.ipynb +156 -0
- data/bottle_2/gt_Raw.ipynb +819 -0
- data/bottle_2/gt_filtered.ply +0 -0
- data/bottle_2/h +0 -0
- data/bottle_2/inference_ICP.ipynb +503 -0
- data/bottle_2/inference_ICP.py +298 -0
- data/bottle_2/initial_guess(kiss_match).ipynb +240 -0
- data/bottle_2/initial_guess(kiss_match).py +103 -0
- data/bottle_2/merged.py +496 -0
- data/bottle_2/output_trans.txt +0 -0
- data/bottle_2/ply_files.json +115 -0
- data/bottle_2/run_all.py +45 -0
- data/car/downsample_car.ipynb +351 -0
- data/car/inference.ipynb +214 -0
- data/glasses/all_infer.ipynb +0 -0
- data/glasses/bottle.csv +25 -0
- data/glasses/dataset_pandas.ipynb +668 -0
- data/glasses/eyeglasses.csv +25 -0
- data/glasses/eyeglasses_data_num.csv +6 -0
- data/glasses/filename.txt +1 -0
- data/glasses/filter_tea .ipynb +474 -0
- data/glasses/gt_Raw.ipynb +834 -0
- data/glasses/gt_filtered.ply +0 -0
- data/glasses/inference_ICP.ipynb +482 -0
- data/glasses/initial_guess(kiss_match).ipynb +238 -0
- data/glasses/merged.py +494 -0
- data/glasses/output_trans.txt +0 -0
- data/glasses/ply_files.json +117 -0
- data/glasses/run_all.py +45 -0
data/airplane/gt_filtered.ply
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data/airplane/inference.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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| 9 |
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"name": "stdout",
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| 10 |
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"output_type": "stream",
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| 11 |
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"text": [
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| 12 |
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"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: noisy_filtered.ply\u001b[0;m\n",
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"Source shape: (50, 3)\n"
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]
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},
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{
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"name": "stderr",
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| 18 |
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"output_type": "stream",
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"text": [
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"RPly: Unexpected end of file\n",
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"RPly: Error reading 'view_px' of 'camera' number 0\n"
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]
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}
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],
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"source": [
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"import open3d as o3d\n",
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"import numpy as np\n",
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"\n",
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"source_path = \"noisy_filtered.ply\"\n",
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| 30 |
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"source_pcd = o3d.io.read_point_cloud(source_path)\n",
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"\n",
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"source_pcd_array = np.asarray(source_pcd.points)\n",
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| 33 |
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"print(\"Source shape:\", source_pcd_array.shape)\n",
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"\n",
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"# o3d.visualization.draw_geometries([source_pcd])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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| 41 |
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"metadata": {},
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| 42 |
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"outputs": [
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{
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"name": "stdout",
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| 45 |
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"output_type": "stream",
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| 46 |
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"text": [
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| 47 |
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"Transformed shape: (368, 3)\n"
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| 48 |
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]
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| 49 |
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}
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],
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| 51 |
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"source": [
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| 52 |
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"transformed_path = \"res/m3reg_pc.ply\"\n",
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| 53 |
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"transformed_pcd = o3d.io.read_point_cloud(transformed_path)\n",
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"\n",
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| 55 |
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"transformed_pcd_array = np.asarray(transformed_pcd.points)\n",
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| 56 |
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"print(\"Transformed shape:\", transformed_pcd_array.shape)\n",
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"\n",
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| 58 |
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"# o3d.visualization.draw_geometries([transformed_pcd])"
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| 59 |
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]
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| 60 |
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},
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| 61 |
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{
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| 62 |
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"cell_type": "code",
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| 63 |
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"execution_count": 9,
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| 64 |
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"metadata": {},
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| 65 |
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"outputs": [
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| 66 |
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{
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| 67 |
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"name": "stdout",
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| 68 |
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"output_type": "stream",
|
| 69 |
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"text": [
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| 70 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: gt_filtered.ply\u001b[0;m\n",
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| 71 |
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"Target shape: (2048, 3)\n"
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| 72 |
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]
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| 73 |
+
},
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| 74 |
+
{
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| 75 |
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"name": "stderr",
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| 76 |
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"output_type": "stream",
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| 77 |
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"text": [
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| 78 |
+
"RPly: Unexpected end of file\n",
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| 79 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 80 |
+
]
|
| 81 |
+
}
|
| 82 |
+
],
|
| 83 |
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"source": [
|
| 84 |
+
"target_path = \"gt_filtered.ply\"\n",
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| 85 |
+
"target_pcd = o3d.io.read_point_cloud(target_path)\n",
|
| 86 |
+
"\n",
|
| 87 |
+
"target_pcd_array = np.asarray(target_pcd.points)\n",
|
| 88 |
+
"print(\"Target shape:\", target_pcd_array.shape)\n",
|
| 89 |
+
"\n",
|
| 90 |
+
"# o3d.visualization.draw_geometries([target_pcd])"
|
| 91 |
+
]
|
| 92 |
+
},
|
| 93 |
+
{
|
| 94 |
+
"cell_type": "code",
|
| 95 |
+
"execution_count": 4,
|
| 96 |
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"metadata": {},
|
| 97 |
+
"outputs": [],
|
| 98 |
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"source": [
|
| 99 |
+
"source_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 100 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 101 |
+
"\n",
|
| 102 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 103 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 104 |
+
"vis.add_geometry(source_pcd)\n",
|
| 105 |
+
"vis.add_geometry(target_pcd)\n",
|
| 106 |
+
"\n",
|
| 107 |
+
"vis.run()\n",
|
| 108 |
+
"vis.destroy_window()"
|
| 109 |
+
]
|
| 110 |
+
},
|
| 111 |
+
{
|
| 112 |
+
"cell_type": "code",
|
| 113 |
+
"execution_count": 5,
|
| 114 |
+
"metadata": {},
|
| 115 |
+
"outputs": [],
|
| 116 |
+
"source": [
|
| 117 |
+
"transformed_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 118 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 119 |
+
"\n",
|
| 120 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 121 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 122 |
+
"vis.add_geometry(transformed_pcd)\n",
|
| 123 |
+
"vis.add_geometry(target_pcd)\n",
|
| 124 |
+
"\n",
|
| 125 |
+
"vis.run()\n",
|
| 126 |
+
"vis.destroy_window()"
|
| 127 |
+
]
|
| 128 |
+
}
|
| 129 |
+
],
|
| 130 |
+
"metadata": {
|
| 131 |
+
"kernelspec": {
|
| 132 |
+
"display_name": "Python 3",
|
| 133 |
+
"language": "python",
|
| 134 |
+
"name": "python3"
|
| 135 |
+
},
|
| 136 |
+
"language_info": {
|
| 137 |
+
"codemirror_mode": {
|
| 138 |
+
"name": "ipython",
|
| 139 |
+
"version": 3
|
| 140 |
+
},
|
| 141 |
+
"file_extension": ".py",
|
| 142 |
+
"mimetype": "text/x-python",
|
| 143 |
+
"name": "python",
|
| 144 |
+
"nbconvert_exporter": "python",
|
| 145 |
+
"pygments_lexer": "ipython3",
|
| 146 |
+
"version": "3.10.12"
|
| 147 |
+
}
|
| 148 |
+
},
|
| 149 |
+
"nbformat": 4,
|
| 150 |
+
"nbformat_minor": 2
|
| 151 |
+
}
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data/airplane/noisy_filtered.ply
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| 1 |
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ply
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| 2 |
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format ascii 1.0
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| 3 |
+
element vertex 50
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| 4 |
+
property float x
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| 5 |
+
property float y
|
| 6 |
+
property float z
|
| 7 |
+
element camera 1
|
| 8 |
+
property float view_px
|
| 9 |
+
property float view_py
|
| 10 |
+
property float view_pz
|
| 11 |
+
property float x_axisx
|
| 12 |
+
property float x_axisy
|
| 13 |
+
property float x_axisz
|
| 14 |
+
property float y_axisx
|
| 15 |
+
property float y_axisy
|
| 16 |
+
property float y_axisz
|
| 17 |
+
property float z_axisx
|
| 18 |
+
property float z_axisy
|
| 19 |
+
property float z_axisz
|
| 20 |
+
element phoxi_frame_params 1
|
| 21 |
+
property uint32 frame_width
|
| 22 |
+
property uint32 frame_height
|
| 23 |
+
property uint32 frame_index
|
| 24 |
+
property float frame_start_time
|
| 25 |
+
property float frame_duration
|
| 26 |
+
property float frame_computation_duration
|
| 27 |
+
property float frame_transfer_duration
|
| 28 |
+
property int32 total_scan_count
|
| 29 |
+
element camera_matrix 1
|
| 30 |
+
property float cm0
|
| 31 |
+
property float cm1
|
| 32 |
+
property float cm2
|
| 33 |
+
property float cm3
|
| 34 |
+
property float cm4
|
| 35 |
+
property float cm5
|
| 36 |
+
property float cm6
|
| 37 |
+
property float cm7
|
| 38 |
+
property float cm8
|
| 39 |
+
element distortion_matrix 1
|
| 40 |
+
property float dm0
|
| 41 |
+
property float dm1
|
| 42 |
+
property float dm2
|
| 43 |
+
property float dm3
|
| 44 |
+
property float dm4
|
| 45 |
+
property float dm5
|
| 46 |
+
property float dm6
|
| 47 |
+
property float dm7
|
| 48 |
+
property float dm8
|
| 49 |
+
property float dm9
|
| 50 |
+
property float dm10
|
| 51 |
+
property float dm11
|
| 52 |
+
property float dm12
|
| 53 |
+
property float dm13
|
| 54 |
+
element camera_resolution 1
|
| 55 |
+
property float width
|
| 56 |
+
property float height
|
| 57 |
+
element frame_binning 1
|
| 58 |
+
property float horizontal
|
| 59 |
+
property float vertical
|
| 60 |
+
end_header
|
| 61 |
+
0.4184504662339592 1.561481561830392 2.4357119363584943
|
| 62 |
+
0.28113543538848257 1.4354800945399746 2.711070098163805
|
| 63 |
+
0.3537999879780671 1.464643107916649 2.676379194427658
|
| 64 |
+
0.28582722515769604 1.4378941127081055 2.7072586234837295
|
| 65 |
+
0.4029046553639043 1.4964752199864368 2.6170537256699675
|
| 66 |
+
0.2752735024204427 1.438310353298161 2.698530079640078
|
| 67 |
+
0.2747188603012154 1.438578145632901 2.697343572989956
|
| 68 |
+
0.4151371462435055 1.5425405591608732 2.4894137593342722
|
| 69 |
+
0.41421876936904484 1.5309202623204619 2.523151362679278
|
| 70 |
+
0.2731159058596594 1.4393520841736387 2.6939144859465873
|
| 71 |
+
0.37537708373261214 1.4867306062494385 2.6263388215053216
|
| 72 |
+
0.2753290293564973 1.4429628521108748 2.684800575851574
|
| 73 |
+
0.39420230218714425 1.5066147113295425 2.5808645743975993
|
| 74 |
+
0.3942459435383868 1.546694463675788 2.462280612624996
|
| 75 |
+
0.31040864685037306 1.4567370407562743 2.6689545514319883
|
| 76 |
+
0.26896439608522765 1.4460358246536973 2.67118516613031
|
| 77 |
+
0.36659617122401367 1.4939638825354855 2.5986947329169134
|
| 78 |
+
0.3849645691585717 1.5157448547371306 2.5472822618453184
|
| 79 |
+
0.26066886452867344 1.445361765269038 2.6672874109344438
|
| 80 |
+
0.269561571397956 1.4484173957660484 2.66456116494064
|
| 81 |
+
0.38224273664093117 1.5290882547524671 2.5058594080915437
|
| 82 |
+
0.3123341721169729 1.467911257929097 2.6372525229282058
|
| 83 |
+
0.2549985420164609 1.4480995106577055 2.6551572911042904
|
| 84 |
+
0.3626152683280879 1.537589873320385 2.4667569934340174
|
| 85 |
+
0.3465697206123827 1.4986680201436198 2.570547450673959
|
| 86 |
+
0.3566323774538513 1.5150890514176694 2.5290977140853714
|
| 87 |
+
0.31943822035078 1.4799521854685544 2.6066639802481992
|
| 88 |
+
0.24596953114144968 1.4524588981235949 2.6358421668541694
|
| 89 |
+
0.2692521008942427 1.464618498230568 2.6163946047113784
|
| 90 |
+
0.2385636685930142 1.456034597036208 2.619999328997119
|
| 91 |
+
0.3178338383804292 1.5242664150056848 2.474377545218438
|
| 92 |
+
0.3199843640103215 1.5056645086649982 2.5309569887373202
|
| 93 |
+
0.23408334184521973 1.4628770969431952 2.5965665942903327
|
| 94 |
+
0.23902055948897444 1.4659853097294189 2.5908750053826806
|
| 95 |
+
0.22339675105702242 1.4633574888920116 2.5875538152244206
|
| 96 |
+
0.2858667818310713 1.4886963550099082 2.5569388361827206
|
| 97 |
+
0.22195761043701737 1.4640523348355319 2.5844751635012595
|
| 98 |
+
0.22114508844165356 1.4644446367444988 2.582736992674614
|
| 99 |
+
0.21948928105974813 1.4652440937444435 2.579194841074788
|
| 100 |
+
0.2574230662825241 1.4772575759703088 2.570586999050489
|
| 101 |
+
0.29067513317078764 1.5010358689578727 2.5238359184470105
|
| 102 |
+
0.2811918724253972 1.5100806731360605 2.490331757860455
|
| 103 |
+
0.21626874640156896 1.4714783407739982 2.558457090467266
|
| 104 |
+
0.24788976711561433 1.4851312354105584 2.5405132660882166
|
| 105 |
+
0.20993514777223835 1.474536329169504 2.5449080709601026
|
| 106 |
+
0.2529179196127833 1.5005729482210217 2.4983856059813405
|
| 107 |
+
0.19948771978485783 1.474901248523302 2.536406915955533
|
| 108 |
+
0.19541053181840906 1.4768697966137914 2.5276848761603428
|
| 109 |
+
0.19920143521321854 1.4797187807846366 2.521946198988463
|
| 110 |
+
0.23596469507905588 1.4947081822466521 2.5036997365937674
|
data/airplane/random_rotate.ipynb
ADDED
|
@@ -0,0 +1,218 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 13 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 14 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 15 |
+
"(50, 3)\n"
|
| 16 |
+
]
|
| 17 |
+
}
|
| 18 |
+
],
|
| 19 |
+
"source": [
|
| 20 |
+
"import open3d as o3d\n",
|
| 21 |
+
"import numpy as np\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"GT = False\n",
|
| 24 |
+
"\n",
|
| 25 |
+
"if GT: ply_path = \"source.ply\"\n",
|
| 26 |
+
"else: ply_path = \"target.ply\"\n",
|
| 27 |
+
"pcd = o3d.io.read_point_cloud(ply_path)\n",
|
| 28 |
+
"\n",
|
| 29 |
+
"pcd_array = np.asarray(pcd.points)\n",
|
| 30 |
+
"print(pcd_array.shape)\n",
|
| 31 |
+
"\n",
|
| 32 |
+
"o3d.visualization.draw_geometries([pcd])"
|
| 33 |
+
]
|
| 34 |
+
},
|
| 35 |
+
{
|
| 36 |
+
"cell_type": "code",
|
| 37 |
+
"execution_count": 2,
|
| 38 |
+
"metadata": {},
|
| 39 |
+
"outputs": [],
|
| 40 |
+
"source": [
|
| 41 |
+
"CHECK_PERTURB = not GT\n",
|
| 42 |
+
"\n",
|
| 43 |
+
"def random_rotation_matrix():\n",
|
| 44 |
+
" \"\"\"\n",
|
| 45 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 46 |
+
" \n",
|
| 47 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 48 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 49 |
+
" 2. Generate a random angle\n",
|
| 50 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 51 |
+
" \n",
|
| 52 |
+
" Returns:\n",
|
| 53 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 54 |
+
" \"\"\"\n",
|
| 55 |
+
" # Generate random angle between 0 and 2ฯ\n",
|
| 56 |
+
" theta = np.random.uniform(0.5 * np.pi, np.pi)/5\n",
|
| 57 |
+
" \n",
|
| 58 |
+
" # Generate random unit vector for rotation axis\n",
|
| 59 |
+
" phi = np.random.uniform(0, 2 * np.pi)/5\n",
|
| 60 |
+
" cos_theta = np.random.uniform(-1, 1)\n",
|
| 61 |
+
" sin_theta = np.sqrt(1 - cos_theta**2)\n",
|
| 62 |
+
" \n",
|
| 63 |
+
" axis = np.array([\n",
|
| 64 |
+
" sin_theta * np.cos(phi),\n",
|
| 65 |
+
" sin_theta * np.sin(phi),\n",
|
| 66 |
+
" cos_theta\n",
|
| 67 |
+
" ])\n",
|
| 68 |
+
" \n",
|
| 69 |
+
" # Normalize to ensure it's a unit vector\n",
|
| 70 |
+
" axis = axis / np.linalg.norm(axis)\n",
|
| 71 |
+
" \n",
|
| 72 |
+
" # Create the cross-product matrix K\n",
|
| 73 |
+
" K = np.array([\n",
|
| 74 |
+
" [0, -axis[2], axis[1]],\n",
|
| 75 |
+
" [axis[2], 0, -axis[0]],\n",
|
| 76 |
+
" [-axis[1], axis[0], 0]\n",
|
| 77 |
+
" ])\n",
|
| 78 |
+
" \n",
|
| 79 |
+
" # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 80 |
+
" R = (np.eye(3) + \n",
|
| 81 |
+
" np.sin(theta) * K + \n",
|
| 82 |
+
" (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 83 |
+
" \n",
|
| 84 |
+
" return R\n",
|
| 85 |
+
"\n",
|
| 86 |
+
"if CHECK_PERTURB:\n",
|
| 87 |
+
" R_pert = random_rotation_matrix()\n",
|
| 88 |
+
" t_pert = np.random.rand(3, 1)*3 #* 10\n",
|
| 89 |
+
" perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T\n",
|
| 90 |
+
"\n",
|
| 91 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 92 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 93 |
+
"\n",
|
| 94 |
+
" o3d.visualization.draw_geometries([perturbed_pcd])"
|
| 95 |
+
]
|
| 96 |
+
},
|
| 97 |
+
{
|
| 98 |
+
"cell_type": "code",
|
| 99 |
+
"execution_count": 3,
|
| 100 |
+
"metadata": {},
|
| 101 |
+
"outputs": [
|
| 102 |
+
{
|
| 103 |
+
"name": "stdout",
|
| 104 |
+
"output_type": "stream",
|
| 105 |
+
"text": [
|
| 106 |
+
"True\n"
|
| 107 |
+
]
|
| 108 |
+
}
|
| 109 |
+
],
|
| 110 |
+
"source": [
|
| 111 |
+
"def write_ply(points, output_path):\n",
|
| 112 |
+
" \"\"\"\n",
|
| 113 |
+
" Write points and parameters to a PLY file\n",
|
| 114 |
+
" \n",
|
| 115 |
+
" Parameters:\n",
|
| 116 |
+
" points: numpy array of shape (N, 3) containing point coordinates\n",
|
| 117 |
+
" output_path: path to save the PLY file\n",
|
| 118 |
+
" \"\"\"\n",
|
| 119 |
+
" with open(output_path, 'w') as f:\n",
|
| 120 |
+
" # Write header\n",
|
| 121 |
+
" f.write(\"ply\\n\")\n",
|
| 122 |
+
" f.write(\"format ascii 1.0\\n\")\n",
|
| 123 |
+
" \n",
|
| 124 |
+
" # Write vertex element\n",
|
| 125 |
+
" f.write(f\"element vertex {len(points)}\\n\")\n",
|
| 126 |
+
" f.write(\"property float x\\n\")\n",
|
| 127 |
+
" f.write(\"property float y\\n\")\n",
|
| 128 |
+
" f.write(\"property float z\\n\")\n",
|
| 129 |
+
" \n",
|
| 130 |
+
" # Write camera element\n",
|
| 131 |
+
" f.write(\"element camera 1\\n\")\n",
|
| 132 |
+
" f.write(\"property float view_px\\n\")\n",
|
| 133 |
+
" f.write(\"property float view_py\\n\")\n",
|
| 134 |
+
" f.write(\"property float view_pz\\n\")\n",
|
| 135 |
+
" f.write(\"property float x_axisx\\n\")\n",
|
| 136 |
+
" f.write(\"property float x_axisy\\n\")\n",
|
| 137 |
+
" f.write(\"property float x_axisz\\n\")\n",
|
| 138 |
+
" f.write(\"property float y_axisx\\n\")\n",
|
| 139 |
+
" f.write(\"property float y_axisy\\n\")\n",
|
| 140 |
+
" f.write(\"property float y_axisz\\n\")\n",
|
| 141 |
+
" f.write(\"property float z_axisx\\n\")\n",
|
| 142 |
+
" f.write(\"property float z_axisy\\n\")\n",
|
| 143 |
+
" f.write(\"property float z_axisz\\n\")\n",
|
| 144 |
+
" \n",
|
| 145 |
+
" # Write phoxi frame parameters\n",
|
| 146 |
+
" f.write(\"element phoxi_frame_params 1\\n\")\n",
|
| 147 |
+
" f.write(\"property uint32 frame_width\\n\")\n",
|
| 148 |
+
" f.write(\"property uint32 frame_height\\n\")\n",
|
| 149 |
+
" f.write(\"property uint32 frame_index\\n\")\n",
|
| 150 |
+
" f.write(\"property float frame_start_time\\n\")\n",
|
| 151 |
+
" f.write(\"property float frame_duration\\n\")\n",
|
| 152 |
+
" f.write(\"property float frame_computation_duration\\n\")\n",
|
| 153 |
+
" f.write(\"property float frame_transfer_duration\\n\")\n",
|
| 154 |
+
" f.write(\"property int32 total_scan_count\\n\")\n",
|
| 155 |
+
" \n",
|
| 156 |
+
" # Write camera matrix\n",
|
| 157 |
+
" f.write(\"element camera_matrix 1\\n\")\n",
|
| 158 |
+
" for i in range(9):\n",
|
| 159 |
+
" f.write(f\"property float cm{i}\\n\")\n",
|
| 160 |
+
" \n",
|
| 161 |
+
" # Write distortion matrix\n",
|
| 162 |
+
" f.write(\"element distortion_matrix 1\\n\")\n",
|
| 163 |
+
" for i in range(14):\n",
|
| 164 |
+
" f.write(f\"property float dm{i}\\n\")\n",
|
| 165 |
+
" \n",
|
| 166 |
+
" # Write camera resolution\n",
|
| 167 |
+
" f.write(\"element camera_resolution 1\\n\")\n",
|
| 168 |
+
" f.write(\"property float width\\n\")\n",
|
| 169 |
+
" f.write(\"property float height\\n\")\n",
|
| 170 |
+
" \n",
|
| 171 |
+
" # Write frame binning\n",
|
| 172 |
+
" f.write(\"element frame_binning 1\\n\")\n",
|
| 173 |
+
" f.write(\"property float horizontal\\n\")\n",
|
| 174 |
+
" f.write(\"property float vertical\\n\")\n",
|
| 175 |
+
" \n",
|
| 176 |
+
" # End header\n",
|
| 177 |
+
" f.write(\"end_header\\n\")\n",
|
| 178 |
+
" \n",
|
| 179 |
+
" # Write vertex data\n",
|
| 180 |
+
" for point in points:\n",
|
| 181 |
+
" f.write(f\"{point[0]} {point[1]} {point[2]}\\n\")\n",
|
| 182 |
+
"\n",
|
| 183 |
+
" print(True)\n",
|
| 184 |
+
"\n",
|
| 185 |
+
"if GT: write_ply(pcd_array, \"gt_filtered.ply\")\n",
|
| 186 |
+
"else: write_ply(perturbed_pcd_array, \"noisy_filtered.ply\")"
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
{
|
| 190 |
+
"cell_type": "code",
|
| 191 |
+
"execution_count": null,
|
| 192 |
+
"metadata": {},
|
| 193 |
+
"outputs": [],
|
| 194 |
+
"source": []
|
| 195 |
+
}
|
| 196 |
+
],
|
| 197 |
+
"metadata": {
|
| 198 |
+
"kernelspec": {
|
| 199 |
+
"display_name": "vision",
|
| 200 |
+
"language": "python",
|
| 201 |
+
"name": "python3"
|
| 202 |
+
},
|
| 203 |
+
"language_info": {
|
| 204 |
+
"codemirror_mode": {
|
| 205 |
+
"name": "ipython",
|
| 206 |
+
"version": 3
|
| 207 |
+
},
|
| 208 |
+
"file_extension": ".py",
|
| 209 |
+
"mimetype": "text/x-python",
|
| 210 |
+
"name": "python",
|
| 211 |
+
"nbconvert_exporter": "python",
|
| 212 |
+
"pygments_lexer": "ipython3",
|
| 213 |
+
"version": "3.9.20"
|
| 214 |
+
}
|
| 215 |
+
},
|
| 216 |
+
"nbformat": 4,
|
| 217 |
+
"nbformat_minor": 2
|
| 218 |
+
}
|
data/airplane/source.ply
ADDED
|
Binary file (49.3 kB). View file
|
|
|
data/airplane/target.ply
ADDED
|
Binary file (1.35 kB). View file
|
|
|
data/bottle/filter_tea.ipynb
ADDED
|
@@ -0,0 +1,245 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 8,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"(896000, 3)\n"
|
| 13 |
+
]
|
| 14 |
+
}
|
| 15 |
+
],
|
| 16 |
+
"source": [
|
| 17 |
+
"import open3d as o3d\n",
|
| 18 |
+
"import numpy as np\n",
|
| 19 |
+
"\n",
|
| 20 |
+
"GT = False\n",
|
| 21 |
+
"\n",
|
| 22 |
+
"if GT: ply_path = \"tea_gt.ply\"\n",
|
| 23 |
+
"else: ply_path = \"dataset/100_1.ply\"\n",
|
| 24 |
+
"pcd = o3d.io.read_point_cloud(ply_path)\n",
|
| 25 |
+
"\n",
|
| 26 |
+
"pcd_array = np.asarray(pcd.points)\n",
|
| 27 |
+
"print(pcd_array.shape)\n",
|
| 28 |
+
"\n",
|
| 29 |
+
"o3d.visualization.draw_geometries([pcd])"
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
{
|
| 33 |
+
"cell_type": "code",
|
| 34 |
+
"execution_count": null,
|
| 35 |
+
"metadata": {},
|
| 36 |
+
"outputs": [
|
| 37 |
+
{
|
| 38 |
+
"name": "stdout",
|
| 39 |
+
"output_type": "stream",
|
| 40 |
+
"text": [
|
| 41 |
+
"(5936, 3)\n"
|
| 42 |
+
]
|
| 43 |
+
}
|
| 44 |
+
],
|
| 45 |
+
"source": [
|
| 46 |
+
"new_pcd_array = np.unique(pcd_array, axis=0)\n",
|
| 47 |
+
"new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 500]\n",
|
| 48 |
+
"new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 200]\n",
|
| 49 |
+
"new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100]\n",
|
| 50 |
+
"\n",
|
| 51 |
+
"new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 300]\n",
|
| 52 |
+
"\n",
|
| 53 |
+
"new_pcd_array -= np.mean(new_pcd_array, axis=0)\n",
|
| 54 |
+
"print(new_pcd_array.shape)\n",
|
| 55 |
+
"\n",
|
| 56 |
+
"new_pcd = o3d.geometry.PointCloud()\n",
|
| 57 |
+
"new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)\n",
|
| 58 |
+
"\n",
|
| 59 |
+
"o3d.visualization.draw_geometries([new_pcd])"
|
| 60 |
+
]
|
| 61 |
+
},
|
| 62 |
+
{
|
| 63 |
+
"cell_type": "code",
|
| 64 |
+
"execution_count": 3,
|
| 65 |
+
"metadata": {},
|
| 66 |
+
"outputs": [],
|
| 67 |
+
"source": [
|
| 68 |
+
"CHECK_PERTURB = not GT\n",
|
| 69 |
+
"\n",
|
| 70 |
+
"def random_rotation_matrix():\n",
|
| 71 |
+
" \"\"\"\n",
|
| 72 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 73 |
+
" \n",
|
| 74 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 75 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 76 |
+
" 2. Generate a random angle\n",
|
| 77 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 78 |
+
" \n",
|
| 79 |
+
" Returns:\n",
|
| 80 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 81 |
+
" \"\"\"\n",
|
| 82 |
+
" # Generate random angle between 0 and 2ฯ\n",
|
| 83 |
+
" theta = np.random.uniform(0.5 * np.pi, np.pi)/5\n",
|
| 84 |
+
" \n",
|
| 85 |
+
" # Generate random unit vector for rotation axis\n",
|
| 86 |
+
" phi = np.random.uniform(0, 2 * np.pi)/5\n",
|
| 87 |
+
" cos_theta = np.random.uniform(-1, 1)\n",
|
| 88 |
+
" sin_theta = np.sqrt(1 - cos_theta**2)\n",
|
| 89 |
+
" \n",
|
| 90 |
+
" axis = np.array([\n",
|
| 91 |
+
" sin_theta * np.cos(phi),\n",
|
| 92 |
+
" sin_theta * np.sin(phi),\n",
|
| 93 |
+
" cos_theta\n",
|
| 94 |
+
" ])\n",
|
| 95 |
+
" \n",
|
| 96 |
+
" # Normalize to ensure it's a unit vector\n",
|
| 97 |
+
" axis = axis / np.linalg.norm(axis)\n",
|
| 98 |
+
" \n",
|
| 99 |
+
" # Create the cross-product matrix K\n",
|
| 100 |
+
" K = np.array([\n",
|
| 101 |
+
" [0, -axis[2], axis[1]],\n",
|
| 102 |
+
" [axis[2], 0, -axis[0]],\n",
|
| 103 |
+
" [-axis[1], axis[0], 0]\n",
|
| 104 |
+
" ])\n",
|
| 105 |
+
" \n",
|
| 106 |
+
" # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 107 |
+
" R = (np.eye(3) + \n",
|
| 108 |
+
" np.sin(theta) * K + \n",
|
| 109 |
+
" (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 110 |
+
" \n",
|
| 111 |
+
" return R\n",
|
| 112 |
+
"\n",
|
| 113 |
+
"if CHECK_PERTURB:\n",
|
| 114 |
+
" R_pert = random_rotation_matrix()\n",
|
| 115 |
+
" t_pert = np.random.rand(3, 1)*3 #* 10\n",
|
| 116 |
+
" perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T\n",
|
| 117 |
+
"\n",
|
| 118 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 119 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 120 |
+
"\n",
|
| 121 |
+
" o3d.visualization.draw_geometries([perturbed_pcd])"
|
| 122 |
+
]
|
| 123 |
+
},
|
| 124 |
+
{
|
| 125 |
+
"cell_type": "code",
|
| 126 |
+
"execution_count": 4,
|
| 127 |
+
"metadata": {},
|
| 128 |
+
"outputs": [
|
| 129 |
+
{
|
| 130 |
+
"name": "stdout",
|
| 131 |
+
"output_type": "stream",
|
| 132 |
+
"text": [
|
| 133 |
+
"True\n"
|
| 134 |
+
]
|
| 135 |
+
}
|
| 136 |
+
],
|
| 137 |
+
"source": [
|
| 138 |
+
"def write_ply(points, output_path):\n",
|
| 139 |
+
" \"\"\"\n",
|
| 140 |
+
" Write points and parameters to a PLY file\n",
|
| 141 |
+
" \n",
|
| 142 |
+
" Parameters:\n",
|
| 143 |
+
" points: numpy array of shape (N, 3) containing point coordinates\n",
|
| 144 |
+
" output_path: path to save the PLY file\n",
|
| 145 |
+
" \"\"\"\n",
|
| 146 |
+
" with open(output_path, 'w') as f:\n",
|
| 147 |
+
" # Write header\n",
|
| 148 |
+
" f.write(\"ply\\n\")\n",
|
| 149 |
+
" f.write(\"format ascii 1.0\\n\")\n",
|
| 150 |
+
" \n",
|
| 151 |
+
" # Write vertex element\n",
|
| 152 |
+
" f.write(f\"element vertex {len(points)}\\n\")\n",
|
| 153 |
+
" f.write(\"property float x\\n\")\n",
|
| 154 |
+
" f.write(\"property float y\\n\")\n",
|
| 155 |
+
" f.write(\"property float z\\n\")\n",
|
| 156 |
+
" \n",
|
| 157 |
+
" # Write camera element\n",
|
| 158 |
+
" f.write(\"element camera 1\\n\")\n",
|
| 159 |
+
" f.write(\"property float view_px\\n\")\n",
|
| 160 |
+
" f.write(\"property float view_py\\n\")\n",
|
| 161 |
+
" f.write(\"property float view_pz\\n\")\n",
|
| 162 |
+
" f.write(\"property float x_axisx\\n\")\n",
|
| 163 |
+
" f.write(\"property float x_axisy\\n\")\n",
|
| 164 |
+
" f.write(\"property float x_axisz\\n\")\n",
|
| 165 |
+
" f.write(\"property float y_axisx\\n\")\n",
|
| 166 |
+
" f.write(\"property float y_axisy\\n\")\n",
|
| 167 |
+
" f.write(\"property float y_axisz\\n\")\n",
|
| 168 |
+
" f.write(\"property float z_axisx\\n\")\n",
|
| 169 |
+
" f.write(\"property float z_axisy\\n\")\n",
|
| 170 |
+
" f.write(\"property float z_axisz\\n\")\n",
|
| 171 |
+
" \n",
|
| 172 |
+
" # Write phoxi frame parameters\n",
|
| 173 |
+
" f.write(\"element phoxi_frame_params 1\\n\")\n",
|
| 174 |
+
" f.write(\"property uint32 frame_width\\n\")\n",
|
| 175 |
+
" f.write(\"property uint32 frame_height\\n\")\n",
|
| 176 |
+
" f.write(\"property uint32 frame_index\\n\")\n",
|
| 177 |
+
" f.write(\"property float frame_start_time\\n\")\n",
|
| 178 |
+
" f.write(\"property float frame_duration\\n\")\n",
|
| 179 |
+
" f.write(\"property float frame_computation_duration\\n\")\n",
|
| 180 |
+
" f.write(\"property float frame_transfer_duration\\n\")\n",
|
| 181 |
+
" f.write(\"property int32 total_scan_count\\n\")\n",
|
| 182 |
+
" \n",
|
| 183 |
+
" # Write camera matrix\n",
|
| 184 |
+
" f.write(\"element camera_matrix 1\\n\")\n",
|
| 185 |
+
" for i in range(9):\n",
|
| 186 |
+
" f.write(f\"property float cm{i}\\n\")\n",
|
| 187 |
+
" \n",
|
| 188 |
+
" # Write distortion matrix\n",
|
| 189 |
+
" f.write(\"element distortion_matrix 1\\n\")\n",
|
| 190 |
+
" for i in range(14):\n",
|
| 191 |
+
" f.write(f\"property float dm{i}\\n\")\n",
|
| 192 |
+
" \n",
|
| 193 |
+
" # Write camera resolution\n",
|
| 194 |
+
" f.write(\"element camera_resolution 1\\n\")\n",
|
| 195 |
+
" f.write(\"property float width\\n\")\n",
|
| 196 |
+
" f.write(\"property float height\\n\")\n",
|
| 197 |
+
" \n",
|
| 198 |
+
" # Write frame binning\n",
|
| 199 |
+
" f.write(\"element frame_binning 1\\n\")\n",
|
| 200 |
+
" f.write(\"property float horizontal\\n\")\n",
|
| 201 |
+
" f.write(\"property float vertical\\n\")\n",
|
| 202 |
+
" \n",
|
| 203 |
+
" # End header\n",
|
| 204 |
+
" f.write(\"end_header\\n\")\n",
|
| 205 |
+
" \n",
|
| 206 |
+
" # Write vertex data\n",
|
| 207 |
+
" for point in points:\n",
|
| 208 |
+
" f.write(f\"{point[0]} {point[1]} {point[2]}\\n\")\n",
|
| 209 |
+
"\n",
|
| 210 |
+
" print(True)\n",
|
| 211 |
+
"\n",
|
| 212 |
+
"if GT: write_ply(new_pcd_array, \"tea_gt_filtered.ply\")\n",
|
| 213 |
+
"else: write_ply(perturbed_pcd_array, \"tea_noisy_filtered.ply\")"
|
| 214 |
+
]
|
| 215 |
+
},
|
| 216 |
+
{
|
| 217 |
+
"cell_type": "code",
|
| 218 |
+
"execution_count": null,
|
| 219 |
+
"metadata": {},
|
| 220 |
+
"outputs": [],
|
| 221 |
+
"source": []
|
| 222 |
+
}
|
| 223 |
+
],
|
| 224 |
+
"metadata": {
|
| 225 |
+
"kernelspec": {
|
| 226 |
+
"display_name": "Python 3",
|
| 227 |
+
"language": "python",
|
| 228 |
+
"name": "python3"
|
| 229 |
+
},
|
| 230 |
+
"language_info": {
|
| 231 |
+
"codemirror_mode": {
|
| 232 |
+
"name": "ipython",
|
| 233 |
+
"version": 3
|
| 234 |
+
},
|
| 235 |
+
"file_extension": ".py",
|
| 236 |
+
"mimetype": "text/x-python",
|
| 237 |
+
"name": "python",
|
| 238 |
+
"nbconvert_exporter": "python",
|
| 239 |
+
"pygments_lexer": "ipython3",
|
| 240 |
+
"version": "3.10.12"
|
| 241 |
+
}
|
| 242 |
+
},
|
| 243 |
+
"nbformat": 4,
|
| 244 |
+
"nbformat_minor": 2
|
| 245 |
+
}
|
data/bottle/inference.ipynb
ADDED
|
@@ -0,0 +1,209 @@
|
|
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|
|
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|
|
|
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|
|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"# conda activate vision\n",
|
| 10 |
+
"# cd build\n",
|
| 11 |
+
"# cmake -DCMAKE_BUILD_TYPE=Release ..\n",
|
| 12 |
+
"# make\n",
|
| 13 |
+
"# ./FRICP ./data/bottle/tea_gt_filtered.ply ./data/bottle/tea_noisy_filtered.ply ./data/bottle/res/ 3"
|
| 14 |
+
]
|
| 15 |
+
},
|
| 16 |
+
{
|
| 17 |
+
"cell_type": "markdown",
|
| 18 |
+
"metadata": {},
|
| 19 |
+
"source": [
|
| 20 |
+
"### Source PCD"
|
| 21 |
+
]
|
| 22 |
+
},
|
| 23 |
+
{
|
| 24 |
+
"cell_type": "code",
|
| 25 |
+
"execution_count": 2,
|
| 26 |
+
"metadata": {},
|
| 27 |
+
"outputs": [
|
| 28 |
+
{
|
| 29 |
+
"name": "stdout",
|
| 30 |
+
"output_type": "stream",
|
| 31 |
+
"text": [
|
| 32 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 33 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 34 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 35 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: tea_noisy_filtered.ply\u001b[0;m\n",
|
| 36 |
+
"Source shape: (23076, 3)\n"
|
| 37 |
+
]
|
| 38 |
+
},
|
| 39 |
+
{
|
| 40 |
+
"name": "stderr",
|
| 41 |
+
"output_type": "stream",
|
| 42 |
+
"text": [
|
| 43 |
+
"RPly: Unexpected end of file\n",
|
| 44 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 45 |
+
]
|
| 46 |
+
}
|
| 47 |
+
],
|
| 48 |
+
"source": [
|
| 49 |
+
"import open3d as o3d\n",
|
| 50 |
+
"import numpy as np\n",
|
| 51 |
+
"\n",
|
| 52 |
+
"source_path = \"tea_noisy_filtered.ply\"\n",
|
| 53 |
+
"source_pcd = o3d.io.read_point_cloud(source_path)\n",
|
| 54 |
+
"\n",
|
| 55 |
+
"source_pcd_array = np.asarray(source_pcd.points)\n",
|
| 56 |
+
"print(\"Source shape:\", source_pcd_array.shape)\n",
|
| 57 |
+
"\n",
|
| 58 |
+
"o3d.visualization.draw_geometries([source_pcd])"
|
| 59 |
+
]
|
| 60 |
+
},
|
| 61 |
+
{
|
| 62 |
+
"cell_type": "markdown",
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"source": [
|
| 65 |
+
"### Target PCD"
|
| 66 |
+
]
|
| 67 |
+
},
|
| 68 |
+
{
|
| 69 |
+
"cell_type": "code",
|
| 70 |
+
"execution_count": 7,
|
| 71 |
+
"metadata": {},
|
| 72 |
+
"outputs": [
|
| 73 |
+
{
|
| 74 |
+
"name": "stdout",
|
| 75 |
+
"output_type": "stream",
|
| 76 |
+
"text": [
|
| 77 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: tea_gt_filtered.ply\u001b[0;m\n",
|
| 78 |
+
"Target shape: (50363, 3)\n"
|
| 79 |
+
]
|
| 80 |
+
},
|
| 81 |
+
{
|
| 82 |
+
"name": "stderr",
|
| 83 |
+
"output_type": "stream",
|
| 84 |
+
"text": [
|
| 85 |
+
"RPly: Unexpected end of file\n",
|
| 86 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 87 |
+
]
|
| 88 |
+
}
|
| 89 |
+
],
|
| 90 |
+
"source": [
|
| 91 |
+
"target_path = \"tea_gt_filtered.ply\"\n",
|
| 92 |
+
"target_pcd = o3d.io.read_point_cloud(target_path)\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"target_pcd_array = np.asarray(target_pcd.points)\n",
|
| 95 |
+
"print(\"Target shape:\", target_pcd_array.shape)\n",
|
| 96 |
+
"\n",
|
| 97 |
+
"o3d.visualization.draw_geometries([target_pcd])"
|
| 98 |
+
]
|
| 99 |
+
},
|
| 100 |
+
{
|
| 101 |
+
"cell_type": "markdown",
|
| 102 |
+
"metadata": {},
|
| 103 |
+
"source": [
|
| 104 |
+
"### Transformed Source PCD"
|
| 105 |
+
]
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"cell_type": "code",
|
| 109 |
+
"execution_count": 4,
|
| 110 |
+
"metadata": {},
|
| 111 |
+
"outputs": [
|
| 112 |
+
{
|
| 113 |
+
"name": "stdout",
|
| 114 |
+
"output_type": "stream",
|
| 115 |
+
"text": [
|
| 116 |
+
"Transformed shape: (23076, 3)\n"
|
| 117 |
+
]
|
| 118 |
+
}
|
| 119 |
+
],
|
| 120 |
+
"source": [
|
| 121 |
+
"transformed_path = \"res/m3reg_pc.ply\"\n",
|
| 122 |
+
"transformed_pcd = o3d.io.read_point_cloud(transformed_path)\n",
|
| 123 |
+
"\n",
|
| 124 |
+
"transformed_pcd_array = np.asarray(transformed_pcd.points)\n",
|
| 125 |
+
"print(\"Transformed shape:\", transformed_pcd_array.shape)\n",
|
| 126 |
+
"\n",
|
| 127 |
+
"o3d.visualization.draw_geometries([transformed_pcd])"
|
| 128 |
+
]
|
| 129 |
+
},
|
| 130 |
+
{
|
| 131 |
+
"cell_type": "markdown",
|
| 132 |
+
"metadata": {},
|
| 133 |
+
"source": [
|
| 134 |
+
"### Source (Original) + Target"
|
| 135 |
+
]
|
| 136 |
+
},
|
| 137 |
+
{
|
| 138 |
+
"cell_type": "code",
|
| 139 |
+
"execution_count": 5,
|
| 140 |
+
"metadata": {},
|
| 141 |
+
"outputs": [],
|
| 142 |
+
"source": [
|
| 143 |
+
"source_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 144 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 145 |
+
"\n",
|
| 146 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 147 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 148 |
+
"vis.add_geometry(source_pcd)\n",
|
| 149 |
+
"vis.add_geometry(target_pcd)\n",
|
| 150 |
+
"\n",
|
| 151 |
+
"vis.run()\n",
|
| 152 |
+
"vis.destroy_window()"
|
| 153 |
+
]
|
| 154 |
+
},
|
| 155 |
+
{
|
| 156 |
+
"cell_type": "markdown",
|
| 157 |
+
"metadata": {},
|
| 158 |
+
"source": [
|
| 159 |
+
"### Transformed + Target"
|
| 160 |
+
]
|
| 161 |
+
},
|
| 162 |
+
{
|
| 163 |
+
"cell_type": "code",
|
| 164 |
+
"execution_count": 6,
|
| 165 |
+
"metadata": {},
|
| 166 |
+
"outputs": [],
|
| 167 |
+
"source": [
|
| 168 |
+
"transformed_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 169 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 170 |
+
"\n",
|
| 171 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 172 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 173 |
+
"vis.add_geometry(transformed_pcd)\n",
|
| 174 |
+
"vis.add_geometry(target_pcd)\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"vis.run()\n",
|
| 177 |
+
"vis.destroy_window()"
|
| 178 |
+
]
|
| 179 |
+
},
|
| 180 |
+
{
|
| 181 |
+
"cell_type": "code",
|
| 182 |
+
"execution_count": null,
|
| 183 |
+
"metadata": {},
|
| 184 |
+
"outputs": [],
|
| 185 |
+
"source": []
|
| 186 |
+
}
|
| 187 |
+
],
|
| 188 |
+
"metadata": {
|
| 189 |
+
"kernelspec": {
|
| 190 |
+
"display_name": "vision",
|
| 191 |
+
"language": "python",
|
| 192 |
+
"name": "python3"
|
| 193 |
+
},
|
| 194 |
+
"language_info": {
|
| 195 |
+
"codemirror_mode": {
|
| 196 |
+
"name": "ipython",
|
| 197 |
+
"version": 3
|
| 198 |
+
},
|
| 199 |
+
"file_extension": ".py",
|
| 200 |
+
"mimetype": "text/x-python",
|
| 201 |
+
"name": "python",
|
| 202 |
+
"nbconvert_exporter": "python",
|
| 203 |
+
"pygments_lexer": "ipython3",
|
| 204 |
+
"version": "3.9.20"
|
| 205 |
+
}
|
| 206 |
+
},
|
| 207 |
+
"nbformat": 4,
|
| 208 |
+
"nbformat_minor": 2
|
| 209 |
+
}
|
data/bottle/tea_gt_filtered.ply
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/bottle/tea_noisy_filtered.ply
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/bottle_2/RMSE.ipynb
ADDED
|
@@ -0,0 +1,197 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "5cf20b5b",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## Get Transformation file\n"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": null,
|
| 14 |
+
"id": "fe5a09ce",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [],
|
| 17 |
+
"source": [
|
| 18 |
+
"import numpy as np\n",
|
| 19 |
+
"import math\n",
|
| 20 |
+
"import json\n",
|
| 21 |
+
"import os\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
|
| 24 |
+
"fill_rate = ['100', '75', '50', '25', '0']\n",
|
| 25 |
+
"result_path = './Fast-Robust-ICP/Result/'\n",
|
| 26 |
+
"\n",
|
| 27 |
+
"# assign your folder \n",
|
| 28 |
+
"\n",
|
| 29 |
+
"category = categories[0]\n",
|
| 30 |
+
"\n",
|
| 31 |
+
"\n",
|
| 32 |
+
"result_path =result_path + category\n",
|
| 33 |
+
"\n",
|
| 34 |
+
"json_path = result_path + \"ply_files.json\"\n",
|
| 35 |
+
"\n",
|
| 36 |
+
"\n",
|
| 37 |
+
"### Generating T matrix list.\n",
|
| 38 |
+
"\n",
|
| 39 |
+
"# bring the filename json file.\n",
|
| 40 |
+
"try: \n",
|
| 41 |
+
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
|
| 42 |
+
" categorized_files = json.load(f)\n",
|
| 43 |
+
"\n",
|
| 44 |
+
"except FileNotFoundError:\n",
|
| 45 |
+
" print(f\"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.\")\n",
|
| 46 |
+
" exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ\n",
|
| 47 |
+
"\n",
|
| 48 |
+
"\n",
|
| 49 |
+
"## get GT\n",
|
| 50 |
+
"gt = []\n",
|
| 51 |
+
"\n",
|
| 52 |
+
"gt_T =[]\n",
|
| 53 |
+
"\n",
|
| 54 |
+
"for fill in fill_rate:\n",
|
| 55 |
+
" filenames = categorized_files.get(fill, [])\n",
|
| 56 |
+
" T_array = []\n",
|
| 57 |
+
"\n",
|
| 58 |
+
" for file in filenames:\n",
|
| 59 |
+
" gt_name = f\"gt_{file}.txt\"\n",
|
| 60 |
+
" matrix = np.loadtxt(gt_name)\n",
|
| 61 |
+
" T_array.append(matrix)\n",
|
| 62 |
+
" gt_T.append(T_array)\n",
|
| 63 |
+
"\n",
|
| 64 |
+
"\n",
|
| 65 |
+
"\n",
|
| 66 |
+
"print(np.gt_T)\n",
|
| 67 |
+
"\n",
|
| 68 |
+
"\n",
|
| 69 |
+
"\n",
|
| 70 |
+
"\n",
|
| 71 |
+
"\n",
|
| 72 |
+
"\n",
|
| 73 |
+
"\n",
|
| 74 |
+
"# get T matrix array\n",
|
| 75 |
+
"overall_T =[]\n",
|
| 76 |
+
"\n",
|
| 77 |
+
"for fill in fill_rate:\n",
|
| 78 |
+
" filenames = categorized_files.get(fill, [])\n",
|
| 79 |
+
" T_array = []\n",
|
| 80 |
+
"\n",
|
| 81 |
+
" for file in filenames:\n",
|
| 82 |
+
" matrix_path = result_path + file+\".txt\"\n",
|
| 83 |
+
" matrix = np.loadtxt(matrix_path)\n",
|
| 84 |
+
" T_array.append(matrix)\n",
|
| 85 |
+
" overall_T.append(T_array)\n",
|
| 86 |
+
"\n",
|
| 87 |
+
"\n",
|
| 88 |
+
"\n",
|
| 89 |
+
"print(np.overall_T.shape)\n",
|
| 90 |
+
"\n",
|
| 91 |
+
"\n"
|
| 92 |
+
]
|
| 93 |
+
},
|
| 94 |
+
{
|
| 95 |
+
"cell_type": "markdown",
|
| 96 |
+
"id": "9a7cf4b9",
|
| 97 |
+
"metadata": {},
|
| 98 |
+
"source": [
|
| 99 |
+
"# compute RMSE\n"
|
| 100 |
+
]
|
| 101 |
+
},
|
| 102 |
+
{
|
| 103 |
+
"cell_type": "code",
|
| 104 |
+
"execution_count": null,
|
| 105 |
+
"id": "758cc248",
|
| 106 |
+
"metadata": {},
|
| 107 |
+
"outputs": [],
|
| 108 |
+
"source": [
|
| 109 |
+
"def RMSE(T_star, T):\n",
|
| 110 |
+
" diff = T_star - T\n",
|
| 111 |
+
" sq_norms = np.sum(diff**2, axis =1)\n",
|
| 112 |
+
"\n",
|
| 113 |
+
" r = np.sqrt(np.mean(sq_norms))\n",
|
| 114 |
+
"\n",
|
| 115 |
+
" return r\n",
|
| 116 |
+
"\n",
|
| 117 |
+
"def mean(array):\n",
|
| 118 |
+
" return np.mean(array)\n",
|
| 119 |
+
"\n",
|
| 120 |
+
"\n",
|
| 121 |
+
"RMSE_mean = []\n",
|
| 122 |
+
"for gt, overall in zip(gt_T, overall_T):\n",
|
| 123 |
+
" rmse = []\n",
|
| 124 |
+
" for T_star, T in zip(gt, overall):\n",
|
| 125 |
+
" r= RMSE(T_star, T)\n",
|
| 126 |
+
" rmse.append(r)\n",
|
| 127 |
+
" RMSE_mean.append(mean(rmse))\n",
|
| 128 |
+
"\n"
|
| 129 |
+
]
|
| 130 |
+
},
|
| 131 |
+
{
|
| 132 |
+
"cell_type": "markdown",
|
| 133 |
+
"id": "b859fdc3",
|
| 134 |
+
"metadata": {},
|
| 135 |
+
"source": [
|
| 136 |
+
"## Save in json\n",
|
| 137 |
+
" "
|
| 138 |
+
]
|
| 139 |
+
},
|
| 140 |
+
{
|
| 141 |
+
"cell_type": "code",
|
| 142 |
+
"execution_count": 2,
|
| 143 |
+
"id": "8c0faa07",
|
| 144 |
+
"metadata": {},
|
| 145 |
+
"outputs": [
|
| 146 |
+
{
|
| 147 |
+
"ename": "NameError",
|
| 148 |
+
"evalue": "name 'RMSE_mean' is not defined",
|
| 149 |
+
"output_type": "error",
|
| 150 |
+
"traceback": [
|
| 151 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 152 |
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
| 153 |
+
"Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cat \u001b[38;5;129;01min\u001b[39;00m categories:\n\u001b[1;32m 7\u001b[0m rmse_dict[cat] \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m mean, fr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(RMSE_mean,fill_rate):\n\u001b[1;32m 9\u001b[0m rmse_dict[cat][fr] \u001b[38;5;241m=\u001b[39m mean\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmse_Results.json\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
|
| 154 |
+
"\u001b[0;31mNameError\u001b[0m: name 'RMSE_mean' is not defined"
|
| 155 |
+
]
|
| 156 |
+
}
|
| 157 |
+
],
|
| 158 |
+
"source": [
|
| 159 |
+
"categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
|
| 160 |
+
"fill_rate = ['100', '75', '50', '25', '0']\n",
|
| 161 |
+
"\n",
|
| 162 |
+
"rmse_dict = {}\n",
|
| 163 |
+
"\n",
|
| 164 |
+
"for cat in categories:\n",
|
| 165 |
+
" rmse_dict[cat] = {}\n",
|
| 166 |
+
" for mean, fr in zip(RMSE_mean,fill_rate):\n",
|
| 167 |
+
" rmse_dict[cat][fr] = mean\n",
|
| 168 |
+
"\n",
|
| 169 |
+
"with open('rmse_Results.json', 'w') as f:\n",
|
| 170 |
+
" json.dump(rmse_dict, f, indent=4)\n",
|
| 171 |
+
" \n",
|
| 172 |
+
"\n"
|
| 173 |
+
]
|
| 174 |
+
}
|
| 175 |
+
],
|
| 176 |
+
"metadata": {
|
| 177 |
+
"kernelspec": {
|
| 178 |
+
"display_name": "base",
|
| 179 |
+
"language": "python",
|
| 180 |
+
"name": "python3"
|
| 181 |
+
},
|
| 182 |
+
"language_info": {
|
| 183 |
+
"codemirror_mode": {
|
| 184 |
+
"name": "ipython",
|
| 185 |
+
"version": 3
|
| 186 |
+
},
|
| 187 |
+
"file_extension": ".py",
|
| 188 |
+
"mimetype": "text/x-python",
|
| 189 |
+
"name": "python",
|
| 190 |
+
"nbconvert_exporter": "python",
|
| 191 |
+
"pygments_lexer": "ipython3",
|
| 192 |
+
"version": "3.13.5"
|
| 193 |
+
}
|
| 194 |
+
},
|
| 195 |
+
"nbformat": 4,
|
| 196 |
+
"nbformat_minor": 5
|
| 197 |
+
}
|
data/bottle_2/all_infer.ipynb
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/bottle_2/all_infer.py
ADDED
|
@@ -0,0 +1,109 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import open3d as o3d
|
| 2 |
+
import numpy as np
|
| 3 |
+
import json
|
| 4 |
+
import os
|
| 5 |
+
import subprocess
|
| 6 |
+
import shutil # shutil ์ํฌํธ ์ถ๊ฐ
|
| 7 |
+
|
| 8 |
+
# ๋ฐ์ดํฐ์
ํด๋์ JSON ํ์ผ ๊ฒฝ๋ก
|
| 9 |
+
folder = "./dataset"
|
| 10 |
+
json_path = "ply_files.json"
|
| 11 |
+
categories= ['100', '75', '50', '25', '0']
|
| 12 |
+
|
| 13 |
+
try:
|
| 14 |
+
with open(json_path, "r", encoding="utf-8") as f:
|
| 15 |
+
categorized_files = json.load(f)
|
| 16 |
+
except FileNotFoundError:
|
| 17 |
+
print(f"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.")
|
| 18 |
+
exit()
|
| 19 |
+
|
| 20 |
+
print("=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===")
|
| 21 |
+
|
| 22 |
+
for category in categories:
|
| 23 |
+
print(f"\n--- [์นดํ
๊ณ ๋ฆฌ: {category} ์ฒ๋ฆฌ ์์ ---")
|
| 24 |
+
|
| 25 |
+
filenames_in_category = categorized_files.get(category, [])
|
| 26 |
+
|
| 27 |
+
if not filenames_in_category:
|
| 28 |
+
print("์ฒ๋ฆฌํ ํ์ผ์ด ์์ต๋๋ค.")
|
| 29 |
+
continue
|
| 30 |
+
|
| 31 |
+
for filename in filenames_in_category:
|
| 32 |
+
print(f"\n>>> ํ์ผ ์ฒ๋ฆฌ ์ค: {filename}.ply")
|
| 33 |
+
|
| 34 |
+
# ... (์ด์ ํ์ผ ์์ ๋ฐ ์ ์ฅ ์ฝ๋๋ ๊ทธ๋๋ก) ...
|
| 35 |
+
# In[23] ๋ถ๋ถ์ ์ฌ๊ธฐ์ ๊ทธ๋๋ก ๋ก๋๋ค.
|
| 36 |
+
# ...
|
| 37 |
+
|
| 38 |
+
# ### Execute terminal
|
| 39 |
+
|
| 40 |
+
# โญ๏ธ ํด๊ฒฐ ๋ฐฉ๋ฒ 1: FRICP ์คํ ์ ์ด์ ๊ฒฐ๊ณผ ํด๋ ์ญ์
|
| 41 |
+
# ๊ฐ ๋ฃจํ๋ง๋ค ๊นจ๋ํ ์ํ์์ ์์ํ๋๋ก ๋ณด์ฅํฉ๋๋ค.
|
| 42 |
+
if os.path.exists('./res'):
|
| 43 |
+
shutil.rmtree('./res')
|
| 44 |
+
print("์ด์ 'res' ํด๋๋ฅผ ์ญ์ ํ์ต๋๋ค.")
|
| 45 |
+
source_path = f'./initialized_result/initial_{filename}.ply'
|
| 46 |
+
print(f"--- ๋ก๋ฉ ์๋ ์ค์ธ ํ์ผ: {source_path}")
|
| 47 |
+
if not os.path.exists(source_path):
|
| 48 |
+
print("!!!!!! ์๋ฌ: ํด๋น ๊ฒฝ๋ก์ ํ์ผ์ด ์กด์ฌํ์ง ์์ต๋๋ค!")
|
| 49 |
+
continue # ๋ค์ ํ์ผ๋ก ๋์ด๊ฐ
|
| 50 |
+
cmd = [
|
| 51 |
+
'../../FRICP',
|
| 52 |
+
'./gt_filtered.ply',
|
| 53 |
+
f'./noisy_result/noisy_filtered_{filename}.ply',
|
| 54 |
+
'./res', # FRICP๋ ์ด ํด๋์ ๊ฒฐ๊ณผ๋ฅผ ์ ์ฅํฉ๋๋ค.
|
| 55 |
+
'3'
|
| 56 |
+
]
|
| 57 |
+
print(cmd)
|
| 58 |
+
|
| 59 |
+
try:
|
| 60 |
+
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
|
| 61 |
+
print("--- STDOUT (ํ์ค ์ถ๋ ฅ) ---")
|
| 62 |
+
print("๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.")
|
| 63 |
+
print(result.stdout)
|
| 64 |
+
|
| 65 |
+
except FileNotFoundError:
|
| 66 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 67 |
+
print(f"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 68 |
+
print("๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.")
|
| 69 |
+
|
| 70 |
+
except subprocess.CalledProcessError as e:
|
| 71 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 72 |
+
print(f"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})")
|
| 73 |
+
|
| 74 |
+
# STDOUT๊ณผ STDERR์ ๋ชจ๋ ์ถ๋ ฅ
|
| 75 |
+
print("\n--- STDOUT (์ค๋ฅ ๋ฐ์ ์ ํ์ค ์ถ๋ ฅ) ---")
|
| 76 |
+
print(e.stdout)
|
| 77 |
+
|
| 78 |
+
print("\n--- STDERR (์๋ฌ ์์ธ) ---")
|
| 79 |
+
print(e.stderr)
|
| 80 |
+
|
| 81 |
+
continue # ์ค๋ฅ ๋ฐ์ ์ ๋ค์ ํ์ผ๋ก ๋์ด๊ฐ๋๋ค.
|
| 82 |
+
|
| 83 |
+
# ### Change the path for result
|
| 84 |
+
|
| 85 |
+
# โญ๏ธ ํด๊ฒฐ ๋ฐฉ๋ฒ 2: ์ ํํ ํ์ผ ๊ฒฝ๋ก ์ง์
|
| 86 |
+
# FRICP๊ฐ 'res' ํด๋ ์์ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํ๋ฏ๋ก, ๊ฒฝ๋ก๋ฅผ ์ ํํ ๋ช
์ํฉ๋๋ค.
|
| 87 |
+
transformed_path = "./resm3reg_pc.ply"
|
| 88 |
+
destination_path = f"./result/final_result_{filename}.ply"
|
| 89 |
+
transformed_path2 = "./resm3trans.txt"
|
| 90 |
+
destination_path2 = f"./result/final_result_{filename}.txt"
|
| 91 |
+
|
| 92 |
+
# ํ์ผ ์ด๋ ์ , ํ์ผ์ด ์ค์ ๋ก ์กด์ฌํ๋์ง ํ์ธํ๋ ๊ฒ์ด ๋ ์์ ์ ์
๋๋ค.
|
| 93 |
+
if os.path.exists(transformed_path):
|
| 94 |
+
shutil.move(transformed_path, destination_path)
|
| 95 |
+
print(f"Successfully moved '{transformed_path}' to '{destination_path}'")
|
| 96 |
+
else:
|
| 97 |
+
print(f"์ค๋ฅ: '{transformed_path}' ํ์ผ์ ์ฐพ์ ์ ์์ด ์ด๋ํ์ง ๋ชปํ์ต๋๋ค.")
|
| 98 |
+
|
| 99 |
+
if os.path.exists(transformed_path2):
|
| 100 |
+
shutil.move(transformed_path2, destination_path2)
|
| 101 |
+
print(f"Successfully moved '{transformed_path2}' to '{destination_path2}'")
|
| 102 |
+
else:
|
| 103 |
+
print(f"์ค๋ฅ: '{transformed_path2}' ํ์ผ์ ์ฐพ์ ์ ์์ด ์ด๋ํ์ง ๋ชปํ์ต๋๋ค.")
|
| 104 |
+
|
| 105 |
+
# ... (์ดํ ์๊ฐํ ์ฝ๋๋ ๊ทธ๋๋ก) ...
|
| 106 |
+
# In[28], In[29], In[30] ๋ถ๋ถ์ ์ฌ๊ธฐ์ ๊ทธ๋๋ก ๋ก๋๋ค.
|
| 107 |
+
# ...
|
| 108 |
+
|
| 109 |
+
print("\n\n=== ๋ชจ๋ ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์๋ฃ! ===")
|
data/bottle_2/bottle.csv
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
,file_1,file_2,file_3,file_4,file_5,file_6,file_7,file_8,file_9,file_10,file_11,file_12,file_13,file_14,file_15,file_16,file_17,file_18,file_19,file_20,file_21,file_22,file_23,file_24,file_25,mean_Val
|
| 2 |
+
bottle_100_ICP,85.04128575425388,86.05326335360489,84.47635691229785,105.59225246637578,89.68881962097188,77.48888868075612,109.90142895087662,108.41158827464747,104.12338729492919,112.59540331960532,108.57939156883003,61.43662300798038,77.43625723382463,101.00628912848936,87.69185745030735,110.1171883980221,107.09749318118588,110.93064448147923,96.01056065110696,52.07204369198823,0.0,0.0,0.0,0.0,0.0,93.78755117107666
|
| 3 |
+
bottle_75_ICP,109.51695154262917,110.05432879513135,87.09602168795054,107.42510565438391,87.97775218715594,90.18008214964271,107.49642510138136,111.99004662196423,109.65780517399432,110.84820982160869,89.85400075280717,59.342682664850095,90.93817506352208,68.19630770224255,89.18749438130695,113.00008379096332,101.08964382624028,113.16925628472214,106.31775551859322,73.05012962149381,0.0,0.0,0.0,0.0,0.0,96.8194129171292
|
| 4 |
+
bottle_50_ICP,108.19988707450763,108.86639982617186,113.7658626011531,87.95524482799122,84.50313400458252,70.6670324093882,81.32191675937815,103.38106838315674,112.88583164302038,85.98483659718111,113.21596871750606,106.85055452694026,83.42222041172025,93.65132954986254,108.24618930633926,61.71279182076898,67.78796417836334,61.33756980244185,94.5127437017708,107.46030337711433,0.0,0.0,0.0,0.0,0.0,92.78644247596792
|
| 5 |
+
bottle_25_ICP,106.28665082567477,105.03500991805781,85.0842936924201,80.05463768235835,102.5157119364935,42.60840044408896,48.90137071810652,54.18483674044661,104.7672300934581,119.41459165731216,112.03161615580395,102.11945300087962,84.12946301195971,71.7070184123044,90.50514407935916,74.48994221635428,111.6279127246182,117.45599015468306,115.8960102820454,71.4591643972948,0.0,0.0,0.0,0.0,0.0,90.01372240718597
|
| 6 |
+
bottle_0_ICP,112.62597432625142,109.8973077605711,110.484548941997,94.04497548454427,76.34275229761651,34.7846151167867,47.72111874975935,31.61799726124414,90.48139287154483,90.31935268202618,122.65398326877047,0.0,91.10849067425075,79.80308353636845,75.17759836711434,70.72516962944377,0.0,100.87137611768568,118.24678207190455,78.59711788098687,122.18578126571407,85.76030582325366,89.98604431157688,0.0,0.0,87.30646516378147
|
| 7 |
+
bottle_100_FAST ICP,84.7012961566271,86.03440164364748,84.47551127602084,111.30492987046674,89.82886884057739,77.34804836912079,110.13503720394769,108.17647743057096,90.41699894975555,111.52369246196193,108.07993658996557,61.60536876947702,77.20546530825926,102.55830080977084,87.66446520570835,96.73624612146475,110.09185680128066,110.93877690835261,95.81097618356239,59.97001131889388,0.0,0.0,0.0,0.0,0.0,93.2303333109716
|
| 8 |
+
bottle_75_FAST ICP,108.91903083750711,110.14611476344476,87.0766408370491,88.91822265799456,86.67330421906041,89.73030954456551,103.91657749505872,112.06031719397869,109.6321756451082,111.65612149685393,90.01851702551402,59.00030048609143,90.10578244322001,105.47941343779007,89.20763098062307,112.52056825224021,88.03706066434097,113.15424261545164,106.42047041848046,103.79366203687644,0.0,0.0,0.0,0.0,0.0,98.32332315256247
|
| 9 |
+
bottle_50_FAST ICP,92.01737591167311,109.25003301164492,114.40436646225686,88.0188234885353,84.43710099121567,71.59584839577762,81.68548663620125,103.51632532593115,111.66974472329198,84.90005949571722,113.9904039875148,107.20528895255818,82.03003318491324,93.49498815798984,108.55895895166164,61.897191832460365,20.121940810489193,60.48798557051702,95.99154746543061,83.85823004859633,0.0,0.0,0.0,0.0,0.0,88.45658667021883
|
| 10 |
+
bottle_25_FAST ICP,108.62107887499435,106.62932754058345,85.0782101219815,80.05165638078077,97.3966801484607,46.59267483026011,38.79435977004901,54.5374253869146,108.34492155732819,113.63753808765595,110.34940611843918,100.21344801575309,83.70812696796126,72.17352747099841,90.58655718023181,70.0953858766288,108.58117921769693,116.41160652165263,117.73460642580609,71.20335800406437,0.0,0.0,0.0,0.0,0.0,89.03705372491206
|
| 11 |
+
bottle_0_FAST ICP,113.49972032300671,107.84338924548734,110.13650077505487,96.23734594085165,76.3513070271596,34.74384858215673,76.3265701038637,40.35838504020668,90.20588322140146,89.83927590668787,107.88731754397071,0.0,91.13245466695298,79.89456838259234,75.35401903641245,57.02607371022735,0.0,117.28994796803546,128.90243372842963,73.90202762120708,117.55854212928806,85.43391740704234,90.04361893576521,0.0,0.0,88.56986415694287
|
| 12 |
+
bottle_100_Robust ICP,81.70543470363305,82.39656376244879,80.09634283942138,111.18603815214006,79.56262675714508,81.525871385867,108.99878767337788,105.05008919172559,93.7531099700586,113.05580473344197,109.85216816503079,61.82723651422891,75.9341125490483,101.84710583012524,66.43210090246328,107.52198599079344,65.1420656529651,110.88598597109053,96.87432011114093,51.74274627309617,0.0,0.0,0.0,0.0,0.0,89.2695248564621
|
| 13 |
+
bottle_75_Robust ICP,112.74079014842333,113.97671069127504,81.1498410176695,102.31169626939742,83.48006574428902,93.32785615162496,104.4472195377852,111.92726166676424,113.26277874067482,109.67946531368244,80.64641992338044,46.46467323157595,91.85744681971214,71.02952087763815,91.82095626249375,114.12411176270146,108.7386756655741,114.61065779035664,107.71003970466465,99.08840813331993,0.0,0.0,0.0,0.0,0.0,97.61972977265016
|
| 14 |
+
bottle_50_Robust ICP,111.47069153618989,111.39110924258998,113.34206315059971,81.48689273332852,84.20602084772226,86.68698324337633,91.34275668397333,108.27653881118654,113.48448234485916,85.4559975898279,117.92588550020439,98.24020716938688,78.42448297082153,106.30037026331838,111.02478369595981,62.35282659674115,31.632497313776717,54.08884351296583,98.70260854219246,110.86054012630366,0.0,0.0,0.0,0.0,0.0,92.83482909376623
|
| 15 |
+
bottle_25_Robust ICP,106.82072836978332,106.4622018053545,81.32469793180212,94.04023934411366,95.93115254977074,50.12393614537032,29.66312970848825,54.753536496227,108.10992790888147,115.62596760465885,110.42669287336633,97.63358735071552,83.36255873758938,71.49869839764217,88.8559068451969,94.90119730111823,107.08644373383686,113.16466701066639,114.8377499998422,99.81545985680388,0.0,0.0,0.0,0.0,0.0,91.2219239985614
|
| 16 |
+
bottle_0_Robust ICP,115.26609033419432,109.6036922420519,94.49770725900457,94.21587989484117,79.61813020373685,30.72760435110564,15.881949746999696,47.17320353090825,67.4286317708186,100.31608672673835,126.7902516099921,0.0,75.23806788157623,55.91837949180391,77.27641205038687,61.51679877424573,0.0,63.70168502116878,123.13251672610497,65.67202122462396,122.56200660721422,78.88745981227382,88.25914633783503,0.0,0.0,80.65160579036309
|
| 17 |
+
bottle_100_Sparse ICP,77.81938558201557,79.23948919838341,82.57688321948443,98.90976816693983,84.78868462122259,83.5531335365124,108.3971242310478,107.34410276724469,106.69560467663753,107.16997062533052,102.88248072481444,49.7186328789136,51.094176706592265,77.5906493907245,87.86070843278522,104.43098296393003,108.63312093860273,109.53991810355184,77.04265027297463,57.084762229776,0.0,0.0,0.0,0.0,0.0,88.11861146337421
|
| 18 |
+
bottle_75_Sparse ICP,103.99827425295919,104.37779153144412,68.68538784551674,97.25552336920758,86.72701043524076,96.838347376969,112.54176904469927,103.41153891428786,109.4229441082182,105.86267701099024,76.11011397317024,66.80255904773338,63.606657059473896,79.03516238434135,90.4674735681281,107.43503004811424,102.50539303646858,111.67784215158278,102.32768487983813,70.99235565588936,0.0,0.0,0.0,0.0,0.0,93.00407678471365
|
| 19 |
+
bottle_50_Sparse ICP,101.79896199942294,102.11006337106423,113.3930555064267,86.26737303207376,55.34544027233182,69.95258338904436,91.26932325790018,100.65731202845619,114.30136008913202,95.54291618696308,108.01212505586417,101.53625000729649,93.2583135501972,100.08792520119134,91.25137477325299,77.6873279321758,48.24807263514985,75.32073439015521,74.30310381106145,103.111405745478,0.0,0.0,0.0,0.0,0.0,90.1727511117319
|
| 20 |
+
bottle_25_Sparse ICP,103.99712394949904,103.61349752389927,94.62256697094382,79.27976119635457,72.89199770216626,41.22770313658797,58.12239299579378,54.927784315056755,68.85909222780111,104.62830979553958,112.88382337654501,100.55198422936014,95.25869527118465,81.57718999018991,77.5592075454821,94.36985996430036,114.66205773160543,114.71204109488971,93.89082326379265,82.84512462503706,0.0,0.0,0.0,0.0,0.0,87.52405184530146
|
| 21 |
+
bottle_0_Sparse ICP,114.32680656953005,112.77026993397001,122.00792045466763,89.45524146678042,83.81796832644702,31.71932256121298,42.8170997722106,22.007086609697787,79.79521860696805,115.08496274365683,127.54274713138065,0.0,84.64138278343356,56.22417113226267,75.67829913140086,77.68010923546002,0.0,39.49974039202994,112.29732068998992,79.17847919424567,125.93509724997001,90.74655019608869,85.68044281521355,0.0,0.0,84.23363033317223
|
| 22 |
+
ICP,104.33414990466338,103.9812619307074,96.18141676716371,95.01444322313071,88.20563400936408,63.14580376013254,79.0684520559004,81.91710745629183,104.38312941538936,103.8324788155467,109.26699209274355,65.94986264013008,85.40692127905548,82.87280566585345,90.1616567168854,86.00903517111048,77.52060278208153,100.75296736820239,106.19677044508418,76.52775179377561,24.437156253142813,17.152061164650732,17.997208862315375,0.0,0.0,92.14271882702823
|
| 23 |
+
FAST ICP,101.55170042076168,103.9806532409616,96.23424589447264,92.90619566772581,86.93745224529475,64.00214594437617,82.17160624182407,83.72978607552042,102.05394481937707,102.31133748977538,106.06511625308084,65.60488124477595,84.83637251426134,90.72015965182831,90.27432627092746,79.65509315860429,65.36640749876156,103.65651191680188,108.97200684434183,78.54545780592763,23.511708425857613,17.086783481408467,18.00872378715304,0.0,0.0,91.52343220312157
|
| 24 |
+
FAST AND ROBUST ICP,105.60074701844478,104.76605554874405,90.08213043969945,96.64814927876417,84.55959922053277,68.47845025546886,70.06676867012487,85.43612593936233,99.20778614705853,104.8266643936699,109.1282836143948,60.833140853181455,80.96333379174952,81.31881497210557,87.08203195130012,88.08338408512,62.519936473230565,91.29036786124962,108.25144701678906,85.43583512282952,24.512401321442844,15.777491962454764,17.651829267567006,0.0,0.0,90.3195227023606
|
| 25 |
+
SPARSE ICP,100.38811047068536,100.4222223117522,96.25716279940787,90.23353344627124,76.7142202714817,64.65821800006536,82.62954186033033,77.66956492694867,95.81484394175138,105.65776727249605,105.4862580523549,63.721885232660725,77.57184507417631,78.90301961974194,84.56341269020986,92.32066202879608,74.80972886836533,90.15005522644189,91.97231658353135,78.64242549008522,25.187019449994004,18.149310039217738,17.136088563042712,0.0,0.0,88.6106243076587
|
data/bottle_2/bottle2.csv
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
,mean_Val
|
| 2 |
+
ICP,59.67950506176781
|
| 3 |
+
FAST ICP,59.40161196875058
|
| 4 |
+
FAST AND ROBUST ICP,53.11171395265664
|
| 5 |
+
SPARSE ICP,57.38276739438777
|
data/bottle_2/bottle2_data_num.csv
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
,Counts
|
| 2 |
+
bottle2_100_ICP,20
|
| 3 |
+
bottle2_75_ICP,20
|
| 4 |
+
bottle2_50_ICP,20
|
| 5 |
+
bottle2_25_ICP,20
|
| 6 |
+
bottle2_0_ICP,21
|
data/bottle_2/cut_files.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"25": [
|
| 3 |
+
"0_23",
|
| 4 |
+
"0_21",
|
| 5 |
+
"0_22"
|
| 6 |
+
]
|
| 7 |
+
}
|
data/bottle_2/dataset_pandas.ipynb
ADDED
|
@@ -0,0 +1,606 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "781eee9c",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## using pandas\n"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 9,
|
| 14 |
+
"id": "70fc5658",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [],
|
| 17 |
+
"source": [
|
| 18 |
+
"import pandas as pd\n",
|
| 19 |
+
"import numpy as np\n",
|
| 20 |
+
"import os\n",
|
| 21 |
+
"import json\n",
|
| 22 |
+
"## column : file no 1~25\n",
|
| 23 |
+
"\n",
|
| 24 |
+
"# array 4X4\n",
|
| 25 |
+
"# for i in range(rows):\n",
|
| 26 |
+
"# for j in range(cols):\n",
|
| 27 |
+
"# object_array[i,j] = np.zeros((4,4))\n",
|
| 28 |
+
"\n",
|
| 29 |
+
"\n",
|
| 30 |
+
"data = np.zeros((20,25))\n",
|
| 31 |
+
"\n",
|
| 32 |
+
"\n",
|
| 33 |
+
"\n",
|
| 34 |
+
"## row : bottle_0, bottle_25 ... gt 0 25 --> 10 rows. \n",
|
| 35 |
+
"\n",
|
| 36 |
+
"categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
|
| 37 |
+
"\n",
|
| 38 |
+
"category = categories[0]\n",
|
| 39 |
+
"fill_rate = ['100', '75', '50', '25', '0']\n",
|
| 40 |
+
"\n",
|
| 41 |
+
"columns = [f'file_{i}' for i in range(1,26)]\n",
|
| 42 |
+
"\n",
|
| 43 |
+
"\n",
|
| 44 |
+
"\n"
|
| 45 |
+
]
|
| 46 |
+
},
|
| 47 |
+
{
|
| 48 |
+
"cell_type": "markdown",
|
| 49 |
+
"id": "22195309",
|
| 50 |
+
"metadata": {},
|
| 51 |
+
"source": [
|
| 52 |
+
"## Get transformation file "
|
| 53 |
+
]
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"cell_type": "code",
|
| 57 |
+
"execution_count": null,
|
| 58 |
+
"id": "d3dcc164",
|
| 59 |
+
"metadata": {},
|
| 60 |
+
"outputs": [],
|
| 61 |
+
"source": []
|
| 62 |
+
},
|
| 63 |
+
{
|
| 64 |
+
"cell_type": "code",
|
| 65 |
+
"execution_count": 10,
|
| 66 |
+
"id": "86c0ea73",
|
| 67 |
+
"metadata": {},
|
| 68 |
+
"outputs": [
|
| 69 |
+
{
|
| 70 |
+
"data": {
|
| 71 |
+
"text/plain": [
|
| 72 |
+
"<bound method DataFrame.info of file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25\n",
|
| 73 |
+
"bottle2_100_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 74 |
+
"bottle2_75_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 75 |
+
"bottle2_50_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 76 |
+
"bottle2_25_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 77 |
+
"bottle2_0_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 78 |
+
"bottle2_100_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 79 |
+
"bottle2_75_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 80 |
+
"bottle2_50_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 81 |
+
"bottle2_25_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 82 |
+
"bottle2_0_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 83 |
+
"bottle2_100_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 84 |
+
"bottle2_75_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 85 |
+
"bottle2_50_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 86 |
+
"bottle2_25_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 87 |
+
"bottle2_0_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 88 |
+
"bottle2_100_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 89 |
+
"bottle2_75_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 90 |
+
"bottle2_50_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 91 |
+
"bottle2_25_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
|
| 92 |
+
"bottle2_0_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0>"
|
| 93 |
+
]
|
| 94 |
+
},
|
| 95 |
+
"execution_count": 10,
|
| 96 |
+
"metadata": {},
|
| 97 |
+
"output_type": "execute_result"
|
| 98 |
+
}
|
| 99 |
+
],
|
| 100 |
+
"source": [
|
| 101 |
+
"## Tmatrix FOlder access -> save in pandas\n",
|
| 102 |
+
"robust_no = ['0','2','3','6']\n",
|
| 103 |
+
"new_row_names = []\n",
|
| 104 |
+
"# ๊ฒฐ๊ณผ๋ฅผ ์ ์ฅํ ๋์
๋๋ฆฌ๋ฅผ ์นดํ
๊ณ ๋ฆฌ๋ณ๋ก ์ด๊ธฐํํฉ๋๋ค.\n",
|
| 105 |
+
"grouped_files = {fill: [] for fill in fill_rate}\n",
|
| 106 |
+
"\n",
|
| 107 |
+
"for no in robust_no:\n",
|
| 108 |
+
" \n",
|
| 109 |
+
" ## get txt file\n",
|
| 110 |
+
"\n",
|
| 111 |
+
" ######################## We got the txt file list#################\n",
|
| 112 |
+
" for fills in fill_rate:\n",
|
| 113 |
+
" \n",
|
| 114 |
+
" if no =='0':\n",
|
| 115 |
+
" name = \"ICP\"\n",
|
| 116 |
+
" elif no == '2':\n",
|
| 117 |
+
" name = \"FAST ICP\"\n",
|
| 118 |
+
" elif no =='3':\n",
|
| 119 |
+
" name = \"Robust ICP\"\n",
|
| 120 |
+
" else:\n",
|
| 121 |
+
" name = \"Sparse ICP\"\n",
|
| 122 |
+
"\n",
|
| 123 |
+
" new_row_names.append(f\"{category}_{fills}_{name}\")\n",
|
| 124 |
+
"\n",
|
| 125 |
+
"df = pd.DataFrame(data, index=new_row_names, columns=columns, dtype=object)\n",
|
| 126 |
+
"# 2. df.index์ ์๋ก์ด ์ด๋ฆ ๋ฆฌ์คํธ๋ฅผ ๋ฐ๋ก ํ ๋น object for array 4x4\n",
|
| 127 |
+
"\n",
|
| 128 |
+
"df.info"
|
| 129 |
+
]
|
| 130 |
+
},
|
| 131 |
+
{
|
| 132 |
+
"cell_type": "markdown",
|
| 133 |
+
"id": "173149df",
|
| 134 |
+
"metadata": {},
|
| 135 |
+
"source": [
|
| 136 |
+
"## RMSE function"
|
| 137 |
+
]
|
| 138 |
+
},
|
| 139 |
+
{
|
| 140 |
+
"cell_type": "code",
|
| 141 |
+
"execution_count": 11,
|
| 142 |
+
"id": "5334ae14",
|
| 143 |
+
"metadata": {},
|
| 144 |
+
"outputs": [
|
| 145 |
+
{
|
| 146 |
+
"name": "stdout",
|
| 147 |
+
"output_type": "stream",
|
| 148 |
+
"text": [
|
| 149 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './result3/result_3_100_1.txt' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n"
|
| 150 |
+
]
|
| 151 |
+
}
|
| 152 |
+
],
|
| 153 |
+
"source": [
|
| 154 |
+
"def RMSE(T_star, T):\n",
|
| 155 |
+
" diff = T_star - T\n",
|
| 156 |
+
" sq_norms = np.sum(diff**2, axis =1)\n",
|
| 157 |
+
"\n",
|
| 158 |
+
" r = np.sqrt(np.mean(sq_norms))\n",
|
| 159 |
+
"\n",
|
| 160 |
+
" return r\n",
|
| 161 |
+
"\n",
|
| 162 |
+
"## get T from Result Txt file\n",
|
| 163 |
+
"def get_T(file_path):\n",
|
| 164 |
+
"\n",
|
| 165 |
+
" try:\n",
|
| 166 |
+
" with open(file_path, 'r') as f:\n",
|
| 167 |
+
" T_matrix = np.loadtxt(file_path)\n",
|
| 168 |
+
" return T_matrix\n",
|
| 169 |
+
" except FileNotFoundError:\n",
|
| 170 |
+
" # try ๋ธ๋ก์์ FileNotFoundError๊ฐ ๋ฐ์ํ์ ๋๋ง ์ด ์ฝ๋๊ฐ ์คํ๋ฉ๋๋ค.\n",
|
| 171 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : '{file_path}' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\")\n",
|
| 172 |
+
" return None # ํ์ผ์ด ์์ผ๋ฏ๋ก None์ ๋ฐํ\n",
|
| 173 |
+
"\n",
|
| 174 |
+
"\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"\n",
|
| 177 |
+
"def get_GT_T(file_path,data_name):\n",
|
| 178 |
+
"\n",
|
| 179 |
+
" try:\n",
|
| 180 |
+
" with open(file_path, 'r') as f:\n",
|
| 181 |
+
" loaded_data = json.load(f)\n",
|
| 182 |
+
" noisy_data = loaded_data[data_name]\n",
|
| 183 |
+
" T_matrix = noisy_data['matrix_world']\n",
|
| 184 |
+
" np.array(T_matrix)\n",
|
| 185 |
+
" return T_matrix\n",
|
| 186 |
+
"\n",
|
| 187 |
+
" except FileNotFoundError:\n",
|
| 188 |
+
" # try ๋ธ๋ก์์ FileNotFoundError๊ฐ ๋ฐ์ํ์ ๋๋ง ์ด ์ฝ๋๊ฐ ์คํ๋ฉ๋๋ค.\n",
|
| 189 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : '{file_path}' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\")\n",
|
| 190 |
+
" return None # ํ์ผ์ด ์์ผ๋ฏ๋ก None์ ๋ฐํ\n",
|
| 191 |
+
"\n",
|
| 192 |
+
" except KeyError as e:\n",
|
| 193 |
+
" # try ๋ธ๋ก์์ KeyError๊ฐ ๋ฐ์ํ์ ๋ ์คํ๋ฉ๋๋ค. (e.g., 'matrix_world' ํค๊ฐ ์์)\n",
|
| 194 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : ํ์ผ '{os.path.basename(file_path)}' ์์ ํ์ํ ํค({e})๊ฐ ์์ต๋๋ค.\")\n",
|
| 195 |
+
" return None\n",
|
| 196 |
+
" \n",
|
| 197 |
+
" \n",
|
| 198 |
+
"\n",
|
| 199 |
+
"def compute_RMSE(gt_files):\n",
|
| 200 |
+
" \n",
|
| 201 |
+
" robust_no = ['0','2','3','6']\n",
|
| 202 |
+
" \n",
|
| 203 |
+
" for no in robust_no:\n",
|
| 204 |
+
" if no =='0':\n",
|
| 205 |
+
" name = \"ICP\"\n",
|
| 206 |
+
" elif no == '2':\n",
|
| 207 |
+
" name = \"FAST ICP\"\n",
|
| 208 |
+
" elif no =='3':\n",
|
| 209 |
+
" name = \"Robust ICP\"\n",
|
| 210 |
+
" else:\n",
|
| 211 |
+
" name = \"Sparse ICP\"\n",
|
| 212 |
+
"\n",
|
| 213 |
+
" for key, value_list in gt_files.items():\n",
|
| 214 |
+
" rmse = []\n",
|
| 215 |
+
" np.array(rmse)\n",
|
| 216 |
+
" # get gt_T and noisy_T\n",
|
| 217 |
+
" for value in value_list:\n",
|
| 218 |
+
" profix = value.split('_')[1]\n",
|
| 219 |
+
" gt_path = f\"./gt_raw/noisy_filtered_{key}_{profix}.json\"\n",
|
| 220 |
+
" gt_name = f\"noisy_filtered_{key}_{profix}\"\n",
|
| 221 |
+
"\n",
|
| 222 |
+
" #### RESULT FOLDER PATH.\n",
|
| 223 |
+
" result_path = f'./result{no}/result_{key}_{profix}.txt'\n",
|
| 224 |
+
" icp_T = get_T(result_path)\n",
|
| 225 |
+
" gt_T = get_GT_T(gt_path,gt_name)\n",
|
| 226 |
+
" \n",
|
| 227 |
+
" \n",
|
| 228 |
+
"\n",
|
| 229 |
+
" if (gt_T is None or icp_T is None):\n",
|
| 230 |
+
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = 0.0\n",
|
| 231 |
+
"\n",
|
| 232 |
+
" else:\n",
|
| 233 |
+
" ## conpute rmse\n",
|
| 234 |
+
" r = RMSE(gt_T, icp_T)\n",
|
| 235 |
+
" \n",
|
| 236 |
+
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = r\n",
|
| 237 |
+
"\n",
|
| 238 |
+
"\n",
|
| 239 |
+
"noisy_T = get_T(\"./result3/result_3_100_1.txt\")\n",
|
| 240 |
+
"gt_T = get_GT_T(\"./gt/noisy_filtered_100_1.json\",\"noisy_filtered_100_1\")\n",
|
| 241 |
+
"\n"
|
| 242 |
+
]
|
| 243 |
+
},
|
| 244 |
+
{
|
| 245 |
+
"cell_type": "markdown",
|
| 246 |
+
"id": "587f5b2d",
|
| 247 |
+
"metadata": {},
|
| 248 |
+
"source": [
|
| 249 |
+
"## Bring GT"
|
| 250 |
+
]
|
| 251 |
+
},
|
| 252 |
+
{
|
| 253 |
+
"cell_type": "code",
|
| 254 |
+
"execution_count": 12,
|
| 255 |
+
"id": "c4883f09",
|
| 256 |
+
"metadata": {},
|
| 257 |
+
"outputs": [
|
| 258 |
+
{
|
| 259 |
+
"name": "stdout",
|
| 260 |
+
"output_type": "stream",
|
| 261 |
+
"text": [
|
| 262 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_12.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 263 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_17.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 264 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_12.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 265 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_17.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 266 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_12.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 267 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_17.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 268 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_12.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 269 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_0_17.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 270 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 271 |
+
"bottle2_100_ICP 57.726431 58.073827 57.979086 69.954194 53.573036 52.09264 70.639847 66.144313 72.694435 72.247312 67.095363 39.427028 36.097347 51.937576 60.991109 70.36314 70.532546 72.729103 55.116216 54.524579 0.0 0.0 0.0 0.0 0.0 60.496956\n",
|
| 272 |
+
"bottle2_75_ICP 66.557193 67.623246 58.069773 68.352285 45.998694 63.978648 59.741997 71.648534 70.064837 53.158668 60.973961 38.136257 36.259578 55.429318 69.340292 69.000672 71.16037 60.483901 55.862601 66.854347 0.0 0.0 0.0 0.0 0.0 60.434759\n",
|
| 273 |
+
"bottle2_50_ICP 54.377858 53.393512 66.12583 47.561213 51.31821 57.560825 68.805384 55.289464 73.306761 70.184317 65.496406 71.789794 67.639152 54.298582 60.46459 38.607896 35.885329 37.012867 57.479033 71.525565 0.0 0.0 0.0 0.0 0.0 57.906129\n",
|
| 274 |
+
"bottle2_25_ICP 71.730017 70.795363 63.555661 67.25048 63.613194 51.285702 42.303407 39.39284 65.657843 67.373311 79.379446 51.375709 55.391288 51.114255 56.139717 53.657441 70.10359 71.862892 82.068982 67.205189 0.0 0.0 0.0 0.0 0.0 62.062816\n",
|
| 275 |
+
"bottle2_0_ICP 80.541255 78.927351 86.367711 51.188665 47.259758 23.529144 26.495752 26.307019 58.988365 87.186705 94.964133 0.0 60.931521 56.640394 30.727522 30.044528 0.0 89.849671 79.279781 24.165655 94.358841 33.815192 45.865195 0.0 0.0 57.496865\n",
|
| 276 |
+
"bottle2_100_FAST ICP 57.688938 58.073827 57.95261 69.895066 53.288431 52.095725 70.636632 66.160966 72.69402 72.207366 66.179926 39.479096 36.090186 51.322082 60.814999 70.445922 70.538539 72.740172 55.116972 54.539657 0.0 0.0 0.0 0.0 0.0 60.398057\n",
|
| 277 |
+
"bottle2_75_FAST ICP 44.860989 67.613803 58.069773 68.358055 46.042018 64.441146 59.721992 71.641551 70.052412 53.481736 60.979005 38.131182 55.091599 55.153897 69.289835 68.607169 71.218541 59.599214 55.860476 67.014762 0.0 0.0 0.0 0.0 0.0 60.261458\n",
|
| 278 |
+
"bottle2_50_FAST ICP 48.445113 53.266916 66.128142 47.394348 51.068578 57.519886 68.855187 66.043425 73.383533 70.153381 65.461271 71.790779 68.849646 54.293063 60.622404 38.606699 35.904505 36.900701 57.872143 72.136896 0.0 0.0 0.0 0.0 0.0 58.234831\n",
|
| 279 |
+
"bottle2_25_FAST ICP 71.730556 70.813613 48.390064 67.208482 63.630603 51.294102 42.303407 39.394111 65.684234 67.362821 79.37104 51.333991 51.569497 51.046581 56.147149 53.361444 67.824738 71.863382 82.072543 67.188839 0.0 0.0 0.0 0.0 0.0 60.979560\n",
|
| 280 |
+
"bottle2_0_FAST ICP 80.541086 78.927351 86.369584 49.968808 47.255769 23.557333 26.504626 26.359362 58.95614 87.183452 94.944673 0.0 60.904371 56.000499 30.738225 27.684303 0.0 89.849728 79.276977 22.581162 94.359098 33.984328 43.870373 0.0 0.0 57.134155\n",
|
| 281 |
+
"bottle2_100_Robust ICP 50.504351 49.133166 49.608769 65.247935 42.131387 43.924281 68.181318 59.094124 67.919525 67.379707 50.458574 52.717507 34.114118 54.92686 57.805806 65.611106 61.177957 65.368603 41.45572 50.579692 0.0 0.0 0.0 0.0 0.0 54.867025\n",
|
| 282 |
+
"bottle2_75_Robust ICP 65.171352 54.045867 36.901146 54.330906 47.420552 65.597031 55.602239 66.911923 67.495546 36.590494 52.79024 32.480709 50.646411 48.142464 56.953986 62.867727 57.595423 52.695511 51.982744 50.382476 0.0 0.0 0.0 0.0 0.0 53.330237\n",
|
| 283 |
+
"bottle2_50_Robust ICP 47.771693 45.012185 57.661057 42.412898 44.792427 56.455638 59.622745 50.11804 57.469541 62.813152 55.040781 61.801269 59.122552 53.439211 61.519585 28.646356 55.147605 37.786525 62.005449 61.623284 0.0 0.0 0.0 0.0 0.0 53.013100\n",
|
| 284 |
+
"bottle2_25_Robust ICP 68.372297 65.913029 60.802011 62.199418 62.664916 48.949447 49.991884 6.183673 49.365622 57.576716 59.482653 47.27592 61.409181 39.522688 46.994318 45.567914 57.165478 60.199405 58.092839 65.6003 0.0 0.0 0.0 0.0 0.0 53.666485\n",
|
| 285 |
+
"bottle2_0_Robust ICP 64.716537 62.513045 62.81728 49.088234 45.262582 33.32966 21.138026 11.118374 47.963994 76.110225 73.256406 0.0 60.237215 77.414676 49.737045 9.669534 0.0 73.509738 69.848925 36.882767 73.228013 19.113386 47.360497 0.0 0.0 50.681722\n",
|
| 286 |
+
"bottle2_100_Sparse ICP 53.412883 53.800208 52.790838 57.532525 53.838994 48.981065 62.133405 60.96027 76.258964 67.811151 60.9657 39.873357 41.373595 55.00382 59.390746 62.465343 67.976456 65.191948 52.286949 45.004554 0.0 0.0 0.0 0.0 0.0 56.852639\n",
|
| 287 |
+
"bottle2_75_Sparse ICP 60.758925 62.083034 53.335445 51.852443 48.473864 57.978259 61.524882 65.698803 66.304336 58.831034 56.198062 36.067757 44.468705 56.804776 63.106554 65.245407 69.781086 65.129953 65.362751 51.36387 0.0 0.0 0.0 0.0 0.0 58.018497\n",
|
| 288 |
+
"bottle2_50_Sparse ICP 63.234282 62.944344 65.182522 48.120253 52.935785 52.932735 55.108551 62.309733 76.492421 62.961988 66.740594 66.057711 64.091673 57.605262 52.913206 48.140719 39.945907 32.604047 50.441113 66.71904 0.0 0.0 0.0 0.0 0.0 57.374094\n",
|
| 289 |
+
"bottle2_25_Sparse ICP 68.338245 67.504931 60.979872 57.887682 58.421357 30.656694 31.248467 43.173549 62.545731 68.838698 78.791105 53.815284 47.591621 49.769237 52.289646 57.737586 62.937188 66.062745 79.519469 57.498068 0.0 0.0 0.0 0.0 0.0 57.780359\n",
|
| 290 |
+
"bottle2_0_Sparse ICP 74.964649 64.046069 72.359844 54.094445 53.676616 27.231209 23.413694 20.608975 55.196291 91.152314 82.327922 0.0 72.626788 63.141163 28.504973 23.181661 0.0 74.259527 74.642275 65.102882 82.026225 27.648789 64.4469 0.0 0.0 56.888248\n"
|
| 291 |
+
]
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"name": "stderr",
|
| 295 |
+
"output_type": "stream",
|
| 296 |
+
"text": [
|
| 297 |
+
"/tmp/ipykernel_270442/3042233176.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
| 298 |
+
" df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n"
|
| 299 |
+
]
|
| 300 |
+
}
|
| 301 |
+
],
|
| 302 |
+
"source": [
|
| 303 |
+
"json_path = \"ply_files.json\"\n",
|
| 304 |
+
"try: \n",
|
| 305 |
+
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
|
| 306 |
+
" gt_files = json.load(f)\n",
|
| 307 |
+
"except FileNotFoundError:\n",
|
| 308 |
+
" print(f\"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.\")\n",
|
| 309 |
+
" exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ\n",
|
| 310 |
+
"\n",
|
| 311 |
+
"\n",
|
| 312 |
+
"\n",
|
| 313 |
+
"### get \n",
|
| 314 |
+
"\n",
|
| 315 |
+
"\n",
|
| 316 |
+
"\n",
|
| 317 |
+
"compute_RMSE(gt_files)\n",
|
| 318 |
+
"\n",
|
| 319 |
+
"##get mean value\n",
|
| 320 |
+
"df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n",
|
| 321 |
+
"\n",
|
| 322 |
+
"\n",
|
| 323 |
+
"\n",
|
| 324 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 325 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 326 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 327 |
+
"\n",
|
| 328 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 329 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 330 |
+
"\n",
|
| 331 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๊ฟ์ ํ ์ง ๋ง์ง\n",
|
| 332 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 333 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 334 |
+
"\n",
|
| 335 |
+
"# ์: DataFrame ์ถ๋ ฅ\n",
|
| 336 |
+
"print(df)\n",
|
| 337 |
+
" \n",
|
| 338 |
+
"\n",
|
| 339 |
+
"\n"
|
| 340 |
+
]
|
| 341 |
+
},
|
| 342 |
+
{
|
| 343 |
+
"cell_type": "markdown",
|
| 344 |
+
"id": "7493fb27",
|
| 345 |
+
"metadata": {},
|
| 346 |
+
"source": [
|
| 347 |
+
"## GET RMSE MEAN by ICP Methods\n",
|
| 348 |
+
"\n"
|
| 349 |
+
]
|
| 350 |
+
},
|
| 351 |
+
{
|
| 352 |
+
"cell_type": "code",
|
| 353 |
+
"execution_count": 13,
|
| 354 |
+
"id": "e49285b9",
|
| 355 |
+
"metadata": {},
|
| 356 |
+
"outputs": [
|
| 357 |
+
{
|
| 358 |
+
"name": "stdout",
|
| 359 |
+
"output_type": "stream",
|
| 360 |
+
"text": [
|
| 361 |
+
"[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]\n",
|
| 362 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 363 |
+
"ICP 66.186551 65.76266 66.419612 60.861367 52.352578 49.689392 53.597278 51.756434 68.142448 70.030063 73.581862 40.145757 51.263777 53.884025 55.532646 52.334736 49.536367 66.387687 65.961323 56.855067 18.871768 6.763038 9.173039 0.0 0.0 59.679505\n",
|
| 364 |
+
"FAST ICP 60.653336 65.739102 63.382035 60.564952 52.25708 49.781638 53.604369 53.919883 68.154068 70.077751 73.387183 40.14701 54.50106 53.563224 55.522523 51.741107 49.097265 66.190639 66.039822 56.692263 18.87182 6.796866 8.774075 0.0 0.0 59.401612\n",
|
| 365 |
+
"FAST AND ROBUST ICP 59.307246 55.323458 53.558052 54.655878 48.454373 49.651212 50.907243 38.685227 58.042846 60.094059 58.205731 38.855081 53.105895 54.68918 54.602148 42.472527 46.217293 57.911956 56.677135 53.013704 14.645603 3.822677 9.472099 0.0 0.0 53.111714\n",
|
| 366 |
+
"SPARSE ICP 64.141797 62.075717 60.929704 53.89747 53.469323 43.555993 46.6858 50.550266 67.359549 69.919037 69.004677 39.162822 54.030476 56.464851 51.241025 51.354143 48.128127 60.649644 64.450511 57.137683 16.405245 5.529758 12.88938 0.0 0.0 57.382767\n",
|
| 367 |
+
"<class 'pandas.core.frame.DataFrame'>\n"
|
| 368 |
+
]
|
| 369 |
+
}
|
| 370 |
+
],
|
| 371 |
+
"source": [
|
| 372 |
+
"df_mean = np.zeros((5,5))\n",
|
| 373 |
+
"\n",
|
| 374 |
+
"## make 25 lengths array\n",
|
| 375 |
+
"\n",
|
| 376 |
+
"grouping = []\n",
|
| 377 |
+
"\n",
|
| 378 |
+
"for i in range(0,len(df)):\n",
|
| 379 |
+
" grouping.append(i)\n",
|
| 380 |
+
"\n",
|
| 381 |
+
"grouping = np.arange(len(df)) //5\n",
|
| 382 |
+
"\n",
|
| 383 |
+
"print(grouping)\n",
|
| 384 |
+
"block_avg_df = df.groupby(grouping).mean()\n",
|
| 385 |
+
"\n",
|
| 386 |
+
"\n",
|
| 387 |
+
"ICP_Method = ['ICP', 'FAST ICP', 'FAST AND ROBUST ICP', 'SPARSE ICP']\n",
|
| 388 |
+
"\n",
|
| 389 |
+
"\n",
|
| 390 |
+
"\n",
|
| 391 |
+
"block_avg_df.index = ICP_Method\n",
|
| 392 |
+
"\n",
|
| 393 |
+
"\n",
|
| 394 |
+
"print(block_avg_df)\n",
|
| 395 |
+
"\n",
|
| 396 |
+
"print(type(block_avg_df))\n",
|
| 397 |
+
"\n",
|
| 398 |
+
"\n"
|
| 399 |
+
]
|
| 400 |
+
},
|
| 401 |
+
{
|
| 402 |
+
"cell_type": "code",
|
| 403 |
+
"execution_count": null,
|
| 404 |
+
"id": "14ebb074",
|
| 405 |
+
"metadata": {},
|
| 406 |
+
"outputs": [],
|
| 407 |
+
"source": []
|
| 408 |
+
},
|
| 409 |
+
{
|
| 410 |
+
"cell_type": "markdown",
|
| 411 |
+
"id": "d03a908e",
|
| 412 |
+
"metadata": {},
|
| 413 |
+
"source": [
|
| 414 |
+
"## merge in Pandas"
|
| 415 |
+
]
|
| 416 |
+
},
|
| 417 |
+
{
|
| 418 |
+
"cell_type": "code",
|
| 419 |
+
"execution_count": 14,
|
| 420 |
+
"id": "92386801",
|
| 421 |
+
"metadata": {},
|
| 422 |
+
"outputs": [
|
| 423 |
+
{
|
| 424 |
+
"name": "stdout",
|
| 425 |
+
"output_type": "stream",
|
| 426 |
+
"text": [
|
| 427 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 428 |
+
"bottle2_100_ICP 57.726431 58.073827 57.979086 69.954194 53.573036 52.09264 70.639847 66.144313 72.694435 72.247312 67.095363 39.427028 36.097347 51.937576 60.991109 70.36314 70.532546 72.729103 55.116216 54.524579 0.0 0.0 0.0 0.0 0.0 60.496956\n",
|
| 429 |
+
"bottle2_75_ICP 66.557193 67.623246 58.069773 68.352285 45.998694 63.978648 59.741997 71.648534 70.064837 53.158668 60.973961 38.136257 36.259578 55.429318 69.340292 69.000672 71.16037 60.483901 55.862601 66.854347 0.0 0.0 0.0 0.0 0.0 60.434759\n",
|
| 430 |
+
"bottle2_50_ICP 54.377858 53.393512 66.12583 47.561213 51.31821 57.560825 68.805384 55.289464 73.306761 70.184317 65.496406 71.789794 67.639152 54.298582 60.46459 38.607896 35.885329 37.012867 57.479033 71.525565 0.0 0.0 0.0 0.0 0.0 57.906129\n",
|
| 431 |
+
"bottle2_25_ICP 71.730017 70.795363 63.555661 67.25048 63.613194 51.285702 42.303407 39.39284 65.657843 67.373311 79.379446 51.375709 55.391288 51.114255 56.139717 53.657441 70.10359 71.862892 82.068982 67.205189 0.0 0.0 0.0 0.0 0.0 62.062816\n",
|
| 432 |
+
"bottle2_0_ICP 80.541255 78.927351 86.367711 51.188665 47.259758 23.529144 26.495752 26.307019 58.988365 87.186705 94.964133 0.0 60.931521 56.640394 30.727522 30.044528 0.0 89.849671 79.279781 24.165655 94.358841 33.815192 45.865195 0.0 0.0 57.496865\n",
|
| 433 |
+
"bottle2_100_FAST ICP 57.688938 58.073827 57.95261 69.895066 53.288431 52.095725 70.636632 66.160966 72.69402 72.207366 66.179926 39.479096 36.090186 51.322082 60.814999 70.445922 70.538539 72.740172 55.116972 54.539657 0.0 0.0 0.0 0.0 0.0 60.398057\n",
|
| 434 |
+
"bottle2_75_FAST ICP 44.860989 67.613803 58.069773 68.358055 46.042018 64.441146 59.721992 71.641551 70.052412 53.481736 60.979005 38.131182 55.091599 55.153897 69.289835 68.607169 71.218541 59.599214 55.860476 67.014762 0.0 0.0 0.0 0.0 0.0 60.261458\n",
|
| 435 |
+
"bottle2_50_FAST ICP 48.445113 53.266916 66.128142 47.394348 51.068578 57.519886 68.855187 66.043425 73.383533 70.153381 65.461271 71.790779 68.849646 54.293063 60.622404 38.606699 35.904505 36.900701 57.872143 72.136896 0.0 0.0 0.0 0.0 0.0 58.234831\n",
|
| 436 |
+
"bottle2_25_FAST ICP 71.730556 70.813613 48.390064 67.208482 63.630603 51.294102 42.303407 39.394111 65.684234 67.362821 79.37104 51.333991 51.569497 51.046581 56.147149 53.361444 67.824738 71.863382 82.072543 67.188839 0.0 0.0 0.0 0.0 0.0 60.979560\n",
|
| 437 |
+
"bottle2_0_FAST ICP 80.541086 78.927351 86.369584 49.968808 47.255769 23.557333 26.504626 26.359362 58.95614 87.183452 94.944673 0.0 60.904371 56.000499 30.738225 27.684303 0.0 89.849728 79.276977 22.581162 94.359098 33.984328 43.870373 0.0 0.0 57.134155\n",
|
| 438 |
+
"bottle2_100_Robust ICP 50.504351 49.133166 49.608769 65.247935 42.131387 43.924281 68.181318 59.094124 67.919525 67.379707 50.458574 52.717507 34.114118 54.92686 57.805806 65.611106 61.177957 65.368603 41.45572 50.579692 0.0 0.0 0.0 0.0 0.0 54.867025\n",
|
| 439 |
+
"bottle2_75_Robust ICP 65.171352 54.045867 36.901146 54.330906 47.420552 65.597031 55.602239 66.911923 67.495546 36.590494 52.79024 32.480709 50.646411 48.142464 56.953986 62.867727 57.595423 52.695511 51.982744 50.382476 0.0 0.0 0.0 0.0 0.0 53.330237\n",
|
| 440 |
+
"bottle2_50_Robust ICP 47.771693 45.012185 57.661057 42.412898 44.792427 56.455638 59.622745 50.11804 57.469541 62.813152 55.040781 61.801269 59.122552 53.439211 61.519585 28.646356 55.147605 37.786525 62.005449 61.623284 0.0 0.0 0.0 0.0 0.0 53.013100\n",
|
| 441 |
+
"bottle2_25_Robust ICP 68.372297 65.913029 60.802011 62.199418 62.664916 48.949447 49.991884 6.183673 49.365622 57.576716 59.482653 47.27592 61.409181 39.522688 46.994318 45.567914 57.165478 60.199405 58.092839 65.6003 0.0 0.0 0.0 0.0 0.0 53.666485\n",
|
| 442 |
+
"bottle2_0_Robust ICP 64.716537 62.513045 62.81728 49.088234 45.262582 33.32966 21.138026 11.118374 47.963994 76.110225 73.256406 0.0 60.237215 77.414676 49.737045 9.669534 0.0 73.509738 69.848925 36.882767 73.228013 19.113386 47.360497 0.0 0.0 50.681722\n",
|
| 443 |
+
"bottle2_100_Sparse ICP 53.412883 53.800208 52.790838 57.532525 53.838994 48.981065 62.133405 60.96027 76.258964 67.811151 60.9657 39.873357 41.373595 55.00382 59.390746 62.465343 67.976456 65.191948 52.286949 45.004554 0.0 0.0 0.0 0.0 0.0 56.852639\n",
|
| 444 |
+
"bottle2_75_Sparse ICP 60.758925 62.083034 53.335445 51.852443 48.473864 57.978259 61.524882 65.698803 66.304336 58.831034 56.198062 36.067757 44.468705 56.804776 63.106554 65.245407 69.781086 65.129953 65.362751 51.36387 0.0 0.0 0.0 0.0 0.0 58.018497\n",
|
| 445 |
+
"bottle2_50_Sparse ICP 63.234282 62.944344 65.182522 48.120253 52.935785 52.932735 55.108551 62.309733 76.492421 62.961988 66.740594 66.057711 64.091673 57.605262 52.913206 48.140719 39.945907 32.604047 50.441113 66.71904 0.0 0.0 0.0 0.0 0.0 57.374094\n",
|
| 446 |
+
"bottle2_25_Sparse ICP 68.338245 67.504931 60.979872 57.887682 58.421357 30.656694 31.248467 43.173549 62.545731 68.838698 78.791105 53.815284 47.591621 49.769237 52.289646 57.737586 62.937188 66.062745 79.519469 57.498068 0.0 0.0 0.0 0.0 0.0 57.780359\n",
|
| 447 |
+
"bottle2_0_Sparse ICP 74.964649 64.046069 72.359844 54.094445 53.676616 27.231209 23.413694 20.608975 55.196291 91.152314 82.327922 0.0 72.626788 63.141163 28.504973 23.181661 0.0 74.259527 74.642275 65.102882 82.026225 27.648789 64.4469 0.0 0.0 56.888248\n",
|
| 448 |
+
"ICP 66.186551 65.76266 66.419612 60.861367 52.352578 49.689392 53.597278 51.756434 68.142448 70.030063 73.581862 40.145757 51.263777 53.884025 55.532646 52.334736 49.536367 66.387687 65.961323 56.855067 18.871768 6.763038 9.173039 0.0 0.0 59.679505\n",
|
| 449 |
+
"FAST ICP 60.653336 65.739102 63.382035 60.564952 52.25708 49.781638 53.604369 53.919883 68.154068 70.077751 73.387183 40.14701 54.50106 53.563224 55.522523 51.741107 49.097265 66.190639 66.039822 56.692263 18.87182 6.796866 8.774075 0.0 0.0 59.401612\n",
|
| 450 |
+
"FAST AND ROBUST ICP 59.307246 55.323458 53.558052 54.655878 48.454373 49.651212 50.907243 38.685227 58.042846 60.094059 58.205731 38.855081 53.105895 54.68918 54.602148 42.472527 46.217293 57.911956 56.677135 53.013704 14.645603 3.822677 9.472099 0.0 0.0 53.111714\n",
|
| 451 |
+
"SPARSE ICP 64.141797 62.075717 60.929704 53.89747 53.469323 43.555993 46.6858 50.550266 67.359549 69.919037 69.004677 39.162822 54.030476 56.464851 51.241025 51.354143 48.128127 60.649644 64.450511 57.137683 16.405245 5.529758 12.88938 0.0 0.0 57.382767\n"
|
| 452 |
+
]
|
| 453 |
+
}
|
| 454 |
+
],
|
| 455 |
+
"source": [
|
| 456 |
+
"combined_df = pd.concat([df, block_avg_df], ignore_index=False)\n",
|
| 457 |
+
"\n",
|
| 458 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 459 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 460 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 461 |
+
"\n",
|
| 462 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 463 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 464 |
+
"\n",
|
| 465 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๊ฟ์ ํ ์ง ๋ง์ง\n",
|
| 466 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 467 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 468 |
+
"\n",
|
| 469 |
+
"print(combined_df)"
|
| 470 |
+
]
|
| 471 |
+
},
|
| 472 |
+
{
|
| 473 |
+
"cell_type": "markdown",
|
| 474 |
+
"id": "a9b19689",
|
| 475 |
+
"metadata": {},
|
| 476 |
+
"source": [
|
| 477 |
+
"## Save bottle csv"
|
| 478 |
+
]
|
| 479 |
+
},
|
| 480 |
+
{
|
| 481 |
+
"cell_type": "code",
|
| 482 |
+
"execution_count": 15,
|
| 483 |
+
"id": "9e8dcfae",
|
| 484 |
+
"metadata": {},
|
| 485 |
+
"outputs": [
|
| 486 |
+
{
|
| 487 |
+
"name": "stdout",
|
| 488 |
+
"output_type": "stream",
|
| 489 |
+
"text": [
|
| 490 |
+
"ICP 59.679505\n",
|
| 491 |
+
"FAST ICP 59.401612\n",
|
| 492 |
+
"FAST AND ROBUST ICP 53.111714\n",
|
| 493 |
+
"SPARSE ICP 57.382767\n",
|
| 494 |
+
"Name: mean_Val, dtype: float64\n"
|
| 495 |
+
]
|
| 496 |
+
}
|
| 497 |
+
],
|
| 498 |
+
"source": [
|
| 499 |
+
"sliced_data = combined_df.loc['ICP':'SPARSE ICP', 'mean_Val']\n",
|
| 500 |
+
"print(sliced_data)\n",
|
| 501 |
+
"sliced_data.to_csv(f'{category}.csv', index=True)"
|
| 502 |
+
]
|
| 503 |
+
},
|
| 504 |
+
{
|
| 505 |
+
"cell_type": "markdown",
|
| 506 |
+
"id": "fdbb5b00",
|
| 507 |
+
"metadata": {},
|
| 508 |
+
"source": [
|
| 509 |
+
"## Load num of dataset in each category. + save array"
|
| 510 |
+
]
|
| 511 |
+
},
|
| 512 |
+
{
|
| 513 |
+
"cell_type": "code",
|
| 514 |
+
"execution_count": 16,
|
| 515 |
+
"id": "7461379a",
|
| 516 |
+
"metadata": {},
|
| 517 |
+
"outputs": [
|
| 518 |
+
{
|
| 519 |
+
"name": "stdout",
|
| 520 |
+
"output_type": "stream",
|
| 521 |
+
"text": [
|
| 522 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val Counts\n",
|
| 523 |
+
"bottle2_100_ICP 57.726431 58.073827 57.979086 69.954194 53.573036 52.09264 70.639847 66.144313 72.694435 72.247312 67.095363 39.427028 36.097347 51.937576 60.991109 70.36314 70.532546 72.729103 55.116216 54.524579 0.0 0.0 0.0 0.0 0.0 60.496956 20\n",
|
| 524 |
+
"bottle2_75_ICP 66.557193 67.623246 58.069773 68.352285 45.998694 63.978648 59.741997 71.648534 70.064837 53.158668 60.973961 38.136257 36.259578 55.429318 69.340292 69.000672 71.16037 60.483901 55.862601 66.854347 0.0 0.0 0.0 0.0 0.0 60.434759 20\n",
|
| 525 |
+
"bottle2_50_ICP 54.377858 53.393512 66.12583 47.561213 51.31821 57.560825 68.805384 55.289464 73.306761 70.184317 65.496406 71.789794 67.639152 54.298582 60.46459 38.607896 35.885329 37.012867 57.479033 71.525565 0.0 0.0 0.0 0.0 0.0 57.906129 20\n",
|
| 526 |
+
"bottle2_25_ICP 71.730017 70.795363 63.555661 67.25048 63.613194 51.285702 42.303407 39.39284 65.657843 67.373311 79.379446 51.375709 55.391288 51.114255 56.139717 53.657441 70.10359 71.862892 82.068982 67.205189 0.0 0.0 0.0 0.0 0.0 62.062816 20\n",
|
| 527 |
+
"bottle2_0_ICP 80.541255 78.927351 86.367711 51.188665 47.259758 23.529144 26.495752 26.307019 58.988365 87.186705 94.964133 0.0 60.931521 56.640394 30.727522 30.044528 0.0 89.849671 79.279781 24.165655 94.358841 33.815192 45.865195 0.0 0.0 57.496865 21\n",
|
| 528 |
+
"bottle2_100_FAST ICP 57.688938 58.073827 57.95261 69.895066 53.288431 52.095725 70.636632 66.160966 72.69402 72.207366 66.179926 39.479096 36.090186 51.322082 60.814999 70.445922 70.538539 72.740172 55.116972 54.539657 0.0 0.0 0.0 0.0 0.0 60.398057 20\n",
|
| 529 |
+
"bottle2_75_FAST ICP 44.860989 67.613803 58.069773 68.358055 46.042018 64.441146 59.721992 71.641551 70.052412 53.481736 60.979005 38.131182 55.091599 55.153897 69.289835 68.607169 71.218541 59.599214 55.860476 67.014762 0.0 0.0 0.0 0.0 0.0 60.261458 20\n",
|
| 530 |
+
"bottle2_50_FAST ICP 48.445113 53.266916 66.128142 47.394348 51.068578 57.519886 68.855187 66.043425 73.383533 70.153381 65.461271 71.790779 68.849646 54.293063 60.622404 38.606699 35.904505 36.900701 57.872143 72.136896 0.0 0.0 0.0 0.0 0.0 58.234831 20\n",
|
| 531 |
+
"bottle2_25_FAST ICP 71.730556 70.813613 48.390064 67.208482 63.630603 51.294102 42.303407 39.394111 65.684234 67.362821 79.37104 51.333991 51.569497 51.046581 56.147149 53.361444 67.824738 71.863382 82.072543 67.188839 0.0 0.0 0.0 0.0 0.0 60.979560 20\n",
|
| 532 |
+
"bottle2_0_FAST ICP 80.541086 78.927351 86.369584 49.968808 47.255769 23.557333 26.504626 26.359362 58.95614 87.183452 94.944673 0.0 60.904371 56.000499 30.738225 27.684303 0.0 89.849728 79.276977 22.581162 94.359098 33.984328 43.870373 0.0 0.0 57.134155 21\n",
|
| 533 |
+
"bottle2_100_Robust ICP 50.504351 49.133166 49.608769 65.247935 42.131387 43.924281 68.181318 59.094124 67.919525 67.379707 50.458574 52.717507 34.114118 54.92686 57.805806 65.611106 61.177957 65.368603 41.45572 50.579692 0.0 0.0 0.0 0.0 0.0 54.867025 20\n",
|
| 534 |
+
"bottle2_75_Robust ICP 65.171352 54.045867 36.901146 54.330906 47.420552 65.597031 55.602239 66.911923 67.495546 36.590494 52.79024 32.480709 50.646411 48.142464 56.953986 62.867727 57.595423 52.695511 51.982744 50.382476 0.0 0.0 0.0 0.0 0.0 53.330237 20\n",
|
| 535 |
+
"bottle2_50_Robust ICP 47.771693 45.012185 57.661057 42.412898 44.792427 56.455638 59.622745 50.11804 57.469541 62.813152 55.040781 61.801269 59.122552 53.439211 61.519585 28.646356 55.147605 37.786525 62.005449 61.623284 0.0 0.0 0.0 0.0 0.0 53.013100 20\n",
|
| 536 |
+
"bottle2_25_Robust ICP 68.372297 65.913029 60.802011 62.199418 62.664916 48.949447 49.991884 6.183673 49.365622 57.576716 59.482653 47.27592 61.409181 39.522688 46.994318 45.567914 57.165478 60.199405 58.092839 65.6003 0.0 0.0 0.0 0.0 0.0 53.666485 20\n",
|
| 537 |
+
"bottle2_0_Robust ICP 64.716537 62.513045 62.81728 49.088234 45.262582 33.32966 21.138026 11.118374 47.963994 76.110225 73.256406 0.0 60.237215 77.414676 49.737045 9.669534 0.0 73.509738 69.848925 36.882767 73.228013 19.113386 47.360497 0.0 0.0 50.681722 21\n",
|
| 538 |
+
"bottle2_100_Sparse ICP 53.412883 53.800208 52.790838 57.532525 53.838994 48.981065 62.133405 60.96027 76.258964 67.811151 60.9657 39.873357 41.373595 55.00382 59.390746 62.465343 67.976456 65.191948 52.286949 45.004554 0.0 0.0 0.0 0.0 0.0 56.852639 20\n",
|
| 539 |
+
"bottle2_75_Sparse ICP 60.758925 62.083034 53.335445 51.852443 48.473864 57.978259 61.524882 65.698803 66.304336 58.831034 56.198062 36.067757 44.468705 56.804776 63.106554 65.245407 69.781086 65.129953 65.362751 51.36387 0.0 0.0 0.0 0.0 0.0 58.018497 20\n",
|
| 540 |
+
"bottle2_50_Sparse ICP 63.234282 62.944344 65.182522 48.120253 52.935785 52.932735 55.108551 62.309733 76.492421 62.961988 66.740594 66.057711 64.091673 57.605262 52.913206 48.140719 39.945907 32.604047 50.441113 66.71904 0.0 0.0 0.0 0.0 0.0 57.374094 20\n",
|
| 541 |
+
"bottle2_25_Sparse ICP 68.338245 67.504931 60.979872 57.887682 58.421357 30.656694 31.248467 43.173549 62.545731 68.838698 78.791105 53.815284 47.591621 49.769237 52.289646 57.737586 62.937188 66.062745 79.519469 57.498068 0.0 0.0 0.0 0.0 0.0 57.780359 20\n",
|
| 542 |
+
"bottle2_0_Sparse ICP 74.964649 64.046069 72.359844 54.094445 53.676616 27.231209 23.413694 20.608975 55.196291 91.152314 82.327922 0.0 72.626788 63.141163 28.504973 23.181661 0.0 74.259527 74.642275 65.102882 82.026225 27.648789 64.4469 0.0 0.0 56.888248 21\n",
|
| 543 |
+
"###################\n",
|
| 544 |
+
"bottle2_100_ICP 20\n",
|
| 545 |
+
"bottle2_75_ICP 20\n",
|
| 546 |
+
"bottle2_50_ICP 20\n",
|
| 547 |
+
"bottle2_25_ICP 20\n",
|
| 548 |
+
"bottle2_0_ICP 21\n",
|
| 549 |
+
"Name: Counts, dtype: int64\n"
|
| 550 |
+
]
|
| 551 |
+
}
|
| 552 |
+
],
|
| 553 |
+
"source": [
|
| 554 |
+
"\n",
|
| 555 |
+
"\n",
|
| 556 |
+
"df['Counts'] = (df != 0).sum(axis=1)-1\n",
|
| 557 |
+
"\n",
|
| 558 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 559 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 560 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 561 |
+
"\n",
|
| 562 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 563 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 564 |
+
"\n",
|
| 565 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๊ฟ์ ํ ์ง ๋ง์ง\n",
|
| 566 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 567 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 568 |
+
"\n",
|
| 569 |
+
"print(df)\n",
|
| 570 |
+
"\n",
|
| 571 |
+
"\n",
|
| 572 |
+
"\n",
|
| 573 |
+
"sliced_data = df.loc['bottle2_100_ICP':'bottle2_0_ICP', 'Counts']\n",
|
| 574 |
+
"print(f\"###################\\n{sliced_data}\")\n",
|
| 575 |
+
"sliced_data.to_csv(f'{category}_data_num.csv', index=True)"
|
| 576 |
+
]
|
| 577 |
+
},
|
| 578 |
+
{
|
| 579 |
+
"cell_type": "markdown",
|
| 580 |
+
"id": "530262b0",
|
| 581 |
+
"metadata": {},
|
| 582 |
+
"source": []
|
| 583 |
+
}
|
| 584 |
+
],
|
| 585 |
+
"metadata": {
|
| 586 |
+
"kernelspec": {
|
| 587 |
+
"display_name": "icp",
|
| 588 |
+
"language": "python",
|
| 589 |
+
"name": "python3"
|
| 590 |
+
},
|
| 591 |
+
"language_info": {
|
| 592 |
+
"codemirror_mode": {
|
| 593 |
+
"name": "ipython",
|
| 594 |
+
"version": 3
|
| 595 |
+
},
|
| 596 |
+
"file_extension": ".py",
|
| 597 |
+
"mimetype": "text/x-python",
|
| 598 |
+
"name": "python",
|
| 599 |
+
"nbconvert_exporter": "python",
|
| 600 |
+
"pygments_lexer": "ipython3",
|
| 601 |
+
"version": "3.10.19"
|
| 602 |
+
}
|
| 603 |
+
},
|
| 604 |
+
"nbformat": 4,
|
| 605 |
+
"nbformat_minor": 5
|
| 606 |
+
}
|
data/bottle_2/filename.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
100_7
|
data/bottle_2/filter_tea .ipynb
ADDED
|
@@ -0,0 +1,459 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 13 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 14 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 15 |
+
"[-0.92560461 61.60377172 58.09118409]\n"
|
| 16 |
+
]
|
| 17 |
+
}
|
| 18 |
+
],
|
| 19 |
+
"source": [
|
| 20 |
+
"import open3d as o3d\n",
|
| 21 |
+
"import numpy as np\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"GT = False\n",
|
| 24 |
+
"\n",
|
| 25 |
+
"mesh = o3d.io.read_triangle_mesh(\"./bottle.stl\")\n",
|
| 26 |
+
"pointcloud = mesh.sample_points_poisson_disk(50000)\n",
|
| 27 |
+
"coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 28 |
+
"mesh.compute_vertex_normals()\n",
|
| 29 |
+
"mesh_triangles = np.asarray(mesh.triangles)\n",
|
| 30 |
+
"vertex_positions = np.asarray(mesh.vertices)\n",
|
| 31 |
+
"triangle_normals = np.asarray(mesh.triangle_normals)\n",
|
| 32 |
+
"# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ\n",
|
| 33 |
+
"centroid = mesh.get_center()\n",
|
| 34 |
+
"print(centroid)\n",
|
| 35 |
+
"filtered_triangles = []\n",
|
| 36 |
+
"for i, triangle in enumerate(mesh_triangles):\n",
|
| 37 |
+
" # ์ผ๊ฐํ์ ์ค์ฌ์ ๊ณ์ฐ\n",
|
| 38 |
+
" tri_center = vertex_positions[triangle].mean(axis=0)\n",
|
| 39 |
+
" # ๊ฐ์ฒด ์ค์ฌ์์ ์ผ๊ฐํ ์ค์ฌ์ผ๋ก ํฅํ๋ ๋ฒกํฐ\n",
|
| 40 |
+
" vec_to_center = tri_center - centroid\n",
|
| 41 |
+
" # ๋ฒ์ ๋ฒกํฐ์ ๋ฐฉํฅ ๋ฒกํฐ๋ฅผ ๋ด์ \n",
|
| 42 |
+
" dot_product = np.dot(triangle_normals[i], vec_to_center)\n",
|
| 43 |
+
" # ๋ด์ ๊ฐ์ด ์์์ด๋ฉด ๋ฐ๊นฅ์ชฝ ๋ฉด์ผ๋ก ํ๋จ\n",
|
| 44 |
+
" if dot_product > 0:\n",
|
| 45 |
+
" filtered_triangles.append(triangle)\n",
|
| 46 |
+
"# 3. ํํฐ๋ง๋ ๋ฉด์ผ๋ก ์๋ก์ด ๋ฉ์ฌ ์์ฑ\n",
|
| 47 |
+
"outer_mesh = o3d.geometry.TriangleMesh()\n",
|
| 48 |
+
"outer_mesh.vertices = mesh.vertices\n",
|
| 49 |
+
"outer_mesh.triangles = o3d.utility.Vector3iVector(np.array(filtered_triangles))\n",
|
| 50 |
+
"# 4. ์๋ก์ด ๋ฉ์ฌ์์ ํฌ์ธํธ ํด๋ผ์ฐ๋ ์ํ๋ง\n",
|
| 51 |
+
"# n_points๋ ์ํ๋งํ ํฌ์ธํธ ๊ฐ์\n",
|
| 52 |
+
"pcd = outer_mesh.sample_points_uniformly(number_of_points=50000)\n",
|
| 53 |
+
"# ๊ฒฐ๊ณผ ์๊ฐํ\n",
|
| 54 |
+
"# o3d.visualization.draw_geometries([pcd,coord_frame ])\n",
|
| 55 |
+
"pcd_array = np.asarray(pcd.points)"
|
| 56 |
+
]
|
| 57 |
+
},
|
| 58 |
+
{
|
| 59 |
+
"cell_type": "code",
|
| 60 |
+
"execution_count": 2,
|
| 61 |
+
"metadata": {},
|
| 62 |
+
"outputs": [
|
| 63 |
+
{
|
| 64 |
+
"name": "stdout",
|
| 65 |
+
"output_type": "stream",
|
| 66 |
+
"text": [
|
| 67 |
+
"100_7\n",
|
| 68 |
+
"(896000, 3)\n"
|
| 69 |
+
]
|
| 70 |
+
}
|
| 71 |
+
],
|
| 72 |
+
"source": [
|
| 73 |
+
"import open3d as o3d\n",
|
| 74 |
+
"import numpy as np\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"GT = False\n",
|
| 77 |
+
"file_names = []\n",
|
| 78 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 79 |
+
" for line in f:\n",
|
| 80 |
+
" file_names.append(line.strip())\n",
|
| 81 |
+
"filename = file_names[0]\n",
|
| 82 |
+
"print(filename)\n",
|
| 83 |
+
"\n",
|
| 84 |
+
"\n",
|
| 85 |
+
"\n",
|
| 86 |
+
"\n",
|
| 87 |
+
"if not GT: \n",
|
| 88 |
+
" ply_path = f\"./dataset/{filename}.ply\"\n",
|
| 89 |
+
"\n",
|
| 90 |
+
" pcd = o3d.io.read_point_cloud(ply_path)\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"pcd_array = np.asarray(pcd.points)\n",
|
| 95 |
+
"print(pcd_array.shape)\n",
|
| 96 |
+
"\n",
|
| 97 |
+
"coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 98 |
+
"o3d.visualization.draw_geometries([pcd, coord_frame])"
|
| 99 |
+
]
|
| 100 |
+
},
|
| 101 |
+
{
|
| 102 |
+
"cell_type": "code",
|
| 103 |
+
"execution_count": 3,
|
| 104 |
+
"metadata": {},
|
| 105 |
+
"outputs": [
|
| 106 |
+
{
|
| 107 |
+
"name": "stdout",
|
| 108 |
+
"output_type": "stream",
|
| 109 |
+
"text": [
|
| 110 |
+
"[ 16.7051863 -37.94668466 564.59663212]\n"
|
| 111 |
+
]
|
| 112 |
+
}
|
| 113 |
+
],
|
| 114 |
+
"source": [
|
| 115 |
+
" \n",
|
| 116 |
+
"if GT==False:\n",
|
| 117 |
+
"\n",
|
| 118 |
+
" new_pcd_array = np.unique(pcd_array, axis=0)\n",
|
| 119 |
+
"\n",
|
| 120 |
+
" # new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 580]\n",
|
| 121 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 1000]\n",
|
| 122 |
+
"\n",
|
| 123 |
+
" # new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100] \n",
|
| 124 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -1000] #diagonal\n",
|
| 125 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 120]\n",
|
| 126 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 0] > -1000]\n",
|
| 127 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 1000] #diagonal\n",
|
| 128 |
+
" # new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 100] \n",
|
| 129 |
+
" # new_pcd_array -= np.mean(new_pcd_array, axis=0)\n",
|
| 130 |
+
" print(np.mean(new_pcd_array, axis=0))\n",
|
| 131 |
+
"\n",
|
| 132 |
+
" new_pcd = o3d.geometry.PointCloud()\n",
|
| 133 |
+
" new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)\n",
|
| 134 |
+
"\n",
|
| 135 |
+
" theta = np.radians(90)\n",
|
| 136 |
+
" # theta = np.radians(-90)\n",
|
| 137 |
+
"\n",
|
| 138 |
+
"\n",
|
| 139 |
+
" rotation_y = np.array([\n",
|
| 140 |
+
" [np.cos(theta), 0, np.sin(theta)],\n",
|
| 141 |
+
" [0, 1, 0 ],\n",
|
| 142 |
+
" [-np.sin(theta),0, np.cos(theta)]\n",
|
| 143 |
+
" ])\n",
|
| 144 |
+
"\n",
|
| 145 |
+
" rotation_x = np.array([\n",
|
| 146 |
+
" [1, 0, 0 ],\n",
|
| 147 |
+
" [0, np.cos(theta), -np.sin(theta)],\n",
|
| 148 |
+
" [0, np.sin(theta), np.cos(theta)]\n",
|
| 149 |
+
"\n",
|
| 150 |
+
" ])\n",
|
| 151 |
+
" rotation_z = np.array([\n",
|
| 152 |
+
" [np.cos(theta), -np.sin(theta), 0],\n",
|
| 153 |
+
" [np.sin(theta), np.cos(theta), 0],\n",
|
| 154 |
+
" [0, 0, 1]\n",
|
| 155 |
+
"\n",
|
| 156 |
+
" ])\n",
|
| 157 |
+
"\n",
|
| 158 |
+
"\n",
|
| 159 |
+
" new_pcd_array = np.asarray(new_pcd.points)\n",
|
| 160 |
+
"\n",
|
| 161 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 162 |
+
" o3d.visualization.draw_geometries([new_pcd, coord_frame])"
|
| 163 |
+
]
|
| 164 |
+
},
|
| 165 |
+
{
|
| 166 |
+
"cell_type": "markdown",
|
| 167 |
+
"metadata": {},
|
| 168 |
+
"source": [
|
| 169 |
+
"## Delete ground plane "
|
| 170 |
+
]
|
| 171 |
+
},
|
| 172 |
+
{
|
| 173 |
+
"cell_type": "code",
|
| 174 |
+
"execution_count": 4,
|
| 175 |
+
"metadata": {},
|
| 176 |
+
"outputs": [
|
| 177 |
+
{
|
| 178 |
+
"name": "stdout",
|
| 179 |
+
"output_type": "stream",
|
| 180 |
+
"text": [
|
| 181 |
+
"Plane equation: -0.01x + -0.00y + 1.00z + -579.50 = 0\n"
|
| 182 |
+
]
|
| 183 |
+
}
|
| 184 |
+
],
|
| 185 |
+
"source": [
|
| 186 |
+
" \n",
|
| 187 |
+
"if GT==False:\n",
|
| 188 |
+
" \n",
|
| 189 |
+
" plane_model, inliers = new_pcd.segment_plane(distance_threshold=2.5,\n",
|
| 190 |
+
" ransac_n=10,\n",
|
| 191 |
+
" num_iterations=1000)\n",
|
| 192 |
+
" [a, b, c, d] = plane_model\n",
|
| 193 |
+
" print(f\"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0\")\n",
|
| 194 |
+
" \n",
|
| 195 |
+
" \n",
|
| 196 |
+
" \n",
|
| 197 |
+
" inlier_cloud = new_pcd.select_by_index(inliers)\n",
|
| 198 |
+
" inlier_cloud.paint_uniform_color([1.0, 0, 1.0])\n",
|
| 199 |
+
" outlier_cloud = new_pcd.select_by_index(inliers, invert=True)\n",
|
| 200 |
+
" o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],\n",
|
| 201 |
+
" zoom=0.8,\n",
|
| 202 |
+
" front=[-0.4999, -0.1659, -0.8499],\n",
|
| 203 |
+
" lookat=[2.1813, 2.0619, 2.0999],\n",
|
| 204 |
+
" up=[0.1204, -0.9852, 0.1215])\n",
|
| 205 |
+
" \n",
|
| 206 |
+
" new_pcd = outlier_cloud\n",
|
| 207 |
+
"\n",
|
| 208 |
+
" new_pcd_array = np.asarray(new_pcd.points)\n",
|
| 209 |
+
"\n",
|
| 210 |
+
"\n",
|
| 211 |
+
" \n",
|
| 212 |
+
" \n",
|
| 213 |
+
" "
|
| 214 |
+
]
|
| 215 |
+
},
|
| 216 |
+
{
|
| 217 |
+
"cell_type": "markdown",
|
| 218 |
+
"metadata": {},
|
| 219 |
+
"source": [
|
| 220 |
+
"### Changing the source position \"gt_filtered\"\n"
|
| 221 |
+
]
|
| 222 |
+
},
|
| 223 |
+
{
|
| 224 |
+
"cell_type": "code",
|
| 225 |
+
"execution_count": 8,
|
| 226 |
+
"metadata": {},
|
| 227 |
+
"outputs": [],
|
| 228 |
+
"source": [
|
| 229 |
+
"\n",
|
| 230 |
+
"CHECK_PERTURB = GT\n",
|
| 231 |
+
"GT = False\n",
|
| 232 |
+
"def random_rotation_matrix():\n",
|
| 233 |
+
" \"\"\"\n",
|
| 234 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 235 |
+
" \n",
|
| 236 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 237 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 238 |
+
" 2. Generate a random angle\n",
|
| 239 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 240 |
+
" \n",
|
| 241 |
+
" Returns:\n",
|
| 242 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 243 |
+
" \"\"\"\n",
|
| 244 |
+
" ## for ground target\n",
|
| 245 |
+
" # Generate random angle ฯ/2\n",
|
| 246 |
+
" theta = 0\n",
|
| 247 |
+
"\n",
|
| 248 |
+
" \n",
|
| 249 |
+
" # axis is -y\n",
|
| 250 |
+
" axis = np.array([\n",
|
| 251 |
+
" 1,\n",
|
| 252 |
+
" 0,\n",
|
| 253 |
+
" 0,\n",
|
| 254 |
+
" ])\n",
|
| 255 |
+
" \n",
|
| 256 |
+
" # for lying target\n",
|
| 257 |
+
" # theta will be pi/2\n",
|
| 258 |
+
" # theta = np.pi/2\n",
|
| 259 |
+
" # axis = np.array([\n",
|
| 260 |
+
" # 0,\n",
|
| 261 |
+
" # 1,\n",
|
| 262 |
+
" # 0,\n",
|
| 263 |
+
" # ])\n",
|
| 264 |
+
" \n",
|
| 265 |
+
"\n",
|
| 266 |
+
"\n",
|
| 267 |
+
"\n",
|
| 268 |
+
" # Normalize to ensure it's a unit vector\n",
|
| 269 |
+
" axis = axis / np.linalg.norm(axis)\n",
|
| 270 |
+
" \n",
|
| 271 |
+
"\n",
|
| 272 |
+
"\n",
|
| 273 |
+
" # Create the cross-product matrix K skew-symmetric\n",
|
| 274 |
+
" K = np.array([\n",
|
| 275 |
+
" [0, -axis[2], axis[1]],\n",
|
| 276 |
+
" [axis[2], 0, -axis[0]],\n",
|
| 277 |
+
" [-axis[1], axis[0], 0]\n",
|
| 278 |
+
" ])\n",
|
| 279 |
+
" \n",
|
| 280 |
+
" # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 281 |
+
" R = (np.eye(3) + \n",
|
| 282 |
+
" np.sin(theta) * K + \n",
|
| 283 |
+
" (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 284 |
+
" \n",
|
| 285 |
+
" return R\n",
|
| 286 |
+
"\n",
|
| 287 |
+
"if CHECK_PERTURB:\n",
|
| 288 |
+
" R_pert = random_rotation_matrix()\n",
|
| 289 |
+
" print(R_pert)\n",
|
| 290 |
+
" t_pert = np.array([\n",
|
| 291 |
+
" 0,\n",
|
| 292 |
+
" 0,\n",
|
| 293 |
+
" 0\n",
|
| 294 |
+
" ])\n",
|
| 295 |
+
"\n",
|
| 296 |
+
" \n",
|
| 297 |
+
" perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T\n",
|
| 298 |
+
"\n",
|
| 299 |
+
"\n",
|
| 300 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 301 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 302 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 303 |
+
" o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])"
|
| 304 |
+
]
|
| 305 |
+
},
|
| 306 |
+
{
|
| 307 |
+
"cell_type": "markdown",
|
| 308 |
+
"metadata": {},
|
| 309 |
+
"source": [
|
| 310 |
+
"### Rotate randomly in Target \"noisy filtered\""
|
| 311 |
+
]
|
| 312 |
+
},
|
| 313 |
+
{
|
| 314 |
+
"cell_type": "code",
|
| 315 |
+
"execution_count": 9,
|
| 316 |
+
"metadata": {},
|
| 317 |
+
"outputs": [],
|
| 318 |
+
"source": [
|
| 319 |
+
"CHECK_PERTURB = not GT\n",
|
| 320 |
+
"\n",
|
| 321 |
+
"if CHECK_PERTURB:\n",
|
| 322 |
+
" # R_pert = random_rotation_matrix()\n",
|
| 323 |
+
" # print(R_pert)\n",
|
| 324 |
+
" # t_pert = np.random.rand(3, 1)*3 #* 10\n",
|
| 325 |
+
"\n",
|
| 326 |
+
" \n",
|
| 327 |
+
" # perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T\n",
|
| 328 |
+
" perturbed_pcd_array = new_pcd_array\n",
|
| 329 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 330 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 331 |
+
" \n",
|
| 332 |
+
" # ๊ฐ์ฒด์ ์ค์ฌ์ (0, 0, 0)์ผ๋ก ๋ฐ๋ก ์ด๋\n",
|
| 333 |
+
" \n",
|
| 334 |
+
"\n",
|
| 335 |
+
" perturbed_pcd_array = np.asarray(perturbed_pcd.points)\n",
|
| 336 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 337 |
+
"\n",
|
| 338 |
+
"\n",
|
| 339 |
+
"\n",
|
| 340 |
+
"\n",
|
| 341 |
+
" o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])\n"
|
| 342 |
+
]
|
| 343 |
+
},
|
| 344 |
+
{
|
| 345 |
+
"cell_type": "code",
|
| 346 |
+
"execution_count": 7,
|
| 347 |
+
"metadata": {},
|
| 348 |
+
"outputs": [
|
| 349 |
+
{
|
| 350 |
+
"name": "stdout",
|
| 351 |
+
"output_type": "stream",
|
| 352 |
+
"text": [
|
| 353 |
+
"True\n"
|
| 354 |
+
]
|
| 355 |
+
}
|
| 356 |
+
],
|
| 357 |
+
"source": [
|
| 358 |
+
"def write_ply(points, output_path):\n",
|
| 359 |
+
" \"\"\"\n",
|
| 360 |
+
" Write points and parameters to a PLY file\n",
|
| 361 |
+
" \n",
|
| 362 |
+
" Parameters:\n",
|
| 363 |
+
" points: numpy array of shape (N, 3) containing point coordinates\n",
|
| 364 |
+
" output_path: path to save the PLY file\n",
|
| 365 |
+
" \"\"\"\n",
|
| 366 |
+
" with open(output_path, 'w') as f:\n",
|
| 367 |
+
" # Write header\n",
|
| 368 |
+
" f.write(\"ply\\n\")\n",
|
| 369 |
+
" f.write(\"format ascii 1.0\\n\")\n",
|
| 370 |
+
" \n",
|
| 371 |
+
" # Write vertex element\n",
|
| 372 |
+
" f.write(f\"element vertex {len(points)}\\n\")\n",
|
| 373 |
+
" f.write(\"property float x\\n\")\n",
|
| 374 |
+
" f.write(\"property float y\\n\")\n",
|
| 375 |
+
" f.write(\"property float z\\n\")\n",
|
| 376 |
+
" \n",
|
| 377 |
+
" # Write camera element\n",
|
| 378 |
+
" f.write(\"element camera 1\\n\")\n",
|
| 379 |
+
" f.write(\"property float view_px\\n\")\n",
|
| 380 |
+
" f.write(\"property float view_py\\n\")\n",
|
| 381 |
+
" f.write(\"property float view_pz\\n\")\n",
|
| 382 |
+
" f.write(\"property float x_axisx\\n\")\n",
|
| 383 |
+
" f.write(\"property float x_axisy\\n\")\n",
|
| 384 |
+
" f.write(\"property float x_axisz\\n\")\n",
|
| 385 |
+
" f.write(\"property float y_axisx\\n\")\n",
|
| 386 |
+
" f.write(\"property float y_axisy\\n\")\n",
|
| 387 |
+
" f.write(\"property float y_axisz\\n\")\n",
|
| 388 |
+
" f.write(\"property float z_axisx\\n\")\n",
|
| 389 |
+
" f.write(\"property float z_axisy\\n\")\n",
|
| 390 |
+
" f.write(\"property float z_axisz\\n\")\n",
|
| 391 |
+
" \n",
|
| 392 |
+
" # Write phoxi frame parameters\n",
|
| 393 |
+
" f.write(\"element phoxi_frame_params 1\\n\")\n",
|
| 394 |
+
" f.write(\"property uint32 frame_width\\n\")\n",
|
| 395 |
+
" f.write(\"property uint32 frame_height\\n\")\n",
|
| 396 |
+
" f.write(\"property uint32 frame_index\\n\")\n",
|
| 397 |
+
" f.write(\"property float frame_start_time\\n\")\n",
|
| 398 |
+
" f.write(\"property float frame_duration\\n\")\n",
|
| 399 |
+
" f.write(\"property float frame_computation_duration\\n\")\n",
|
| 400 |
+
" f.write(\"property float frame_transfer_duration\\n\")\n",
|
| 401 |
+
" f.write(\"property int32 total_scan_count\\n\")\n",
|
| 402 |
+
" \n",
|
| 403 |
+
" # Write camera matrix\n",
|
| 404 |
+
" f.write(\"element camera_matrix 1\\n\")\n",
|
| 405 |
+
" for i in range(9):\n",
|
| 406 |
+
" f.write(f\"property float cm{i}\\n\")\n",
|
| 407 |
+
" \n",
|
| 408 |
+
" # Write distortion matrix\n",
|
| 409 |
+
" f.write(\"element distortion_matrix 1\\n\")\n",
|
| 410 |
+
" for i in range(14):\n",
|
| 411 |
+
" f.write(f\"property float dm{i}\\n\")\n",
|
| 412 |
+
" \n",
|
| 413 |
+
" # Write camera resolution\n",
|
| 414 |
+
" f.write(\"element camera_resolution 1\\n\")\n",
|
| 415 |
+
" f.write(\"property float width\\n\")\n",
|
| 416 |
+
" f.write(\"property float height\\n\")\n",
|
| 417 |
+
" \n",
|
| 418 |
+
" # Write frame binning\n",
|
| 419 |
+
" f.write(\"element frame_binning 1\\n\")\n",
|
| 420 |
+
" f.write(\"property float horizontal\\n\")\n",
|
| 421 |
+
" f.write(\"property float vertical\\n\")\n",
|
| 422 |
+
" \n",
|
| 423 |
+
" # End header\n",
|
| 424 |
+
" f.write(\"end_header\\n\")\n",
|
| 425 |
+
" \n",
|
| 426 |
+
" # Write vertex data\n",
|
| 427 |
+
" for point in points:\n",
|
| 428 |
+
" f.write(f\"{point[0]} {point[1]} {point[2]}\\n\")\n",
|
| 429 |
+
"\n",
|
| 430 |
+
" print(True)\n",
|
| 431 |
+
"\n",
|
| 432 |
+
"if GT: write_ply(perturbed_pcd_array, f\"gt_filtered.ply\")\n",
|
| 433 |
+
"else: write_ply(perturbed_pcd_array, f\"./noisy_result/noisy_filtered_{filename}.ply\")\n",
|
| 434 |
+
"# write_ply(new_pcd_array, \"gt_filtered.ply\")"
|
| 435 |
+
]
|
| 436 |
+
}
|
| 437 |
+
],
|
| 438 |
+
"metadata": {
|
| 439 |
+
"kernelspec": {
|
| 440 |
+
"display_name": "icp",
|
| 441 |
+
"language": "python",
|
| 442 |
+
"name": "python3"
|
| 443 |
+
},
|
| 444 |
+
"language_info": {
|
| 445 |
+
"codemirror_mode": {
|
| 446 |
+
"name": "ipython",
|
| 447 |
+
"version": 3
|
| 448 |
+
},
|
| 449 |
+
"file_extension": ".py",
|
| 450 |
+
"mimetype": "text/x-python",
|
| 451 |
+
"name": "python",
|
| 452 |
+
"nbconvert_exporter": "python",
|
| 453 |
+
"pygments_lexer": "ipython3",
|
| 454 |
+
"version": "3.10.19"
|
| 455 |
+
}
|
| 456 |
+
},
|
| 457 |
+
"nbformat": 4,
|
| 458 |
+
"nbformat_minor": 2
|
| 459 |
+
}
|
data/bottle_2/filter_tea.py
ADDED
|
@@ -0,0 +1,400 @@
|
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| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding: utf-8
|
| 3 |
+
|
| 4 |
+
# In[ ]:
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
import open3d as o3d
|
| 8 |
+
import numpy as np
|
| 9 |
+
|
| 10 |
+
GT = False
|
| 11 |
+
if GT==True:
|
| 12 |
+
mesh = o3d.io.read_triangle_mesh("./bottle2.stl")
|
| 13 |
+
pointcloud = mesh.sample_points_poisson_disk(50000)
|
| 14 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 15 |
+
|
| 16 |
+
mesh.compute_vertex_normals()
|
| 17 |
+
mesh_triangles = np.asarray(mesh.triangles)
|
| 18 |
+
vertex_positions = np.asarray(mesh.vertices)
|
| 19 |
+
triangle_normals = np.asarray(mesh.triangle_normals)
|
| 20 |
+
|
| 21 |
+
# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ
|
| 22 |
+
centroid = mesh.get_center()
|
| 23 |
+
filtered_triangles = []
|
| 24 |
+
for i, triangle in enumerate(mesh_triangles):
|
| 25 |
+
# ์ผ๊ฐํ์ ์ค์ฌ์ ๊ณ์ฐ
|
| 26 |
+
tri_center = vertex_positions[triangle].mean(axis=0)
|
| 27 |
+
# ๊ฐ์ฒด ์ค์ฌ์์ ์ผ๊ฐํ ์ค์ฌ์ผ๋ก ํฅํ๋ ๋ฒกํฐ
|
| 28 |
+
vec_to_center = tri_center - centroid
|
| 29 |
+
# ๋ฒ์ ๋ฒกํฐ์ ๋ฐฉํฅ ๋ฒกํฐ๋ฅผ ๋ด์
|
| 30 |
+
dot_product = np.dot(triangle_normals[i], vec_to_center)
|
| 31 |
+
# ๋ด์ ๊ฐ์ด ์์์ด๋ฉด ๋ฐ๊นฅ์ชฝ ๋ฉด์ผ๋ก ํ๋จ
|
| 32 |
+
if dot_product > 0:
|
| 33 |
+
filtered_triangles.append(triangle)
|
| 34 |
+
# 3. ํํฐ๋ง๋ ๋ฉด์ผ๋ก ์๋ก์ด ๋ฉ์ฌ ์์ฑ
|
| 35 |
+
outer_mesh = o3d.geometry.TriangleMesh()
|
| 36 |
+
outer_mesh.vertices = mesh.vertices
|
| 37 |
+
outer_mesh.triangles = o3d.utility.Vector3iVector(np.array(filtered_triangles))
|
| 38 |
+
# 4. ์๋ก์ด ๋ฉ์ฌ์์ ํฌ์ธํธ ํด๋ผ์ฐ๋ ์ํ๋ง
|
| 39 |
+
# n_points๋ ์ํ๋งํ ํฌ์ธํธ ๊ฐ์
|
| 40 |
+
pcd = outer_mesh.sample_points_uniformly(number_of_points=50000)
|
| 41 |
+
# ๊ฒฐ๊ณผ ์๊ฐํ
|
| 42 |
+
o3d.visualization.draw_geometries([pcd,coord_frame ])
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
pcd_array = np.asarray(pcd.points)
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
# In[160]:
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
import open3d as o3d
|
| 54 |
+
import numpy as np
|
| 55 |
+
|
| 56 |
+
file_names = []
|
| 57 |
+
with open('filename.txt', 'r') as f:
|
| 58 |
+
for line in f:
|
| 59 |
+
file_names.append(line.strip())
|
| 60 |
+
filename = file_names[0]
|
| 61 |
+
print(filename)
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
if not GT:
|
| 67 |
+
ply_path = f"./dataset/{filename}.ply"
|
| 68 |
+
|
| 69 |
+
pcd = o3d.io.read_point_cloud(ply_path)
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
pcd_array = np.asarray(pcd.points)
|
| 74 |
+
print(pcd_array.shape)
|
| 75 |
+
|
| 76 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 77 |
+
o3d.visualization.draw_geometries([pcd, coord_frame])
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
# In[161]:
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
if GT==False:
|
| 84 |
+
|
| 85 |
+
new_pcd_array = np.unique(pcd_array, axis=0)
|
| 86 |
+
|
| 87 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 580]
|
| 88 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 1000]
|
| 89 |
+
|
| 90 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100]
|
| 91 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -1000] #diagonal
|
| 92 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 120]
|
| 93 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] > -1000]
|
| 94 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 1000] #diagonal
|
| 95 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 100]
|
| 96 |
+
# new_pcd_array -= np.mean(new_pcd_array, axis=0)
|
| 97 |
+
print(np.mean(new_pcd_array, axis=0))
|
| 98 |
+
|
| 99 |
+
new_pcd = o3d.geometry.PointCloud()
|
| 100 |
+
new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)
|
| 101 |
+
|
| 102 |
+
theta = np.radians(90)
|
| 103 |
+
# theta = np.radians(-90)
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
rotation_y = np.array([
|
| 107 |
+
[np.cos(theta), 0, np.sin(theta)],
|
| 108 |
+
[0, 1, 0 ],
|
| 109 |
+
[-np.sin(theta),0, np.cos(theta)]
|
| 110 |
+
])
|
| 111 |
+
|
| 112 |
+
rotation_x = np.array([
|
| 113 |
+
[1, 0, 0 ],
|
| 114 |
+
[0, np.cos(theta), -np.sin(theta)],
|
| 115 |
+
[0, np.sin(theta), np.cos(theta)]
|
| 116 |
+
|
| 117 |
+
])
|
| 118 |
+
rotation_z = np.array([
|
| 119 |
+
[np.cos(theta), -np.sin(theta), 0],
|
| 120 |
+
[np.sin(theta), np.cos(theta), 0],
|
| 121 |
+
[0, 0, 1]
|
| 122 |
+
|
| 123 |
+
])
|
| 124 |
+
|
| 125 |
+
|
| 126 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 127 |
+
|
| 128 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 129 |
+
o3d.visualization.draw_geometries([new_pcd, coord_frame])
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
# ## Delete ground plane
|
| 133 |
+
|
| 134 |
+
# In[162]:
|
| 135 |
+
|
| 136 |
+
|
| 137 |
+
if GT==False:
|
| 138 |
+
|
| 139 |
+
plane_model, inliers = new_pcd.segment_plane(distance_threshold=2.5,
|
| 140 |
+
ransac_n=10,
|
| 141 |
+
num_iterations=1000)
|
| 142 |
+
[a, b, c, d] = plane_model
|
| 143 |
+
print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
|
| 144 |
+
|
| 145 |
+
|
| 146 |
+
|
| 147 |
+
inlier_cloud = new_pcd.select_by_index(inliers)
|
| 148 |
+
inlier_cloud.paint_uniform_color([1.0, 0, 1.0])
|
| 149 |
+
outlier_cloud = new_pcd.select_by_index(inliers, invert=True)
|
| 150 |
+
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
|
| 151 |
+
zoom=0.8,
|
| 152 |
+
front=[-0.4999, -0.1659, -0.8499],
|
| 153 |
+
lookat=[2.1813, 2.0619, 2.0999],
|
| 154 |
+
up=[0.1204, -0.9852, 0.1215])
|
| 155 |
+
|
| 156 |
+
new_pcd = outlier_cloud
|
| 157 |
+
|
| 158 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+
# ### Changing the source position "gt_filtered"
|
| 164 |
+
#
|
| 165 |
+
|
| 166 |
+
# In[163]:
|
| 167 |
+
|
| 168 |
+
|
| 169 |
+
CHECK_PERTURB = GT
|
| 170 |
+
|
| 171 |
+
def random_rotation_matrix():
|
| 172 |
+
"""
|
| 173 |
+
Generate a random 3x3 rotation matrix (SO(3) matrix).
|
| 174 |
+
|
| 175 |
+
Uses the method described by James Arvo in "Fast Random Rotation Matrices" (1992):
|
| 176 |
+
1. Generate a random unit vector for rotation axis
|
| 177 |
+
2. Generate a random angle
|
| 178 |
+
3. Create rotation matrix using Rodriguez rotation formula
|
| 179 |
+
|
| 180 |
+
Returns:
|
| 181 |
+
numpy.ndarray: A 3x3 random rotation matrix
|
| 182 |
+
"""
|
| 183 |
+
## for ground target
|
| 184 |
+
# Generate random angle ฯ/2
|
| 185 |
+
theta = -np.pi/2
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
# axis is -y
|
| 189 |
+
axis = np.array([
|
| 190 |
+
1,
|
| 191 |
+
0,
|
| 192 |
+
0,
|
| 193 |
+
])
|
| 194 |
+
|
| 195 |
+
# for lying target
|
| 196 |
+
# theta will be pi/2
|
| 197 |
+
# theta = np.pi/2
|
| 198 |
+
# axis = np.array([
|
| 199 |
+
# 0,
|
| 200 |
+
# 1,
|
| 201 |
+
# 0,
|
| 202 |
+
# ])
|
| 203 |
+
|
| 204 |
+
|
| 205 |
+
|
| 206 |
+
|
| 207 |
+
# Normalize to ensure it's a unit vector
|
| 208 |
+
axis = axis / np.linalg.norm(axis)
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
|
| 212 |
+
# Create the cross-product matrix K skew-symmetric
|
| 213 |
+
K = np.array([
|
| 214 |
+
[0, -axis[2], axis[1]],
|
| 215 |
+
[axis[2], 0, -axis[0]],
|
| 216 |
+
[-axis[1], axis[0], 0]
|
| 217 |
+
])
|
| 218 |
+
|
| 219 |
+
# Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ
|
| 220 |
+
R = (np.eye(3) +
|
| 221 |
+
np.sin(theta) * K +
|
| 222 |
+
(1 - np.cos(theta)) * np.dot(K, K))
|
| 223 |
+
|
| 224 |
+
return R
|
| 225 |
+
|
| 226 |
+
if CHECK_PERTURB:
|
| 227 |
+
R_pert = random_rotation_matrix()
|
| 228 |
+
print(R_pert)
|
| 229 |
+
t_pert = np.array([
|
| 230 |
+
0,
|
| 231 |
+
0,
|
| 232 |
+
0
|
| 233 |
+
])
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T
|
| 237 |
+
|
| 238 |
+
|
| 239 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 240 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 241 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 242 |
+
o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
# ### Rotate randomly in Target "noisy filtered"
|
| 246 |
+
|
| 247 |
+
# In[164]:
|
| 248 |
+
|
| 249 |
+
|
| 250 |
+
CHECK_PERTURB = not GT
|
| 251 |
+
|
| 252 |
+
def random_rotation_matrix():
|
| 253 |
+
"""
|
| 254 |
+
Generate a random 3x3 rotation matrix (SO(3) matrix).
|
| 255 |
+
|
| 256 |
+
Uses the method described by James Arvo in "Fast Random Rotation Matrices" (1992):
|
| 257 |
+
1. Generate a random unit vector for rotation axis
|
| 258 |
+
2. Generate a random angle
|
| 259 |
+
3. Create rotation matrix using Rodriguez rotation formula
|
| 260 |
+
|
| 261 |
+
Returns:
|
| 262 |
+
numpy.ndarray: A 3x3 random rotation matrix
|
| 263 |
+
"""
|
| 264 |
+
# # Generate random angle between 0 and 2ฯ
|
| 265 |
+
# theta = np.random.uniform(0, 2 * np.pi)/4
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
# # Generate random unit vector for rotation axis
|
| 269 |
+
# phi = np.random.uniform(0, 2 * np.pi)/3
|
| 270 |
+
# cos_theta = np.random.uniform(-1, 1)/5
|
| 271 |
+
# sin_theta = np.sqrt(1 - cos_theta**2)
|
| 272 |
+
|
| 273 |
+
# axis = np.array([
|
| 274 |
+
# sin_theta * np.cos(phi),
|
| 275 |
+
# sin_theta * np.sin(phi),
|
| 276 |
+
# cos_theta
|
| 277 |
+
# ])
|
| 278 |
+
|
| 279 |
+
# # Normalize to ensure it's a unit vector
|
| 280 |
+
# axis = axis / np.linalg.norm(axis)
|
| 281 |
+
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
# # Create the cross-product matrix K skew-symmetric
|
| 285 |
+
# K = np.array([
|
| 286 |
+
# [0, -axis[2], axis[1]],
|
| 287 |
+
# [axis[2], 0, -axis[0]],
|
| 288 |
+
# [-axis[1], axis[0], 0]
|
| 289 |
+
# ])
|
| 290 |
+
|
| 291 |
+
# # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ
|
| 292 |
+
# R = (np.eye(3) +
|
| 293 |
+
# np.sin(theta) * K +
|
| 294 |
+
# (1 - np.cos(theta)) * np.dot(K, K))
|
| 295 |
+
|
| 296 |
+
# return R
|
| 297 |
+
|
| 298 |
+
if CHECK_PERTURB:
|
| 299 |
+
# R_pert = random_rotation_matrix()
|
| 300 |
+
# print(R_pert)
|
| 301 |
+
# t_pert = np.random.rand(3, 1)*3 #* 10
|
| 302 |
+
|
| 303 |
+
|
| 304 |
+
# perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T
|
| 305 |
+
perturbed_pcd_array = new_pcd_array
|
| 306 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 307 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 308 |
+
|
| 309 |
+
# # ๊ฐ์ฒด์ ์ค์ฌ์ (0, 0, 0)์ผ๋ก ๋ฐ๋ก ์ด๋
|
| 310 |
+
# perturbed_pcd.translate((0, 0, 0), relative=False)
|
| 311 |
+
# perturbed_pcd_array = np.asarray(perturbed_pcd.points)
|
| 312 |
+
# coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
|
| 317 |
+
o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 318 |
+
|
| 319 |
+
|
| 320 |
+
# In[165]:
|
| 321 |
+
|
| 322 |
+
|
| 323 |
+
def write_ply(points, output_path):
|
| 324 |
+
"""
|
| 325 |
+
Write points and parameters to a PLY file
|
| 326 |
+
|
| 327 |
+
Parameters:
|
| 328 |
+
points: numpy array of shape (N, 3) containing point coordinates
|
| 329 |
+
output_path: path to save the PLY file
|
| 330 |
+
"""
|
| 331 |
+
with open(output_path, 'w') as f:
|
| 332 |
+
# Write header
|
| 333 |
+
f.write("ply\n")
|
| 334 |
+
f.write("format ascii 1.0\n")
|
| 335 |
+
|
| 336 |
+
# Write vertex element
|
| 337 |
+
f.write(f"element vertex {len(points)}\n")
|
| 338 |
+
f.write("property float x\n")
|
| 339 |
+
f.write("property float y\n")
|
| 340 |
+
f.write("property float z\n")
|
| 341 |
+
|
| 342 |
+
# Write camera element
|
| 343 |
+
f.write("element camera 1\n")
|
| 344 |
+
f.write("property float view_px\n")
|
| 345 |
+
f.write("property float view_py\n")
|
| 346 |
+
f.write("property float view_pz\n")
|
| 347 |
+
f.write("property float x_axisx\n")
|
| 348 |
+
f.write("property float x_axisy\n")
|
| 349 |
+
f.write("property float x_axisz\n")
|
| 350 |
+
f.write("property float y_axisx\n")
|
| 351 |
+
f.write("property float y_axisy\n")
|
| 352 |
+
f.write("property float y_axisz\n")
|
| 353 |
+
f.write("property float z_axisx\n")
|
| 354 |
+
f.write("property float z_axisy\n")
|
| 355 |
+
f.write("property float z_axisz\n")
|
| 356 |
+
|
| 357 |
+
# Write phoxi frame parameters
|
| 358 |
+
f.write("element phoxi_frame_params 1\n")
|
| 359 |
+
f.write("property uint32 frame_width\n")
|
| 360 |
+
f.write("property uint32 frame_height\n")
|
| 361 |
+
f.write("property uint32 frame_index\n")
|
| 362 |
+
f.write("property float frame_start_time\n")
|
| 363 |
+
f.write("property float frame_duration\n")
|
| 364 |
+
f.write("property float frame_computation_duration\n")
|
| 365 |
+
f.write("property float frame_transfer_duration\n")
|
| 366 |
+
f.write("property int32 total_scan_count\n")
|
| 367 |
+
|
| 368 |
+
# Write camera matrix
|
| 369 |
+
f.write("element camera_matrix 1\n")
|
| 370 |
+
for i in range(9):
|
| 371 |
+
f.write(f"property float cm{i}\n")
|
| 372 |
+
|
| 373 |
+
# Write distortion matrix
|
| 374 |
+
f.write("element distortion_matrix 1\n")
|
| 375 |
+
for i in range(14):
|
| 376 |
+
f.write(f"property float dm{i}\n")
|
| 377 |
+
|
| 378 |
+
# Write camera resolution
|
| 379 |
+
f.write("element camera_resolution 1\n")
|
| 380 |
+
f.write("property float width\n")
|
| 381 |
+
f.write("property float height\n")
|
| 382 |
+
|
| 383 |
+
# Write frame binning
|
| 384 |
+
f.write("element frame_binning 1\n")
|
| 385 |
+
f.write("property float horizontal\n")
|
| 386 |
+
f.write("property float vertical\n")
|
| 387 |
+
|
| 388 |
+
# End header
|
| 389 |
+
f.write("end_header\n")
|
| 390 |
+
|
| 391 |
+
# Write vertex data
|
| 392 |
+
for point in points:
|
| 393 |
+
f.write(f"{point[0]} {point[1]} {point[2]}\n")
|
| 394 |
+
|
| 395 |
+
print(True)
|
| 396 |
+
|
| 397 |
+
if GT: write_ply(perturbed_pcd_array, f"gt_filtered.ply")
|
| 398 |
+
else: write_ply(perturbed_pcd_array, f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 399 |
+
# write_ply(new_pcd_array, "gt_filtered.ply")
|
| 400 |
+
|
data/bottle_2/generategt.ipynb
ADDED
|
@@ -0,0 +1,156 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "0c5517fb",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## import Blender.txt\n",
|
| 9 |
+
"\n"
|
| 10 |
+
]
|
| 11 |
+
},
|
| 12 |
+
{
|
| 13 |
+
"cell_type": "code",
|
| 14 |
+
"execution_count": 12,
|
| 15 |
+
"id": "fd9f6425",
|
| 16 |
+
"metadata": {},
|
| 17 |
+
"outputs": [
|
| 18 |
+
{
|
| 19 |
+
"name": "stdout",
|
| 20 |
+
"output_type": "stream",
|
| 21 |
+
"text": [
|
| 22 |
+
"100_13\n",
|
| 23 |
+
"[[ 9.98290e-01 1.27898e-02 -5.70343e-02 8.50504e-01]\n",
|
| 24 |
+
" [-2.31538e-02 9.82475e-01 -1.84952e-01 -8.78984e+00]\n",
|
| 25 |
+
" [ 5.36693e-02 1.85956e-01 9.81091e-01 -6.32466e+01]\n",
|
| 26 |
+
" [ 0.00000e+00 0.00000e+00 0.00000e+00 1.00000e+00]]\n",
|
| 27 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./gt_filtered.ply\u001b[0;m\n",
|
| 28 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./noisy_result/noisy_filtered_100_13.ply\u001b[0;m\n",
|
| 29 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./noisy_result/noisy_filtered_100_13.ply\u001b[0;m\n"
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
{
|
| 33 |
+
"name": "stderr",
|
| 34 |
+
"output_type": "stream",
|
| 35 |
+
"text": [
|
| 36 |
+
"RPly: Unexpected end of file\n",
|
| 37 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n",
|
| 38 |
+
"RPly: Unexpected end of file\n",
|
| 39 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n",
|
| 40 |
+
"RPly: Unexpected end of file\n",
|
| 41 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 42 |
+
]
|
| 43 |
+
}
|
| 44 |
+
],
|
| 45 |
+
"source": [
|
| 46 |
+
"import json\n",
|
| 47 |
+
"import numpy as np\n",
|
| 48 |
+
"import open3d as o3d\n",
|
| 49 |
+
"\n",
|
| 50 |
+
"\n",
|
| 51 |
+
"def get_T(file_path):\n",
|
| 52 |
+
" with open(file_path, 'r') as f:\n",
|
| 53 |
+
" T_matrix = np.loadtxt(file_path)\n",
|
| 54 |
+
" print(T_matrix)\n",
|
| 55 |
+
" return T_matrix\n",
|
| 56 |
+
" \n",
|
| 57 |
+
"filenames = []\n",
|
| 58 |
+
"with open(\"filename.txt\", \"r\") as f:\n",
|
| 59 |
+
" for line in f:\n",
|
| 60 |
+
" filenames.append(line.strip())\n",
|
| 61 |
+
"\n",
|
| 62 |
+
"\n",
|
| 63 |
+
"filename = filenames[0]\n",
|
| 64 |
+
"print(filename)\n",
|
| 65 |
+
"\n",
|
| 66 |
+
"with open(f\"./gt/noisy_filtered_{filename}.json\", 'r') as f:\n",
|
| 67 |
+
" loaded_data = json.load(f)\n",
|
| 68 |
+
"\n",
|
| 69 |
+
"noisy_data = loaded_data[f'noisy_filtered_{filename}']\n",
|
| 70 |
+
"T_matrix = noisy_data['matrix_world']\n",
|
| 71 |
+
"\n",
|
| 72 |
+
" \n",
|
| 73 |
+
"infer_path = f\"./result3/result_{filename}.txt\"\n",
|
| 74 |
+
"infer_T = get_T(infer_path)\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"\n",
|
| 77 |
+
"\n",
|
| 78 |
+
"##Translated\n",
|
| 79 |
+
"\n",
|
| 80 |
+
"gt_path = \"./gt_filtered.ply\"\n",
|
| 81 |
+
"noisy_path = f\"./noisy_result/noisy_filtered_{filename}.ply\"\n",
|
| 82 |
+
"\n",
|
| 83 |
+
"\n",
|
| 84 |
+
"gt_pcd = o3d.io.read_point_cloud(gt_path)\n",
|
| 85 |
+
"gt_pcd.paint_uniform_color([0,0,1])\n",
|
| 86 |
+
"noisy_pcd = o3d.io.read_point_cloud(noisy_path)\n",
|
| 87 |
+
"noisy_pcd.paint_uniform_color([1,0,0])\n",
|
| 88 |
+
"infer_pcd = o3d.io.read_point_cloud(noisy_path)\n",
|
| 89 |
+
"infer_pcd.paint_uniform_color([0,1,0])\n",
|
| 90 |
+
"\n",
|
| 91 |
+
"## move and check gt and noisy\n",
|
| 92 |
+
"\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"\n",
|
| 95 |
+
"# infer_pcd.transform(infer_T)\n",
|
| 96 |
+
"noisy_pcd.transform(T_matrix)\n",
|
| 97 |
+
"\n",
|
| 98 |
+
"\n",
|
| 99 |
+
"o3d.visualization.draw_geometries([gt_pcd, noisy_pcd, infer_pcd])\n"
|
| 100 |
+
]
|
| 101 |
+
},
|
| 102 |
+
{
|
| 103 |
+
"cell_type": "code",
|
| 104 |
+
"execution_count": null,
|
| 105 |
+
"id": "fbf13a76",
|
| 106 |
+
"metadata": {},
|
| 107 |
+
"outputs": [],
|
| 108 |
+
"source": []
|
| 109 |
+
},
|
| 110 |
+
{
|
| 111 |
+
"cell_type": "code",
|
| 112 |
+
"execution_count": null,
|
| 113 |
+
"id": "0509ac65",
|
| 114 |
+
"metadata": {},
|
| 115 |
+
"outputs": [],
|
| 116 |
+
"source": []
|
| 117 |
+
},
|
| 118 |
+
{
|
| 119 |
+
"cell_type": "markdown",
|
| 120 |
+
"id": "eccad9e9",
|
| 121 |
+
"metadata": {},
|
| 122 |
+
"source": [
|
| 123 |
+
"## write GT\n"
|
| 124 |
+
]
|
| 125 |
+
},
|
| 126 |
+
{
|
| 127 |
+
"cell_type": "code",
|
| 128 |
+
"execution_count": null,
|
| 129 |
+
"id": "e0a339ca",
|
| 130 |
+
"metadata": {},
|
| 131 |
+
"outputs": [],
|
| 132 |
+
"source": []
|
| 133 |
+
}
|
| 134 |
+
],
|
| 135 |
+
"metadata": {
|
| 136 |
+
"kernelspec": {
|
| 137 |
+
"display_name": "Python 3",
|
| 138 |
+
"language": "python",
|
| 139 |
+
"name": "python3"
|
| 140 |
+
},
|
| 141 |
+
"language_info": {
|
| 142 |
+
"codemirror_mode": {
|
| 143 |
+
"name": "ipython",
|
| 144 |
+
"version": 3
|
| 145 |
+
},
|
| 146 |
+
"file_extension": ".py",
|
| 147 |
+
"mimetype": "text/x-python",
|
| 148 |
+
"name": "python",
|
| 149 |
+
"nbconvert_exporter": "python",
|
| 150 |
+
"pygments_lexer": "ipython3",
|
| 151 |
+
"version": "3.10.12"
|
| 152 |
+
}
|
| 153 |
+
},
|
| 154 |
+
"nbformat": 4,
|
| 155 |
+
"nbformat_minor": 5
|
| 156 |
+
}
|
data/bottle_2/gt_Raw.ipynb
ADDED
|
@@ -0,0 +1,819 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "7d7011e4",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## load gt and translate"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 1,
|
| 14 |
+
"id": "878f605d",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [
|
| 17 |
+
{
|
| 18 |
+
"name": "stdout",
|
| 19 |
+
"output_type": "stream",
|
| 20 |
+
"text": [
|
| 21 |
+
"=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 100\n",
|
| 24 |
+
"100_19\n",
|
| 25 |
+
"<class 'numpy.ndarray'>\n",
|
| 26 |
+
"[ 51.19733434 -10.83484204 -387.45794023] [[-2.93318480e-01 -8.46543729e-01 4.44216162e-01 1.75000000e+01]\n",
|
| 27 |
+
" [ 5.14243305e-01 -5.31416714e-01 -6.73164248e-01 1.43500000e+02]\n",
|
| 28 |
+
" [ 8.05926859e-01 3.09836771e-02 5.91203749e-01 -2.00000000e+01]\n",
|
| 29 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 30 |
+
"100_10\n",
|
| 31 |
+
"<class 'numpy.ndarray'>\n",
|
| 32 |
+
"[ 25.67619933 -15.94907366 -345.08978903] [[-6.65230572e-01 3.54238040e-07 -7.46638000e-01 7.05000000e+01]\n",
|
| 33 |
+
" [ 7.46638000e-01 4.32703331e-07 -6.65230572e-01 1.19000000e+02]\n",
|
| 34 |
+
" [ 8.74227766e-08 -1.00000000e+00 -5.52335052e-07 1.10500000e+02]\n",
|
| 35 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 36 |
+
"100_1\n",
|
| 37 |
+
"<class 'numpy.ndarray'>\n",
|
| 38 |
+
"[ 54.14357065 -18.01762774 -324.78425313] [[ 7.28970468e-01 -3.22459824e-02 -6.83785200e-01 7.25000000e+01]\n",
|
| 39 |
+
" [ 6.84545159e-01 3.43387984e-02 7.28161275e-01 -2.25000000e+01]\n",
|
| 40 |
+
" [ 8.74227766e-08 -9.98889923e-01 4.71058004e-02 1.00000000e+02]\n",
|
| 41 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 42 |
+
"100_4\n",
|
| 43 |
+
"<class 'numpy.ndarray'>\n",
|
| 44 |
+
"[ 54.71764735 -17.40441832 -330.81992476] [[-2.38532797e-01 -5.08246794e-02 -9.69803572e-01 9.95000000e+01]\n",
|
| 45 |
+
" [ 9.71134424e-01 -1.24836117e-02 -2.38205910e-01 6.65000000e+01]\n",
|
| 46 |
+
" [ 8.74227766e-08 -9.98629570e-01 5.23353480e-02 1.03000000e+02]\n",
|
| 47 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 48 |
+
"100_6\n",
|
| 49 |
+
"<class 'numpy.ndarray'>\n",
|
| 50 |
+
"[ 24.2281063 -17.30561922 -347.65201352] [[ 4.06735718e-01 2.39131302e-02 9.13232863e-01 -8.50000000e+01]\n",
|
| 51 |
+
" [-9.13545847e-01 1.06466869e-02 4.06596333e-01 5.00000000e+00]\n",
|
| 52 |
+
" [ 8.74227766e-08 -9.99657333e-01 2.61761285e-02 1.04000000e+02]\n",
|
| 53 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 54 |
+
"100_5\n",
|
| 55 |
+
"<class 'numpy.ndarray'>\n",
|
| 56 |
+
"[ 31.02152667 -16.37354795 -337.47553193] [[ 8.05927813e-01 2.47906260e-02 5.91494560e-01 -5.90000000e+01]\n",
|
| 57 |
+
" [-5.92013836e-01 3.37481424e-02 8.05220902e-01 -3.25000000e+01]\n",
|
| 58 |
+
" [ 8.74227766e-08 -9.99122858e-01 4.18749601e-02 1.05000000e+02]\n",
|
| 59 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 60 |
+
"100_17\n",
|
| 61 |
+
"<class 'numpy.ndarray'>\n",
|
| 62 |
+
"[ 41.83540301 -11.42143584 -357.66950004] [[ 0.34551173 -0.83580726 0.42667067 16.5 ]\n",
|
| 63 |
+
" [ -0.52599114 -0.54902297 -0.64954376 156. ]\n",
|
| 64 |
+
" [ 0.77714539 0. -0.6293211 82.5 ]\n",
|
| 65 |
+
" [ 0. 0. 0. 1. ]]\n",
|
| 66 |
+
"100_15\n",
|
| 67 |
+
"<class 'numpy.ndarray'>\n",
|
| 68 |
+
"[ 24.84687131 -12.3077729 -366.56053808] [[ 4.75527972e-01 -8.59782219e-01 -1.86138809e-01 7.10000000e+01]\n",
|
| 69 |
+
" [-8.23639393e-01 -5.09470284e-01 2.49114692e-01 6.75000000e+01]\n",
|
| 70 |
+
" [-3.09016585e-01 3.48502435e-02 -9.50417936e-01 1.11000000e+02]\n",
|
| 71 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 72 |
+
"100_12\n",
|
| 73 |
+
"<class 'numpy.ndarray'>\n",
|
| 74 |
+
"[ 41.42189298 -14.12100997 -352.32385985] [[-4.44922507e-01 -8.68288934e-01 -2.19358906e-01 7.30000000e+01]\n",
|
| 75 |
+
" [ 7.17583358e-01 -4.92189586e-01 4.92771238e-01 4.00000000e+01]\n",
|
| 76 |
+
" [-5.35833955e-01 6.18367195e-02 8.42055917e-01 -4.00000000e+01]\n",
|
| 77 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 78 |
+
"100_16\n",
|
| 79 |
+
"<class 'numpy.ndarray'>\n",
|
| 80 |
+
"[ 35.45306223 -8.66726484 -360.26216223] [[ 5.50878942e-01 -8.34482431e-01 -1.30962841e-02 5.65000000e+01]\n",
|
| 81 |
+
" [-8.19793046e-01 -5.38107276e-01 -1.95907772e-01 1.04500000e+02]\n",
|
| 82 |
+
" [ 1.56434372e-01 1.18657708e-01 -9.80534852e-01 1.20000000e+02]\n",
|
| 83 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 84 |
+
"100_14\n",
|
| 85 |
+
"<class 'numpy.ndarray'>\n",
|
| 86 |
+
"[ -4.11060848 -10.1532769 -348.5691703 ] [[ 3.01073521e-01 -8.68400633e-01 -3.93998891e-01 9.70000000e+01]\n",
|
| 87 |
+
" [-6.39815569e-01 -4.90323544e-01 5.91792881e-01 2.20000000e+01]\n",
|
| 88 |
+
" [-7.07100272e-01 7.39134625e-02 -7.03239679e-01 9.00000000e+01]\n",
|
| 89 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 90 |
+
"100_7\n",
|
| 91 |
+
"<class 'numpy.ndarray'>\n",
|
| 92 |
+
"[ 41.27617588 -17.8722464 -352.18525802] [[-4.06179309e-01 7.38384351e-02 9.10805285e-01 -9.45000000e+01]\n",
|
| 93 |
+
" [-9.12293434e-01 2.43186895e-02 -4.08814460e-01 8.85000000e+01]\n",
|
| 94 |
+
" [-5.23358099e-02 -9.96973693e-01 5.74845709e-02 1.01000000e+02]\n",
|
| 95 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 96 |
+
"100_13\n",
|
| 97 |
+
"<class 'numpy.ndarray'>\n",
|
| 98 |
+
"[ 27.57481428 -11.96131775 -340.67913273] [[ 4.17015217e-02 -8.65965843e-01 -4.98361468e-01 1.05500000e+02]\n",
|
| 99 |
+
" [-6.64780587e-02 -5.00094891e-01 8.63415182e-01 -1.00000000e+00]\n",
|
| 100 |
+
" [-9.96916056e-01 -2.87562096e-03 -7.84224495e-02 4.65000000e+01]\n",
|
| 101 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 102 |
+
"100_9\n",
|
| 103 |
+
"<class 'numpy.ndarray'>\n",
|
| 104 |
+
"[ 33.07159082 -19.30269462 -355.05924509] [[-9.94521916e-01 -4.92398394e-03 -1.04412429e-01 2.00000000e+01]\n",
|
| 105 |
+
" [ 1.04528464e-01 -4.68477383e-02 -9.93417859e-01 1.55000000e+02]\n",
|
| 106 |
+
" [ 8.74227766e-08 -9.98889923e-01 4.71058004e-02 1.01000000e+02]\n",
|
| 107 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 108 |
+
"100_18\n",
|
| 109 |
+
"<class 'numpy.ndarray'>\n",
|
| 110 |
+
"[ 29.45425976 -10.39854392 -371.09839178] [[-1.61214992e-02 -9.00317252e-01 4.34935510e-01 1.65000000e+01]\n",
|
| 111 |
+
" [ 3.29080857e-02 -4.35234129e-01 -8.99715662e-01 1.68000000e+02]\n",
|
| 112 |
+
" [ 9.99328375e-01 -1.91871048e-04 3.66443433e-02 4.00000000e+01]\n",
|
| 113 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 114 |
+
"100_2\n",
|
| 115 |
+
"<class 'numpy.ndarray'>\n",
|
| 116 |
+
"[ 54.11831137 -18.01841884 -324.7759575 ] [[ 7.49945462e-01 -3.64737324e-02 -6.60493314e-01 7.10000000e+01]\n",
|
| 117 |
+
" [ 6.61168039e-01 9.71948169e-03 7.50174880e-01 -2.60000000e+01]\n",
|
| 118 |
+
" [-2.09420230e-02 -9.99287367e-01 3.14043462e-02 1.02500000e+02]\n",
|
| 119 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 120 |
+
"100_11\n",
|
| 121 |
+
"<class 'numpy.ndarray'>\n",
|
| 122 |
+
"[ 22.03579253 -17.03875545 -340.16779864] [[-5.23394831e-02 -4.70412374e-02 -9.97520804e-01 9.65000000e+01]\n",
|
| 123 |
+
" [ 9.98629332e-01 -2.46540597e-03 -5.22813834e-02 5.85000000e+01]\n",
|
| 124 |
+
" [ 8.74227766e-08 -9.98889923e-01 4.71058004e-02 1.01500000e+02]\n",
|
| 125 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 126 |
+
"100_20\n",
|
| 127 |
+
"<class 'numpy.ndarray'>\n",
|
| 128 |
+
"[ 50.40114729 -13.0845525 -351.94462587] [[-5.15631497e-01 -8.28933716e-01 2.16778919e-01 3.70000000e+01]\n",
|
| 129 |
+
" [ 8.41432929e-01 -5.37620902e-01 -5.43539152e-02 9.00000000e+01]\n",
|
| 130 |
+
" [ 1.61600679e-01 1.54378325e-01 9.74706411e-01 -6.50000000e+01]\n",
|
| 131 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 132 |
+
"100_3\n",
|
| 133 |
+
"<class 'numpy.ndarray'>\n",
|
| 134 |
+
"[ 41.17514239 -18.68515214 -329.11994646] [[ 8.91007781e-01 -2.37595402e-02 -4.53365833e-01 5.55000000e+01]\n",
|
| 135 |
+
" [ 4.53987986e-01 4.66312431e-02 8.89786720e-01 -3.70000000e+01]\n",
|
| 136 |
+
" [ 8.74227766e-08 -9.98629570e-01 5.23353480e-02 1.01500000e+02]\n",
|
| 137 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 138 |
+
"100_8\n",
|
| 139 |
+
"<class 'numpy.ndarray'>\n",
|
| 140 |
+
"[ 39.3421297 -18.34427337 -353.48498967] [[-8.57876718e-01 -2.60340068e-02 5.13195634e-01 -5.50000000e+01]\n",
|
| 141 |
+
" [-5.13428628e-01 2.72471388e-03 -8.58127952e-01 1.36000000e+02]\n",
|
| 142 |
+
" [ 2.09421981e-02 -9.99657333e-01 -1.57040730e-02 1.07500000e+02]\n",
|
| 143 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 144 |
+
"\n",
|
| 145 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 75\n",
|
| 146 |
+
"75_6\n",
|
| 147 |
+
"<class 'numpy.ndarray'>\n",
|
| 148 |
+
"[ -10.28079148 -13.57670978 -333.97319473] [[ 2.84015656e-01 5.01801819e-02 9.57505643e-01 -9.55000000e+01]\n",
|
| 149 |
+
" [-9.58819628e-01 1.48639735e-02 2.83626437e-01 2.20000000e+01]\n",
|
| 150 |
+
" [ 8.74227766e-08 -9.98629570e-01 5.23353480e-02 1.07000000e+02]\n",
|
| 151 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 152 |
+
"75_12\n",
|
| 153 |
+
"<class 'numpy.ndarray'>\n",
|
| 154 |
+
"[ 53.5993972 -14.16602359 -353.43173679] [[-2.09197178e-01 -8.75313044e-01 -4.35962826e-01 1.03000000e+02]\n",
|
| 155 |
+
" [ 2.72632271e-01 -4.80357140e-01 8.33623827e-01 -3.00000000e+00]\n",
|
| 156 |
+
" [-9.39099669e-01 5.55342138e-02 3.39127928e-01 1.50000000e+01]\n",
|
| 157 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 158 |
+
"75_9\n",
|
| 159 |
+
"<class 'numpy.ndarray'>\n",
|
| 160 |
+
"[ -16.15560412 -16.54161382 -339.99475785] [[-6.33384287e-01 3.72045321e-07 -7.73837388e-01 7.65000000e+01]\n",
|
| 161 |
+
" [ 7.73837388e-01 4.17491378e-07 -6.33384287e-01 1.13500000e+02]\n",
|
| 162 |
+
" [ 8.74227766e-08 -1.00000000e+00 -5.52335052e-07 1.07000000e+02]\n",
|
| 163 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 164 |
+
"75_4\n",
|
| 165 |
+
"<class 'numpy.ndarray'>\n",
|
| 166 |
+
"[ 14.88823613 -16.12019282 -322.59421087] [[ 1.04506835e-01 -3.34254205e-02 -9.93962288e-01 9.75000000e+01]\n",
|
| 167 |
+
" [ 9.94303644e-01 -1.75337940e-02 1.05132356e-01 3.65000000e+01]\n",
|
| 168 |
+
" [-2.09420230e-02 -9.99287426e-01 3.14026177e-02 1.06000000e+02]\n",
|
| 169 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 170 |
+
"75_11\n",
|
| 171 |
+
"<class 'numpy.ndarray'>\n",
|
| 172 |
+
"[ 4.07306872 -16.04775377 -354.76939747] [[ 7.28970468e-01 1.43375667e-02 -6.84394956e-01 6.55000000e+01]\n",
|
| 173 |
+
" [ 6.84545159e-01 -1.52679132e-02 7.28810549e-01 -2.25000000e+01]\n",
|
| 174 |
+
" [ 8.74227766e-08 -9.99780655e-01 -2.09445693e-02 1.11500000e+02]\n",
|
| 175 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 176 |
+
"75_7\n",
|
| 177 |
+
"<class 'numpy.ndarray'>\n",
|
| 178 |
+
"[ -19.98757666 -19.97969048 -323.16157819] [[-6.57375097e-01 3.94375920e-02 7.52530813e-01 -7.65000000e+01]\n",
|
| 179 |
+
" [-7.53563523e-01 -3.44037041e-02 -6.56474233e-01 1.17000000e+02]\n",
|
| 180 |
+
" [ 8.74227766e-08 -9.98629570e-01 5.23348711e-02 9.80000000e+01]\n",
|
| 181 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 182 |
+
"75_14\n",
|
| 183 |
+
"<class 'numpy.ndarray'>\n",
|
| 184 |
+
"[ 2.75472692 -8.87751636 -360.52708487] [[ 3.65883231e-01 -8.72372746e-01 -3.24183911e-01 9.15000000e+01]\n",
|
| 185 |
+
" [-7.18088210e-01 -4.86214906e-01 4.97940153e-01 3.35000000e+01]\n",
|
| 186 |
+
" [-5.92012465e-01 5.06046824e-02 -8.04338455e-01 1.00000000e+02]\n",
|
| 187 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 188 |
+
"75_8\n",
|
| 189 |
+
"<class 'numpy.ndarray'>\n",
|
| 190 |
+
"[ 6.31429544 -20.70514197 -333.38406434] [[-9.98341978e-01 -2.10930570e-03 -5.75226769e-02 2.50000000e+01]\n",
|
| 191 |
+
" [ 5.75613379e-02 -3.65822129e-02 -9.97671485e-01 1.49000000e+02]\n",
|
| 192 |
+
" [ 8.74227766e-08 -9.99328434e-01 3.66429724e-02 1.00000000e+02]\n",
|
| 193 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 194 |
+
"75_16\n",
|
| 195 |
+
"<class 'numpy.ndarray'>\n",
|
| 196 |
+
"[ -18.92732154 -3.39734423 -361.08917408] [[ 4.85276163e-01 -8.50462437e-01 2.03028768e-01 4.70000000e+01]\n",
|
| 197 |
+
" [-7.47261703e-01 -5.23964822e-01 -4.08730775e-01 1.22000000e+02]\n",
|
| 198 |
+
" [ 4.53990102e-01 4.66316864e-02 -8.89785647e-01 1.16500000e+02]\n",
|
| 199 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 200 |
+
"75_17\n",
|
| 201 |
+
"<class 'numpy.ndarray'>\n",
|
| 202 |
+
"[ -9.48478114 -7.6458447 -376.1634084 ] [[ 3.70562911e-01 -8.56798828e-01 3.58579069e-01 2.75000000e+01]\n",
|
| 203 |
+
" [-4.77728516e-01 -5.06902754e-01 -7.17513144e-01 1.51000000e+02]\n",
|
| 204 |
+
" [ 7.96529114e-01 9.45803002e-02 -5.97156584e-01 8.05000000e+01]\n",
|
| 205 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 206 |
+
"75_2\n",
|
| 207 |
+
"<class 'numpy.ndarray'>\n",
|
| 208 |
+
"[ 40.87666511 -16.50623297 -345.97803966] [[-1.04529373e-01 -3.12379207e-02 -9.94031072e-01 9.45000000e+01]\n",
|
| 209 |
+
" [ 9.94521797e-01 -3.28317867e-03 -1.04477800e-01 7.05000000e+01]\n",
|
| 210 |
+
" [ 8.74227766e-08 -9.99506593e-01 3.14099826e-02 1.06500000e+02]\n",
|
| 211 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 212 |
+
"75_3\n",
|
| 213 |
+
"<class 'numpy.ndarray'>\n",
|
| 214 |
+
"[ 44.66045392 -16.332629 -318.40189155] [[ 5.31401873e-01 -1.33055728e-02 -8.47015381e-01 8.50000000e+01]\n",
|
| 215 |
+
" [ 8.47119868e-01 8.34674481e-03 5.31336308e-01 1.00000000e+00]\n",
|
| 216 |
+
" [ 8.74227766e-08 -9.99876618e-01 1.57068912e-02 1.06500000e+02]\n",
|
| 217 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 218 |
+
"75_1\n",
|
| 219 |
+
"<class 'numpy.ndarray'>\n",
|
| 220 |
+
"[ 41.09635179 -16.49375851 -345.7756453 ] [[-9.41086635e-02 -3.12710628e-02 -9.95070696e-01 9.85000000e+01]\n",
|
| 221 |
+
" [ 9.95561957e-01 -2.95590935e-03 -9.40622315e-02 6.60000000e+01]\n",
|
| 222 |
+
" [ 8.74227766e-08 -9.99506593e-01 3.14104594e-02 1.04000000e+02]\n",
|
| 223 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 224 |
+
"75_15\n",
|
| 225 |
+
"<class 'numpy.ndarray'>\n",
|
| 226 |
+
"[ -4.89271883 -12.75829091 -363.93432522] [[ 4.97526824e-01 -8.63186777e-01 -8.58815610e-02 6.35000000e+01]\n",
|
| 227 |
+
" [-8.61743987e-01 -5.03162324e-01 6.50001392e-02 7.60000000e+01]\n",
|
| 228 |
+
" [-9.93196219e-02 4.16686051e-02 -9.94182765e-01 1.13500000e+02]\n",
|
| 229 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 230 |
+
"75_20\n",
|
| 231 |
+
"<class 'numpy.ndarray'>\n",
|
| 232 |
+
"[ 8.30658836 -8.95998312 -370.69422623] [[-4.02583301e-01 -8.33207130e-01 3.79067987e-01 2.90000000e+01]\n",
|
| 233 |
+
" [ 6.64743483e-01 -5.50803840e-01 -5.04709125e-01 1.27000000e+02]\n",
|
| 234 |
+
" [ 6.29319310e-01 4.87955064e-02 7.75613427e-01 -3.65000000e+01]\n",
|
| 235 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 236 |
+
"75_10\n",
|
| 237 |
+
"<class 'numpy.ndarray'>\n",
|
| 238 |
+
"[ -12.03577189 -15.94498601 -348.25758199] [[-9.93204340e-02 -2.60467101e-02 -9.94714558e-01 9.65000000e+01]\n",
|
| 239 |
+
" [ 9.95055497e-01 -2.59973761e-03 -9.92864072e-02 6.40000000e+01]\n",
|
| 240 |
+
" [ 8.74227766e-08 -9.99657333e-01 2.61761285e-02 1.07000000e+02]\n",
|
| 241 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 242 |
+
"75_13\n",
|
| 243 |
+
"<class 'numpy.ndarray'>\n",
|
| 244 |
+
"[ 17.26730099 -11.93908139 -344.08557292] [[ 8.25190097e-02 -8.49209785e-01 -5.21568179e-01 1.08500000e+02]\n",
|
| 245 |
+
" [-3.43716562e-01 -5.15492618e-01 7.84937143e-01 8.00000000e+00]\n",
|
| 246 |
+
" [-9.35440838e-01 1.14499390e-01 -3.34425420e-01 5.80000000e+01]\n",
|
| 247 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 248 |
+
"75_5\n",
|
| 249 |
+
"<class 'numpy.ndarray'>\n",
|
| 250 |
+
"[ -13.14375458 -12.79746753 -336.26320302] [[ 8.30012262e-01 -1.16808610e-02 5.57622790e-01 -5.40000000e+01]\n",
|
| 251 |
+
" [-5.57745099e-01 -1.73831116e-02 8.29830229e-01 -2.95000000e+01]\n",
|
| 252 |
+
" [ 8.74227766e-08 -9.99780655e-01 -2.09431387e-02 1.13000000e+02]\n",
|
| 253 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 254 |
+
"75_19\n",
|
| 255 |
+
"<class 'numpy.ndarray'>\n",
|
| 256 |
+
"[ -5.15010367 -5.35148716 -376.2158517 ] [[-1.00854911e-01 -8.38657498e-01 5.35239995e-01 1.35000000e+01]\n",
|
| 257 |
+
" [ 2.54722267e-01 -5.41818321e-01 -8.00967813e-01 1.63000000e+02]\n",
|
| 258 |
+
" [ 9.61740553e-01 5.55559993e-02 2.68269777e-01 1.15000000e+01]\n",
|
| 259 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 260 |
+
"75_18\n",
|
| 261 |
+
"<class 'numpy.ndarray'>\n",
|
| 262 |
+
"[ -1.27348381 -3.52856113 -363.11010876] [[ 1.41220719e-01 -8.79481137e-01 4.54499364e-01 2.15000000e+01]\n",
|
| 263 |
+
" [-1.38294190e-01 -4.72124577e-01 -8.70616496e-01 1.68500000e+02]\n",
|
| 264 |
+
" [ 9.80271101e-01 6.00944757e-02 -1.88300893e-01 4.50000000e+01]\n",
|
| 265 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 266 |
+
"\n",
|
| 267 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 50\n",
|
| 268 |
+
"50_18\n",
|
| 269 |
+
"<class 'numpy.ndarray'>\n",
|
| 270 |
+
"[ 28.26907896 -17.23835968 -360.79007548] [[-1.70527339e-01 -8.65532279e-01 -4.70929146e-01 1.02000000e+02]\n",
|
| 271 |
+
" [ 2.27120772e-01 -4.99586582e-01 8.35960150e-01 -5.50000000e+00]\n",
|
| 272 |
+
" [-9.58820403e-01 3.55962664e-02 2.81773537e-01 2.15000000e+01]\n",
|
| 273 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 274 |
+
"50_8\n",
|
| 275 |
+
"<class 'numpy.ndarray'>\n",
|
| 276 |
+
"[ 40.10537581 -17.06463583 -349.99374741] [[-4.77158964e-01 4.43686872e-07 -8.78817022e-01 7.70000000e+01]\n",
|
| 277 |
+
" [ 8.78817022e-01 3.40380240e-07 -4.77158964e-01 9.75000000e+01]\n",
|
| 278 |
+
" [ 8.74227766e-08 -1.00000000e+00 -5.52335052e-07 1.07500000e+02]\n",
|
| 279 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 280 |
+
"50_13\n",
|
| 281 |
+
"<class 'numpy.ndarray'>\n",
|
| 282 |
+
"[ 38.87510909 -16.11698246 -391.01243428] [[ 5.17667353e-01 8.54664147e-01 -3.96198146e-02 -4.90000000e+01]\n",
|
| 283 |
+
" [ 8.44749629e-01 -5.17910421e-01 -1.34784400e-01 9.60000000e+01]\n",
|
| 284 |
+
" [-1.35714903e-01 3.63046639e-02 -9.90082562e-01 1.15000000e+02]\n",
|
| 285 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 286 |
+
"50_15\n",
|
| 287 |
+
"<class 'numpy.ndarray'>\n",
|
| 288 |
+
"[ 9.54939332 -17.56393199 -373.96317135] [[ 2.90624380e-01 8.73271525e-01 3.91068190e-01 -9.30000000e+01]\n",
|
| 289 |
+
" [ 5.15770555e-01 -4.87224042e-01 7.04693913e-01 8.50000000e+00]\n",
|
| 290 |
+
" [ 8.05926919e-01 -3.09977727e-03 -5.92006922e-01 9.35000000e+01]\n",
|
| 291 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 292 |
+
"50_7\n",
|
| 293 |
+
"<class 'numpy.ndarray'>\n",
|
| 294 |
+
"[ 52.13405573 -16.19737249 -357.43718045] [[ 1.82236373e-01 5.59017678e-07 -9.83254731e-01 9.00000000e+01]\n",
|
| 295 |
+
" [ 9.83254731e-01 -1.46966714e-08 1.82236373e-01 3.20000000e+01]\n",
|
| 296 |
+
" [ 8.74227766e-08 -1.00000000e+00 -5.52335052e-07 1.09500000e+02]\n",
|
| 297 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 298 |
+
"50_4\n",
|
| 299 |
+
"<class 'numpy.ndarray'>\n",
|
| 300 |
+
"[ 42.48308957 -13.34240294 -343.44224666] [[ 4.76922661e-01 1.49888871e-02 8.78817439e-01 -8.05000000e+01]\n",
|
| 301 |
+
" [-8.78383815e-01 -2.76041720e-02 4.77158129e-01 2.50000000e+00]\n",
|
| 302 |
+
" [ 3.14110965e-02 -9.99506533e-01 9.41284725e-07 1.12500000e+02]\n",
|
| 303 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 304 |
+
"50_5\n",
|
| 305 |
+
"<class 'numpy.ndarray'>\n",
|
| 306 |
+
"[ 37.35970129 -14.24316042 -336.57444623] [[ 8.65977049e-01 1.40418659e-03 5.00081718e-01 -4.85000000e+01]\n",
|
| 307 |
+
" [-4.99974072e-01 2.33705416e-02 8.65724981e-01 -3.70000000e+01]\n",
|
| 308 |
+
" [-1.04715424e-02 -9.99725878e-01 2.09404100e-02 1.09000000e+02]\n",
|
| 309 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 310 |
+
"50_19\n",
|
| 311 |
+
"<class 'numpy.ndarray'>\n",
|
| 312 |
+
"[ 18.13099468 -15.09828699 -373.56972069] [[ 1.69344455e-01 -8.62255633e-01 -4.77323413e-01 1.02500000e+02]\n",
|
| 313 |
+
" [-3.75056893e-01 -5.04259884e-01 7.77852356e-01 3.50000000e+00]\n",
|
| 314 |
+
" [-9.11402643e-01 4.72984463e-02 -4.08788532e-01 6.85000000e+01]\n",
|
| 315 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 316 |
+
"50_16\n",
|
| 317 |
+
"<class 'numpy.ndarray'>\n",
|
| 318 |
+
"[ 54.96526723 -6.24934125 -372.58995266] [[-4.84127194e-01 -8.51330519e-01 2.02131584e-01 4.65000000e+01]\n",
|
| 319 |
+
" [ 8.18616331e-01 -5.22263169e-01 -2.38973722e-01 1.03000000e+02]\n",
|
| 320 |
+
" [ 3.09011519e-01 4.97745462e-02 9.49754894e-01 -5.35000000e+01]\n",
|
| 321 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 322 |
+
"50_20\n",
|
| 323 |
+
"<class 'numpy.ndarray'>\n",
|
| 324 |
+
"[ 6.9073331 -12.50545741 -376.94878724] [[ 4.52463269e-01 -8.56338322e-01 2.48921052e-01 3.45000000e+01]\n",
|
| 325 |
+
" [-6.88811421e-01 -5.12875915e-01 -5.12344778e-01 1.30000000e+02]\n",
|
| 326 |
+
" [ 5.66406071e-01 6.03575222e-02 -8.21913123e-01 1.10000000e+02]\n",
|
| 327 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 328 |
+
"50_14\n",
|
| 329 |
+
"<class 'numpy.ndarray'>\n",
|
| 330 |
+
"[ 26.84217318 -13.37724083 -373.07124676] [[ 4.91954267e-01 8.43542457e-01 2.15446383e-01 -7.50000000e+01]\n",
|
| 331 |
+
" [ 7.93443859e-01 -5.36261320e-01 2.87872612e-01 5.85000000e+01]\n",
|
| 332 |
+
" [ 3.58368307e-01 2.93244496e-02 -9.33119595e-01 1.23500000e+02]\n",
|
| 333 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 334 |
+
"50_12\n",
|
| 335 |
+
"<class 'numpy.ndarray'>\n",
|
| 336 |
+
"[ 59.89943686 -13.35773397 -398.58164983] [[ 4.00223613e-01 8.52581263e-01 -3.36045057e-01 -2.70000000e+01]\n",
|
| 337 |
+
" [ 6.09276235e-01 -5.21465302e-01 -5.97374618e-01 1.39000000e+02]\n",
|
| 338 |
+
" [-6.84546232e-01 3.43391597e-02 -7.28160203e-01 9.50000000e+01]\n",
|
| 339 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 340 |
+
"50_11\n",
|
| 341 |
+
"<class 'numpy.ndarray'>\n",
|
| 342 |
+
"[ 58.33317475 -12.89082022 -326.86482127] [[-7.99684465e-01 3.77000421e-02 5.99235713e-01 -5.95000000e+01]\n",
|
| 343 |
+
" [-6.00420475e-01 -5.02118543e-02 -7.98106492e-01 1.36500000e+02]\n",
|
| 344 |
+
" [ 8.74227766e-08 -9.98026788e-01 6.27895147e-02 1.06500000e+02]\n",
|
| 345 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 346 |
+
"50_9\n",
|
| 347 |
+
"<class 'numpy.ndarray'>\n",
|
| 348 |
+
"[ 27.92755044 -15.93361787 -333.39666851] [[-8.49892676e-01 -2.48230007e-02 -5.26370823e-01 4.90000000e+01]\n",
|
| 349 |
+
" [ 5.26955843e-01 -4.00352329e-02 -8.48949194e-01 1.45000000e+02]\n",
|
| 350 |
+
" [ 8.74227766e-08 -9.98889863e-01 4.71062772e-02 1.06500000e+02]\n",
|
| 351 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 352 |
+
"50_6\n",
|
| 353 |
+
"<class 'numpy.ndarray'>\n",
|
| 354 |
+
"[ 43.07009535 -13.964573 -345.44811509] [[ 9.99972582e-01 5.15297474e-03 5.31978253e-03 4.50000000e+00]\n",
|
| 355 |
+
" [-5.23811160e-03 -1.57352034e-02 9.99862492e-01 -4.85000000e+01]\n",
|
| 356 |
+
" [ 5.23597375e-03 -9.99862909e-01 -1.57077797e-02 1.12000000e+02]\n",
|
| 357 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 358 |
+
"50_17\n",
|
| 359 |
+
"<class 'numpy.ndarray'>\n",
|
| 360 |
+
"[ 21.19433361 -13.9616235 -385.61167404] [[-4.56769258e-01 -8.58143985e-01 -2.34415770e-01 7.85000000e+01]\n",
|
| 361 |
+
" [ 7.03364849e-01 -5.09721696e-01 4.95440930e-01 3.60000000e+01]\n",
|
| 362 |
+
" [-5.44646442e-01 6.14223853e-02 8.36413503e-01 -3.90000000e+01]\n",
|
| 363 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 364 |
+
"50_1\n",
|
| 365 |
+
"<class 'numpy.ndarray'>\n",
|
| 366 |
+
"[ 59.52117866 -15.3738714 -347.71641335] [[-1.19972751e-01 2.48980802e-02 9.92464900e-01 -9.15000000e+01]\n",
|
| 367 |
+
" [-9.91396725e-01 -5.57048060e-02 -1.18446149e-01 6.85000000e+01]\n",
|
| 368 |
+
" [ 5.23359850e-02 -9.98136818e-01 3.13669369e-02 1.08000000e+02]\n",
|
| 369 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 370 |
+
"50_10\n",
|
| 371 |
+
"<class 'numpy.ndarray'>\n",
|
| 372 |
+
"[ 38.45517695 -25.38650074 -331.90212336] [[-9.99602497e-01 8.94374773e-03 -2.67361123e-02 -3.00000000e+00]\n",
|
| 373 |
+
" [ 2.61755064e-02 -5.78178689e-02 -9.97983932e-01 1.55500000e+02]\n",
|
| 374 |
+
" [-1.04715424e-02 -9.98287082e-01 5.75607792e-02 9.30000000e+01]\n",
|
| 375 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 376 |
+
"50_3\n",
|
| 377 |
+
"<class 'numpy.ndarray'>\n",
|
| 378 |
+
"[ 47.77424912 -14.1188276 -331.79743582] [[-7.67154515e-01 2.28467248e-02 6.41055346e-01 -6.55000000e+01]\n",
|
| 379 |
+
" [-6.41440928e-01 -3.54810432e-02 -7.66351461e-01 1.31000000e+02]\n",
|
| 380 |
+
" [ 5.23668900e-03 -9.99109149e-01 4.18742672e-02 1.09000000e+02]\n",
|
| 381 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 382 |
+
"50_2\n",
|
| 383 |
+
"<class 'numpy.ndarray'>\n",
|
| 384 |
+
"[ 59.42157252 -15.51625781 -347.62839332] [[-1.14937671e-01 -5.58722718e-07 9.93372679e-01 -9.10000000e+01]\n",
|
| 385 |
+
" [-9.93372679e-01 -2.33593003e-08 -1.14937671e-01 6.45000000e+01]\n",
|
| 386 |
+
" [ 8.74227766e-08 -1.00000000e+00 -5.52335052e-07 1.10500000e+02]\n",
|
| 387 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 388 |
+
"\n",
|
| 389 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 25\n",
|
| 390 |
+
"25_6\n",
|
| 391 |
+
"<class 'numpy.ndarray'>\n",
|
| 392 |
+
"[ 8.61615697 -31.8045368 -351.10071425] [[-2.61689126e-02 8.90969396e-01 4.53308612e-01 -9.00000000e+01]\n",
|
| 393 |
+
" [-4.53255996e-02 -4.54055071e-01 8.89819980e-01 -2.00000000e+01]\n",
|
| 394 |
+
" [ 9.98629451e-01 2.73913727e-03 5.22658490e-02 4.20000000e+01]\n",
|
| 395 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 396 |
+
"25_19\n",
|
| 397 |
+
"<class 'numpy.ndarray'>\n",
|
| 398 |
+
"[ -3.05976304 -13.79157959 -338.19754376] [[-7.10789740e-01 2.20553949e-02 -7.03058660e-01 5.30000000e+01]\n",
|
| 399 |
+
" [ 7.03385055e-01 2.97254249e-02 -7.10187197e-01 1.40500000e+02]\n",
|
| 400 |
+
" [ 5.23525849e-03 -9.99314725e-01 -3.66419628e-02 1.12000000e+02]\n",
|
| 401 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 402 |
+
"25_17\n",
|
| 403 |
+
"<class 'numpy.ndarray'>\n",
|
| 404 |
+
"[ 47.73491514 -7.13858754 -353.31326402] [[-8.57488573e-01 4.48732153e-02 5.12542367e-01 -4.95000000e+01]\n",
|
| 405 |
+
" [-5.13196528e-01 -3.64979031e-03 -8.58263373e-01 1.47000000e+02]\n",
|
| 406 |
+
" [-3.66423652e-02 -9.98986006e-01 2.61584315e-02 1.16000000e+02]\n",
|
| 407 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 408 |
+
"25_9\n",
|
| 409 |
+
"<class 'numpy.ndarray'>\n",
|
| 410 |
+
"[ 23.41583283 -14.79229727 -365.86318633] [[-4.22979087e-01 8.85387242e-01 -1.92816377e-01 -2.65000000e+01]\n",
|
| 411 |
+
" [-8.30143154e-01 -4.63932246e-01 -3.09239805e-01 1.27000000e+02]\n",
|
| 412 |
+
" [-3.63250703e-01 2.92632300e-02 9.31231737e-01 -3.05000000e+01]\n",
|
| 413 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 414 |
+
"25_11\n",
|
| 415 |
+
"<class 'numpy.ndarray'>\n",
|
| 416 |
+
"[ 31.82878222 -14.1290978 -328.08744427] [[-9.99931514e-01 -5.06819226e-03 1.05502456e-02 -7.50000000e+00]\n",
|
| 417 |
+
" [-1.04691898e-02 -1.57619193e-02 -9.99820948e-01 1.63500000e+02]\n",
|
| 418 |
+
" [ 5.23357699e-03 -9.99862909e-01 1.57077797e-02 1.07000000e+02]\n",
|
| 419 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 420 |
+
"25_20\n",
|
| 421 |
+
"<class 'numpy.ndarray'>\n",
|
| 422 |
+
"[ 19.60748626 -17.66528259 -358.65433521] [[ 3.66388112e-02 -4.76490818e-02 -9.98191953e-01 8.45000000e+01]\n",
|
| 423 |
+
" [ 9.99205112e-01 -1.39529109e-02 3.73420455e-02 4.75000000e+01]\n",
|
| 424 |
+
" [-1.57069974e-02 -9.98766661e-01 4.70999889e-02 1.05500000e+02]\n",
|
| 425 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 426 |
+
"25_14\n",
|
| 427 |
+
"<class 'numpy.ndarray'>\n",
|
| 428 |
+
"[ 19.89107552 -18.53665048 -356.73167077] [[ 9.50848758e-01 2.76911701e-03 -3.09643239e-01 2.90000000e+01]\n",
|
| 429 |
+
" [ 3.08946699e-01 -7.61085898e-02 9.48029220e-01 -5.15000000e+01]\n",
|
| 430 |
+
" [-2.09413078e-02 -9.97095704e-01 -7.32232705e-02 1.13000000e+02]\n",
|
| 431 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 432 |
+
"25_4\n",
|
| 433 |
+
"<class 'numpy.ndarray'>\n",
|
| 434 |
+
"[ 14.5972955 -19.92519179 -379.18381659] [[ 4.04507875e-01 8.49457979e-01 3.38813305e-01 -7.50000000e+01]\n",
|
| 435 |
+
" [ 7.00630009e-01 -5.25954723e-01 4.82171327e-01 4.40000000e+01]\n",
|
| 436 |
+
" [ 5.87784767e-01 4.23406847e-02 -8.07908595e-01 1.04500000e+02]\n",
|
| 437 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 438 |
+
"25_16\n",
|
| 439 |
+
"<class 'numpy.ndarray'>\n",
|
| 440 |
+
"[ 23.9261915 -6.66387486 -387.36830495] [[ 1.04527801e-01 -6.58727515e-07 9.94521976e-01 -8.20000000e+01]\n",
|
| 441 |
+
" [-9.94521976e-01 -1.57138928e-07 1.04527801e-01 4.30000000e+01]\n",
|
| 442 |
+
" [ 8.74227766e-08 -1.00000000e+00 -6.71544342e-07 1.20500000e+02]\n",
|
| 443 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 444 |
+
"25_5\n",
|
| 445 |
+
"<class 'numpy.ndarray'>\n",
|
| 446 |
+
"[ 6.65038734 -24.28279954 -377.78655465] [[ 3.07082146e-01 8.68339777e-01 3.89469624e-01 -7.95000000e+01]\n",
|
| 447 |
+
" [ 5.58580637e-01 -4.95790064e-01 6.64966106e-01 2.70000000e+01]\n",
|
| 448 |
+
" [ 7.70511687e-01 1.33509627e-02 -6.37286067e-01 9.70000000e+01]\n",
|
| 449 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 450 |
+
"25_2\n",
|
| 451 |
+
"<class 'numpy.ndarray'>\n",
|
| 452 |
+
"[ 50.34458922 -6.53171928 -392.03081776] [[ 2.70119667e-01 8.85806918e-01 -3.77334684e-01 -3.10000000e+01]\n",
|
| 453 |
+
" [ 4.20756996e-01 -4.61101443e-01 -7.81248391e-01 1.49500000e+02]\n",
|
| 454 |
+
" [-8.66024792e-01 5.22643477e-02 -4.97262031e-01 7.40000000e+01]\n",
|
| 455 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 456 |
+
"25_10\n",
|
| 457 |
+
"<class 'numpy.ndarray'>\n",
|
| 458 |
+
"[ 19.80598694 5.54998488 -349.78725865] [[-1.55652106e-01 9.53439653e-01 -2.58312285e-01 -3.90000000e+01]\n",
|
| 459 |
+
" [-3.49600285e-01 -2.97745287e-01 -8.88328433e-01 1.76500000e+02]\n",
|
| 460 |
+
" [-9.23878789e-01 -4.79641519e-02 3.79667461e-01 2.25000000e+01]\n",
|
| 461 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 462 |
+
"25_3\n",
|
| 463 |
+
"<class 'numpy.ndarray'>\n",
|
| 464 |
+
"[ 32.64899358 -13.09385106 -384.42097825] [[ 5.41654408e-01 8.39874089e-01 -3.49570401e-02 -5.20000000e+01]\n",
|
| 465 |
+
" [ 8.34076822e-01 -5.42156577e-01 -1.01892397e-01 9.55000000e+01]\n",
|
| 466 |
+
" [-1.04528971e-01 2.60336101e-02 -9.94181037e-01 1.14000000e+02]\n",
|
| 467 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 468 |
+
"25_8\n",
|
| 469 |
+
"<class 'numpy.ndarray'>\n",
|
| 470 |
+
"[ 2.7573541 -14.86633456 -356.21006468] [[-5.17571390e-01 8.51803780e-01 8.09332281e-02 -5.95000000e+01]\n",
|
| 471 |
+
" [-8.15557301e-01 -5.19725084e-01 2.54464358e-01 6.60000000e+01]\n",
|
| 472 |
+
" [ 2.58816719e-01 6.56977817e-02 9.63689625e-01 -5.30000000e+01]\n",
|
| 473 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 474 |
+
"25_13\n",
|
| 475 |
+
"<class 'numpy.ndarray'>\n",
|
| 476 |
+
"[ 27.29416278 -18.17496673 -357.47004385] [[ 3.43616605e-01 -1.52306622e-02 -9.38986480e-01 7.90000000e+01]\n",
|
| 477 |
+
" [ 9.38978672e-01 -1.11511489e-02 3.43794614e-01 -2.50000000e+00]\n",
|
| 478 |
+
" [-1.57069974e-02 -9.99821842e-01 1.04695475e-02 1.07000000e+02]\n",
|
| 479 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 480 |
+
"25_7\n",
|
| 481 |
+
"<class 'numpy.ndarray'>\n",
|
| 482 |
+
"[ -4.18285571 -33.39064946 -355.47568268] [[-3.12351257e-01 8.49359274e-01 4.25470978e-01 -8.05000000e+01]\n",
|
| 483 |
+
" [-4.97926295e-01 -5.27806222e-01 6.88106120e-01 1.00000000e+00]\n",
|
| 484 |
+
" [ 8.09015512e-01 3.07762506e-03 5.87779224e-01 5.00000000e-01]\n",
|
| 485 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 486 |
+
"25_12\n",
|
| 487 |
+
"<class 'numpy.ndarray'>\n",
|
| 488 |
+
"[ 14.94080716 -12.84743322 -362.73966275] [[-5.44161558e-01 -1.67256054e-02 -8.38813722e-01 6.25000000e+01]\n",
|
| 489 |
+
" [ 8.37934792e-01 -6.07356206e-02 -5.42380333e-01 1.09500000e+02]\n",
|
| 490 |
+
" [-4.18742336e-02 -9.98013735e-01 4.70649563e-02 1.07500000e+02]\n",
|
| 491 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 492 |
+
"25_1\n",
|
| 493 |
+
"<class 'numpy.ndarray'>\n",
|
| 494 |
+
"[ 50.87395532 -6.51268418 -391.69625226] [[ 0.36555567 0.85089946 -0.3772786 -27.5 ]\n",
|
| 495 |
+
" [ 0.35424927 -0.50201368 -0.78898019 154. ]\n",
|
| 496 |
+
" [ -0.86074185 0.15476552 -0.48494446 68.5 ]\n",
|
| 497 |
+
" [ 0. 0. 0. 1. ]]\n",
|
| 498 |
+
"25_15\n",
|
| 499 |
+
"<class 'numpy.ndarray'>\n",
|
| 500 |
+
"[ 13.02554317 -11.10753714 -382.33100474] [[ 7.05521405e-01 -4.03015725e-02 7.07541764e-01 -5.15000000e+01]\n",
|
| 501 |
+
" [-7.08676636e-01 -3.43199782e-02 7.04698205e-01 -2.55000000e+01]\n",
|
| 502 |
+
" [-4.11762809e-03 -9.98597980e-01 -5.27742617e-02 1.18500000e+02]\n",
|
| 503 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 504 |
+
"25_18\n",
|
| 505 |
+
"<class 'numpy.ndarray'>\n",
|
| 506 |
+
"[ 19.24908623 -10.19467014 -340.9759356 ] [[-9.92011368e-01 -5.07827625e-02 1.15475222e-01 -1.35000000e+01]\n",
|
| 507 |
+
" [-1.14779614e-01 -1.64168198e-02 -9.93255317e-01 1.63000000e+02]\n",
|
| 508 |
+
" [ 5.23359850e-02 -9.98574793e-01 1.04568461e-02 1.14000000e+02]\n",
|
| 509 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 510 |
+
"\n",
|
| 511 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 0\n",
|
| 512 |
+
"0_12\n",
|
| 513 |
+
"0_17\n",
|
| 514 |
+
"0_16\n",
|
| 515 |
+
"<class 'numpy.ndarray'>\n",
|
| 516 |
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"[ 30.33364947 -48.24128986 -365.61265488] [[-1.72922775e-01 9.19383526e-01 3.53315264e-01 -7.85000000e+01]\n",
|
| 517 |
+
" [-2.85529107e-01 -3.90108436e-01 8.75379086e-01 -4.05000000e+01]\n",
|
| 518 |
+
" [ 9.42640364e-01 5.04911914e-02 3.29969376e-01 2.40000000e+01]\n",
|
| 519 |
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" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 520 |
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"0_15\n",
|
| 521 |
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"<class 'numpy.ndarray'>\n",
|
| 522 |
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"[ 47.83855301 -40.63630415 -374.67333886] [[-8.56738165e-02 -8.49586189e-01 -5.20445287e-01 8.50000000e+01]\n",
|
| 523 |
+
" [ 1.24656409e-01 -5.27401686e-01 8.40421438e-01 -1.55000000e+01]\n",
|
| 524 |
+
" [-9.88494217e-01 7.12527009e-03 1.51090875e-01 2.85000000e+01]\n",
|
| 525 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 526 |
+
"0_2\n",
|
| 527 |
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"<class 'numpy.ndarray'>\n",
|
| 528 |
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"[ 53.98529817 -23.34183389 -318.12190071] [[-9.96255994e-01 -8.23962316e-03 -8.60589445e-02 1.40000000e+01]\n",
|
| 529 |
+
" [ 8.63362178e-02 -4.32123058e-02 -9.95328486e-01 1.60500000e+02]\n",
|
| 530 |
+
" [ 4.48232563e-03 -9.99031961e-01 4.37618978e-02 9.40000000e+01]\n",
|
| 531 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 532 |
+
"0_5\n",
|
| 533 |
+
"<class 'numpy.ndarray'>\n",
|
| 534 |
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"[ 52.34242901 -11.5680847 -364.04068114] [[ 2.73865104e-01 -5.74945211e-02 -9.60048079e-01 7.30000000e+01]\n",
|
| 535 |
+
" [ 9.61411834e-01 -1.08027589e-02 2.74901092e-01 1.25000000e+01]\n",
|
| 536 |
+
" [-2.61764750e-02 -9.98287380e-01 5.23174144e-02 1.10500000e+02]\n",
|
| 537 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 538 |
+
"0_14\n",
|
| 539 |
+
"<class 'numpy.ndarray'>\n",
|
| 540 |
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"[ 32.66934429 -31.03419982 -419.4400454 ] [[ 3.93467128e-01 8.50530863e-01 3.48971128e-01 -4.90000000e+01]\n",
|
| 541 |
+
" [ 6.81505263e-01 -5.24617076e-01 5.10223031e-01 6.25000000e+01]\n",
|
| 542 |
+
" [ 6.17036641e-01 3.70696560e-02 -7.86060810e-01 8.50000000e+01]\n",
|
| 543 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 544 |
+
"0_9\n",
|
| 545 |
+
"<class 'numpy.ndarray'>\n",
|
| 546 |
+
"[ 33.45435988 -9.86960392 -390.26269769] [[-5.52335052e-07 1.57068912e-02 9.99876618e-01 -7.10000000e+01]\n",
|
| 547 |
+
" [-1.00000000e+00 -9.60874615e-08 -5.50893787e-07 6.00000000e+01]\n",
|
| 548 |
+
" [ 8.74227766e-08 -9.99876618e-01 1.57068912e-02 1.13500000e+02]\n",
|
| 549 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 550 |
+
"0_22\n",
|
| 551 |
+
"<class 'numpy.ndarray'>\n",
|
| 552 |
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"[ 24.14234403 -44.48885363 -376.81869704] [[ 4.61720601e-02 -8.39288235e-01 -5.41722655e-01 8.65000000e+01]\n",
|
| 553 |
+
" [-1.94662526e-01 -5.39464176e-01 8.19197714e-01 -2.40000000e+01]\n",
|
| 554 |
+
" [-9.79782939e-01 6.76290542e-02 -1.88286141e-01 5.50000000e+01]\n",
|
| 555 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 556 |
+
"0_4\n",
|
| 557 |
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"<class 'numpy.ndarray'>\n",
|
| 558 |
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"[ 22.34787415 -12.38490572 -361.83273303] [[-2.43611336e-01 -4.94846478e-02 -9.68609691e-01 6.60000000e+01]\n",
|
| 559 |
+
" [ 9.69858825e-01 -1.78214479e-02 -2.43015021e-01 8.50000000e+01]\n",
|
| 560 |
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" [-5.23651438e-03 -9.98615861e-01 5.23346290e-02 1.09500000e+02]\n",
|
| 561 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 562 |
+
"0_18\n",
|
| 563 |
+
"<class 'numpy.ndarray'>\n",
|
| 564 |
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"[ 94.3018499 9.95712859 -403.02370763] [[-4.40655112e-01 8.44817400e-01 -3.03490698e-01 -3.35000000e+01]\n",
|
| 565 |
+
" [-7.63237119e-01 -5.30567527e-01 -3.68737340e-01 1.61000000e+02]\n",
|
| 566 |
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" [-4.72538024e-01 6.91493675e-02 8.78593266e-01 -4.00000000e+00]\n",
|
| 567 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 568 |
+
"0_8\n",
|
| 569 |
+
"<class 'numpy.ndarray'>\n",
|
| 570 |
+
"[ 4.89130441 -10.65416859 -363.34594537] [[-6.29009902e-01 -2.90704630e-02 -7.76853561e-01 5.40000000e+01]\n",
|
| 571 |
+
" [-7.76762486e-01 6.38768151e-02 6.26545906e-01 -3.65000000e+01]\n",
|
| 572 |
+
" [ 3.14089544e-02 9.97534275e-01 -6.27599955e-02 -1.85000000e+01]\n",
|
| 573 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 574 |
+
"0_7\n",
|
| 575 |
+
"<class 'numpy.ndarray'>\n",
|
| 576 |
+
"[ 33.38694694 -8.22702268 -321.43350163] [[-9.99945164e-01 1.04718581e-02 -1.09615452e-04 -1.50000000e+00]\n",
|
| 577 |
+
" [ 0.00000000e+00 1.04670478e-02 9.99945223e-01 -8.50000000e+01]\n",
|
| 578 |
+
" [ 1.04724318e-02 9.99890387e-01 -1.04664741e-02 -2.30000000e+01]\n",
|
| 579 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 580 |
+
"0_11\n",
|
| 581 |
+
"<class 'numpy.ndarray'>\n",
|
| 582 |
+
"[ 39.39567817 -7.81152401 -331.79231827] [[-9.88440156e-01 1.50076434e-01 -2.15206817e-02 -9.00000000e+00]\n",
|
| 583 |
+
" [ 1.03513040e-02 -7.48123527e-02 -9.97143924e-01 1.81500000e+02]\n",
|
| 584 |
+
" [-1.51257813e-01 -9.85839844e-01 7.23940507e-02 1.11000000e+02]\n",
|
| 585 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 586 |
+
"0_13\n",
|
| 587 |
+
"<class 'numpy.ndarray'>\n",
|
| 588 |
+
"[ 47.8849601 -21.10832864 -406.85110541] [[ 5.03184497e-01 8.54882419e-01 1.26417741e-01 -5.70000000e+01]\n",
|
| 589 |
+
" [ 8.50838244e-01 -5.15692055e-01 1.00677565e-01 7.25000000e+01]\n",
|
| 590 |
+
" [ 1.51260108e-01 5.69016635e-02 -9.86854911e-01 9.90000000e+01]\n",
|
| 591 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 592 |
+
"0_23\n",
|
| 593 |
+
"<class 'numpy.ndarray'>\n",
|
| 594 |
+
"[ 34.1425256 -13.99516051 -436.24154435] [[-3.52828532e-01 3.82114016e-02 -9.34907436e-01 5.45000000e+01]\n",
|
| 595 |
+
" [ 9.32251334e-01 -7.12009743e-02 -3.54736269e-01 9.50000000e+01]\n",
|
| 596 |
+
" [-8.01212862e-02 -9.96729791e-01 -1.05008949e-02 1.07500000e+02]\n",
|
| 597 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 598 |
+
"0_10\n",
|
| 599 |
+
"<class 'numpy.ndarray'>\n",
|
| 600 |
+
"[ 75.30314 -10.69616655 -360.85009292] [[-4.57888782e-01 -1.05290443e-01 8.82752419e-01 -5.35000000e+01]\n",
|
| 601 |
+
" [-8.87143850e-01 -1.01783918e-02 -4.61380690e-01 1.36000000e+02]\n",
|
| 602 |
+
" [ 5.75639792e-02 -9.94389415e-01 -8.87472034e-02 1.18000000e+02]\n",
|
| 603 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 604 |
+
"0_19\n",
|
| 605 |
+
"<class 'numpy.ndarray'>\n",
|
| 606 |
+
"[ 54.61536245 5.98952087 -390.19254055] [[-3.62248421e-02 9.26083982e-01 -3.75574470e-01 -2.85000000e+01]\n",
|
| 607 |
+
" [-1.19981252e-01 -3.77133012e-01 -9.18354630e-01 1.87000000e+02]\n",
|
| 608 |
+
" [-9.92115021e-01 1.17946425e-02 1.24774300e-01 3.80000000e+01]\n",
|
| 609 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 610 |
+
"0_1\n",
|
| 611 |
+
"<class 'numpy.ndarray'>\n",
|
| 612 |
+
"[ 54.25867277 -22.806361 -318.17092856] [[-9.64558959e-01 -1.13992482e-01 -2.37974271e-01 3.15000000e+01]\n",
|
| 613 |
+
" [ 2.42280513e-01 -2.53391284e-02 -9.69875276e-01 1.61000000e+02]\n",
|
| 614 |
+
" [ 1.04528435e-01 -9.93158400e-01 5.20592406e-02 9.80000000e+01]\n",
|
| 615 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 616 |
+
"0_6\n",
|
| 617 |
+
"<class 'numpy.ndarray'>\n",
|
| 618 |
+
"[ 77.66232163 -15.57184449 -330.88443051] [[-8.38258147e-01 7.89983943e-03 5.45216382e-01 -3.50000000e+01]\n",
|
| 619 |
+
" [ 5.44367671e-01 6.97381571e-02 8.35942805e-01 -6.90000000e+01]\n",
|
| 620 |
+
" [-3.14185731e-02 9.97534037e-01 -6.27589971e-02 -1.30000000e+01]\n",
|
| 621 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 622 |
+
"0_21\n",
|
| 623 |
+
"<class 'numpy.ndarray'>\n",
|
| 624 |
+
"[ 75.01018329 -10.05817754 -341.89935495] [[-9.92029309e-01 8.33121538e-02 9.45351794e-02 -1.25000000e+01]\n",
|
| 625 |
+
" [-1.02217443e-01 -9.33565423e-02 -9.90371704e-01 1.80000000e+02]\n",
|
| 626 |
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" [-7.36845210e-02 -9.92140949e-01 1.01128384e-01 1.08500000e+02]\n",
|
| 627 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 628 |
+
"0_20\n",
|
| 629 |
+
"<class 'numpy.ndarray'>\n",
|
| 630 |
+
"[ 27.12234241 -2.73171326 -424.23229279] [[ 2.70503014e-01 8.54203105e-01 -4.44032878e-01 -4.10000000e+01]\n",
|
| 631 |
+
" [ 4.31221068e-01 -5.19878030e-01 -7.37411201e-01 1.32000000e+02]\n",
|
| 632 |
+
" [-8.60741854e-01 7.99562782e-03 -5.08978963e-01 6.45000000e+01]\n",
|
| 633 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 634 |
+
"0_3\n",
|
| 635 |
+
"<class 'numpy.ndarray'>\n",
|
| 636 |
+
"[ 18.07637881 -9.88668362 -329.27505922] [[-9.54033732e-01 -2.76956767e-01 -1.14518963e-01 3.10000000e+01]\n",
|
| 637 |
+
" [ 1.51104078e-01 -1.14518076e-01 -9.81862068e-01 1.76000000e+02]\n",
|
| 638 |
+
" [ 2.58818835e-01 -9.54033852e-01 1.51103407e-01 9.90000000e+01]\n",
|
| 639 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n"
|
| 640 |
+
]
|
| 641 |
+
}
|
| 642 |
+
],
|
| 643 |
+
"source": [
|
| 644 |
+
"import json\n",
|
| 645 |
+
"import numpy as np\n",
|
| 646 |
+
"\n",
|
| 647 |
+
"name = \"bottle2\"\n",
|
| 648 |
+
"folder = \"./dataset\"\n",
|
| 649 |
+
"json_path = \"ply_files.json\"\n",
|
| 650 |
+
"\n",
|
| 651 |
+
"try:\n",
|
| 652 |
+
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
|
| 653 |
+
" categorized_files = json.load(f)\n",
|
| 654 |
+
"except FileNotFoundError:\n",
|
| 655 |
+
" print(f\"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.\")\n",
|
| 656 |
+
" exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ\n",
|
| 657 |
+
"\n",
|
| 658 |
+
"# 3. ๋ชจ๋ ์นดํ
๊ณ ๋ฆฌ์ ํ์ผ์ ์ํํ๋ ๋ฐ๋ณต๋ฌธ\n",
|
| 659 |
+
"print(\"=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===\")\n",
|
| 660 |
+
"categories = [\"100\", \"75\", \"50\", \"25\", \"0\"]\n",
|
| 661 |
+
"\n",
|
| 662 |
+
"# resolutions ๋์
๋๋ฆฌ๋ฅผ ๊ธฐ์ค์ผ๋ก ์ธ๋ถ ๋ฃจํ๋ฅผ ์คํํฉ๋๋ค.\n",
|
| 663 |
+
"for category in categories:\n",
|
| 664 |
+
" \n",
|
| 665 |
+
" print(f\"\\n--- [์นดํ
๊ณ ๋ฆฌ: {category}\")\n",
|
| 666 |
+
" \n",
|
| 667 |
+
" # JSON์์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ ํ์ผ ๋ฆฌ์คํธ๋ฅผ ๊ฐ์ ธ์ต๋๋ค.\n",
|
| 668 |
+
" # .get(category, [])๋ฅผ ์ฌ์ฉํ๋ฉด JSON์ ํด๋น ์นดํ
๊ณ ๋ฆฌ๊ฐ ์์ด๋ ์ค๋ฅ ์์ด ๋น ๋ฆฌ์คํธ๋ฅผ ๋ฐํํฉ๋๋ค.\n",
|
| 669 |
+
" filenames_in_category = categorized_files.get(category, [])\n",
|
| 670 |
+
" \n",
|
| 671 |
+
" if not filenames_in_category:\n",
|
| 672 |
+
" print(\"์ฒ๋ฆฌํ ํ์ผ์ด ์์ต๋๋ค.\")\n",
|
| 673 |
+
" continue # ํ์ผ์ด ์์ผ๋ฉด ๋ค์ ์นดํ
๊ณ ๋ฆฌ๋ก ๋์ด๊ฐ\n",
|
| 674 |
+
"\n",
|
| 675 |
+
" # ๋ด๋ถ ๋ฃจํ์์ ํด๋น ์นดํ
๊ณ ๋ฆฌ์ ๋ชจ๋ ํ์ผ์ ํ๋์ฉ ์ฒ๋ฆฌํฉ๋๋ค.\n",
|
| 676 |
+
" for filename in filenames_in_category:\n",
|
| 677 |
+
" gt_path =f\"./gt/noisy_filtered_{filename}.json\"\n",
|
| 678 |
+
" print(filename)\n",
|
| 679 |
+
" try:\n",
|
| 680 |
+
" with open(gt_path, \"r\", encoding='utf-8') as f:\n",
|
| 681 |
+
" gt_processed = json.load(f)\n",
|
| 682 |
+
" gt = np.array(gt_processed[f\"noisy_filtered_{filename}\"][\"matrix_world\"])\n",
|
| 683 |
+
"\n",
|
| 684 |
+
" print(type(gt))\n",
|
| 685 |
+
" ## get translted \n",
|
| 686 |
+
" center_path = f\"./centroid/{filename}.txt\"\n",
|
| 687 |
+
" translated = np.loadtxt(center_path) \n",
|
| 688 |
+
" print(translated, gt)\n",
|
| 689 |
+
" ## generate translate T\n",
|
| 690 |
+
" tran_T = np.eye(4)\n",
|
| 691 |
+
" tran_T[0:3,3] = translated\n",
|
| 692 |
+
" \n",
|
| 693 |
+
"\n",
|
| 694 |
+
" final_T = gt @ tran_T\n",
|
| 695 |
+
" real_final_T = np.linalg.inv(final_T)\n",
|
| 696 |
+
"\n",
|
| 697 |
+
" gt_list = real_final_T.tolist()\n",
|
| 698 |
+
" gt_processed[f\"noisy_filtered_{filename}\"][\"matrix_world\"] = gt_list\n",
|
| 699 |
+
"\n",
|
| 700 |
+
" with open(f'./gt_raw/noisy_filtered_{filename}.json', 'w', encoding='utf-8') as f:\n",
|
| 701 |
+
" json.dump(gt_processed, f, ensure_ascii=False, indent=4)\n",
|
| 702 |
+
"\n",
|
| 703 |
+
"\n",
|
| 704 |
+
" except FileNotFoundError:\n",
|
| 705 |
+
" continue"
|
| 706 |
+
]
|
| 707 |
+
},
|
| 708 |
+
{
|
| 709 |
+
"cell_type": "markdown",
|
| 710 |
+
"id": "a0277328",
|
| 711 |
+
"metadata": {},
|
| 712 |
+
"source": [
|
| 713 |
+
"## verify"
|
| 714 |
+
]
|
| 715 |
+
},
|
| 716 |
+
{
|
| 717 |
+
"cell_type": "code",
|
| 718 |
+
"execution_count": 10,
|
| 719 |
+
"id": "463b3159",
|
| 720 |
+
"metadata": {},
|
| 721 |
+
"outputs": [
|
| 722 |
+
{
|
| 723 |
+
"name": "stdout",
|
| 724 |
+
"output_type": "stream",
|
| 725 |
+
"text": [
|
| 726 |
+
"100_7\n",
|
| 727 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./gt_filtered.ply\u001b[0;m\n"
|
| 728 |
+
]
|
| 729 |
+
},
|
| 730 |
+
{
|
| 731 |
+
"name": "stderr",
|
| 732 |
+
"output_type": "stream",
|
| 733 |
+
"text": [
|
| 734 |
+
"RPly: Unexpected end of file\n",
|
| 735 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 736 |
+
]
|
| 737 |
+
}
|
| 738 |
+
],
|
| 739 |
+
"source": [
|
| 740 |
+
"import json\n",
|
| 741 |
+
"import numpy as np\n",
|
| 742 |
+
"import open3d as o3d\n",
|
| 743 |
+
"\n",
|
| 744 |
+
"\n",
|
| 745 |
+
"def get_T(file_path):\n",
|
| 746 |
+
" with open(file_path, 'r') as f:\n",
|
| 747 |
+
" T_matrix = np.loadtxt(file_path)\n",
|
| 748 |
+
" print(T_matrix)\n",
|
| 749 |
+
" return T_matrix\n",
|
| 750 |
+
" \n",
|
| 751 |
+
"filenames = []\n",
|
| 752 |
+
"with open(\"filename.txt\", \"r\") as f:\n",
|
| 753 |
+
" for line in f:\n",
|
| 754 |
+
" filenames.append(line.strip())\n",
|
| 755 |
+
"\n",
|
| 756 |
+
"\n",
|
| 757 |
+
"filename = filenames[0]\n",
|
| 758 |
+
"print(filename)\n",
|
| 759 |
+
"\n",
|
| 760 |
+
"with open(f\"./gt_raw/noisy_filtered_{filename}.json\", 'r') as f:\n",
|
| 761 |
+
" loaded_data = json.load(f)\n",
|
| 762 |
+
"\n",
|
| 763 |
+
"\n",
|
| 764 |
+
"\n",
|
| 765 |
+
"noisy_data = loaded_data[f'noisy_filtered_{filename}']\n",
|
| 766 |
+
"T_matrix = noisy_data['matrix_world']\n",
|
| 767 |
+
"\n",
|
| 768 |
+
"\n",
|
| 769 |
+
"##Translated\n",
|
| 770 |
+
"\n",
|
| 771 |
+
"gt_path = \"./gt_filtered.ply\"\n",
|
| 772 |
+
"noisy_path = f\"./dataset/{filename}.ply\"\n",
|
| 773 |
+
"translated_path = f\"./result3/result_{filename}.ply\"\n",
|
| 774 |
+
"\n",
|
| 775 |
+
"\n",
|
| 776 |
+
"\n",
|
| 777 |
+
"gt_pcd = o3d.io.read_point_cloud(gt_path)\n",
|
| 778 |
+
"gt_pcd.paint_uniform_color([0,0,1])\n",
|
| 779 |
+
"noisy_pcd = o3d.io.read_point_cloud(noisy_path)\n",
|
| 780 |
+
"noisy_pcd.paint_uniform_color([1,0,0])\n",
|
| 781 |
+
"\n",
|
| 782 |
+
"translated_noisy_pcd = o3d.io.read_point_cloud(translated_path)\n",
|
| 783 |
+
"translated_noisy_pcd.paint_uniform_color([0,1,0])\n",
|
| 784 |
+
"\n",
|
| 785 |
+
"\n",
|
| 786 |
+
"gt = np.array(T_matrix)\n",
|
| 787 |
+
"\n",
|
| 788 |
+
"## move and check gt and noisy\n",
|
| 789 |
+
"\n",
|
| 790 |
+
"o3d.visualization.draw_geometries([gt_pcd, noisy_pcd, translated_noisy_pcd])\n",
|
| 791 |
+
"# noisy_pcd.transform(tran_T)\n",
|
| 792 |
+
"gt_pcd.transform(gt)\n",
|
| 793 |
+
"\n",
|
| 794 |
+
"o3d.visualization.draw_geometries([noisy_pcd, translated_noisy_pcd])\n"
|
| 795 |
+
]
|
| 796 |
+
}
|
| 797 |
+
],
|
| 798 |
+
"metadata": {
|
| 799 |
+
"kernelspec": {
|
| 800 |
+
"display_name": "Python 3",
|
| 801 |
+
"language": "python",
|
| 802 |
+
"name": "python3"
|
| 803 |
+
},
|
| 804 |
+
"language_info": {
|
| 805 |
+
"codemirror_mode": {
|
| 806 |
+
"name": "ipython",
|
| 807 |
+
"version": 3
|
| 808 |
+
},
|
| 809 |
+
"file_extension": ".py",
|
| 810 |
+
"mimetype": "text/x-python",
|
| 811 |
+
"name": "python",
|
| 812 |
+
"nbconvert_exporter": "python",
|
| 813 |
+
"pygments_lexer": "ipython3",
|
| 814 |
+
"version": "3.10.12"
|
| 815 |
+
}
|
| 816 |
+
},
|
| 817 |
+
"nbformat": 4,
|
| 818 |
+
"nbformat_minor": 5
|
| 819 |
+
}
|
data/bottle_2/gt_filtered.ply
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/bottle_2/h
ADDED
|
File without changes
|
data/bottle_2/inference_ICP.ipynb
ADDED
|
@@ -0,0 +1,503 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 32,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"# conda activate vision\n",
|
| 10 |
+
"# cd build\n",
|
| 11 |
+
"# cmake -DCMAKE_BUILD_TYPE=Release ..\n",
|
| 12 |
+
"# make\n",
|
| 13 |
+
"# ./FRICP ./data/bottle/tea_gt_filtered.ply ./data/bottle/tea_noisy_filtered.ply ./data/bottle/res/ 3\n",
|
| 14 |
+
"\n",
|
| 15 |
+
"\n",
|
| 16 |
+
"# 100_16 is the best thing. "
|
| 17 |
+
]
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"execution_count": 2,
|
| 22 |
+
"metadata": {},
|
| 23 |
+
"outputs": [
|
| 24 |
+
{
|
| 25 |
+
"name": "stdout",
|
| 26 |
+
"output_type": "stream",
|
| 27 |
+
"text": [
|
| 28 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 29 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 30 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 31 |
+
"100_19\n"
|
| 32 |
+
]
|
| 33 |
+
}
|
| 34 |
+
],
|
| 35 |
+
"source": [
|
| 36 |
+
"import open3d as o3d\n",
|
| 37 |
+
"import numpy as np\n",
|
| 38 |
+
"\n",
|
| 39 |
+
"file_names = []\n",
|
| 40 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 41 |
+
" for line in f:\n",
|
| 42 |
+
" file_names.append(line.strip())\n",
|
| 43 |
+
"filename = file_names[0]\n",
|
| 44 |
+
"print(filename)\n",
|
| 45 |
+
"\n"
|
| 46 |
+
]
|
| 47 |
+
},
|
| 48 |
+
{
|
| 49 |
+
"cell_type": "markdown",
|
| 50 |
+
"metadata": {},
|
| 51 |
+
"source": [
|
| 52 |
+
"# Modify initial file"
|
| 53 |
+
]
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"cell_type": "code",
|
| 57 |
+
"execution_count": 34,
|
| 58 |
+
"metadata": {},
|
| 59 |
+
"outputs": [
|
| 60 |
+
{
|
| 61 |
+
"name": "stdout",
|
| 62 |
+
"output_type": "stream",
|
| 63 |
+
"text": [
|
| 64 |
+
"\n",
|
| 65 |
+
"์์
์๋ฃ!\n",
|
| 66 |
+
"'./initialized_result/initial_100_4.ply' ํ์ผ์ด ์ฑ๊ณต์ ์ผ๋ก ์์ฑ๋์์ต๋๋ค.\n"
|
| 67 |
+
]
|
| 68 |
+
}
|
| 69 |
+
],
|
| 70 |
+
"source": [
|
| 71 |
+
"\n",
|
| 72 |
+
"output_filename = f'./initialized_result/initial_{filename}.ply'\n",
|
| 73 |
+
"\n",
|
| 74 |
+
"# 1. read file\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"with open(f'./initialized_result/initial_{filename}.ply', 'r') as f:\n",
|
| 77 |
+
" lines = f.readlines()\n",
|
| 78 |
+
"\n",
|
| 79 |
+
"# 2. seperate data and header \n",
|
| 80 |
+
"header_lines = []\n",
|
| 81 |
+
"data_lines = []\n",
|
| 82 |
+
"is_header = True\n",
|
| 83 |
+
"\n",
|
| 84 |
+
"for line in lines:\n",
|
| 85 |
+
" if \"end_header\" in line:\n",
|
| 86 |
+
" is_header = False\n",
|
| 87 |
+
" continue\n",
|
| 88 |
+
" \n",
|
| 89 |
+
" if is_header:\n",
|
| 90 |
+
" header_lines.append(line)\n",
|
| 91 |
+
" \n",
|
| 92 |
+
" else: \n",
|
| 93 |
+
" parts = line.strip().split()\n",
|
| 94 |
+
" if len(parts) >= 3:\n",
|
| 95 |
+
" data_lines.append(f\"{parts[0]} {parts[1]} {parts[2]}\\n\")\n",
|
| 96 |
+
"\n",
|
| 97 |
+
"\n",
|
| 98 |
+
"# 3. modify header\n",
|
| 99 |
+
"# vertex\n",
|
| 100 |
+
"num_points = len(data_lines)\n",
|
| 101 |
+
"\n",
|
| 102 |
+
"# generate new header\n",
|
| 103 |
+
"\n",
|
| 104 |
+
"new_header = f\"\"\"ply\n",
|
| 105 |
+
"format ascii 1.0\n",
|
| 106 |
+
"element vertex {num_points}\n",
|
| 107 |
+
"property float x\n",
|
| 108 |
+
"property float y\n",
|
| 109 |
+
"property float z\n",
|
| 110 |
+
"element camera 1\n",
|
| 111 |
+
"property float view_px\n",
|
| 112 |
+
"property float view_py\n",
|
| 113 |
+
"property float view_pz\n",
|
| 114 |
+
"property float x_axisx\n",
|
| 115 |
+
"property float x_axisy\n",
|
| 116 |
+
"property float x_axisz\n",
|
| 117 |
+
"property float y_axisx\n",
|
| 118 |
+
"property float y_axisy\n",
|
| 119 |
+
"property float y_axisz\n",
|
| 120 |
+
"property float z_axisx\n",
|
| 121 |
+
"property float z_axisy\n",
|
| 122 |
+
"property float z_axisz\n",
|
| 123 |
+
"element phoxi_frame_params 1\n",
|
| 124 |
+
"property uint32 frame_width\n",
|
| 125 |
+
"property uint32 frame_height\n",
|
| 126 |
+
"property uint32 frame_index\n",
|
| 127 |
+
"property float frame_start_time\n",
|
| 128 |
+
"property float frame_duration\n",
|
| 129 |
+
"property float frame_computation_duration\n",
|
| 130 |
+
"property float frame_transfer_duration\n",
|
| 131 |
+
"property int32 total_scan_count\n",
|
| 132 |
+
"element camera_matrix 1\n",
|
| 133 |
+
"property float cm0\n",
|
| 134 |
+
"property float cm1\n",
|
| 135 |
+
"property float cm2\n",
|
| 136 |
+
"property float cm3\n",
|
| 137 |
+
"property float cm4\n",
|
| 138 |
+
"property float cm5\n",
|
| 139 |
+
"property float cm6\n",
|
| 140 |
+
"property float cm7\n",
|
| 141 |
+
"property float cm8\n",
|
| 142 |
+
"element distortion_matrix 1\n",
|
| 143 |
+
"property float dm0\n",
|
| 144 |
+
"property float dm1\n",
|
| 145 |
+
"property float dm2\n",
|
| 146 |
+
"property float dm3\n",
|
| 147 |
+
"property float dm4\n",
|
| 148 |
+
"property float dm5\n",
|
| 149 |
+
"property float dm6\n",
|
| 150 |
+
"property float dm7\n",
|
| 151 |
+
"property float dm8\n",
|
| 152 |
+
"property float dm9\n",
|
| 153 |
+
"property float dm10\n",
|
| 154 |
+
"property float dm11\n",
|
| 155 |
+
"property float dm12\n",
|
| 156 |
+
"property float dm13\n",
|
| 157 |
+
"element camera_resolution 1\n",
|
| 158 |
+
"property float width\n",
|
| 159 |
+
"property float height\n",
|
| 160 |
+
"element frame_binning 1\n",
|
| 161 |
+
"property float horizontal\n",
|
| 162 |
+
"property float vertical\n",
|
| 163 |
+
"end_header\n",
|
| 164 |
+
"\"\"\"\n",
|
| 165 |
+
"\n",
|
| 166 |
+
"#4. write 4file \n",
|
| 167 |
+
"\n",
|
| 168 |
+
"with open(output_filename,'w') as f:\n",
|
| 169 |
+
" f.write(new_header)\n",
|
| 170 |
+
"\n",
|
| 171 |
+
" for line in data_lines:\n",
|
| 172 |
+
" f.write(line)\n",
|
| 173 |
+
"\n",
|
| 174 |
+
"\n",
|
| 175 |
+
"print(\"\\n์์
์๋ฃ!\")\n",
|
| 176 |
+
"print(f\"'{output_filename}' ํ์ผ์ด ์ฑ๊ณต์ ์ผ๋ก ์์ฑ๋์์ต๋๋ค.\")\n"
|
| 177 |
+
]
|
| 178 |
+
},
|
| 179 |
+
{
|
| 180 |
+
"cell_type": "markdown",
|
| 181 |
+
"metadata": {},
|
| 182 |
+
"source": [
|
| 183 |
+
"### Source PCD"
|
| 184 |
+
]
|
| 185 |
+
},
|
| 186 |
+
{
|
| 187 |
+
"cell_type": "code",
|
| 188 |
+
"execution_count": null,
|
| 189 |
+
"metadata": {},
|
| 190 |
+
"outputs": [],
|
| 191 |
+
"source": []
|
| 192 |
+
},
|
| 193 |
+
{
|
| 194 |
+
"cell_type": "code",
|
| 195 |
+
"execution_count": 35,
|
| 196 |
+
"metadata": {},
|
| 197 |
+
"outputs": [
|
| 198 |
+
{
|
| 199 |
+
"name": "stdout",
|
| 200 |
+
"output_type": "stream",
|
| 201 |
+
"text": [
|
| 202 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./initialized_result/initial_100_4.ply\u001b[0;m\n",
|
| 203 |
+
"Source shape: (36526, 3)\n"
|
| 204 |
+
]
|
| 205 |
+
},
|
| 206 |
+
{
|
| 207 |
+
"name": "stderr",
|
| 208 |
+
"output_type": "stream",
|
| 209 |
+
"text": [
|
| 210 |
+
"RPly: Unexpected end of file\n",
|
| 211 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 212 |
+
]
|
| 213 |
+
}
|
| 214 |
+
],
|
| 215 |
+
"source": [
|
| 216 |
+
"\n",
|
| 217 |
+
"\n",
|
| 218 |
+
"\n",
|
| 219 |
+
"source_path = f\"./initialized_result/initial_{filename}.ply\"\n",
|
| 220 |
+
"\n",
|
| 221 |
+
"source_pcd = o3d.io.read_point_cloud(source_path)\n",
|
| 222 |
+
"\n",
|
| 223 |
+
"\n",
|
| 224 |
+
"\n",
|
| 225 |
+
"source_pcd_array = np.asarray(source_pcd.points)\n",
|
| 226 |
+
"print(\"Source shape:\", source_pcd_array.shape)\n",
|
| 227 |
+
"\n",
|
| 228 |
+
"coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 229 |
+
"o3d.visualization.draw_geometries([source_pcd,coord_frame])"
|
| 230 |
+
]
|
| 231 |
+
},
|
| 232 |
+
{
|
| 233 |
+
"cell_type": "code",
|
| 234 |
+
"execution_count": null,
|
| 235 |
+
"metadata": {},
|
| 236 |
+
"outputs": [],
|
| 237 |
+
"source": []
|
| 238 |
+
},
|
| 239 |
+
{
|
| 240 |
+
"cell_type": "markdown",
|
| 241 |
+
"metadata": {},
|
| 242 |
+
"source": [
|
| 243 |
+
"### Target PCD"
|
| 244 |
+
]
|
| 245 |
+
},
|
| 246 |
+
{
|
| 247 |
+
"cell_type": "code",
|
| 248 |
+
"execution_count": 13,
|
| 249 |
+
"metadata": {},
|
| 250 |
+
"outputs": [
|
| 251 |
+
{
|
| 252 |
+
"name": "stderr",
|
| 253 |
+
"output_type": "stream",
|
| 254 |
+
"text": [
|
| 255 |
+
"RPly: Unexpected end of file\n"
|
| 256 |
+
]
|
| 257 |
+
},
|
| 258 |
+
{
|
| 259 |
+
"name": "stdout",
|
| 260 |
+
"output_type": "stream",
|
| 261 |
+
"text": [
|
| 262 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: gt_filtered.ply\u001b[0;m\n",
|
| 263 |
+
"Target shape: (50000, 3)\n"
|
| 264 |
+
]
|
| 265 |
+
},
|
| 266 |
+
{
|
| 267 |
+
"name": "stderr",
|
| 268 |
+
"output_type": "stream",
|
| 269 |
+
"text": [
|
| 270 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 271 |
+
]
|
| 272 |
+
}
|
| 273 |
+
],
|
| 274 |
+
"source": [
|
| 275 |
+
"target_path = f\"gt_filtered.ply\"\n",
|
| 276 |
+
"target_pcd = o3d.io.read_point_cloud(target_path)\n",
|
| 277 |
+
"\n",
|
| 278 |
+
"target_pcd_array = np.asarray(target_pcd.points)\n",
|
| 279 |
+
"print(\"Target shape:\", target_pcd_array.shape)\n",
|
| 280 |
+
"\n",
|
| 281 |
+
"o3d.visualization.draw_geometries([target_pcd])"
|
| 282 |
+
]
|
| 283 |
+
},
|
| 284 |
+
{
|
| 285 |
+
"cell_type": "markdown",
|
| 286 |
+
"metadata": {},
|
| 287 |
+
"source": [
|
| 288 |
+
"## Execute termianl"
|
| 289 |
+
]
|
| 290 |
+
},
|
| 291 |
+
{
|
| 292 |
+
"cell_type": "code",
|
| 293 |
+
"execution_count": 3,
|
| 294 |
+
"metadata": {},
|
| 295 |
+
"outputs": [
|
| 296 |
+
{
|
| 297 |
+
"name": "stdout",
|
| 298 |
+
"output_type": "stream",
|
| 299 |
+
"text": [
|
| 300 |
+
"/home/cam/ICP_DATA/Fast-Robust-ICP/data/bottle_2\n",
|
| 301 |
+
"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\n",
|
| 302 |
+
"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\n",
|
| 303 |
+
"source: 3x58759\n",
|
| 304 |
+
"target: 3x50000\n",
|
| 305 |
+
"scale = 616.692\n",
|
| 306 |
+
"begin registration...\n",
|
| 307 |
+
"Registration done!\n",
|
| 308 |
+
"\n"
|
| 309 |
+
]
|
| 310 |
+
}
|
| 311 |
+
],
|
| 312 |
+
"source": [
|
| 313 |
+
"# ./FRICP ./data/bottle_2/gt_filtered.ply ./data/bottle_2/result/noisy_filtered_100_1.ply ./data/bottle_2/res 3 execute\n",
|
| 314 |
+
"import os\n",
|
| 315 |
+
"print(os.getcwd())\n",
|
| 316 |
+
"\n",
|
| 317 |
+
"import subprocess\n",
|
| 318 |
+
"\n",
|
| 319 |
+
"cmd = [\n",
|
| 320 |
+
" '../../FRICP',\n",
|
| 321 |
+
" './gt_filtered.ply',\n",
|
| 322 |
+
" f'./noisy_result/noisy_filtered_{filename}.ply',\n",
|
| 323 |
+
" './res',\n",
|
| 324 |
+
" '3'\n",
|
| 325 |
+
"]\n",
|
| 326 |
+
"\n",
|
| 327 |
+
"try:\n",
|
| 328 |
+
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
| 329 |
+
"\n",
|
| 330 |
+
" print(\"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\")\n",
|
| 331 |
+
" print(\"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\")\n",
|
| 332 |
+
" print(result.stdout)\n",
|
| 333 |
+
"\n",
|
| 334 |
+
"except FileNotFoundError:\n",
|
| 335 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 336 |
+
" print(f\"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.\")\n",
|
| 337 |
+
" print(\"๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.\")\n",
|
| 338 |
+
"\n",
|
| 339 |
+
"except subprocess.CalledProcessError as e:\n",
|
| 340 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 341 |
+
" print(f\"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})\")\n",
|
| 342 |
+
" print(\"\\n--- STDERR (์๋ฌ ์์ธ) ---\")\n",
|
| 343 |
+
" print(e.stderr)\n"
|
| 344 |
+
]
|
| 345 |
+
},
|
| 346 |
+
{
|
| 347 |
+
"cell_type": "markdown",
|
| 348 |
+
"metadata": {},
|
| 349 |
+
"source": [
|
| 350 |
+
"### Change the path for result\n"
|
| 351 |
+
]
|
| 352 |
+
},
|
| 353 |
+
{
|
| 354 |
+
"cell_type": "code",
|
| 355 |
+
"execution_count": 7,
|
| 356 |
+
"metadata": {},
|
| 357 |
+
"outputs": [
|
| 358 |
+
{
|
| 359 |
+
"name": "stdout",
|
| 360 |
+
"output_type": "stream",
|
| 361 |
+
"text": [
|
| 362 |
+
"Successfully moved and renamed 'resm3reg_pc.ply' to './result/final_result_100_19.ply'\n",
|
| 363 |
+
"Successfully moved and renamed 'resm3trans.txt' to './result/final_result_100_19.txt'\n"
|
| 364 |
+
]
|
| 365 |
+
}
|
| 366 |
+
],
|
| 367 |
+
"source": [
|
| 368 |
+
"import shutil\n",
|
| 369 |
+
"import os\n",
|
| 370 |
+
"\n",
|
| 371 |
+
"transformed_path = \"resm3reg_pc.ply\"\n",
|
| 372 |
+
"destination_path = f\"./result/final_result_{filename}.ply\"\n",
|
| 373 |
+
"transformed_path2 = \"resm3trans.txt\"\n",
|
| 374 |
+
"destination_path2 = f\"./result/final_result_{filename}.txt\"\n",
|
| 375 |
+
"\n",
|
| 376 |
+
"shutil.move(transformed_path, destination_path)\n",
|
| 377 |
+
"print(f\"Successfully moved and renamed '{transformed_path}' to '{destination_path}'\")\n",
|
| 378 |
+
"\n",
|
| 379 |
+
"\n",
|
| 380 |
+
"\n",
|
| 381 |
+
"shutil.move(transformed_path2, destination_path2)\n",
|
| 382 |
+
"print(f\"Successfully moved and renamed '{transformed_path2}' to '{destination_path2}'\")\n",
|
| 383 |
+
"\n",
|
| 384 |
+
"\n"
|
| 385 |
+
]
|
| 386 |
+
},
|
| 387 |
+
{
|
| 388 |
+
"cell_type": "markdown",
|
| 389 |
+
"metadata": {},
|
| 390 |
+
"source": [
|
| 391 |
+
"### Transformed Source PCD"
|
| 392 |
+
]
|
| 393 |
+
},
|
| 394 |
+
{
|
| 395 |
+
"cell_type": "code",
|
| 396 |
+
"execution_count": 10,
|
| 397 |
+
"metadata": {},
|
| 398 |
+
"outputs": [
|
| 399 |
+
{
|
| 400 |
+
"name": "stdout",
|
| 401 |
+
"output_type": "stream",
|
| 402 |
+
"text": [
|
| 403 |
+
"Transformed shape: (58759, 3)\n"
|
| 404 |
+
]
|
| 405 |
+
}
|
| 406 |
+
],
|
| 407 |
+
"source": [
|
| 408 |
+
"\n",
|
| 409 |
+
"transformed_pcd = o3d.io.read_point_cloud(destination_path)\n",
|
| 410 |
+
"\n",
|
| 411 |
+
"transformed_pcd_array = np.asarray(transformed_pcd.points)\n",
|
| 412 |
+
"print(\"Transformed shape:\", transformed_pcd_array.shape)\n",
|
| 413 |
+
"\n",
|
| 414 |
+
"o3d.visualization.draw_geometries([transformed_pcd])"
|
| 415 |
+
]
|
| 416 |
+
},
|
| 417 |
+
{
|
| 418 |
+
"cell_type": "markdown",
|
| 419 |
+
"metadata": {},
|
| 420 |
+
"source": [
|
| 421 |
+
"### Source (Original) + Target"
|
| 422 |
+
]
|
| 423 |
+
},
|
| 424 |
+
{
|
| 425 |
+
"cell_type": "code",
|
| 426 |
+
"execution_count": 17,
|
| 427 |
+
"metadata": {},
|
| 428 |
+
"outputs": [
|
| 429 |
+
{
|
| 430 |
+
"ename": "NameError",
|
| 431 |
+
"evalue": "name 'source_pcd' is not defined",
|
| 432 |
+
"output_type": "error",
|
| 433 |
+
"traceback": [
|
| 434 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 435 |
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
| 436 |
+
"Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msource_pcd\u001b[49m\u001b[38;5;241m.\u001b[39mpaint_uniform_color([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 2\u001b[0m target_pcd\u001b[38;5;241m.\u001b[39mpaint_uniform_color([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 4\u001b[0m vis \u001b[38;5;241m=\u001b[39m o3d\u001b[38;5;241m.\u001b[39mvisualization\u001b[38;5;241m.\u001b[39mVisualizer()\n",
|
| 437 |
+
"\u001b[0;31mNameError\u001b[0m: name 'source_pcd' is not defined"
|
| 438 |
+
]
|
| 439 |
+
}
|
| 440 |
+
],
|
| 441 |
+
"source": [
|
| 442 |
+
"source_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 443 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 444 |
+
"\n",
|
| 445 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 446 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 447 |
+
"vis.add_geometry(source_pcd)\n",
|
| 448 |
+
"vis.add_geometry(target_pcd)\n",
|
| 449 |
+
"vis.add_geometry(coord_frame)\n",
|
| 450 |
+
"vis.run()\n",
|
| 451 |
+
"\n",
|
| 452 |
+
"\n",
|
| 453 |
+
"vis.destroy_window()"
|
| 454 |
+
]
|
| 455 |
+
},
|
| 456 |
+
{
|
| 457 |
+
"cell_type": "markdown",
|
| 458 |
+
"metadata": {},
|
| 459 |
+
"source": [
|
| 460 |
+
"### Transformed + Target"
|
| 461 |
+
]
|
| 462 |
+
},
|
| 463 |
+
{
|
| 464 |
+
"cell_type": "code",
|
| 465 |
+
"execution_count": null,
|
| 466 |
+
"metadata": {},
|
| 467 |
+
"outputs": [],
|
| 468 |
+
"source": [
|
| 469 |
+
"transformed_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 470 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 471 |
+
"\n",
|
| 472 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 473 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 474 |
+
"vis.add_geometry(transformed_pcd)\n",
|
| 475 |
+
"vis.add_geometry(target_pcd)\n",
|
| 476 |
+
"\n",
|
| 477 |
+
"vis.run()\n",
|
| 478 |
+
"vis.destroy_window()"
|
| 479 |
+
]
|
| 480 |
+
}
|
| 481 |
+
],
|
| 482 |
+
"metadata": {
|
| 483 |
+
"kernelspec": {
|
| 484 |
+
"display_name": "Python 3",
|
| 485 |
+
"language": "python",
|
| 486 |
+
"name": "python3"
|
| 487 |
+
},
|
| 488 |
+
"language_info": {
|
| 489 |
+
"codemirror_mode": {
|
| 490 |
+
"name": "ipython",
|
| 491 |
+
"version": 3
|
| 492 |
+
},
|
| 493 |
+
"file_extension": ".py",
|
| 494 |
+
"mimetype": "text/x-python",
|
| 495 |
+
"name": "python",
|
| 496 |
+
"nbconvert_exporter": "python",
|
| 497 |
+
"pygments_lexer": "ipython3",
|
| 498 |
+
"version": "3.10.12"
|
| 499 |
+
}
|
| 500 |
+
},
|
| 501 |
+
"nbformat": 4,
|
| 502 |
+
"nbformat_minor": 2
|
| 503 |
+
}
|
data/bottle_2/inference_ICP.py
ADDED
|
@@ -0,0 +1,298 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding: utf-8
|
| 3 |
+
|
| 4 |
+
# In[21]:
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
# conda activate vision
|
| 8 |
+
# cd build
|
| 9 |
+
# cmake -DCMAKE_BUILD_TYPE=Release ..
|
| 10 |
+
# make
|
| 11 |
+
# ./FRICP ./data/bottle/tea_gt_filtered.ply ./data/bottle/tea_noisy_filtered.ply ./data/bottle/res/ 3
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
# 100_16 is the best thing.
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
# In[22]:
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
import open3d as o3d
|
| 21 |
+
import numpy as np
|
| 22 |
+
|
| 23 |
+
file_names = []
|
| 24 |
+
with open('filename.txt', 'r') as f:
|
| 25 |
+
for line in f:
|
| 26 |
+
file_names.append(line.strip())
|
| 27 |
+
filename = file_names[0]
|
| 28 |
+
print(filename)
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
# # Modify initial file
|
| 33 |
+
|
| 34 |
+
# In[23]:
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
output_filename = f'./initialized_result/initial_{filename}.ply'
|
| 38 |
+
|
| 39 |
+
# 1. read file
|
| 40 |
+
|
| 41 |
+
with open(f'./initialized_result/initial_{filename}.ply', 'r') as f:
|
| 42 |
+
lines = f.readlines()
|
| 43 |
+
|
| 44 |
+
# 2. seperate data and header
|
| 45 |
+
header_lines = []
|
| 46 |
+
data_lines = []
|
| 47 |
+
is_header = True
|
| 48 |
+
|
| 49 |
+
for line in lines:
|
| 50 |
+
if "end_header" in line:
|
| 51 |
+
is_header = False
|
| 52 |
+
continue
|
| 53 |
+
|
| 54 |
+
if is_header:
|
| 55 |
+
header_lines.append(line)
|
| 56 |
+
|
| 57 |
+
else:
|
| 58 |
+
parts = line.strip().split()
|
| 59 |
+
if len(parts) >= 3:
|
| 60 |
+
data_lines.append(f"{parts[0]} {parts[1]} {parts[2]}\n")
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
# 3. modify header
|
| 64 |
+
# vertex
|
| 65 |
+
num_points = len(data_lines)
|
| 66 |
+
|
| 67 |
+
# generate new header
|
| 68 |
+
|
| 69 |
+
new_header = f"""ply
|
| 70 |
+
format ascii 1.0
|
| 71 |
+
element vertex {num_points}
|
| 72 |
+
property float x
|
| 73 |
+
property float y
|
| 74 |
+
property float z
|
| 75 |
+
element camera 1
|
| 76 |
+
property float view_px
|
| 77 |
+
property float view_py
|
| 78 |
+
property float view_pz
|
| 79 |
+
property float x_axisx
|
| 80 |
+
property float x_axisy
|
| 81 |
+
property float x_axisz
|
| 82 |
+
property float y_axisx
|
| 83 |
+
property float y_axisy
|
| 84 |
+
property float y_axisz
|
| 85 |
+
property float z_axisx
|
| 86 |
+
property float z_axisy
|
| 87 |
+
property float z_axisz
|
| 88 |
+
element phoxi_frame_params 1
|
| 89 |
+
property uint32 frame_width
|
| 90 |
+
property uint32 frame_height
|
| 91 |
+
property uint32 frame_index
|
| 92 |
+
property float frame_start_time
|
| 93 |
+
property float frame_duration
|
| 94 |
+
property float frame_computation_duration
|
| 95 |
+
property float frame_transfer_duration
|
| 96 |
+
property int32 total_scan_count
|
| 97 |
+
element camera_matrix 1
|
| 98 |
+
property float cm0
|
| 99 |
+
property float cm1
|
| 100 |
+
property float cm2
|
| 101 |
+
property float cm3
|
| 102 |
+
property float cm4
|
| 103 |
+
property float cm5
|
| 104 |
+
property float cm6
|
| 105 |
+
property float cm7
|
| 106 |
+
property float cm8
|
| 107 |
+
element distortion_matrix 1
|
| 108 |
+
property float dm0
|
| 109 |
+
property float dm1
|
| 110 |
+
property float dm2
|
| 111 |
+
property float dm3
|
| 112 |
+
property float dm4
|
| 113 |
+
property float dm5
|
| 114 |
+
property float dm6
|
| 115 |
+
property float dm7
|
| 116 |
+
property float dm8
|
| 117 |
+
property float dm9
|
| 118 |
+
property float dm10
|
| 119 |
+
property float dm11
|
| 120 |
+
property float dm12
|
| 121 |
+
property float dm13
|
| 122 |
+
element camera_resolution 1
|
| 123 |
+
property float width
|
| 124 |
+
property float height
|
| 125 |
+
element frame_binning 1
|
| 126 |
+
property float horizontal
|
| 127 |
+
property float vertical
|
| 128 |
+
end_header
|
| 129 |
+
"""
|
| 130 |
+
|
| 131 |
+
#4. write 4file
|
| 132 |
+
|
| 133 |
+
with open(output_filename,'w') as f:
|
| 134 |
+
f.write(new_header)
|
| 135 |
+
|
| 136 |
+
for line in data_lines:
|
| 137 |
+
f.write(line)
|
| 138 |
+
|
| 139 |
+
|
| 140 |
+
print("\n์์
์๋ฃ!")
|
| 141 |
+
print(f"'{output_filename}' ํ์ผ์ด ์ฑ๊ณต์ ์ผ๋ก ์์ฑ๋์์ต๋๋ค.")
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
# ### Source PCD
|
| 145 |
+
|
| 146 |
+
# In[ ]:
|
| 147 |
+
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
# In[24]:
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
source_path = f"./initialized_result/initial_{filename}.ply"
|
| 156 |
+
|
| 157 |
+
source_pcd = o3d.io.read_point_cloud(source_path)
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
source_pcd_array = np.asarray(source_pcd.points)
|
| 162 |
+
print("Source shape:", source_pcd_array.shape)
|
| 163 |
+
|
| 164 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 165 |
+
o3d.visualization.draw_geometries([source_pcd,coord_frame])
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
# In[ ]:
|
| 169 |
+
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
# ### Target PCD
|
| 175 |
+
|
| 176 |
+
# In[25]:
|
| 177 |
+
|
| 178 |
+
|
| 179 |
+
target_path = f"gt_filtered.ply"
|
| 180 |
+
target_pcd = o3d.io.read_point_cloud(target_path)
|
| 181 |
+
|
| 182 |
+
target_pcd_array = np.asarray(target_pcd.points)
|
| 183 |
+
print("Target shape:", target_pcd_array.shape)
|
| 184 |
+
|
| 185 |
+
o3d.visualization.draw_geometries([target_pcd, coord_frame])
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
# ## Execute termianl
|
| 189 |
+
|
| 190 |
+
# In[26]:
|
| 191 |
+
|
| 192 |
+
|
| 193 |
+
# ./FRICP ./data/bottle_2/gt_filtered.ply ./data/bottle_2/result/noisy_filtered_100_1.ply ./data/bottle_2/res 3 execute
|
| 194 |
+
import os
|
| 195 |
+
print(os.getcwd())
|
| 196 |
+
|
| 197 |
+
import subprocess
|
| 198 |
+
|
| 199 |
+
cmd = [
|
| 200 |
+
'../../FRICP',
|
| 201 |
+
'./gt_filtered.ply',
|
| 202 |
+
f'./initialized_result/initial_{filename}.ply',
|
| 203 |
+
'./res',
|
| 204 |
+
'3'
|
| 205 |
+
]
|
| 206 |
+
|
| 207 |
+
try:
|
| 208 |
+
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
|
| 209 |
+
|
| 210 |
+
print("--- STDOUT (ํ์ค ์ถ๋ ฅ) ---")
|
| 211 |
+
print("๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.")
|
| 212 |
+
print(result.stdout)
|
| 213 |
+
|
| 214 |
+
except FileNotFoundError:
|
| 215 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 216 |
+
print(f"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 217 |
+
print("๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.")
|
| 218 |
+
|
| 219 |
+
except subprocess.CalledProcessError as e:
|
| 220 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 221 |
+
print(f"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})")
|
| 222 |
+
print("\n--- STDERR (์๋ฌ ์์ธ) ---")
|
| 223 |
+
print(e.stderr)
|
| 224 |
+
|
| 225 |
+
|
| 226 |
+
# ### Change the path for result
|
| 227 |
+
#
|
| 228 |
+
|
| 229 |
+
# In[27]:
|
| 230 |
+
|
| 231 |
+
|
| 232 |
+
import shutil
|
| 233 |
+
import os
|
| 234 |
+
|
| 235 |
+
transformed_path = "resm3reg_pc.ply"
|
| 236 |
+
destination_path = f"./result/final_result_{filename}.ply"
|
| 237 |
+
transformed_path2 = "resm3trans.txt"
|
| 238 |
+
destination_path2 = f"./result/final_result_{filename}.txt"
|
| 239 |
+
|
| 240 |
+
shutil.move(transformed_path, destination_path)
|
| 241 |
+
print(f"Successfully moved and renamed '{transformed_path}' to '{destination_path}'")
|
| 242 |
+
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
shutil.move(transformed_path2, destination_path2)
|
| 246 |
+
print(f"Successfully moved and renamed '{transformed_path2}' to '{destination_path2}'")
|
| 247 |
+
|
| 248 |
+
|
| 249 |
+
|
| 250 |
+
|
| 251 |
+
# ### Transformed Source PCD
|
| 252 |
+
|
| 253 |
+
# In[28]:
|
| 254 |
+
|
| 255 |
+
|
| 256 |
+
transformed_pcd = o3d.io.read_point_cloud(destination_path)
|
| 257 |
+
|
| 258 |
+
transformed_pcd_array = np.asarray(transformed_pcd.points)
|
| 259 |
+
print("Transformed shape:", transformed_pcd_array.shape)
|
| 260 |
+
|
| 261 |
+
o3d.visualization.draw_geometries([transformed_pcd, coord_frame])
|
| 262 |
+
|
| 263 |
+
|
| 264 |
+
# ### Source (Original) + Target
|
| 265 |
+
|
| 266 |
+
# In[29]:
|
| 267 |
+
|
| 268 |
+
|
| 269 |
+
source_pcd.paint_uniform_color([1, 0, 0])
|
| 270 |
+
target_pcd.paint_uniform_color([0, 1, 0])
|
| 271 |
+
|
| 272 |
+
vis = o3d.visualization.Visualizer()
|
| 273 |
+
vis.create_window(window_name="Point Cloud Viewer", width=1200, height=800, visible=True)
|
| 274 |
+
vis.add_geometry(source_pcd)
|
| 275 |
+
vis.add_geometry(target_pcd)
|
| 276 |
+
vis.add_geometry(coord_frame)
|
| 277 |
+
vis.run()
|
| 278 |
+
|
| 279 |
+
|
| 280 |
+
vis.destroy_window()
|
| 281 |
+
|
| 282 |
+
|
| 283 |
+
# ### Transformed + Target
|
| 284 |
+
|
| 285 |
+
# In[30]:
|
| 286 |
+
|
| 287 |
+
|
| 288 |
+
transformed_pcd.paint_uniform_color([1, 0, 0])
|
| 289 |
+
target_pcd.paint_uniform_color([0, 1, 0])
|
| 290 |
+
|
| 291 |
+
vis = o3d.visualization.Visualizer()
|
| 292 |
+
vis.create_window(window_name="Point Cloud Viewer", width=1200, height=800, visible=True)
|
| 293 |
+
vis.add_geometry(transformed_pcd)
|
| 294 |
+
vis.add_geometry(target_pcd)
|
| 295 |
+
vis.add_geometry(coord_frame)
|
| 296 |
+
vis.run()
|
| 297 |
+
vis.destroy_window()
|
| 298 |
+
|
data/bottle_2/initial_guess(kiss_match).ipynb
ADDED
|
@@ -0,0 +1,240 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "c97d9003",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## PCD file transformation"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 13,
|
| 14 |
+
"id": "57266b06",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [
|
| 17 |
+
{
|
| 18 |
+
"name": "stdout",
|
| 19 |
+
"output_type": "stream",
|
| 20 |
+
"text": [
|
| 21 |
+
"0_23\n",
|
| 22 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./gt_filtered.ply\u001b[0;m\n",
|
| 23 |
+
"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\n"
|
| 24 |
+
]
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"name": "stderr",
|
| 28 |
+
"output_type": "stream",
|
| 29 |
+
"text": [
|
| 30 |
+
"RPly: Unexpected end of file\n",
|
| 31 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 32 |
+
]
|
| 33 |
+
}
|
| 34 |
+
],
|
| 35 |
+
"source": [
|
| 36 |
+
"import open3d as o3d\n",
|
| 37 |
+
"import numpy as np\n",
|
| 38 |
+
"\n",
|
| 39 |
+
"file_names = []\n",
|
| 40 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 41 |
+
" for line in f:\n",
|
| 42 |
+
" file_names.append(line.strip())\n",
|
| 43 |
+
"filename = file_names[0]\n",
|
| 44 |
+
"print(filename)\n",
|
| 45 |
+
"\n",
|
| 46 |
+
"\n",
|
| 47 |
+
"# PLY ํ์ผ ์ฝ๊ธฐ\n",
|
| 48 |
+
"pcd = o3d.io.read_point_cloud(\"./gt_filtered.ply\")\n",
|
| 49 |
+
"\n",
|
| 50 |
+
"# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)\n",
|
| 51 |
+
"o3d.io.write_point_cloud(\"./initialize_pcdfile/gt_filtered.pcd\", pcd)\n",
|
| 52 |
+
"\n",
|
| 53 |
+
"# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:\n",
|
| 54 |
+
"# o3d.io.write_point_cloud(\"output_ascii.pcd\", pcd, write_ascii=True)\n",
|
| 55 |
+
"\n",
|
| 56 |
+
"print(\"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\")"
|
| 57 |
+
]
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"cell_type": "code",
|
| 61 |
+
"execution_count": 14,
|
| 62 |
+
"id": "8b0bc642",
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"outputs": [
|
| 65 |
+
{
|
| 66 |
+
"name": "stdout",
|
| 67 |
+
"output_type": "stream",
|
| 68 |
+
"text": [
|
| 69 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./noisy_result/noisy_filtered_0_23.ply\u001b[0;m\n",
|
| 70 |
+
"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\n"
|
| 71 |
+
]
|
| 72 |
+
},
|
| 73 |
+
{
|
| 74 |
+
"name": "stderr",
|
| 75 |
+
"output_type": "stream",
|
| 76 |
+
"text": [
|
| 77 |
+
"RPly: Unexpected end of file\n",
|
| 78 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 79 |
+
]
|
| 80 |
+
}
|
| 81 |
+
],
|
| 82 |
+
"source": [
|
| 83 |
+
"# PLY ํ์ผ ์ฝ๊ธฐ\n",
|
| 84 |
+
"pcd = o3d.io.read_point_cloud(f\"./noisy_result/noisy_filtered_{filename}.ply\")\n",
|
| 85 |
+
"\n",
|
| 86 |
+
"# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)\n",
|
| 87 |
+
"o3d.io.write_point_cloud(f\"./initialize_pcdfile/first_{filename}.pcd\", pcd)\n",
|
| 88 |
+
"\n",
|
| 89 |
+
"# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:\n",
|
| 90 |
+
"# o3d.io.write_point_cloud(\"output_ascii.pcd\", pcd, write_ascii=True)\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"print(\"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\")"
|
| 93 |
+
]
|
| 94 |
+
},
|
| 95 |
+
{
|
| 96 |
+
"cell_type": "markdown",
|
| 97 |
+
"id": "fcdc0f5e",
|
| 98 |
+
"metadata": {},
|
| 99 |
+
"source": [
|
| 100 |
+
"## Execute initial Guess"
|
| 101 |
+
]
|
| 102 |
+
},
|
| 103 |
+
{
|
| 104 |
+
"cell_type": "code",
|
| 105 |
+
"execution_count": 15,
|
| 106 |
+
"id": "5d191e44",
|
| 107 |
+
"metadata": {},
|
| 108 |
+
"outputs": [
|
| 109 |
+
{
|
| 110 |
+
"name": "stdout",
|
| 111 |
+
"output_type": "stream",
|
| 112 |
+
"text": [
|
| 113 |
+
"/home/cam/Fast-Robust-ICP/data/bottle_2\n",
|
| 114 |
+
"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\n",
|
| 115 |
+
"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\n",
|
| 116 |
+
"Loaded source point cloud: (4980, 3)\n",
|
| 117 |
+
"Loaded target point cloud: (50000, 3)\n",
|
| 118 |
+
"Resolution: 1.0\n",
|
| 119 |
+
"Yaw Augmentation Angle: None\n",
|
| 120 |
+
"============== Time ==============\n",
|
| 121 |
+
"Voxelization: 0.00235131 sec\n",
|
| 122 |
+
"Extraction : 0.0493407 sec\n",
|
| 123 |
+
"Pruning : 0.00378771 sec\n",
|
| 124 |
+
"Matching : 0.0432693 sec\n",
|
| 125 |
+
"Solving : 8.312e-06 sec\n",
|
| 126 |
+
"----------------------------------\n",
|
| 127 |
+
"\u001b[1;32mTotal : 0.0987573 sec\u001b[0m\n",
|
| 128 |
+
"====== # of correspondences ======\n",
|
| 129 |
+
"# initial pairs : 88\n",
|
| 130 |
+
"# pruned pairs : 4\n",
|
| 131 |
+
"----------------------------------\n",
|
| 132 |
+
"\u001b[1;36m# rot inliers : 4\n",
|
| 133 |
+
"# trans inliers : 4\u001b[0m\n",
|
| 134 |
+
"==================================\n",
|
| 135 |
+
"\u001b[1;33m=> Registration might have failed :(\u001b[0m\n",
|
| 136 |
+
"\n",
|
| 137 |
+
"<_kiss_matcher.RegistrationSolution object at 0x76de1ebf2130>\n",
|
| 138 |
+
"ply complete.\n",
|
| 139 |
+
"1.0์ด ๋์ ์๊ฐํ ์ฐฝ์ ํ์ํฉ๋๋ค...\n",
|
| 140 |
+
"Visualization complete.\n",
|
| 141 |
+
"\n"
|
| 142 |
+
]
|
| 143 |
+
}
|
| 144 |
+
],
|
| 145 |
+
"source": [
|
| 146 |
+
"import os\n",
|
| 147 |
+
"print(os.getcwd())\n",
|
| 148 |
+
"\n",
|
| 149 |
+
"import subprocess\n",
|
| 150 |
+
"\n",
|
| 151 |
+
"cmd = [\n",
|
| 152 |
+
" 'python3',\n",
|
| 153 |
+
" '../../../KISS-Matcher/python/examples/run_kiss_matcher.py',\n",
|
| 154 |
+
" '--src_path',\n",
|
| 155 |
+
" f'./initialize_pcdfile/first_{filename}.pcd',\n",
|
| 156 |
+
" '--tgt_path',\n",
|
| 157 |
+
" './initialize_pcdfile/gt_filtered.pcd',\n",
|
| 158 |
+
" '--resolution',\n",
|
| 159 |
+
" '1'\n",
|
| 160 |
+
" \n",
|
| 161 |
+
"\n",
|
| 162 |
+
"\n",
|
| 163 |
+
"]\n",
|
| 164 |
+
"try:\n",
|
| 165 |
+
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
| 166 |
+
"\n",
|
| 167 |
+
" print(\"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\")\n",
|
| 168 |
+
" print(\"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\")\n",
|
| 169 |
+
" print(result.stdout)\n",
|
| 170 |
+
"\n",
|
| 171 |
+
"except FileNotFoundError:\n",
|
| 172 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 173 |
+
" print(f\"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.\")\n",
|
| 174 |
+
" print(\"๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.\")\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"except subprocess.CalledProcessError as e:\n",
|
| 177 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 178 |
+
" print(f\"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})\")\n",
|
| 179 |
+
" print(\"\\n--- STDERR (์๋ฌ ์์ธ) ---\")\n",
|
| 180 |
+
" print(e.stderr)\n"
|
| 181 |
+
]
|
| 182 |
+
},
|
| 183 |
+
{
|
| 184 |
+
"cell_type": "markdown",
|
| 185 |
+
"id": "0128f9e3",
|
| 186 |
+
"metadata": {},
|
| 187 |
+
"source": [
|
| 188 |
+
"## Saving initialized data\n"
|
| 189 |
+
]
|
| 190 |
+
},
|
| 191 |
+
{
|
| 192 |
+
"cell_type": "code",
|
| 193 |
+
"execution_count": 16,
|
| 194 |
+
"id": "63441612",
|
| 195 |
+
"metadata": {},
|
| 196 |
+
"outputs": [
|
| 197 |
+
{
|
| 198 |
+
"name": "stdout",
|
| 199 |
+
"output_type": "stream",
|
| 200 |
+
"text": [
|
| 201 |
+
"Successfully moved and renamed 'output.ply' to './initialized_result/initial_0_23.ply'\n"
|
| 202 |
+
]
|
| 203 |
+
}
|
| 204 |
+
],
|
| 205 |
+
"source": [
|
| 206 |
+
"import shutil\n",
|
| 207 |
+
"import os\n",
|
| 208 |
+
"\n",
|
| 209 |
+
"transformed_path = \"output.ply\"\n",
|
| 210 |
+
"destination_path = f\"./initialized_result/initial_{filename}.ply\"\n",
|
| 211 |
+
"\n",
|
| 212 |
+
"\n",
|
| 213 |
+
"shutil.move(transformed_path, destination_path)\n",
|
| 214 |
+
"print(f\"Successfully moved and renamed '{transformed_path}' to '{destination_path}'\")\n",
|
| 215 |
+
"\n"
|
| 216 |
+
]
|
| 217 |
+
}
|
| 218 |
+
],
|
| 219 |
+
"metadata": {
|
| 220 |
+
"kernelspec": {
|
| 221 |
+
"display_name": "Python 3",
|
| 222 |
+
"language": "python",
|
| 223 |
+
"name": "python3"
|
| 224 |
+
},
|
| 225 |
+
"language_info": {
|
| 226 |
+
"codemirror_mode": {
|
| 227 |
+
"name": "ipython",
|
| 228 |
+
"version": 3
|
| 229 |
+
},
|
| 230 |
+
"file_extension": ".py",
|
| 231 |
+
"mimetype": "text/x-python",
|
| 232 |
+
"name": "python",
|
| 233 |
+
"nbconvert_exporter": "python",
|
| 234 |
+
"pygments_lexer": "ipython3",
|
| 235 |
+
"version": "3.10.12"
|
| 236 |
+
}
|
| 237 |
+
},
|
| 238 |
+
"nbformat": 4,
|
| 239 |
+
"nbformat_minor": 5
|
| 240 |
+
}
|
data/bottle_2/initial_guess(kiss_match).py
ADDED
|
@@ -0,0 +1,103 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding: utf-8
|
| 3 |
+
|
| 4 |
+
# ## PCD file transformation
|
| 5 |
+
|
| 6 |
+
# In[18]:
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
import open3d as o3d
|
| 10 |
+
import numpy as np
|
| 11 |
+
|
| 12 |
+
file_names = []
|
| 13 |
+
with open('filename.txt', 'r') as f:
|
| 14 |
+
for line in f:
|
| 15 |
+
file_names.append(line.strip())
|
| 16 |
+
filename = file_names[0]
|
| 17 |
+
print(filename)
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 21 |
+
pcd = o3d.io.read_point_cloud("./gt_filtered.ply")
|
| 22 |
+
|
| 23 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 24 |
+
o3d.io.write_point_cloud("./initialize_pcdfile/gt_filtered.pcd", pcd)
|
| 25 |
+
|
| 26 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 27 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 28 |
+
|
| 29 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
# In[19]:
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 36 |
+
pcd = o3d.io.read_point_cloud(f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 37 |
+
|
| 38 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 39 |
+
o3d.io.write_point_cloud(f"./initialize_pcdfile/first_{filename}.pcd", pcd)
|
| 40 |
+
|
| 41 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 42 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 43 |
+
|
| 44 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
# ## Execute initial Guess
|
| 48 |
+
|
| 49 |
+
# In[20]:
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
import os
|
| 53 |
+
print(os.getcwd())
|
| 54 |
+
|
| 55 |
+
import subprocess
|
| 56 |
+
|
| 57 |
+
cmd = [
|
| 58 |
+
'python3',
|
| 59 |
+
'../../../KISS-Matcher/python/examples/run_kiss_matcher.py',
|
| 60 |
+
'--src_path',
|
| 61 |
+
f'./initialize_pcdfile/first_{filename}.pcd',
|
| 62 |
+
'--tgt_path',
|
| 63 |
+
'./initialize_pcdfile/gt_filtered.pcd',
|
| 64 |
+
'--resolution',
|
| 65 |
+
'1'
|
| 66 |
+
|
| 67 |
+
]
|
| 68 |
+
try:
|
| 69 |
+
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
|
| 70 |
+
|
| 71 |
+
print("--- STDOUT (ํ์ค ์ถ๋ ฅ) ---")
|
| 72 |
+
print("๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.")
|
| 73 |
+
print(result.stdout)
|
| 74 |
+
|
| 75 |
+
except FileNotFoundError:
|
| 76 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 77 |
+
print(f"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 78 |
+
print("๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.")
|
| 79 |
+
|
| 80 |
+
except subprocess.CalledProcessError as e:
|
| 81 |
+
print("--- ์๋ฌ ๋ฐ์! ---")
|
| 82 |
+
print(f"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})")
|
| 83 |
+
print("\n--- STDERR (์๋ฌ ์์ธ) ---")
|
| 84 |
+
print(e.stderr)
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
# ## Saving initialized data
|
| 88 |
+
#
|
| 89 |
+
|
| 90 |
+
# In[21]:
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
import shutil
|
| 94 |
+
import os
|
| 95 |
+
|
| 96 |
+
transformed_path = "output.ply"
|
| 97 |
+
destination_path = f"./initialized_result/initial_{filename}.ply"
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
shutil.move(transformed_path, destination_path)
|
| 101 |
+
print(f"Successfully moved and renamed '{transformed_path}' to '{destination_path}'")
|
| 102 |
+
|
| 103 |
+
|
data/bottle_2/merged.py
ADDED
|
@@ -0,0 +1,496 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding: utf-8
|
| 3 |
+
|
| 4 |
+
# In[ ]:
|
| 5 |
+
|
| 6 |
+
import json
|
| 7 |
+
import os
|
| 8 |
+
import open3d as o3d
|
| 9 |
+
import numpy as np
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
mesh = o3d.io.read_triangle_mesh("./bottle.stl")
|
| 13 |
+
pointcloud = mesh.sample_points_poisson_disk(50000)
|
| 14 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 15 |
+
mesh.compute_vertex_normals()
|
| 16 |
+
mesh_triangles = np.asarray(mesh.triangles)
|
| 17 |
+
vertex_positions = np.asarray(mesh.vertices)
|
| 18 |
+
triangle_normals = np.asarray(mesh.triangle_normals)
|
| 19 |
+
# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ
|
| 20 |
+
centroid = mesh.get_center()
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
# ๋ฐ์ดํฐ์
ํด๋์ JSON ํ์ผ ๊ฒฝ๋ก
|
| 24 |
+
folder = "./dataset"
|
| 25 |
+
json_path = "ply_files.json"
|
| 26 |
+
|
| 27 |
+
# 1. ๊ฐ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ resolution ๊ฐ์ ๋์
๋๋ฆฌ๋ก ์ ์ํฉ๋๋ค.
|
| 28 |
+
# ์ด ๊ฐ์ ์กฐ์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ๋ณ ์ค์ ์ ๋ณ๊ฒฝํ ์ ์์ต๋๋ค.
|
| 29 |
+
resolutions = {
|
| 30 |
+
"100": 1.0,
|
| 31 |
+
"75": 0.8,
|
| 32 |
+
"50": 0.8,
|
| 33 |
+
"25": 0.8,
|
| 34 |
+
"0": 0.8
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
# 2. ๋ถ๋ฅ๋ ํ์ผ ๋ชฉ๋ก์ด ๋ด๊ธด JSON ํ์ผ์ ์ฝ์ด์ต๋๋ค.
|
| 38 |
+
try:
|
| 39 |
+
with open(json_path, "r", encoding="utf-8") as f:
|
| 40 |
+
categorized_files = json.load(f)
|
| 41 |
+
except FileNotFoundError:
|
| 42 |
+
print(f"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.")
|
| 43 |
+
exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ
|
| 44 |
+
|
| 45 |
+
# 3. ๋ชจ๋ ์นดํ
๊ณ ๋ฆฌ์ ํ์ผ์ ์ํํ๋ ๋ฐ๋ณต๋ฌธ
|
| 46 |
+
print("=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===")
|
| 47 |
+
|
| 48 |
+
# resolutions ๋์
๋๋ฆฌ๋ฅผ ๊ธฐ์ค์ผ๋ก ์ธ๋ถ ๋ฃจํ๋ฅผ ์คํํฉ๋๋ค.
|
| 49 |
+
for category, resolution in resolutions.items():
|
| 50 |
+
|
| 51 |
+
print(f"\n--- [์นดํ
๊ณ ๋ฆฌ: {category}, ํด์๋: {resolution}] ์ฒ๋ฆฌ ์์ ---")
|
| 52 |
+
|
| 53 |
+
# JSON์์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ ํ์ผ ๋ฆฌ์คํธ๋ฅผ ๊ฐ์ ธ์ต๋๋ค.
|
| 54 |
+
# .get(category, [])๋ฅผ ์ฌ์ฉํ๋ฉด JSON์ ํด๋น ์นดํ
๊ณ ๋ฆฌ๊ฐ ์์ด๋ ์ค๋ฅ ์์ด ๋น ๋ฆฌ์คํธ๋ฅผ ๋ฐํํฉ๋๋ค.
|
| 55 |
+
filenames_in_category = categorized_files.get(category, [])
|
| 56 |
+
|
| 57 |
+
if not filenames_in_category:
|
| 58 |
+
print("์ฒ๋ฆฌํ ํ์ผ์ด ์์ต๋๋ค.")
|
| 59 |
+
continue # ํ์ผ์ด ์์ผ๋ฉด ๋ค์ ์นดํ
๊ณ ๋ฆฌ๋ก ๋์ด๊ฐ
|
| 60 |
+
|
| 61 |
+
# ๋ด๋ถ ๋ฃจํ์์ ํด๋น ์นดํ
๊ณ ๋ฆฌ์ ๋ชจ๋ ํ์ผ์ ํ๋์ฉ ์ฒ๋ฆฌํฉ๋๋ค.
|
| 62 |
+
for filename in filenames_in_category:
|
| 63 |
+
|
| 64 |
+
# ์ค์ ํ์ผ ๊ฒฝ๋ก๋ฅผ ๋ง๋ญ๋๋ค. (JSON์๋ ํ์ฅ์๊ฐ ์์ผ๋ฏ๋ก .ply๋ฅผ ๋ถ์ฌ์ค๋๋ค)
|
| 65 |
+
file_path = os.path.join(folder, f"{filename}.ply")
|
| 66 |
+
|
| 67 |
+
print(f" - ํ์ผ ์ฒ๋ฆฌ ์ค: {file_path} (ํด์๋: {resolution})")
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
filename = filename
|
| 71 |
+
# PLY ํ์ผ ๋ก๋
|
| 72 |
+
pcd = o3d.io.read_point_cloud(f"./dataset/{filename}.ply")
|
| 73 |
+
|
| 74 |
+
GT = False
|
| 75 |
+
if GT==True:
|
| 76 |
+
mesh = o3d.io.read_triangle_mesh("./bottle2.stl")
|
| 77 |
+
pointcloud = mesh.sample_points_poisson_disk(50000)
|
| 78 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 79 |
+
|
| 80 |
+
mesh.compute_vertex_normals()
|
| 81 |
+
mesh_triangles = np.asarray(mesh.triangles)
|
| 82 |
+
vertex_positions = np.asarray(mesh.vertices)
|
| 83 |
+
triangle_normals = np.asarray(mesh.triangle_normals)
|
| 84 |
+
|
| 85 |
+
# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ
|
| 86 |
+
centroid = mesh.get_center()
|
| 87 |
+
filtered_triangles = []
|
| 88 |
+
for i, triangle in enumerate(mesh_triangles):
|
| 89 |
+
# ์ผ๊ฐํ์ ์ค์ฌ์ ๊ณ์ฐ
|
| 90 |
+
tri_center = vertex_positions[triangle].mean(axis=0)
|
| 91 |
+
# ๊ฐ์ฒด ์ค์ฌ์์ ์ผ๊ฐํ ์ค์ฌ์ผ๋ก ํฅํ๋ ๋ฒกํฐ
|
| 92 |
+
vec_to_center = tri_center - centroid
|
| 93 |
+
# ๋ฒ์ ๋ฒกํฐ์ ๋ฐฉํฅ ๋ฒกํฐ๋ฅผ ๋ด์
|
| 94 |
+
dot_product = np.dot(triangle_normals[i], vec_to_center)
|
| 95 |
+
# ๋ด์ ๊ฐ์ด ์์์ด๋ฉด ๋ฐ๊นฅ์ชฝ ๋ฉด์ผ๋ก ํ๋จ
|
| 96 |
+
if dot_product > 0:
|
| 97 |
+
filtered_triangles.append(triangle)
|
| 98 |
+
# 3. ํํฐ๋ง๋ ๋ฉด์ผ๋ก ์๋ก์ด ๋ฉ์ฌ ์์ฑ
|
| 99 |
+
outer_mesh = o3d.geometry.TriangleMesh()
|
| 100 |
+
outer_mesh.vertices = mesh.vertices
|
| 101 |
+
outer_mesh.triangles = o3d.utility.Vector3iVector(np.array(filtered_triangles))
|
| 102 |
+
# 4. ์๋ก์ด ๋ฉ์ฌ์์ ํฌ์ธํธ ํด๋ผ์ฐ๋ ์ํ๋ง
|
| 103 |
+
# n_points๋ ์ํ๋งํ ํฌ์ธํธ ๊ฐ์
|
| 104 |
+
pcd = outer_mesh.sample_points_uniformly(number_of_points=50000)
|
| 105 |
+
# ๊ฒฐ๊ณผ ์๊ฐํ
|
| 106 |
+
# o3d.visualization.draw_geometries([pcd,coord_frame ])
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
|
| 111 |
+
pcd_array = np.asarray(pcd.points)
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
# In[160]:
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
import open3d as o3d
|
| 118 |
+
import numpy as np
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
if not GT:
|
| 122 |
+
ply_path = f"./dataset/{filename}.ply"
|
| 123 |
+
|
| 124 |
+
pcd = o3d.io.read_point_cloud(ply_path)
|
| 125 |
+
print(ply_path)
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
pcd_array = np.asarray(pcd.points)
|
| 129 |
+
print(pcd_array.shape)
|
| 130 |
+
|
| 131 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 132 |
+
# o3d.visualization.draw_geometries([pcd, coord_frame])
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
# In[161]:
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
if GT==False:
|
| 139 |
+
|
| 140 |
+
new_pcd_array = np.unique(pcd_array, axis=0)
|
| 141 |
+
|
| 142 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 580]
|
| 143 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 1000]
|
| 144 |
+
|
| 145 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100]
|
| 146 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -1000] #diagonal
|
| 147 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 120]
|
| 148 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] > -1000]
|
| 149 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 1000] #diagonal
|
| 150 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 100]
|
| 151 |
+
# new_pcd_array -= np.mean(new_pcd_array, axis=0)
|
| 152 |
+
print(np.mean(new_pcd_array, axis=0))
|
| 153 |
+
|
| 154 |
+
new_pcd = o3d.geometry.PointCloud()
|
| 155 |
+
new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)
|
| 156 |
+
|
| 157 |
+
theta = np.radians(90)
|
| 158 |
+
# theta = np.radians(-90)
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 162 |
+
|
| 163 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 164 |
+
# o3d.visualization.draw_geometries([new_pcd, coord_frame])
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
# ## Delete ground plane
|
| 168 |
+
|
| 169 |
+
# In[162]:
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
if GT==False:
|
| 173 |
+
|
| 174 |
+
plane_model, inliers = new_pcd.segment_plane(distance_threshold=1,
|
| 175 |
+
ransac_n=10,
|
| 176 |
+
num_iterations=1000)
|
| 177 |
+
[a, b, c, d] = plane_model
|
| 178 |
+
print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
inlier_cloud = new_pcd.select_by_index(inliers)
|
| 183 |
+
inlier_cloud.paint_uniform_color([1.0, 0, 1.0])
|
| 184 |
+
outlier_cloud = new_pcd.select_by_index(inliers, invert=True)
|
| 185 |
+
# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
|
| 186 |
+
# zoom=0.8,
|
| 187 |
+
# front=[-0.4999, -0.1659, -0.8499],
|
| 188 |
+
# lookat=[2.1813, 2.0619, 2.0999],
|
| 189 |
+
# up=[0.1204, -0.9852, 0.1215])
|
| 190 |
+
|
| 191 |
+
new_pcd = outlier_cloud
|
| 192 |
+
|
| 193 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 194 |
+
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
# ### Changing the source position "gt_filtered"
|
| 199 |
+
#
|
| 200 |
+
|
| 201 |
+
# In[163]:
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
CHECK_PERTURB = GT
|
| 205 |
+
|
| 206 |
+
def random_rotation_matrix():
|
| 207 |
+
"""
|
| 208 |
+
Generate a random 3x3 rotation matrix (SO(3) matrix).
|
| 209 |
+
|
| 210 |
+
Uses the method described by James Arvo in "Fast Random Rotation Matrices" (1992):
|
| 211 |
+
1. Generate a random unit vector for rotation axis
|
| 212 |
+
2. Generate a random angle
|
| 213 |
+
3. Create rotation matrix using Rodriguez rotation formula
|
| 214 |
+
|
| 215 |
+
Returns:
|
| 216 |
+
numpy.ndarray: A 3x3 random rotation matrix
|
| 217 |
+
"""
|
| 218 |
+
## for ground target
|
| 219 |
+
# Generate random angle ฯ/2
|
| 220 |
+
theta = 0
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
# axis is -y
|
| 224 |
+
axis = np.array([
|
| 225 |
+
1,
|
| 226 |
+
0,
|
| 227 |
+
0,
|
| 228 |
+
])
|
| 229 |
+
|
| 230 |
+
# for lying target
|
| 231 |
+
# theta will be pi/2
|
| 232 |
+
# theta = np.pi/2
|
| 233 |
+
# axis = np.array([
|
| 234 |
+
# 0,
|
| 235 |
+
# 1,
|
| 236 |
+
# 0,
|
| 237 |
+
# ])
|
| 238 |
+
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
|
| 242 |
+
# Normalize to ensure it's a unit vector
|
| 243 |
+
axis = axis / np.linalg.norm(axis)
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
|
| 247 |
+
# Create the cross-product matrix K skew-symmetric
|
| 248 |
+
K = np.array([
|
| 249 |
+
[0, -axis[2], axis[1]],
|
| 250 |
+
[axis[2], 0, -axis[0]],
|
| 251 |
+
[-axis[1], axis[0], 0]
|
| 252 |
+
])
|
| 253 |
+
|
| 254 |
+
# Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ
|
| 255 |
+
R = (np.eye(3) +
|
| 256 |
+
np.sin(theta) * K +
|
| 257 |
+
(1 - np.cos(theta)) * np.dot(K, K))
|
| 258 |
+
|
| 259 |
+
return R
|
| 260 |
+
|
| 261 |
+
if CHECK_PERTURB:
|
| 262 |
+
R_pert = random_rotation_matrix()
|
| 263 |
+
print(R_pert)
|
| 264 |
+
t_pert = np.array([
|
| 265 |
+
0,
|
| 266 |
+
0,
|
| 267 |
+
0
|
| 268 |
+
])
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T
|
| 272 |
+
|
| 273 |
+
|
| 274 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 275 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 276 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 277 |
+
# o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 278 |
+
|
| 279 |
+
|
| 280 |
+
# ### Rotate randomly in Target "noisy filtered"
|
| 281 |
+
|
| 282 |
+
# In[164]:
|
| 283 |
+
|
| 284 |
+
|
| 285 |
+
CHECK_PERTURB = not GT
|
| 286 |
+
|
| 287 |
+
|
| 288 |
+
if CHECK_PERTURB:
|
| 289 |
+
# R_pert = random_rotation_matrix()
|
| 290 |
+
# print(R_pert)
|
| 291 |
+
# t_pert = np.random.rand(3, 1)*3 #* 10
|
| 292 |
+
|
| 293 |
+
|
| 294 |
+
# perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T
|
| 295 |
+
perturbed_pcd_array = new_pcd_array
|
| 296 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 297 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 298 |
+
|
| 299 |
+
|
| 300 |
+
now_centeroid = perturbed_pcd.get_center()
|
| 301 |
+
perturbed_pcd.translate(centroid, relative=False)
|
| 302 |
+
|
| 303 |
+
## get centeroid vector
|
| 304 |
+
|
| 305 |
+
translation_vector = centroid - now_centeroid
|
| 306 |
+
|
| 307 |
+
np.savetxt(f"./centroid/{filename}.txt",translation_vector)
|
| 308 |
+
|
| 309 |
+
##### changed
|
| 310 |
+
|
| 311 |
+
perturbed_pcd_array = np.asarray(perturbed_pcd.points)
|
| 312 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
|
| 317 |
+
|
| 318 |
+
# o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 319 |
+
|
| 320 |
+
|
| 321 |
+
# In[165]:
|
| 322 |
+
|
| 323 |
+
|
| 324 |
+
def write_ply(points, output_path):
|
| 325 |
+
"""
|
| 326 |
+
Write points and parameters to a PLY file
|
| 327 |
+
|
| 328 |
+
Parameters:
|
| 329 |
+
points: numpy array of shape (N, 3) containing point coordinates
|
| 330 |
+
output_path: path to save the PLY file
|
| 331 |
+
"""
|
| 332 |
+
with open(output_path, 'w') as f:
|
| 333 |
+
# Write header
|
| 334 |
+
f.write("ply\n")
|
| 335 |
+
f.write("format ascii 1.0\n")
|
| 336 |
+
|
| 337 |
+
# Write vertex element
|
| 338 |
+
f.write(f"element vertex {len(points)}\n")
|
| 339 |
+
f.write("property float x\n")
|
| 340 |
+
f.write("property float y\n")
|
| 341 |
+
f.write("property float z\n")
|
| 342 |
+
|
| 343 |
+
# Write camera element
|
| 344 |
+
f.write("element camera 1\n")
|
| 345 |
+
f.write("property float view_px\n")
|
| 346 |
+
f.write("property float view_py\n")
|
| 347 |
+
f.write("property float view_pz\n")
|
| 348 |
+
f.write("property float x_axisx\n")
|
| 349 |
+
f.write("property float x_axisy\n")
|
| 350 |
+
f.write("property float x_axisz\n")
|
| 351 |
+
f.write("property float y_axisx\n")
|
| 352 |
+
f.write("property float y_axisy\n")
|
| 353 |
+
f.write("property float y_axisz\n")
|
| 354 |
+
f.write("property float z_axisx\n")
|
| 355 |
+
f.write("property float z_axisy\n")
|
| 356 |
+
f.write("property float z_axisz\n")
|
| 357 |
+
|
| 358 |
+
# Write phoxi frame parameters
|
| 359 |
+
f.write("element phoxi_frame_params 1\n")
|
| 360 |
+
f.write("property uint32 frame_width\n")
|
| 361 |
+
f.write("property uint32 frame_height\n")
|
| 362 |
+
f.write("property uint32 frame_index\n")
|
| 363 |
+
f.write("property float frame_start_time\n")
|
| 364 |
+
f.write("property float frame_duration\n")
|
| 365 |
+
f.write("property float frame_computation_duration\n")
|
| 366 |
+
f.write("property float frame_transfer_duration\n")
|
| 367 |
+
f.write("property int32 total_scan_count\n")
|
| 368 |
+
|
| 369 |
+
# Write camera matrix
|
| 370 |
+
f.write("element camera_matrix 1\n")
|
| 371 |
+
for i in range(9):
|
| 372 |
+
f.write(f"property float cm{i}\n")
|
| 373 |
+
|
| 374 |
+
# Write distortion matrix
|
| 375 |
+
f.write("element distortion_matrix 1\n")
|
| 376 |
+
for i in range(14):
|
| 377 |
+
f.write(f"property float dm{i}\n")
|
| 378 |
+
|
| 379 |
+
# Write camera resolution
|
| 380 |
+
f.write("element camera_resolution 1\n")
|
| 381 |
+
f.write("property float width\n")
|
| 382 |
+
f.write("property float height\n")
|
| 383 |
+
|
| 384 |
+
# Write frame binning
|
| 385 |
+
f.write("element frame_binning 1\n")
|
| 386 |
+
f.write("property float horizontal\n")
|
| 387 |
+
f.write("property float vertical\n")
|
| 388 |
+
|
| 389 |
+
# End header
|
| 390 |
+
f.write("end_header\n")
|
| 391 |
+
|
| 392 |
+
# Write vertex data
|
| 393 |
+
for point in points:
|
| 394 |
+
f.write(f"{point[0]} {point[1]} {point[2]}\n")
|
| 395 |
+
|
| 396 |
+
print(True)
|
| 397 |
+
|
| 398 |
+
if GT: write_ply(perturbed_pcd_array, f"gt_filtered.ply")
|
| 399 |
+
else:
|
| 400 |
+
write_ply(perturbed_pcd_array, f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 401 |
+
write_ply(new_pcd_array,f"./noisy_raw/noisy_filtered_{filename}.ply")
|
| 402 |
+
# write_ply(new_pcd_array, "gt_filtered.ply")
|
| 403 |
+
|
| 404 |
+
#!/usr/bin/env python
|
| 405 |
+
# coding: utf-8
|
| 406 |
+
|
| 407 |
+
# ## PCD file transformation
|
| 408 |
+
|
| 409 |
+
# In[18]:
|
| 410 |
+
|
| 411 |
+
|
| 412 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 413 |
+
pcd = o3d.io.read_point_cloud("./gt_filtered.ply")
|
| 414 |
+
|
| 415 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 416 |
+
o3d.io.write_point_cloud("./initialize_pcdfile/gt_filtered.pcd", pcd)
|
| 417 |
+
|
| 418 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 419 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 420 |
+
|
| 421 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 422 |
+
|
| 423 |
+
|
| 424 |
+
# In[19]:
|
| 425 |
+
|
| 426 |
+
|
| 427 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 428 |
+
pcd = o3d.io.read_point_cloud(f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 429 |
+
|
| 430 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 431 |
+
o3d.io.write_point_cloud(f"./initialize_pcdfile/first_{filename}.pcd", pcd)
|
| 432 |
+
|
| 433 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 434 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 435 |
+
|
| 436 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 437 |
+
|
| 438 |
+
|
| 439 |
+
# ## Execute initial Guess
|
| 440 |
+
|
| 441 |
+
# In[20]:
|
| 442 |
+
|
| 443 |
+
|
| 444 |
+
# import os
|
| 445 |
+
# print(os.getcwd())
|
| 446 |
+
|
| 447 |
+
# import subprocess
|
| 448 |
+
|
| 449 |
+
# cmd = [
|
| 450 |
+
# 'python3',
|
| 451 |
+
# '../../../KISS-Matcher/python/examples/run_kiss_matcher.py',
|
| 452 |
+
# '--src_path',
|
| 453 |
+
# f'./initialize_pcdfile/first_{filename}.pcd',
|
| 454 |
+
# '--tgt_path',
|
| 455 |
+
# './initialize_pcdfile/gt_filtered.pcd',
|
| 456 |
+
# '--resolution',
|
| 457 |
+
# '1'
|
| 458 |
+
|
| 459 |
+
# ]
|
| 460 |
+
# try:
|
| 461 |
+
# result = subprocess.run(cmd, capture_output=True, text=True, check=True)
|
| 462 |
+
|
| 463 |
+
# print("--- STDOUT (ํ์ค ์ถ๋ ฅ) ---")
|
| 464 |
+
# print("๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.")
|
| 465 |
+
# print(result.stdout)
|
| 466 |
+
|
| 467 |
+
# except FileNotFoundError:
|
| 468 |
+
# print("--- ์๋ฌ ๋ฐ์! ---")
|
| 469 |
+
# print(f"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 470 |
+
# print("๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.")
|
| 471 |
+
|
| 472 |
+
# except subprocess.CalledProcessError as e:
|
| 473 |
+
# print("--- ์๋ฌ ๋ฐ์! ---")
|
| 474 |
+
# print(f"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})")
|
| 475 |
+
# print("\n--- STDERR (์๋ฌ ์์ธ) ---")
|
| 476 |
+
# print(e.stderr)
|
| 477 |
+
|
| 478 |
+
|
| 479 |
+
# # ## Saving initialized data
|
| 480 |
+
# #
|
| 481 |
+
|
| 482 |
+
# # In[21]:
|
| 483 |
+
|
| 484 |
+
|
| 485 |
+
# import shutil
|
| 486 |
+
# import os
|
| 487 |
+
|
| 488 |
+
# transformed_path = "output.ply"
|
| 489 |
+
# destination_path = f"./initialized_result/initial_{filename}.ply"
|
| 490 |
+
|
| 491 |
+
|
| 492 |
+
# shutil.move(transformed_path, destination_path)
|
| 493 |
+
# print(f"Successfully moved and renamed '{transformed_path}' to '{destination_path}'")
|
| 494 |
+
|
| 495 |
+
|
| 496 |
+
|
data/bottle_2/output_trans.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/bottle_2/ply_files.json
ADDED
|
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"100": [
|
| 3 |
+
"100_19",
|
| 4 |
+
"100_10",
|
| 5 |
+
"100_1",
|
| 6 |
+
"100_4",
|
| 7 |
+
"100_6",
|
| 8 |
+
"100_5",
|
| 9 |
+
"100_17",
|
| 10 |
+
"100_15",
|
| 11 |
+
"100_12",
|
| 12 |
+
"100_16",
|
| 13 |
+
"100_14",
|
| 14 |
+
"100_7",
|
| 15 |
+
"100_13",
|
| 16 |
+
"100_9",
|
| 17 |
+
"100_18",
|
| 18 |
+
"100_2",
|
| 19 |
+
"100_11",
|
| 20 |
+
"100_20",
|
| 21 |
+
"100_3",
|
| 22 |
+
"100_8"
|
| 23 |
+
],
|
| 24 |
+
"75": [
|
| 25 |
+
"75_6",
|
| 26 |
+
"75_12",
|
| 27 |
+
"75_9",
|
| 28 |
+
"75_4",
|
| 29 |
+
"75_11",
|
| 30 |
+
"75_7",
|
| 31 |
+
"75_14",
|
| 32 |
+
"75_8",
|
| 33 |
+
"75_16",
|
| 34 |
+
"75_17",
|
| 35 |
+
"75_2",
|
| 36 |
+
"75_3",
|
| 37 |
+
"75_1",
|
| 38 |
+
"75_15",
|
| 39 |
+
"75_20",
|
| 40 |
+
"75_10",
|
| 41 |
+
"75_13",
|
| 42 |
+
"75_5",
|
| 43 |
+
"75_19",
|
| 44 |
+
"75_18"
|
| 45 |
+
],
|
| 46 |
+
"50": [
|
| 47 |
+
"50_18",
|
| 48 |
+
"50_8",
|
| 49 |
+
"50_13",
|
| 50 |
+
"50_15",
|
| 51 |
+
"50_7",
|
| 52 |
+
"50_4",
|
| 53 |
+
"50_5",
|
| 54 |
+
"50_19",
|
| 55 |
+
"50_16",
|
| 56 |
+
"50_20",
|
| 57 |
+
"50_14",
|
| 58 |
+
"50_12",
|
| 59 |
+
"50_11",
|
| 60 |
+
"50_9",
|
| 61 |
+
"50_6",
|
| 62 |
+
"50_17",
|
| 63 |
+
"50_1",
|
| 64 |
+
"50_10",
|
| 65 |
+
"50_3",
|
| 66 |
+
"50_2"
|
| 67 |
+
],
|
| 68 |
+
"25": [
|
| 69 |
+
"25_6",
|
| 70 |
+
"25_19",
|
| 71 |
+
"25_17",
|
| 72 |
+
"25_9",
|
| 73 |
+
"25_11",
|
| 74 |
+
"25_20",
|
| 75 |
+
"25_14",
|
| 76 |
+
"25_4",
|
| 77 |
+
"25_16",
|
| 78 |
+
"25_5",
|
| 79 |
+
"25_2",
|
| 80 |
+
"25_10",
|
| 81 |
+
"25_3",
|
| 82 |
+
"25_8",
|
| 83 |
+
"25_13",
|
| 84 |
+
"25_7",
|
| 85 |
+
"25_12",
|
| 86 |
+
"25_1",
|
| 87 |
+
"25_15",
|
| 88 |
+
"25_18"
|
| 89 |
+
],
|
| 90 |
+
"0": [
|
| 91 |
+
"0_12",
|
| 92 |
+
"0_17",
|
| 93 |
+
"0_16",
|
| 94 |
+
"0_15",
|
| 95 |
+
"0_2",
|
| 96 |
+
"0_5",
|
| 97 |
+
"0_14",
|
| 98 |
+
"0_9",
|
| 99 |
+
"0_22",
|
| 100 |
+
"0_4",
|
| 101 |
+
"0_18",
|
| 102 |
+
"0_8",
|
| 103 |
+
"0_7",
|
| 104 |
+
"0_11",
|
| 105 |
+
"0_13",
|
| 106 |
+
"0_23",
|
| 107 |
+
"0_10",
|
| 108 |
+
"0_19",
|
| 109 |
+
"0_1",
|
| 110 |
+
"0_6",
|
| 111 |
+
"0_21",
|
| 112 |
+
"0_20",
|
| 113 |
+
"0_3"
|
| 114 |
+
]
|
| 115 |
+
}
|
data/bottle_2/run_all.py
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import json
|
| 3 |
+
import subprocess
|
| 4 |
+
|
| 5 |
+
import os
|
| 6 |
+
import json
|
| 7 |
+
|
| 8 |
+
# PLY ํ์ผ๋ค์ด ๋ค์ด ์๋ ํด๋ ๊ฒฝ๋ก
|
| 9 |
+
folder = "./dataset"
|
| 10 |
+
|
| 11 |
+
# ๋ถ๋ฅํ ์นดํ
๊ณ ๋ฆฌ๋ฅผ ๋ฏธ๋ฆฌ ์ ์ํฉ๋๋ค.
|
| 12 |
+
categories = ["100", "75", "50", "25", "0"]
|
| 13 |
+
|
| 14 |
+
# ๊ฒฐ๊ณผ๋ฅผ ์ ์ฅํ ๋์
๋๋ฆฌ๋ฅผ ์นดํ
๊ณ ๋ฆฌ๋ณ๋ก ์ด๊ธฐํํฉ๋๋ค.
|
| 15 |
+
grouped_files = {cat: [] for cat in categories}
|
| 16 |
+
|
| 17 |
+
# ํ์ฅ์๊ฐ .ply ์ธ ํ์ผ ๋ชฉ๋ก์ ๊ฐ์ ธ์ต๋๋ค.
|
| 18 |
+
try:
|
| 19 |
+
all_files = os.listdir(folder)
|
| 20 |
+
except FileNotFoundError:
|
| 21 |
+
print(f"์ค๋ฅ: '{folder}' ํด๋๋ฅผ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 22 |
+
all_files = []
|
| 23 |
+
|
| 24 |
+
# ๊ฐ ํ์ผ์ ์ํํ๋ฉฐ ์ ์ ํ ์นดํ
๊ณ ๋ฆฌ์ ์ถ๊ฐํฉ๋๋ค.
|
| 25 |
+
for filename_with_ext in all_files:
|
| 26 |
+
if filename_with_ext.endswith(".ply"):
|
| 27 |
+
# ํ์ฅ์(.ply)๋ฅผ ์ ๊ฑฐํฉ๋๋ค.
|
| 28 |
+
filename = filename_with_ext.removesuffix('.ply')
|
| 29 |
+
|
| 30 |
+
# ํ์ผ๋ช
์ '_' ๊ธฐ์ค์ผ๋ก ๋ถ๋ฆฌํ์ฌ ์ ๋์ด(prefix)๋ฅผ ์ป์ต๋๋ค.
|
| 31 |
+
prefix = filename.split('_')[0]
|
| 32 |
+
|
| 33 |
+
# ์ ๋์ด๊ฐ ์ ์๋ ์นดํ
๊ณ ๋ฆฌ ์ค ํ๋๋ผ๋ฉด, ํด๋น ๋ฆฌ์คํธ์ ํ์ผ๋ช
์ ์ถ๊ฐํฉ๋๋ค.
|
| 34 |
+
if prefix in grouped_files:
|
| 35 |
+
grouped_files[prefix].append(filename)
|
| 36 |
+
|
| 37 |
+
# ๋ถ๋ฅ๋ ๋์
๋๋ฆฌ๋ฅผ JSON ํ์ผ๋ก ์ ์ฅํฉ๋๋ค.
|
| 38 |
+
with open("ply_files.json", "w", encoding="utf-8") as f:
|
| 39 |
+
json.dump(grouped_files, f, ensure_ascii=False, indent=2)
|
| 40 |
+
|
| 41 |
+
print("JSON ์ ์ฅ ์๋ฃ! ์๋์ ๊ฐ์ด ํ์ผ์ด ๋ถ๋ฅ๋์์ต๋๋ค.")
|
| 42 |
+
print(json.dumps(grouped_files, indent=2))
|
| 43 |
+
|
| 44 |
+
# merged.py ์คํ
|
| 45 |
+
subprocess.run(["python3", "merged.py"])
|
data/car/downsample_car.ipynb
ADDED
|
@@ -0,0 +1,351 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
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|
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|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 13 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 14 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 15 |
+
"Reading point cloud from car_source.ply\n",
|
| 16 |
+
"Removing duplicate points (epsilon = 0.001)\n",
|
| 17 |
+
"Performing voxel downsampling with voxel size 0.05\n",
|
| 18 |
+
"\n",
|
| 19 |
+
"Point cloud statistics:\n",
|
| 20 |
+
"Original points: 2016000\n",
|
| 21 |
+
"Points after duplicate removal: 619908\n",
|
| 22 |
+
"Final points after downsampling: 619908\n",
|
| 23 |
+
"Duplicate removal reduction: 69.25%\n",
|
| 24 |
+
"Total reduction: 69.25%\n",
|
| 25 |
+
"Reading point cloud from car_target.ply\n",
|
| 26 |
+
"Removing duplicate points (epsilon = 0.001)\n",
|
| 27 |
+
"Performing voxel downsampling with voxel size 0.05\n",
|
| 28 |
+
"\n",
|
| 29 |
+
"Point cloud statistics:\n",
|
| 30 |
+
"Original points: 2016000\n",
|
| 31 |
+
"Points after duplicate removal: 873663\n",
|
| 32 |
+
"Final points after downsampling: 873663\n",
|
| 33 |
+
"Duplicate removal reduction: 56.66%\n",
|
| 34 |
+
"Total reduction: 56.66%\n"
|
| 35 |
+
]
|
| 36 |
+
}
|
| 37 |
+
],
|
| 38 |
+
"source": [
|
| 39 |
+
"import open3d as o3d\n",
|
| 40 |
+
"import numpy as np\n",
|
| 41 |
+
"\n",
|
| 42 |
+
"def write_ply(points, output_path):\n",
|
| 43 |
+
" \"\"\"\n",
|
| 44 |
+
" Write points and parameters to a PLY file\n",
|
| 45 |
+
" \n",
|
| 46 |
+
" Parameters:\n",
|
| 47 |
+
" points: numpy array of shape (N, 3) containing point coordinates\n",
|
| 48 |
+
" output_path: path to save the PLY file\n",
|
| 49 |
+
" \"\"\"\n",
|
| 50 |
+
" with open(output_path, 'w') as f:\n",
|
| 51 |
+
" # Write header\n",
|
| 52 |
+
" f.write(\"ply\\n\")\n",
|
| 53 |
+
" f.write(\"format ascii 1.0\\n\")\n",
|
| 54 |
+
" \n",
|
| 55 |
+
" # Write vertex element\n",
|
| 56 |
+
" f.write(f\"element vertex {len(points)}\\n\")\n",
|
| 57 |
+
" f.write(\"property float x\\n\")\n",
|
| 58 |
+
" f.write(\"property float y\\n\")\n",
|
| 59 |
+
" f.write(\"property float z\\n\")\n",
|
| 60 |
+
" \n",
|
| 61 |
+
" # Write camera element\n",
|
| 62 |
+
" f.write(\"element camera 1\\n\")\n",
|
| 63 |
+
" f.write(\"property float view_px\\n\")\n",
|
| 64 |
+
" f.write(\"property float view_py\\n\")\n",
|
| 65 |
+
" f.write(\"property float view_pz\\n\")\n",
|
| 66 |
+
" f.write(\"property float x_axisx\\n\")\n",
|
| 67 |
+
" f.write(\"property float x_axisy\\n\")\n",
|
| 68 |
+
" f.write(\"property float x_axisz\\n\")\n",
|
| 69 |
+
" f.write(\"property float y_axisx\\n\")\n",
|
| 70 |
+
" f.write(\"property float y_axisy\\n\")\n",
|
| 71 |
+
" f.write(\"property float y_axisz\\n\")\n",
|
| 72 |
+
" f.write(\"property float z_axisx\\n\")\n",
|
| 73 |
+
" f.write(\"property float z_axisy\\n\")\n",
|
| 74 |
+
" f.write(\"property float z_axisz\\n\")\n",
|
| 75 |
+
" \n",
|
| 76 |
+
" # Write phoxi frame parameters\n",
|
| 77 |
+
" f.write(\"element phoxi_frame_params 1\\n\")\n",
|
| 78 |
+
" f.write(\"property uint32 frame_width\\n\")\n",
|
| 79 |
+
" f.write(\"property uint32 frame_height\\n\")\n",
|
| 80 |
+
" f.write(\"property uint32 frame_index\\n\")\n",
|
| 81 |
+
" f.write(\"property float frame_start_time\\n\")\n",
|
| 82 |
+
" f.write(\"property float frame_duration\\n\")\n",
|
| 83 |
+
" f.write(\"property float frame_computation_duration\\n\")\n",
|
| 84 |
+
" f.write(\"property float frame_transfer_duration\\n\")\n",
|
| 85 |
+
" f.write(\"property int32 total_scan_count\\n\")\n",
|
| 86 |
+
" \n",
|
| 87 |
+
" # Write camera matrix\n",
|
| 88 |
+
" f.write(\"element camera_matrix 1\\n\")\n",
|
| 89 |
+
" for i in range(9):\n",
|
| 90 |
+
" f.write(f\"property float cm{i}\\n\")\n",
|
| 91 |
+
" \n",
|
| 92 |
+
" # Write distortion matrix\n",
|
| 93 |
+
" f.write(\"element distortion_matrix 1\\n\")\n",
|
| 94 |
+
" for i in range(14):\n",
|
| 95 |
+
" f.write(f\"property float dm{i}\\n\")\n",
|
| 96 |
+
" \n",
|
| 97 |
+
" # Write camera resolution\n",
|
| 98 |
+
" f.write(\"element camera_resolution 1\\n\")\n",
|
| 99 |
+
" f.write(\"property float width\\n\")\n",
|
| 100 |
+
" f.write(\"property float height\\n\")\n",
|
| 101 |
+
" \n",
|
| 102 |
+
" # Write frame binning\n",
|
| 103 |
+
" f.write(\"element frame_binning 1\\n\")\n",
|
| 104 |
+
" f.write(\"property float horizontal\\n\")\n",
|
| 105 |
+
" f.write(\"property float vertical\\n\")\n",
|
| 106 |
+
" \n",
|
| 107 |
+
" # End header\n",
|
| 108 |
+
" f.write(\"end_header\\n\")\n",
|
| 109 |
+
" \n",
|
| 110 |
+
" # Write vertex data\n",
|
| 111 |
+
" for point in points:\n",
|
| 112 |
+
" f.write(f\"{point[0]} {point[1]} {point[2]}\\n\")\n",
|
| 113 |
+
"\n",
|
| 114 |
+
" return True\n",
|
| 115 |
+
" \n",
|
| 116 |
+
"def random_rotation_matrix():\n",
|
| 117 |
+
" \"\"\"\n",
|
| 118 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 119 |
+
" \n",
|
| 120 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 121 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 122 |
+
" 2. Generate a random angle\n",
|
| 123 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 124 |
+
" \n",
|
| 125 |
+
" Returns:\n",
|
| 126 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 127 |
+
" \"\"\"\n",
|
| 128 |
+
" # Generate random angle between 0 and 2ฯ\n",
|
| 129 |
+
" theta = np.random.uniform(0, 2 * np.pi)\n",
|
| 130 |
+
" \n",
|
| 131 |
+
" # Generate random unit vector for rotation axis\n",
|
| 132 |
+
" phi = np.random.uniform(0, 2 * np.pi)\n",
|
| 133 |
+
" cos_theta = np.random.uniform(-1, 1)\n",
|
| 134 |
+
" sin_theta = np.sqrt(1 - cos_theta**2)\n",
|
| 135 |
+
" \n",
|
| 136 |
+
" axis = np.array([\n",
|
| 137 |
+
" sin_theta * np.cos(phi),\n",
|
| 138 |
+
" sin_theta * np.sin(phi),\n",
|
| 139 |
+
" cos_theta\n",
|
| 140 |
+
" ])\n",
|
| 141 |
+
" \n",
|
| 142 |
+
" # Normalize to ensure it's a unit vector\n",
|
| 143 |
+
" axis = axis / np.linalg.norm(axis)\n",
|
| 144 |
+
" \n",
|
| 145 |
+
" # Create the cross-product matrix K\n",
|
| 146 |
+
" K = np.array([\n",
|
| 147 |
+
" [0, -axis[2], axis[1]],\n",
|
| 148 |
+
" [axis[2], 0, -axis[0]],\n",
|
| 149 |
+
" [-axis[1], axis[0], 0]\n",
|
| 150 |
+
" ])\n",
|
| 151 |
+
" \n",
|
| 152 |
+
" # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 153 |
+
" R = (np.eye(3) + \n",
|
| 154 |
+
" np.sin(theta) * K + \n",
|
| 155 |
+
" (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 156 |
+
" \n",
|
| 157 |
+
" return R\n",
|
| 158 |
+
"\n",
|
| 159 |
+
"def remove_duplicates(pcd, eps=0.001):\n",
|
| 160 |
+
" \"\"\"\n",
|
| 161 |
+
" Remove duplicate points from point cloud within epsilon distance\n",
|
| 162 |
+
" \n",
|
| 163 |
+
" Parameters:\n",
|
| 164 |
+
" pcd: open3d.geometry.PointCloud\n",
|
| 165 |
+
" eps: float, maximum distance between points to be considered duplicates\n",
|
| 166 |
+
" \n",
|
| 167 |
+
" Returns:\n",
|
| 168 |
+
" open3d.geometry.PointCloud: Point cloud with duplicates removed\n",
|
| 169 |
+
" \"\"\"\n",
|
| 170 |
+
" # Convert to numpy array for processing\n",
|
| 171 |
+
" points = np.asarray(pcd.points)\n",
|
| 172 |
+
" colors = np.asarray(pcd.colors) if pcd.has_colors() else None\n",
|
| 173 |
+
" \n",
|
| 174 |
+
" # Use voxel downsampling with very small voxel size to remove duplicates\n",
|
| 175 |
+
" temp_pcd = o3d.geometry.PointCloud()\n",
|
| 176 |
+
" temp_pcd.points = o3d.utility.Vector3dVector(points)\n",
|
| 177 |
+
" if colors is not None:\n",
|
| 178 |
+
" temp_pcd.colors = o3d.utility.Vector3dVector(colors)\n",
|
| 179 |
+
" \n",
|
| 180 |
+
" # Use voxel downsampling with epsilon size to remove points within eps distance\n",
|
| 181 |
+
" deduped_pcd = temp_pcd.voxel_down_sample(voxel_size=eps)\n",
|
| 182 |
+
" \n",
|
| 183 |
+
" return deduped_pcd\n",
|
| 184 |
+
"\n",
|
| 185 |
+
"def downsample_ply(input_path, output_path, method='voxel', voxel_size=0.05, \n",
|
| 186 |
+
" every_k_points=5, remove_duplicates_eps=0.001, perturb = False):\n",
|
| 187 |
+
" \"\"\"\n",
|
| 188 |
+
" Remove duplicates and downsample a PLY file using different methods.\n",
|
| 189 |
+
" \n",
|
| 190 |
+
" Parameters:\n",
|
| 191 |
+
" input_path (str): Path to input PLY file\n",
|
| 192 |
+
" output_path (str): Path to save downsampled PLY file\n",
|
| 193 |
+
" method (str): Downsampling method ('voxel', 'uniform', or 'random')\n",
|
| 194 |
+
" voxel_size (float): Size of voxel for voxel downsampling\n",
|
| 195 |
+
" every_k_points (int): Keep every kth point for uniform downsampling\n",
|
| 196 |
+
" remove_duplicates_eps (float): Maximum distance between points to be considered duplicates\n",
|
| 197 |
+
" \n",
|
| 198 |
+
" Returns:\n",
|
| 199 |
+
" bool: True if successful, False otherwise\n",
|
| 200 |
+
" \"\"\"\n",
|
| 201 |
+
" try:\n",
|
| 202 |
+
" # Read point cloud\n",
|
| 203 |
+
" print(f\"Reading point cloud from {input_path}\")\n",
|
| 204 |
+
" pcd = o3d.io.read_point_cloud(input_path)\n",
|
| 205 |
+
" original_points = len(np.asarray(pcd.points))\n",
|
| 206 |
+
" \n",
|
| 207 |
+
" # Remove duplicates first\n",
|
| 208 |
+
" print(f\"Removing duplicate points (epsilon = {remove_duplicates_eps})\")\n",
|
| 209 |
+
" pcd = remove_duplicates(pcd, eps=remove_duplicates_eps)\n",
|
| 210 |
+
" after_dedup_points = len(np.asarray(pcd.points))\n",
|
| 211 |
+
" \n",
|
| 212 |
+
" # Perform downsampling based on selected method\n",
|
| 213 |
+
" if method == 'voxel':\n",
|
| 214 |
+
" print(f\"Performing voxel downsampling with voxel size {voxel_size}\")\n",
|
| 215 |
+
" downsampled_pcd = pcd.voxel_down_sample(voxel_size=voxel_size)\n",
|
| 216 |
+
" \n",
|
| 217 |
+
" elif method == 'uniform':\n",
|
| 218 |
+
" print(f\"Performing uniform downsampling, keeping every {every_k_points}th point\")\n",
|
| 219 |
+
" downsampled_pcd = pcd.uniform_down_sample(every_k_points=every_k_points)\n",
|
| 220 |
+
" \n",
|
| 221 |
+
" elif method == 'random':\n",
|
| 222 |
+
" points = np.asarray(pcd.points)\n",
|
| 223 |
+
" colors = np.asarray(pcd.colors) if pcd.has_colors() else None\n",
|
| 224 |
+
" indices = np.random.choice(\n",
|
| 225 |
+
" points.shape[0], \n",
|
| 226 |
+
" size=points.shape[0] // every_k_points, \n",
|
| 227 |
+
" replace=False\n",
|
| 228 |
+
" )\n",
|
| 229 |
+
" downsampled_pcd = o3d.geometry.PointCloud()\n",
|
| 230 |
+
" downsampled_pcd.points = o3d.utility.Vector3dVector(points[indices])\n",
|
| 231 |
+
" if colors is not None:\n",
|
| 232 |
+
" downsampled_pcd.colors = o3d.utility.Vector3dVector(colors[indices])\n",
|
| 233 |
+
" \n",
|
| 234 |
+
" else:\n",
|
| 235 |
+
" raise ValueError(f\"Unknown downsampling method: {method}\")\n",
|
| 236 |
+
" \n",
|
| 237 |
+
" point_cloud = np.asarray(downsampled_pcd.points)\n",
|
| 238 |
+
" if perturb:\n",
|
| 239 |
+
" R_perturb = random_rotation_matrix()\n",
|
| 240 |
+
" t_perturb = np.random.rand(3) * 0.01\n",
|
| 241 |
+
" point_cloud = np.dot(R_perturb, point_cloud.T).T + t_perturb.T\n",
|
| 242 |
+
"\n",
|
| 243 |
+
" # Save downsampled point cloud\n",
|
| 244 |
+
" success = write_ply(point_cloud, output_path)\n",
|
| 245 |
+
" \n",
|
| 246 |
+
" if not success:\n",
|
| 247 |
+
" raise Exception(\"Failed to write point cloud\")\n",
|
| 248 |
+
" \n",
|
| 249 |
+
" # Print statistics\n",
|
| 250 |
+
" final_points = len(np.asarray(downsampled_pcd.points))\n",
|
| 251 |
+
" dedup_reduction = (1 - after_dedup_points/original_points) * 100\n",
|
| 252 |
+
" total_reduction = (1 - final_points/original_points) * 100\n",
|
| 253 |
+
" \n",
|
| 254 |
+
" print(\"\\nPoint cloud statistics:\")\n",
|
| 255 |
+
" print(f\"Original points: {original_points}\")\n",
|
| 256 |
+
" print(f\"Points after duplicate removal: {after_dedup_points}\")\n",
|
| 257 |
+
" print(f\"Final points after downsampling: {final_points}\")\n",
|
| 258 |
+
" print(f\"Duplicate removal reduction: {dedup_reduction:.2f}%\")\n",
|
| 259 |
+
" print(f\"Total reduction: {total_reduction:.2f}%\")\n",
|
| 260 |
+
" \n",
|
| 261 |
+
" return True\n",
|
| 262 |
+
" \n",
|
| 263 |
+
" except Exception as e:\n",
|
| 264 |
+
" print(f\"Error during processing: {str(e)}\")\n",
|
| 265 |
+
" return False\n",
|
| 266 |
+
"\n",
|
| 267 |
+
"mode = 'downsample' # 'downsample', 'voxel', 'uniform'\n",
|
| 268 |
+
"\n",
|
| 269 |
+
"if mode == 'downsample':\n",
|
| 270 |
+
" # Voxel downsampling\n",
|
| 271 |
+
" downsample_ply(\n",
|
| 272 |
+
" \"car_source.ply\",\n",
|
| 273 |
+
" \"car_source_downsample.ply\",\n",
|
| 274 |
+
" method='voxel',\n",
|
| 275 |
+
" voxel_size=0.05,\n",
|
| 276 |
+
" remove_duplicates_eps=0.001,\n",
|
| 277 |
+
" perturb = True\n",
|
| 278 |
+
" )\n",
|
| 279 |
+
" downsample_ply(\n",
|
| 280 |
+
" \"car_target.ply\",\n",
|
| 281 |
+
" \"car_target_downsample.ply\",\n",
|
| 282 |
+
" method='voxel',\n",
|
| 283 |
+
" voxel_size=0.05,\n",
|
| 284 |
+
" remove_duplicates_eps=0.001\n",
|
| 285 |
+
" )\n",
|
| 286 |
+
"\n",
|
| 287 |
+
"if mode == 'voxel': \n",
|
| 288 |
+
" # Uniform downsampling\n",
|
| 289 |
+
" downsample_ply(\n",
|
| 290 |
+
" \"car_source.ply\",\n",
|
| 291 |
+
" \"car_source_downsample.ply\",\n",
|
| 292 |
+
" method='uniform',\n",
|
| 293 |
+
" every_k_points=5,\n",
|
| 294 |
+
" remove_duplicates_eps=0.001\n",
|
| 295 |
+
" )\n",
|
| 296 |
+
" downsample_ply(\n",
|
| 297 |
+
" \"car_target.ply\",\n",
|
| 298 |
+
" \"car_target_downsample.ply\",\n",
|
| 299 |
+
" method='uniform',\n",
|
| 300 |
+
" every_k_points=5,\n",
|
| 301 |
+
" remove_duplicates_eps=0.001\n",
|
| 302 |
+
" )\n",
|
| 303 |
+
"\n",
|
| 304 |
+
"if mode == 'uniform': \n",
|
| 305 |
+
" # Random downsampling\n",
|
| 306 |
+
" downsample_ply(\n",
|
| 307 |
+
" \"car_source.ply\",\n",
|
| 308 |
+
" \"car_source_downsample.ply\",\n",
|
| 309 |
+
" method='random',\n",
|
| 310 |
+
" every_k_points=5,\n",
|
| 311 |
+
" remove_duplicates_eps=0.001\n",
|
| 312 |
+
" )\n",
|
| 313 |
+
" downsample_ply(\n",
|
| 314 |
+
" \"car_target.ply\",\n",
|
| 315 |
+
" \"car_target_downsample.ply\",\n",
|
| 316 |
+
" method='random',\n",
|
| 317 |
+
" every_k_points=5,\n",
|
| 318 |
+
" remove_duplicates_eps=0.001\n",
|
| 319 |
+
" )"
|
| 320 |
+
]
|
| 321 |
+
},
|
| 322 |
+
{
|
| 323 |
+
"cell_type": "code",
|
| 324 |
+
"execution_count": null,
|
| 325 |
+
"metadata": {},
|
| 326 |
+
"outputs": [],
|
| 327 |
+
"source": []
|
| 328 |
+
}
|
| 329 |
+
],
|
| 330 |
+
"metadata": {
|
| 331 |
+
"kernelspec": {
|
| 332 |
+
"display_name": "Python 3",
|
| 333 |
+
"language": "python",
|
| 334 |
+
"name": "python3"
|
| 335 |
+
},
|
| 336 |
+
"language_info": {
|
| 337 |
+
"codemirror_mode": {
|
| 338 |
+
"name": "ipython",
|
| 339 |
+
"version": 3
|
| 340 |
+
},
|
| 341 |
+
"file_extension": ".py",
|
| 342 |
+
"mimetype": "text/x-python",
|
| 343 |
+
"name": "python",
|
| 344 |
+
"nbconvert_exporter": "python",
|
| 345 |
+
"pygments_lexer": "ipython3",
|
| 346 |
+
"version": "3.10.12"
|
| 347 |
+
}
|
| 348 |
+
},
|
| 349 |
+
"nbformat": 4,
|
| 350 |
+
"nbformat_minor": 2
|
| 351 |
+
}
|
data/car/inference.ipynb
ADDED
|
@@ -0,0 +1,214 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"# cd build\n",
|
| 10 |
+
"# cmake -DCMAKE_BUILD_TYPE=Release ..\n",
|
| 11 |
+
"# make\n",
|
| 12 |
+
"# ./FRICP ./data/car/car_target_downsample.ply ./data/car/car_source_downsample.ply ./data/car/res/ 3"
|
| 13 |
+
]
|
| 14 |
+
},
|
| 15 |
+
{
|
| 16 |
+
"cell_type": "markdown",
|
| 17 |
+
"metadata": {},
|
| 18 |
+
"source": [
|
| 19 |
+
"### Source PCD"
|
| 20 |
+
]
|
| 21 |
+
},
|
| 22 |
+
{
|
| 23 |
+
"cell_type": "code",
|
| 24 |
+
"execution_count": 1,
|
| 25 |
+
"metadata": {},
|
| 26 |
+
"outputs": [
|
| 27 |
+
{
|
| 28 |
+
"name": "stdout",
|
| 29 |
+
"output_type": "stream",
|
| 30 |
+
"text": [
|
| 31 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 32 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 33 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
|
| 34 |
+
]
|
| 35 |
+
},
|
| 36 |
+
{
|
| 37 |
+
"name": "stderr",
|
| 38 |
+
"output_type": "stream",
|
| 39 |
+
"text": [
|
| 40 |
+
"RPly: Unexpected end of file\n",
|
| 41 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 42 |
+
]
|
| 43 |
+
},
|
| 44 |
+
{
|
| 45 |
+
"name": "stdout",
|
| 46 |
+
"output_type": "stream",
|
| 47 |
+
"text": [
|
| 48 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: car_source_downsample.ply\u001b[0;m\n",
|
| 49 |
+
"Source shape: (619908, 3)\n"
|
| 50 |
+
]
|
| 51 |
+
}
|
| 52 |
+
],
|
| 53 |
+
"source": [
|
| 54 |
+
"import open3d as o3d\n",
|
| 55 |
+
"import numpy as np\n",
|
| 56 |
+
"\n",
|
| 57 |
+
"source_path = \"car_source_downsample.ply\"\n",
|
| 58 |
+
"source_pcd = o3d.io.read_point_cloud(source_path)\n",
|
| 59 |
+
"\n",
|
| 60 |
+
"source_pcd_array = np.asarray(source_pcd.points)\n",
|
| 61 |
+
"print(\"Source shape:\", source_pcd_array.shape)\n",
|
| 62 |
+
"\n",
|
| 63 |
+
"o3d.visualization.draw_geometries([source_pcd])"
|
| 64 |
+
]
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"cell_type": "markdown",
|
| 68 |
+
"metadata": {},
|
| 69 |
+
"source": [
|
| 70 |
+
"### Target PCD"
|
| 71 |
+
]
|
| 72 |
+
},
|
| 73 |
+
{
|
| 74 |
+
"cell_type": "code",
|
| 75 |
+
"execution_count": 2,
|
| 76 |
+
"metadata": {},
|
| 77 |
+
"outputs": [
|
| 78 |
+
{
|
| 79 |
+
"name": "stdout",
|
| 80 |
+
"output_type": "stream",
|
| 81 |
+
"text": [
|
| 82 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: car_target_downsample.ply\u001b[0;m\n",
|
| 83 |
+
"Target shape: (873663, 3)\n"
|
| 84 |
+
]
|
| 85 |
+
},
|
| 86 |
+
{
|
| 87 |
+
"name": "stderr",
|
| 88 |
+
"output_type": "stream",
|
| 89 |
+
"text": [
|
| 90 |
+
"RPly: Unexpected end of file\n",
|
| 91 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 92 |
+
]
|
| 93 |
+
}
|
| 94 |
+
],
|
| 95 |
+
"source": [
|
| 96 |
+
"target_path = \"car_target_downsample.ply\"\n",
|
| 97 |
+
"target_pcd = o3d.io.read_point_cloud(target_path)\n",
|
| 98 |
+
"\n",
|
| 99 |
+
"target_pcd_array = np.asarray(target_pcd.points)\n",
|
| 100 |
+
"print(\"Target shape:\", target_pcd_array.shape)\n",
|
| 101 |
+
"\n",
|
| 102 |
+
"o3d.visualization.draw_geometries([target_pcd])"
|
| 103 |
+
]
|
| 104 |
+
},
|
| 105 |
+
{
|
| 106 |
+
"cell_type": "markdown",
|
| 107 |
+
"metadata": {},
|
| 108 |
+
"source": [
|
| 109 |
+
"### Transformed Source PCD"
|
| 110 |
+
]
|
| 111 |
+
},
|
| 112 |
+
{
|
| 113 |
+
"cell_type": "code",
|
| 114 |
+
"execution_count": 3,
|
| 115 |
+
"metadata": {},
|
| 116 |
+
"outputs": [
|
| 117 |
+
{
|
| 118 |
+
"name": "stdout",
|
| 119 |
+
"output_type": "stream",
|
| 120 |
+
"text": [
|
| 121 |
+
"Transformed shape: (619908, 3)\n"
|
| 122 |
+
]
|
| 123 |
+
}
|
| 124 |
+
],
|
| 125 |
+
"source": [
|
| 126 |
+
"transformed_path = \"res/m3reg_pc.ply\"\n",
|
| 127 |
+
"transformed_pcd = o3d.io.read_point_cloud(transformed_path)\n",
|
| 128 |
+
"\n",
|
| 129 |
+
"transformed_pcd_array = np.asarray(transformed_pcd.points)\n",
|
| 130 |
+
"print(\"Transformed shape:\", transformed_pcd_array.shape)\n",
|
| 131 |
+
"\n",
|
| 132 |
+
"o3d.visualization.draw_geometries([transformed_pcd])"
|
| 133 |
+
]
|
| 134 |
+
},
|
| 135 |
+
{
|
| 136 |
+
"cell_type": "markdown",
|
| 137 |
+
"metadata": {},
|
| 138 |
+
"source": [
|
| 139 |
+
"### Source (Original) + Target"
|
| 140 |
+
]
|
| 141 |
+
},
|
| 142 |
+
{
|
| 143 |
+
"cell_type": "code",
|
| 144 |
+
"execution_count": 4,
|
| 145 |
+
"metadata": {},
|
| 146 |
+
"outputs": [],
|
| 147 |
+
"source": [
|
| 148 |
+
"source_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 149 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 150 |
+
"\n",
|
| 151 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 152 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 153 |
+
"vis.add_geometry(source_pcd)\n",
|
| 154 |
+
"vis.add_geometry(target_pcd)\n",
|
| 155 |
+
"\n",
|
| 156 |
+
"vis.run()\n",
|
| 157 |
+
"vis.destroy_window()"
|
| 158 |
+
]
|
| 159 |
+
},
|
| 160 |
+
{
|
| 161 |
+
"cell_type": "markdown",
|
| 162 |
+
"metadata": {},
|
| 163 |
+
"source": [
|
| 164 |
+
"### Transformed + Target"
|
| 165 |
+
]
|
| 166 |
+
},
|
| 167 |
+
{
|
| 168 |
+
"cell_type": "code",
|
| 169 |
+
"execution_count": 5,
|
| 170 |
+
"metadata": {},
|
| 171 |
+
"outputs": [],
|
| 172 |
+
"source": [
|
| 173 |
+
"transformed_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 174 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 175 |
+
"\n",
|
| 176 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 177 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 178 |
+
"vis.add_geometry(transformed_pcd)\n",
|
| 179 |
+
"vis.add_geometry(target_pcd)\n",
|
| 180 |
+
"\n",
|
| 181 |
+
"vis.run()\n",
|
| 182 |
+
"vis.destroy_window()"
|
| 183 |
+
]
|
| 184 |
+
},
|
| 185 |
+
{
|
| 186 |
+
"cell_type": "code",
|
| 187 |
+
"execution_count": null,
|
| 188 |
+
"metadata": {},
|
| 189 |
+
"outputs": [],
|
| 190 |
+
"source": []
|
| 191 |
+
}
|
| 192 |
+
],
|
| 193 |
+
"metadata": {
|
| 194 |
+
"kernelspec": {
|
| 195 |
+
"display_name": "Python 3",
|
| 196 |
+
"language": "python",
|
| 197 |
+
"name": "python3"
|
| 198 |
+
},
|
| 199 |
+
"language_info": {
|
| 200 |
+
"codemirror_mode": {
|
| 201 |
+
"name": "ipython",
|
| 202 |
+
"version": 3
|
| 203 |
+
},
|
| 204 |
+
"file_extension": ".py",
|
| 205 |
+
"mimetype": "text/x-python",
|
| 206 |
+
"name": "python",
|
| 207 |
+
"nbconvert_exporter": "python",
|
| 208 |
+
"pygments_lexer": "ipython3",
|
| 209 |
+
"version": "3.10.12"
|
| 210 |
+
}
|
| 211 |
+
},
|
| 212 |
+
"nbformat": 4,
|
| 213 |
+
"nbformat_minor": 2
|
| 214 |
+
}
|
data/glasses/all_infer.ipynb
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/glasses/bottle.csv
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
,file_1,file_2,file_3,file_4,file_5,file_6,file_7,file_8,file_9,file_10,file_11,file_12,file_13,file_14,file_15,file_16,file_17,file_18,file_19,file_20,file_21,file_22,file_23,file_24,file_25,mean_Val
|
| 2 |
+
eyeglasses_100_ICP,75.51653495394899,76.00103199004224,0.0,85.74346143402444,134.16012926507486,129.91987001484316,166.0664964244785,172.3590746833109,77.5675768710639,133.14287598624617,103.73415613056342,98.87027368011256,160.5835290777202,126.17170983418293,123.28168915483552,127.40001477836599,100.59098891564037,126.36969911638593,137.75334679266845,80.57292535399384,0.0,0.0,0.0,0.0,0.0,117.67396760302644
|
| 3 |
+
eyeglasses_75_ICP,102.51712203498685,102.71063672186233,2.5105762817824515,155.0313892300544,73.28915630087278,80.10142286182572,61.6642170552717,133.03230404536268,130.60315811579525,145.51109450361503,152.06334435452948,17.619102965832944,42.92461252779952,151.48193925390873,159.18454688606766,124.2966200861978,152.90101617418273,147.66220349142665,84.52304960168144,53.34520262686054,0.0,0.0,0.0,0.0,0.0,103.64863575599584
|
| 4 |
+
eyeglasses_50_ICP,0.6015741903834354,2.8452058285927064,110.51901319722636,56.28572654309218,53.32635161297196,61.741382380184966,85.95688825251894,121.5059962415175,158.22792441676737,120.48210280365721,163.26046311963538,109.41096489918088,72.88913708060892,38.3178073242256,143.68401792751254,163.7867666636524,135.8775688193156,168.33332742593984,155.0870413589984,153.26899611525445,0.0,0.0,0.0,0.0,0.0,103.77041281006184
|
| 5 |
+
eyeglasses_25_ICP,139.13864680025034,140.51052266668606,151.7093977712709,113.65244680744456,75.1106132184419,56.214407671431076,45.88685609333,130.76833214522333,164.28756894726808,157.78672295255376,144.15098598108406,103.92408061009746,97.0196179056392,45.70141433658228,134.73400503530502,157.3968890047997,112.38124958239436,165.99811915045157,123.34060946414556,155.2221077926652,0.0,0.0,0.0,0.0,0.0,120.74672969685321
|
| 6 |
+
eyeglasses_0_ICP,60.04991082566456,182.90415876501115,59.82950216871568,73.7625146358775,58.28419890950544,79.71643545431141,38.044308881264456,153.36281639968632,135.43326539048297,154.24876415823215,145.39590693775096,25.9106141015137,98.71536711906626,115.79991923606332,71.13979743679549,140.9382098036925,121.71112000247408,149.71217757669916,168.5029121108631,141.76709650392965,152.2609035201492,147.80916326732194,142.52520684702927,91.32113755437325,0.0,112.88105865026974
|
| 7 |
+
eyeglasses_100_FAST ICP,75.5187308434864,76.00496974122805,0.0,86.17471678825584,134.15888577513982,129.91221389336002,166.07323901399576,172.36082441941062,89.6243271911958,132.6794901473997,103.89959657538533,98.87085293288902,160.61277754271094,126.16977217179729,123.27745832745872,45.18673199758907,100.58689325690877,126.330072334676,137.75274333809836,80.5737513939449,0.0,0.0,0.0,0.0,0.0,113.98779198341737
|
| 8 |
+
eyeglasses_75_FAST ICP,102.42608192175467,102.69753459934931,2.840931270289013,155.03208726641134,75.8160345326146,80.10295241662858,61.63266855915283,133.0239563972094,130.49233178010883,145.5079568445103,152.24606133806714,17.620690880483043,42.955108336009914,151.59412455383557,159.1860297924438,124.2964959709984,152.91046244458744,147.66712168080346,84.52533860447731,53.34651226544358,0.0,0.0,0.0,0.0,0.0,103.79602407275893
|
| 9 |
+
eyeglasses_50_FAST ICP,1.5568122641527766,2.5124008238033793,107.10731327027244,57.7703049884877,53.32615621434432,61.7434399205845,85.96034561112776,121.50560896737548,158.22803344183083,120.40908235167409,163.26105285827163,104.76696058626732,72.88884273230175,38.28347404978611,143.68701010899792,163.56997268386675,135.8775595113068,168.33519924022247,155.19887976263263,153.26686036986206,0.0,0.0,0.0,0.0,0.0,103.46276548785845
|
| 10 |
+
eyeglasses_25_FAST ICP,125.73876818197037,122.01364089888638,151.7035979888734,113.99909527085833,77.07966426783474,56.22088237296886,45.883554801008415,130.76805635650382,109.26206578553587,139.0436878413092,144.15162504177013,103.93456808933466,26.27856225215323,45.7049245990998,134.13913279767138,157.8377875390067,112.38026791486097,165.99742936516162,123.36102795766949,155.24514405265427,0.0,0.0,0.0,0.0,0.0,112.03717416875656
|
| 11 |
+
eyeglasses_0_FAST ICP,60.22925039219409,182.91426682701464,59.000265687099265,74.16508873434424,58.2846768555866,79.71697593243557,38.044525599729496,153.36344057961946,135.64692996188683,154.35256865501708,145.39749491018858,26.121627493550182,98.73623949166469,115.79991923606332,71.86613773977069,140.33915878050433,120.86634784228097,149.7210621975309,168.02437633631175,140.21030565488817,151.93284733244684,91.15564581095389,142.5721738353796,91.34590790578044,0.0,110.4086347413434
|
| 12 |
+
eyeglasses_100_Robust ICP,75.75653848738223,76.18774923131436,0.0,79.1605772756743,133.62491286251668,121.68110193399859,157.08526506073426,126.51764298276913,1.691524835728188,154.72376907130626,118.99344836709355,10.038066122292635,160.01322907705529,120.50629571367567,111.25162077590228,125.94461111680737,99.58236331003572,139.26288159580326,137.84414167776615,76.2050844688691,0.0,0.0,0.0,0.0,0.0,106.63530652456447
|
| 13 |
+
eyeglasses_75_Robust ICP,139.472323041692,138.5975619501767,2.217538055916783,152.4279639938108,69.51305960052665,80.18208737513707,57.35927667959797,132.2775433456103,133.4462671230113,148.61241398060793,140.85055762318262,4.2035055350531465,0.9906031273796142,153.51478967747082,157.97682232625993,137.64442581324894,151.64815395202908,144.24224150055846,92.71004373151372,51.65168679359356,0.0,0.0,0.0,0.0,0.0,104.47694326131887
|
| 14 |
+
eyeglasses_50_Robust ICP,1.3755216460504922,2.1346436434518816,106.86398141897781,49.586724372839456,57.05111089595722,60.73855387255383,66.26513023496774,115.65777063879904,156.53618886178342,65.22727234347522,150.09296585681577,11.951002758396335,71.50841505279936,129.97907293385222,145.1082502386099,163.9889719060767,140.41499752641775,165.45304166929444,149.1606391681766,151.98766020836518,0.0,0.0,0.0,0.0,0.0,98.05409576238301
|
| 15 |
+
eyeglasses_25_Robust ICP,124.00422804647823,122.29037724068283,149.5279088214046,103.8515675742576,60.78751301624003,61.451873136563044,44.36715633619058,124.4759652808121,161.5577327755834,140.68845568660743,124.65623044533673,1.907703964125683,2.943728424331599,48.640131069396894,137.43168507121374,146.51890330950468,130.88745850434591,164.68537861955838,120.9301984683491,153.28471614318437,0.0,0.0,0.0,0.0,0.0,106.24444559670835
|
| 16 |
+
eyeglasses_0_Robust ICP,57.73896334463609,174.5634197664402,53.77257745761977,72.23987867784734,60.77683349518314,78.12456409958408,29.164486290794365,151.39898819814792,125.05910366033528,153.60836127418486,139.84897701358068,57.13499174391295,66.96067838797823,78.43371918167516,85.21682627592233,145.4677167515239,84.80187685800591,150.56227891280508,148.18423554442214,138.44904232921172,151.97069566752944,91.47095491526541,146.96371631787102,96.73108905206605,0.0,105.77683230068929
|
| 17 |
+
eyeglasses_100_Sparse ICP,54.17899469739919,54.84785135599243,0.0,79.58295651810589,134.42487325951575,129.5912845285183,111.64873354301028,23.937578213767154,146.34177195227804,19.9163859850053,107.85626997860463,10.241442214310394,3.411256450114343,103.74267007605388,112.35391962260815,83.04203275581155,87.47674214527946,139.46578173028445,141.10823075861555,75.96047070568828,0.0,0.0,0.0,0.0,0.0,85.21732876268226
|
| 18 |
+
eyeglasses_75_Sparse ICP,138.6811860402104,137.5983484388318,10.689334316307113,153.63289804484566,70.67343477971835,60.3707409307818,55.10831909617402,132.331860324879,100.33363542959653,140.9804903562735,88.18570189664554,11.061523528347614,4.694564154331557,148.16072806383298,142.6530373582771,143.1530290534934,151.44309126091727,141.69349434780796,102.54688113191057,46.20934792951648,0.0,0.0,0.0,0.0,0.0,99.01008232413493
|
| 19 |
+
eyeglasses_50_Sparse ICP,3.5262388190194174,5.275081043252968,14.928834398675749,51.16753940868383,48.696201101808406,51.03699551521889,67.22049654631782,115.99281339473909,138.74063199884182,89.58094258111971,158.66161724027734,10.14631940894041,67.39075600290572,150.58801773016555,146.39841694974066,159.9879086408528,147.74844911512986,154.829275347152,147.90853162882757,152.43497118899742,0.0,0.0,0.0,0.0,0.0,94.11300190303336
|
| 20 |
+
eyeglasses_25_Sparse ICP,146.4329287208715,142.51736324418462,149.40720067750553,54.709018630468094,63.46820462112425,57.10632805261897,45.86612421856109,124.40161448144558,149.08774372270864,147.8530027425512,133.90307211836978,24.71464684602617,4.439883309268856,25.607231189008225,97.78549341493859,154.8641361148791,101.46744904405824,159.98903972864412,108.317885673641,152.13559639938507,0.0,0.0,0.0,0.0,0.0,102.20369814751292
|
| 21 |
+
eyeglasses_0_Sparse ICP,109.42336685149034,163.30377136246767,79.15294649993758,72.2072345027589,61.89620784296406,75.85447105176921,31.033353045087114,155.30034537684247,121.32632581984205,121.36576481925033,147.2143541501991,56.87302898049748,137.5654299402161,153.6719796505628,35.234936288114135,149.25144585122138,87.66829662458683,160.30954814766613,155.31398823277286,116.97368227080774,152.9389542387432,109.19550687608314,148.31948275618512,103.78073958433927,0.0,112.71563169851687
|
| 22 |
+
ICP,75.56475776104683,100.9943111944389,64.91369788379907,96.89510773009862,78.8340898613734,81.53870367651928,79.52375334137271,142.20570470302013,133.22389874827553,142.23431208086086,141.72097130471266,71.1470072513475,94.42645274216682,95.49455799699258,126.40481128810325,142.7637000673417,124.69238869880144,151.61510535218062,133.8413918656714,116.83526567854074,30.452180704029843,29.561832653464386,28.505041369405852,18.26422751087465,0.0,111.74416090324141
|
| 23 |
+
FAST ICP,73.09392872071166,97.22856257805635,64.13042164330683,97.42825860967149,79.73308352910402,81.5392929071955,79.51886671700285,142.20437734402375,124.65073763211163,138.39855716798206,141.79116614473656,70.26293999650485,80.2943060709681,95.51044292211643,126.43115375326852,126.24602939439305,124.52430619398899,151.6101769636789,133.7724731998379,116.5285147473586,30.38656946648937,18.231129162190776,28.51443476707592,18.269181581156086,0.0,108.73847809082694
|
| 24 |
+
FAST AND ROBUST ICP,79.66951491324781,102.75475036641319,62.47640115078379,91.4533423788859,76.35068597408475,80.43563608356732,70.84826292045697,130.0655820892277,115.6581634512883,132.57205447123633,134.88843586120188,17.04705402475615,60.48333081390882,106.21480171521414,127.39704093758164,143.91292577943233,121.46697003016686,152.84116445960393,129.76585171804555,114.3156379886448,30.394139133505888,18.29419098305308,29.392743263574204,19.34621781041321,0.0,104.23752468913281
|
| 25 |
+
SPARSE ICP,90.44854302579817,100.70848308894588,50.8356631784852,82.25992942097248,75.83178432102616,74.79196401578143,62.17540528983007,110.39284235833466,131.16602178465342,103.93931729684,127.16420307681929,22.607392195624413,43.500377971367314,116.35412534192469,106.88516072673572,138.05971048325165,115.16080563799433,151.2574278603109,131.0391034851535,108.74281369887899,30.587790847748643,21.83910137521663,29.663896551237023,20.756147916867853,0.0,98.65194856717606
|
data/glasses/dataset_pandas.ipynb
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "781eee9c",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## using pandas\n"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 2,
|
| 14 |
+
"id": "70fc5658",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [],
|
| 17 |
+
"source": [
|
| 18 |
+
"import pandas as pd\n",
|
| 19 |
+
"import numpy as np\n",
|
| 20 |
+
"import os\n",
|
| 21 |
+
"import json\n",
|
| 22 |
+
"## column : file no 1~25\n",
|
| 23 |
+
"\n",
|
| 24 |
+
"# array 4X4\n",
|
| 25 |
+
"# for i in range(rows):\n",
|
| 26 |
+
"# for j in range(cols):\n",
|
| 27 |
+
"# object_array[i,j] = np.zeros((4,4))\n",
|
| 28 |
+
"\n",
|
| 29 |
+
"\n",
|
| 30 |
+
"data = np.zeros((20,25))\n",
|
| 31 |
+
"\n",
|
| 32 |
+
"\n",
|
| 33 |
+
"\n",
|
| 34 |
+
"## row : bottle_0, bottle_25 ... gt 0 25 --> 10 rows. \n",
|
| 35 |
+
"\n",
|
| 36 |
+
"categories = ['bottle2', 'lightbulb', 'lighter', 'eyeglasses', 'magnifying_glass', 'spray']\n",
|
| 37 |
+
"\n",
|
| 38 |
+
"category = categories[3]\n",
|
| 39 |
+
"fill_rate = ['100', '75', '50', '25', '0']\n",
|
| 40 |
+
"\n",
|
| 41 |
+
"columns = [f'file_{i}' for i in range(1,26)]\n",
|
| 42 |
+
"\n",
|
| 43 |
+
"\n",
|
| 44 |
+
"\n"
|
| 45 |
+
]
|
| 46 |
+
},
|
| 47 |
+
{
|
| 48 |
+
"cell_type": "markdown",
|
| 49 |
+
"id": "22195309",
|
| 50 |
+
"metadata": {},
|
| 51 |
+
"source": [
|
| 52 |
+
"## Get transformation file "
|
| 53 |
+
]
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"cell_type": "code",
|
| 57 |
+
"execution_count": null,
|
| 58 |
+
"id": "d3dcc164",
|
| 59 |
+
"metadata": {},
|
| 60 |
+
"outputs": [],
|
| 61 |
+
"source": []
|
| 62 |
+
},
|
| 63 |
+
{
|
| 64 |
+
"cell_type": "code",
|
| 65 |
+
"execution_count": 3,
|
| 66 |
+
"id": "86c0ea73",
|
| 67 |
+
"metadata": {},
|
| 68 |
+
"outputs": [
|
| 69 |
+
{
|
| 70 |
+
"data": {
|
| 71 |
+
"text/plain": [
|
| 72 |
+
"<bound method DataFrame.info of file_1 file_2 file_3 file_4 file_5 file_6 file_7 \\\n",
|
| 73 |
+
"eyeglasses_100_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 74 |
+
"eyeglasses_75_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 75 |
+
"eyeglasses_50_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 76 |
+
"eyeglasses_25_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 77 |
+
"eyeglasses_0_ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 78 |
+
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 79 |
+
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 80 |
+
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 81 |
+
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 82 |
+
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 83 |
+
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 84 |
+
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 85 |
+
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 86 |
+
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 87 |
+
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 88 |
+
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 89 |
+
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 90 |
+
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 91 |
+
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 92 |
+
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 93 |
+
"\n",
|
| 94 |
+
" file_8 file_9 file_10 ... file_16 file_17 file_18 \\\n",
|
| 95 |
+
"eyeglasses_100_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 96 |
+
"eyeglasses_75_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 97 |
+
"eyeglasses_50_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 98 |
+
"eyeglasses_25_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 99 |
+
"eyeglasses_0_ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 100 |
+
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 101 |
+
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 102 |
+
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 103 |
+
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 104 |
+
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 105 |
+
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 106 |
+
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 107 |
+
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 108 |
+
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 109 |
+
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 110 |
+
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 111 |
+
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 112 |
+
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 113 |
+
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 114 |
+
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
|
| 115 |
+
"\n",
|
| 116 |
+
" file_19 file_20 file_21 file_22 file_23 file_24 \\\n",
|
| 117 |
+
"eyeglasses_100_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 118 |
+
"eyeglasses_75_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 119 |
+
"eyeglasses_50_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 120 |
+
"eyeglasses_25_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 121 |
+
"eyeglasses_0_ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 122 |
+
"eyeglasses_100_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 123 |
+
"eyeglasses_75_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 124 |
+
"eyeglasses_50_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 125 |
+
"eyeglasses_25_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 126 |
+
"eyeglasses_0_FAST ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 127 |
+
"eyeglasses_100_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 128 |
+
"eyeglasses_75_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 129 |
+
"eyeglasses_50_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 130 |
+
"eyeglasses_25_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 131 |
+
"eyeglasses_0_Robust ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 132 |
+
"eyeglasses_100_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 133 |
+
"eyeglasses_75_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 134 |
+
"eyeglasses_50_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 135 |
+
"eyeglasses_25_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 136 |
+
"eyeglasses_0_Sparse ICP 0.0 0.0 0.0 0.0 0.0 0.0 \n",
|
| 137 |
+
"\n",
|
| 138 |
+
" file_25 \n",
|
| 139 |
+
"eyeglasses_100_ICP 0.0 \n",
|
| 140 |
+
"eyeglasses_75_ICP 0.0 \n",
|
| 141 |
+
"eyeglasses_50_ICP 0.0 \n",
|
| 142 |
+
"eyeglasses_25_ICP 0.0 \n",
|
| 143 |
+
"eyeglasses_0_ICP 0.0 \n",
|
| 144 |
+
"eyeglasses_100_FAST ICP 0.0 \n",
|
| 145 |
+
"eyeglasses_75_FAST ICP 0.0 \n",
|
| 146 |
+
"eyeglasses_50_FAST ICP 0.0 \n",
|
| 147 |
+
"eyeglasses_25_FAST ICP 0.0 \n",
|
| 148 |
+
"eyeglasses_0_FAST ICP 0.0 \n",
|
| 149 |
+
"eyeglasses_100_Robust ICP 0.0 \n",
|
| 150 |
+
"eyeglasses_75_Robust ICP 0.0 \n",
|
| 151 |
+
"eyeglasses_50_Robust ICP 0.0 \n",
|
| 152 |
+
"eyeglasses_25_Robust ICP 0.0 \n",
|
| 153 |
+
"eyeglasses_0_Robust ICP 0.0 \n",
|
| 154 |
+
"eyeglasses_100_Sparse ICP 0.0 \n",
|
| 155 |
+
"eyeglasses_75_Sparse ICP 0.0 \n",
|
| 156 |
+
"eyeglasses_50_Sparse ICP 0.0 \n",
|
| 157 |
+
"eyeglasses_25_Sparse ICP 0.0 \n",
|
| 158 |
+
"eyeglasses_0_Sparse ICP 0.0 \n",
|
| 159 |
+
"\n",
|
| 160 |
+
"[20 rows x 25 columns]>"
|
| 161 |
+
]
|
| 162 |
+
},
|
| 163 |
+
"execution_count": 3,
|
| 164 |
+
"metadata": {},
|
| 165 |
+
"output_type": "execute_result"
|
| 166 |
+
}
|
| 167 |
+
],
|
| 168 |
+
"source": [
|
| 169 |
+
"## Tmatrix FOlder access -> save in pandas\n",
|
| 170 |
+
"robust_no = ['0','2','3','6']\n",
|
| 171 |
+
"new_row_names = []\n",
|
| 172 |
+
"# ๊ฒฐ๊ณผ๋ฅผ ์ ์ฅํ ๋์
๋๋ฆฌ๋ฅผ ์นดํ
๊ณ ๋ฆฌ๋ณ๋ก ์ด๊ธฐํํฉ๋๋ค.\n",
|
| 173 |
+
"grouped_files = {fill: [] for fill in fill_rate}\n",
|
| 174 |
+
"\n",
|
| 175 |
+
"for no in robust_no:\n",
|
| 176 |
+
" \n",
|
| 177 |
+
" ## get txt file\n",
|
| 178 |
+
"\n",
|
| 179 |
+
" ######################## We got the txt file list#################\n",
|
| 180 |
+
" for fills in fill_rate:\n",
|
| 181 |
+
" \n",
|
| 182 |
+
" if no =='0':\n",
|
| 183 |
+
" name = \"ICP\"\n",
|
| 184 |
+
" elif no == '2':\n",
|
| 185 |
+
" name = \"FAST ICP\"\n",
|
| 186 |
+
" elif no =='3':\n",
|
| 187 |
+
" name = \"Robust ICP\"\n",
|
| 188 |
+
" else:\n",
|
| 189 |
+
" name = \"Sparse ICP\"\n",
|
| 190 |
+
"\n",
|
| 191 |
+
" new_row_names.append(f\"{category}_{fills}_{name}\")\n",
|
| 192 |
+
"\n",
|
| 193 |
+
"df = pd.DataFrame(data, index=new_row_names, columns=columns, dtype=object)\n",
|
| 194 |
+
"# 2. df.index์ ์๋ก์ด ์ด๋ฆ ๋ฆฌ์คํธ๋ฅผ ๋ฐ๋ก ํ ๋น object for array 4x4\n",
|
| 195 |
+
"\n",
|
| 196 |
+
"df.info"
|
| 197 |
+
]
|
| 198 |
+
},
|
| 199 |
+
{
|
| 200 |
+
"cell_type": "markdown",
|
| 201 |
+
"id": "173149df",
|
| 202 |
+
"metadata": {},
|
| 203 |
+
"source": [
|
| 204 |
+
"## RMSE function"
|
| 205 |
+
]
|
| 206 |
+
},
|
| 207 |
+
{
|
| 208 |
+
"cell_type": "code",
|
| 209 |
+
"execution_count": 4,
|
| 210 |
+
"id": "5334ae14",
|
| 211 |
+
"metadata": {},
|
| 212 |
+
"outputs": [
|
| 213 |
+
{
|
| 214 |
+
"name": "stdout",
|
| 215 |
+
"output_type": "stream",
|
| 216 |
+
"text": [
|
| 217 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './result3/result_3_100_1.txt' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n"
|
| 218 |
+
]
|
| 219 |
+
}
|
| 220 |
+
],
|
| 221 |
+
"source": [
|
| 222 |
+
"def RMSE(T_star, T):\n",
|
| 223 |
+
" diff = T_star - T\n",
|
| 224 |
+
" sq_norms = np.sum(diff**2, axis =1)\n",
|
| 225 |
+
"\n",
|
| 226 |
+
" r = np.sqrt(np.mean(sq_norms))\n",
|
| 227 |
+
"\n",
|
| 228 |
+
" return r\n",
|
| 229 |
+
"\n",
|
| 230 |
+
"## get T from Result Txt file\n",
|
| 231 |
+
"def get_T(file_path):\n",
|
| 232 |
+
"\n",
|
| 233 |
+
" try:\n",
|
| 234 |
+
" with open(file_path, 'r') as f:\n",
|
| 235 |
+
" T_matrix = np.loadtxt(file_path)\n",
|
| 236 |
+
" return T_matrix\n",
|
| 237 |
+
" except FileNotFoundError:\n",
|
| 238 |
+
" # try ๋ธ๋ก์์ FileNotFoundError๊ฐ ๋ฐ์ํ์ ๋๋ง ์ด ์ฝ๋๊ฐ ์คํ๋ฉ๋๋ค.\n",
|
| 239 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : '{file_path}' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\")\n",
|
| 240 |
+
" return None # ํ์ผ์ด ์์ผ๋ฏ๋ก None์ ๋ฐํ\n",
|
| 241 |
+
"\n",
|
| 242 |
+
"\n",
|
| 243 |
+
"\n",
|
| 244 |
+
"\n",
|
| 245 |
+
"def get_GT_T(file_path,data_name):\n",
|
| 246 |
+
"\n",
|
| 247 |
+
" try:\n",
|
| 248 |
+
" with open(file_path, 'r') as f:\n",
|
| 249 |
+
" loaded_data = json.load(f)\n",
|
| 250 |
+
" noisy_data = loaded_data[data_name]\n",
|
| 251 |
+
" T_matrix = noisy_data['matrix_world']\n",
|
| 252 |
+
" np.array(T_matrix)\n",
|
| 253 |
+
" return T_matrix\n",
|
| 254 |
+
"\n",
|
| 255 |
+
" except FileNotFoundError:\n",
|
| 256 |
+
" # try ๋ธ๋ก์์ FileNotFoundError๊ฐ ๋ฐ์ํ์ ๋๋ง ์ด ์ฝ๋๊ฐ ์คํ๋ฉ๋๋ค.\n",
|
| 257 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : '{file_path}' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\")\n",
|
| 258 |
+
" return None # ํ์ผ์ด ์์ผ๋ฏ๋ก None์ ๋ฐํ\n",
|
| 259 |
+
"\n",
|
| 260 |
+
" except KeyError as e:\n",
|
| 261 |
+
" # try ๋ธ๋ก์์ KeyError๊ฐ ๋ฐ์ํ์ ๋ ์คํ๋ฉ๋๋ค. (e.g., 'matrix_world' ํค๊ฐ ์์)\n",
|
| 262 |
+
" print(f\"โ ๏ธ ๊ฒฝ๊ณ : ํ์ผ '{os.path.basename(file_path)}' ์์ ํ์ํ ํค({e})๊ฐ ์์ต๋๋ค.\")\n",
|
| 263 |
+
" return None\n",
|
| 264 |
+
" \n",
|
| 265 |
+
" \n",
|
| 266 |
+
"\n",
|
| 267 |
+
"def compute_RMSE(gt_files):\n",
|
| 268 |
+
" \n",
|
| 269 |
+
" robust_no = ['0','2','3','6']\n",
|
| 270 |
+
" \n",
|
| 271 |
+
" for no in robust_no:\n",
|
| 272 |
+
" if no =='0':\n",
|
| 273 |
+
" name = \"ICP\"\n",
|
| 274 |
+
" elif no == '2':\n",
|
| 275 |
+
" name = \"FAST ICP\"\n",
|
| 276 |
+
" elif no =='3':\n",
|
| 277 |
+
" name = \"Robust ICP\"\n",
|
| 278 |
+
" else:\n",
|
| 279 |
+
" name = \"Sparse ICP\"\n",
|
| 280 |
+
"\n",
|
| 281 |
+
" for key, value_list in gt_files.items():\n",
|
| 282 |
+
" rmse = []\n",
|
| 283 |
+
" np.array(rmse)\n",
|
| 284 |
+
" # get gt_T and noisy_T\n",
|
| 285 |
+
" for value in value_list:\n",
|
| 286 |
+
" profix = value.split('_')[1]\n",
|
| 287 |
+
" gt_path = f\"./gt_raw/noisy_filtered_{key}_{profix}.json\"\n",
|
| 288 |
+
" gt_name = f\"noisy_filtered_{key}_{profix}\"\n",
|
| 289 |
+
"\n",
|
| 290 |
+
" #### RESULT FOLDER PATH.\n",
|
| 291 |
+
" result_path = f'./result{no}/result_{key}_{profix}.txt'\n",
|
| 292 |
+
" icp_T = get_T(result_path)\n",
|
| 293 |
+
" gt_T = get_GT_T(gt_path,gt_name)\n",
|
| 294 |
+
" \n",
|
| 295 |
+
" \n",
|
| 296 |
+
"\n",
|
| 297 |
+
" if (gt_T is None or icp_T is None):\n",
|
| 298 |
+
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = 0.0\n",
|
| 299 |
+
"\n",
|
| 300 |
+
" else:\n",
|
| 301 |
+
" ## conpute rmse\n",
|
| 302 |
+
" r = RMSE(gt_T, icp_T)\n",
|
| 303 |
+
" \n",
|
| 304 |
+
" df.loc[f'{category}_{key}_{name}',f'file_{profix}'] = r\n",
|
| 305 |
+
"\n",
|
| 306 |
+
"\n",
|
| 307 |
+
"noisy_T = get_T(\"./result3/result_3_100_1.txt\")\n",
|
| 308 |
+
"gt_T = get_GT_T(\"./gt/noisy_filtered_100_1.json\",\"noisy_filtered_100_1\")\n",
|
| 309 |
+
"\n"
|
| 310 |
+
]
|
| 311 |
+
},
|
| 312 |
+
{
|
| 313 |
+
"cell_type": "markdown",
|
| 314 |
+
"id": "587f5b2d",
|
| 315 |
+
"metadata": {},
|
| 316 |
+
"source": [
|
| 317 |
+
"## Bring GT"
|
| 318 |
+
]
|
| 319 |
+
},
|
| 320 |
+
{
|
| 321 |
+
"cell_type": "code",
|
| 322 |
+
"execution_count": 5,
|
| 323 |
+
"id": "c4883f09",
|
| 324 |
+
"metadata": {},
|
| 325 |
+
"outputs": [
|
| 326 |
+
{
|
| 327 |
+
"name": "stdout",
|
| 328 |
+
"output_type": "stream",
|
| 329 |
+
"text": [
|
| 330 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_100_3.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 331 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_75_21.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 332 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_100_3.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 333 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_75_21.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 334 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_100_3.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 335 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_75_21.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 336 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_100_3.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 337 |
+
"โ ๏ธ ๊ฒฝ๊ณ : './gt_raw/noisy_filtered_75_21.json' ๊ฒฝ๋ก์ ํ์ผ์ด ์์ต๋๋ค. ํด๋น ์ฒ๋ฆฌ๋ฅผ ๊ฑด๋๋๋๋ค.\n",
|
| 338 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 339 |
+
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758\n",
|
| 340 |
+
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948\n",
|
| 341 |
+
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618\n",
|
| 342 |
+
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065\n",
|
| 343 |
+
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664\n",
|
| 344 |
+
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795\n",
|
| 345 |
+
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146\n",
|
| 346 |
+
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514\n",
|
| 347 |
+
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252\n",
|
| 348 |
+
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988\n",
|
| 349 |
+
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198\n",
|
| 350 |
+
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732\n",
|
| 351 |
+
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147\n",
|
| 352 |
+
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664\n",
|
| 353 |
+
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235\n",
|
| 354 |
+
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693\n",
|
| 355 |
+
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038\n",
|
| 356 |
+
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481\n",
|
| 357 |
+
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496\n",
|
| 358 |
+
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357\n"
|
| 359 |
+
]
|
| 360 |
+
},
|
| 361 |
+
{
|
| 362 |
+
"name": "stderr",
|
| 363 |
+
"output_type": "stream",
|
| 364 |
+
"text": [
|
| 365 |
+
"/tmp/ipykernel_285739/3042233176.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
| 366 |
+
" df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n"
|
| 367 |
+
]
|
| 368 |
+
}
|
| 369 |
+
],
|
| 370 |
+
"source": [
|
| 371 |
+
"json_path = \"ply_files.json\"\n",
|
| 372 |
+
"try: \n",
|
| 373 |
+
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
|
| 374 |
+
" gt_files = json.load(f)\n",
|
| 375 |
+
"except FileNotFoundError:\n",
|
| 376 |
+
" print(f\"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.\")\n",
|
| 377 |
+
" exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ\n",
|
| 378 |
+
"\n",
|
| 379 |
+
"\n",
|
| 380 |
+
"\n",
|
| 381 |
+
"### get \n",
|
| 382 |
+
"\n",
|
| 383 |
+
"\n",
|
| 384 |
+
"\n",
|
| 385 |
+
"compute_RMSE(gt_files)\n",
|
| 386 |
+
"\n",
|
| 387 |
+
"##get mean value\n",
|
| 388 |
+
"df['mean_Val'] = df.replace(0, np.nan).mean(axis=1)\n",
|
| 389 |
+
"\n",
|
| 390 |
+
"\n",
|
| 391 |
+
"\n",
|
| 392 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 393 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 394 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 395 |
+
"\n",
|
| 396 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 397 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 398 |
+
"\n",
|
| 399 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๊ฟ์ ํ ์ง ๋ง์ง\n",
|
| 400 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 401 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 402 |
+
"\n",
|
| 403 |
+
"# ์: DataFrame ์ถ๋ ฅ\n",
|
| 404 |
+
"print(df)\n",
|
| 405 |
+
" \n",
|
| 406 |
+
"\n",
|
| 407 |
+
"\n"
|
| 408 |
+
]
|
| 409 |
+
},
|
| 410 |
+
{
|
| 411 |
+
"cell_type": "markdown",
|
| 412 |
+
"id": "7493fb27",
|
| 413 |
+
"metadata": {},
|
| 414 |
+
"source": [
|
| 415 |
+
"## GET RMSE MEAN by ICP Methods\n",
|
| 416 |
+
"\n"
|
| 417 |
+
]
|
| 418 |
+
},
|
| 419 |
+
{
|
| 420 |
+
"cell_type": "code",
|
| 421 |
+
"execution_count": 6,
|
| 422 |
+
"id": "e49285b9",
|
| 423 |
+
"metadata": {},
|
| 424 |
+
"outputs": [
|
| 425 |
+
{
|
| 426 |
+
"name": "stdout",
|
| 427 |
+
"output_type": "stream",
|
| 428 |
+
"text": [
|
| 429 |
+
"[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]\n",
|
| 430 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 431 |
+
"ICP 85.341354 88.887397 47.023139 61.45332 54.934998 76.071344 73.060515 101.697793 114.806848 110.32652 107.365937 78.519844 63.967499 69.409217 77.080832 72.752249 96.577527 113.995142 94.781897 69.170672 23.556134 23.08887 18.382303 12.599637 0.0 84.049010\n",
|
| 432 |
+
"FAST ICP 92.158538 88.49653 47.021592 61.454307 54.938263 76.072606 73.062909 101.700427 114.805221 110.333409 107.197623 78.522964 63.969287 77.772819 77.082034 66.268981 96.57216 113.992938 94.776605 69.172997 23.555429 23.088534 18.366808 12.601635 0.0 84.421939\n",
|
| 433 |
+
"FAST AND ROBUST ICP 81.668689 84.148253 30.419366 69.999343 57.367623 87.657208 86.909857 104.396662 97.951836 107.130861 107.193278 29.211593 24.911955 82.180071 68.26604 93.264397 97.84577 90.549384 87.216121 76.045872 23.974562 23.5607 16.910104 5.35311 0.0 78.851995\n",
|
| 434 |
+
"SPARSE ICP 65.841104 64.161483 34.618481 67.748556 53.99955 70.817248 94.782662 93.190901 94.431494 101.400787 80.180398 28.513659 43.435852 78.420871 76.207549 71.979979 98.883359 83.64568 78.809106 50.974496 23.541922 23.063196 17.463471 7.420005 0.0 72.398813\n",
|
| 435 |
+
"<class 'pandas.core.frame.DataFrame'>\n"
|
| 436 |
+
]
|
| 437 |
+
}
|
| 438 |
+
],
|
| 439 |
+
"source": [
|
| 440 |
+
"df_mean = np.zeros((5,5))\n",
|
| 441 |
+
"\n",
|
| 442 |
+
"## make 25 lengths array\n",
|
| 443 |
+
"\n",
|
| 444 |
+
"grouping = []\n",
|
| 445 |
+
"\n",
|
| 446 |
+
"for i in range(0,len(df)):\n",
|
| 447 |
+
" grouping.append(i)\n",
|
| 448 |
+
"\n",
|
| 449 |
+
"grouping = np.arange(len(df)) //5\n",
|
| 450 |
+
"\n",
|
| 451 |
+
"print(grouping)\n",
|
| 452 |
+
"block_avg_df = df.groupby(grouping).mean()\n",
|
| 453 |
+
"\n",
|
| 454 |
+
"\n",
|
| 455 |
+
"ICP_Method = ['ICP', 'FAST ICP', 'FAST AND ROBUST ICP', 'SPARSE ICP']\n",
|
| 456 |
+
"\n",
|
| 457 |
+
"\n",
|
| 458 |
+
"\n",
|
| 459 |
+
"block_avg_df.index = ICP_Method\n",
|
| 460 |
+
"\n",
|
| 461 |
+
"\n",
|
| 462 |
+
"print(block_avg_df)\n",
|
| 463 |
+
"\n",
|
| 464 |
+
"print(type(block_avg_df))\n",
|
| 465 |
+
"\n",
|
| 466 |
+
"\n"
|
| 467 |
+
]
|
| 468 |
+
},
|
| 469 |
+
{
|
| 470 |
+
"cell_type": "code",
|
| 471 |
+
"execution_count": null,
|
| 472 |
+
"id": "14ebb074",
|
| 473 |
+
"metadata": {},
|
| 474 |
+
"outputs": [],
|
| 475 |
+
"source": []
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"cell_type": "markdown",
|
| 479 |
+
"id": "d03a908e",
|
| 480 |
+
"metadata": {},
|
| 481 |
+
"source": [
|
| 482 |
+
"## merge in Pandas"
|
| 483 |
+
]
|
| 484 |
+
},
|
| 485 |
+
{
|
| 486 |
+
"cell_type": "code",
|
| 487 |
+
"execution_count": 7,
|
| 488 |
+
"id": "92386801",
|
| 489 |
+
"metadata": {},
|
| 490 |
+
"outputs": [
|
| 491 |
+
{
|
| 492 |
+
"name": "stdout",
|
| 493 |
+
"output_type": "stream",
|
| 494 |
+
"text": [
|
| 495 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val\n",
|
| 496 |
+
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758\n",
|
| 497 |
+
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948\n",
|
| 498 |
+
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618\n",
|
| 499 |
+
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065\n",
|
| 500 |
+
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664\n",
|
| 501 |
+
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795\n",
|
| 502 |
+
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146\n",
|
| 503 |
+
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514\n",
|
| 504 |
+
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252\n",
|
| 505 |
+
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988\n",
|
| 506 |
+
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198\n",
|
| 507 |
+
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732\n",
|
| 508 |
+
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147\n",
|
| 509 |
+
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664\n",
|
| 510 |
+
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235\n",
|
| 511 |
+
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693\n",
|
| 512 |
+
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038\n",
|
| 513 |
+
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481\n",
|
| 514 |
+
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496\n",
|
| 515 |
+
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357\n",
|
| 516 |
+
"ICP 85.341354 88.887397 47.023139 61.45332 54.934998 76.071344 73.060515 101.697793 114.806848 110.32652 107.365937 78.519844 63.967499 69.409217 77.080832 72.752249 96.577527 113.995142 94.781897 69.170672 23.556134 23.08887 18.382303 12.599637 0.0 84.049010\n",
|
| 517 |
+
"FAST ICP 92.158538 88.49653 47.021592 61.454307 54.938263 76.072606 73.062909 101.700427 114.805221 110.333409 107.197623 78.522964 63.969287 77.772819 77.082034 66.268981 96.57216 113.992938 94.776605 69.172997 23.555429 23.088534 18.366808 12.601635 0.0 84.421939\n",
|
| 518 |
+
"FAST AND ROBUST ICP 81.668689 84.148253 30.419366 69.999343 57.367623 87.657208 86.909857 104.396662 97.951836 107.130861 107.193278 29.211593 24.911955 82.180071 68.26604 93.264397 97.84577 90.549384 87.216121 76.045872 23.974562 23.5607 16.910104 5.35311 0.0 78.851995\n",
|
| 519 |
+
"SPARSE ICP 65.841104 64.161483 34.618481 67.748556 53.99955 70.817248 94.782662 93.190901 94.431494 101.400787 80.180398 28.513659 43.435852 78.420871 76.207549 71.979979 98.883359 83.64568 78.809106 50.974496 23.541922 23.063196 17.463471 7.420005 0.0 72.398813\n"
|
| 520 |
+
]
|
| 521 |
+
}
|
| 522 |
+
],
|
| 523 |
+
"source": [
|
| 524 |
+
"combined_df = pd.concat([df, block_avg_df], ignore_index=False)\n",
|
| 525 |
+
"\n",
|
| 526 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 527 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 528 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 529 |
+
"\n",
|
| 530 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 531 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 532 |
+
"\n",
|
| 533 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๊ฟ์ ํ ์ง ๋ง์ง\n",
|
| 534 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 535 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 536 |
+
"\n",
|
| 537 |
+
"print(combined_df)"
|
| 538 |
+
]
|
| 539 |
+
},
|
| 540 |
+
{
|
| 541 |
+
"cell_type": "markdown",
|
| 542 |
+
"id": "a9b19689",
|
| 543 |
+
"metadata": {},
|
| 544 |
+
"source": [
|
| 545 |
+
"## Save bottle csv"
|
| 546 |
+
]
|
| 547 |
+
},
|
| 548 |
+
{
|
| 549 |
+
"cell_type": "code",
|
| 550 |
+
"execution_count": 8,
|
| 551 |
+
"id": "9e8dcfae",
|
| 552 |
+
"metadata": {},
|
| 553 |
+
"outputs": [
|
| 554 |
+
{
|
| 555 |
+
"name": "stdout",
|
| 556 |
+
"output_type": "stream",
|
| 557 |
+
"text": [
|
| 558 |
+
"ICP 84.049010\n",
|
| 559 |
+
"FAST ICP 84.421939\n",
|
| 560 |
+
"FAST AND ROBUST ICP 78.851995\n",
|
| 561 |
+
"SPARSE ICP 72.398813\n",
|
| 562 |
+
"Name: mean_Val, dtype: float64\n"
|
| 563 |
+
]
|
| 564 |
+
}
|
| 565 |
+
],
|
| 566 |
+
"source": [
|
| 567 |
+
"sliced_data = combined_df.loc['ICP':'SPARSE ICP', 'mean_Val']\n",
|
| 568 |
+
"print(sliced_data)\n",
|
| 569 |
+
"combined_df.to_csv(f'{category}.csv', index=True)"
|
| 570 |
+
]
|
| 571 |
+
},
|
| 572 |
+
{
|
| 573 |
+
"cell_type": "markdown",
|
| 574 |
+
"id": "1c228eca",
|
| 575 |
+
"metadata": {},
|
| 576 |
+
"source": [
|
| 577 |
+
"## Load num of dataset in each category. + save array"
|
| 578 |
+
]
|
| 579 |
+
},
|
| 580 |
+
{
|
| 581 |
+
"cell_type": "code",
|
| 582 |
+
"execution_count": 9,
|
| 583 |
+
"id": "e81b4de4",
|
| 584 |
+
"metadata": {},
|
| 585 |
+
"outputs": [
|
| 586 |
+
{
|
| 587 |
+
"name": "stdout",
|
| 588 |
+
"output_type": "stream",
|
| 589 |
+
"text": [
|
| 590 |
+
" file_1 file_2 file_3 file_4 file_5 file_6 file_7 file_8 file_9 file_10 file_11 file_12 file_13 file_14 file_15 file_16 file_17 file_18 file_19 file_20 file_21 file_22 file_23 file_24 file_25 mean_Val Counts\n",
|
| 591 |
+
"eyeglasses_100_ICP 49.177524 49.806584 0.0 138.441225 87.915898 120.186261 120.15116 123.894466 89.380514 73.315877 48.166215 6.039374 115.531124 77.997241 88.023412 43.083893 96.244094 117.313122 122.726607 32.79982 0.0 0.0 0.0 0.0 0.0 84.220758 19\n",
|
| 592 |
+
"eyeglasses_75_ICP 87.588102 87.952244 86.888912 44.465704 43.706854 46.803776 83.053832 86.934602 119.085669 118.664201 127.226752 89.041529 25.653662 76.212343 116.570636 110.974039 121.662971 92.39682 92.948404 45.527907 0.0 0.0 0.0 0.0 0.0 85.167948 20\n",
|
| 593 |
+
"eyeglasses_50_ICP 86.077398 85.515931 56.203467 39.658613 55.964432 85.659654 81.994906 86.296592 125.03123 120.92935 120.172806 93.555076 53.094512 52.153707 95.846049 82.616041 85.503566 120.062881 3.460667 90.995474 0.0 0.0 0.0 0.0 0.0 81.039618 20\n",
|
| 594 |
+
"eyeglasses_25_ICP 88.437185 91.31789 47.286129 42.121124 43.6699 44.493015 50.610979 88.285632 121.91528 121.430682 117.920522 89.293436 77.573422 45.97554 43.442207 84.104947 94.560476 119.785534 121.267815 94.969581 0.0 0.0 0.0 0.0 0.0 81.423065 20\n",
|
| 595 |
+
"eyeglasses_0_ICP 115.42656 129.844337 44.737188 42.579934 43.417908 83.214014 29.491695 123.077669 118.621548 117.292488 123.34339 114.669807 47.984773 94.707256 41.521857 42.982327 84.91653 120.417353 133.505992 81.56058 117.780668 115.444352 91.911517 62.998183 0.0 88.393664 24\n",
|
| 596 |
+
"eyeglasses_100_FAST ICP 83.259331 83.645555 0.0 138.441165 87.915654 120.192528 120.16229 123.927474 89.380702 73.341829 48.162544 6.053365 115.531124 77.998005 88.023421 43.043867 96.244094 117.31153 122.726611 32.816015 0.0 0.0 0.0 0.0 0.0 87.798795 19\n",
|
| 597 |
+
"eyeglasses_75_FAST ICP 87.593486 87.954906 86.888759 44.470837 43.710619 46.793999 83.054473 86.934602 119.080369 118.665765 127.226687 89.043718 25.65575 76.255087 116.570636 78.510193 121.658091 92.397359 92.949963 45.527629 0.0 0.0 0.0 0.0 0.0 83.547146 20\n",
|
| 598 |
+
"eyeglasses_50_FAST ICP 86.076355 49.681895 56.206844 39.659207 55.970009 85.6618 81.995082 86.297193 125.030481 120.940702 119.333142 93.555076 53.094512 52.15224 95.846028 82.647037 85.484721 120.062273 3.460217 90.995474 0.0 0.0 0.0 0.0 0.0 79.207514 20\n",
|
| 599 |
+
"eyeglasses_25_FAST ICP 88.436958 91.355311 47.275427 42.120321 43.676167 44.498545 50.610979 88.284004 121.9152 121.430384 117.920431 89.292972 77.581779 45.975742 43.446218 84.110273 94.560495 119.779661 121.248484 94.965692 0.0 0.0 0.0 0.0 0.0 81.424252 20\n",
|
| 600 |
+
"eyeglasses_0_FAST ICP 115.42656 129.844985 44.736931 42.580005 43.418869 83.216159 29.491718 123.058863 118.619352 117.288364 123.345308 114.66969 47.983268 136.483019 41.523866 43.033534 84.913399 120.413866 133.497748 81.560175 117.777147 115.442671 91.834041 63.008174 0.0 90.131988 24\n",
|
| 601 |
+
"eyeglasses_100_Robust ICP 86.706648 87.550122 0.0 163.059025 88.657162 122.168079 122.876288 124.316046 2.024247 46.971363 48.601167 16.502839 3.776909 86.079746 71.630269 163.053315 89.672166 85.70942 124.58407 39.207876 0.0 0.0 0.0 0.0 0.0 82.797198 19\n",
|
| 602 |
+
"eyeglasses_75_Robust ICP 151.41524 150.31776 1.81071 51.252233 46.856081 90.632477 87.766717 88.139124 120.170606 121.48972 121.421837 3.040087 0.633951 92.450141 85.345478 84.340541 124.259725 3.88856 47.404646 50.859014 0.0 0.0 0.0 0.0 0.0 76.174732 20\n",
|
| 603 |
+
"eyeglasses_50_Robust ICP 1.56464 0.751704 52.268807 42.884698 50.529565 88.666486 85.292645 85.090955 123.343195 123.688799 123.172204 11.937183 58.305113 46.758012 85.425053 82.88777 83.149764 121.562022 5.581632 108.902694 0.0 0.0 0.0 0.0 0.0 69.088147 20\n",
|
| 604 |
+
"eyeglasses_25_Robust ICP 45.422566 60.675765 44.991866 48.96448 47.907548 49.815408 86.961364 88.40623 123.518214 123.869926 120.11876 2.261041 4.281067 51.381574 49.784994 88.151235 109.011523 120.460096 123.827282 92.882345 0.0 0.0 0.0 0.0 0.0 74.134664 20\n",
|
| 605 |
+
"eyeglasses_0_Robust ICP 123.234351 121.445912 53.025448 43.83628 52.887759 87.003592 51.652271 136.030958 120.702919 119.634498 122.65242 112.316815 57.562732 134.230881 49.144408 47.889125 83.13567 121.126821 134.682975 88.377431 119.872811 117.8035 84.55052 26.765552 0.0 92.065235 24\n",
|
| 606 |
+
"eyeglasses_100_Sparse ICP 78.881009 79.132542 0.0 161.702818 88.473492 122.745418 119.394692 80.692248 18.86516 36.50056 45.538846 8.924245 107.029653 43.537395 88.652852 69.197221 92.27113 84.338137 95.198425 21.550318 0.0 0.0 0.0 0.0 0.0 75.927693 19\n",
|
| 607 |
+
"eyeglasses_75_Sparse ICP 2.760445 2.500606 5.701879 48.877519 42.806559 52.169592 87.087468 88.496797 118.09202 117.066411 4.664192 4.772974 5.393414 85.533386 88.041404 81.56911 115.74126 3.103941 47.332934 43.208846 0.0 0.0 0.0 0.0 0.0 52.246038 20\n",
|
| 608 |
+
"eyeglasses_50_Sparse ICP 2.662489 8.362011 57.043531 42.963002 49.176657 83.142893 86.212548 84.757268 91.590711 117.766474 118.944513 14.286125 57.324034 122.92677 83.14378 80.079108 95.31367 115.935388 4.063046 103.015609 0.0 0.0 0.0 0.0 0.0 70.935481 20\n",
|
| 609 |
+
"eyeglasses_25_Sparse ICP 129.711864 110.97634 49.67125 41.613502 42.037786 48.471344 87.542938 88.77044 123.064149 118.953458 115.809029 5.083772 3.368678 45.859338 79.97394 83.047998 111.944448 90.690014 111.13601 1.443622 0.0 0.0 0.0 0.0 0.0 74.458496 20\n",
|
| 610 |
+
"eyeglasses_0_Sparse ICP 115.189712 119.835916 60.675744 43.585937 47.503253 47.556993 93.675667 123.237753 120.54543 116.717029 115.945411 109.501177 44.063482 94.247467 41.225771 46.006457 79.146285 124.16092 136.315118 85.654087 117.709611 115.315982 87.317356 37.100023 0.0 88.426357 24\n",
|
| 611 |
+
"###################\n",
|
| 612 |
+
"eyeglasses_100_ICP 19\n",
|
| 613 |
+
"eyeglasses_75_ICP 20\n",
|
| 614 |
+
"eyeglasses_50_ICP 20\n",
|
| 615 |
+
"eyeglasses_25_ICP 20\n",
|
| 616 |
+
"eyeglasses_0_ICP 24\n",
|
| 617 |
+
"Name: Counts, dtype: int64\n"
|
| 618 |
+
]
|
| 619 |
+
}
|
| 620 |
+
],
|
| 621 |
+
"source": [
|
| 622 |
+
"\n",
|
| 623 |
+
"\n",
|
| 624 |
+
"df['Counts'] = (df != 0).sum(axis=1)-1\n",
|
| 625 |
+
"\n",
|
| 626 |
+
"# ๋ชจ๋ ํ/์ด์ ์ ๋ถ ๋ณด์ฌ์ค\n",
|
| 627 |
+
"pd.set_option('display.max_rows', None) # ํ ์ ์ฒด ์ถ๋ ฅ\n",
|
| 628 |
+
"pd.set_option('display.max_columns', None) # ์ด ์ ์ฒด ์ถ๋ ฅ\n",
|
| 629 |
+
"\n",
|
| 630 |
+
"# ๊ฐ ์ด์ ๋๋น ์ ํ ํด์ (๊ธด ๋ฌธ์์ด๋ ์๋ฆฌ์ง ์์)\n",
|
| 631 |
+
"pd.set_option('display.max_colwidth', None)\n",
|
| 632 |
+
"\n",
|
| 633 |
+
"# ํ๋ฉด ๋๋น์ ๋ฐ๋ผ ์ค๋ฐ๏ฟฝ๏ฟฝ๏ฟฝ์ ํ ์ง ๋ง์ง\n",
|
| 634 |
+
"pd.set_option('display.width', None) # None์ด๋ฉด ์๋์ผ๋ก ์ฝ์ ๋๋น๋ฅผ ์ฌ์ฉ\n",
|
| 635 |
+
"pd.set_option('display.expand_frame_repr', False) # True๋ฉด ์ค๋ฐ๊ฟ ํ์ฉ, False๋ฉด ํ ์ค๋ก ์ถ๋ ฅ ์๋\n",
|
| 636 |
+
"\n",
|
| 637 |
+
"print(df)\n",
|
| 638 |
+
"\n",
|
| 639 |
+
"\n",
|
| 640 |
+
"\n",
|
| 641 |
+
"sliced_data = df.loc['eyeglasses_100_ICP':'eyeglasses_0_ICP', 'Counts']\n",
|
| 642 |
+
"print(f\"###################\\n{sliced_data}\")\n",
|
| 643 |
+
"sliced_data.to_csv(f'{category}_data_num.csv', index=True)"
|
| 644 |
+
]
|
| 645 |
+
}
|
| 646 |
+
],
|
| 647 |
+
"metadata": {
|
| 648 |
+
"kernelspec": {
|
| 649 |
+
"display_name": "icp",
|
| 650 |
+
"language": "python",
|
| 651 |
+
"name": "python3"
|
| 652 |
+
},
|
| 653 |
+
"language_info": {
|
| 654 |
+
"codemirror_mode": {
|
| 655 |
+
"name": "ipython",
|
| 656 |
+
"version": 3
|
| 657 |
+
},
|
| 658 |
+
"file_extension": ".py",
|
| 659 |
+
"mimetype": "text/x-python",
|
| 660 |
+
"name": "python",
|
| 661 |
+
"nbconvert_exporter": "python",
|
| 662 |
+
"pygments_lexer": "ipython3",
|
| 663 |
+
"version": "3.10.19"
|
| 664 |
+
}
|
| 665 |
+
},
|
| 666 |
+
"nbformat": 4,
|
| 667 |
+
"nbformat_minor": 5
|
| 668 |
+
}
|
data/glasses/eyeglasses.csv
ADDED
|
@@ -0,0 +1,25 @@
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
| 1 |
+
,file_1,file_2,file_3,file_4,file_5,file_6,file_7,file_8,file_9,file_10,file_11,file_12,file_13,file_14,file_15,file_16,file_17,file_18,file_19,file_20,file_21,file_22,file_23,file_24,file_25,mean_Val
|
| 2 |
+
eyeglasses_100_ICP,49.17752379774512,49.806583865125546,0.0,138.44122465194937,87.91589778921198,120.18626136199036,120.15115993030028,123.89446624334863,89.3805137041566,73.31587653716802,48.16621452861571,6.039374146491476,115.53112399921835,77.99724092235948,88.02341225312288,43.08389328755267,96.24409415747118,117.31312215505255,122.72660693013857,32.799819643652135,0.0,0.0,0.0,0.0,0.0,84.22075841603531
|
| 3 |
+
eyeglasses_75_ICP,87.58810229011992,87.95224423257785,86.88891178891647,44.465703715949644,43.70685366708641,46.80377638552133,83.05383213012615,86.93460229603807,119.08566924703662,118.6642014464694,127.22675215273469,89.04152887828387,25.653661867266834,76.2123426514258,116.57063585495702,110.9740394162759,121.66297112409931,92.39681997996807,92.94840434116387,45.52790692470302,0.0,0.0,0.0,0.0,0.0,85.16794801953601
|
| 4 |
+
eyeglasses_50_ICP,86.07739781846578,85.51593128260045,56.20346741580735,39.658613317852165,55.96443155010566,85.65965410227459,81.99490633545153,86.29659242016291,125.03122971595887,120.92935006316522,120.17280593196269,93.55507599145504,53.09451237091268,52.15370742689203,95.84604948469631,82.61604145711769,85.50356577255059,120.0628808637047,3.4606666948134284,90.99547398802572,0.0,0.0,0.0,0.0,0.0,81.03961770019878
|
| 5 |
+
eyeglasses_25_ICP,88.43718490106615,91.31788965534798,47.28612899442477,42.12112366585242,43.669900073262006,44.49301503853161,50.61097931810664,88.28563224312408,121.91527963511011,121.43068155173343,117.92052154560032,89.29343565174771,77.57342184054598,45.97554012437388,43.44220674545128,84.10494650962362,94.56047600196041,119.78553412438167,121.2678151521993,94.96958147891239,0.0,0.0,0.0,0.0,0.0,81.4230647125678
|
| 6 |
+
eyeglasses_0_ICP,115.42655952774327,129.84433685670615,44.737188308064496,42.57993437851364,43.41790845717933,83.21401396984572,29.491695488730294,123.0776693891887,118.62154798266184,117.29248823060789,123.34338990239955,114.66980659721456,47.98477334193737,94.7072555599628,41.521857356262885,42.98232678008477,84.91652989595941,120.41735290776046,133.505991955395,81.56057963337318,117.78066764603615,115.44435245989825,91.91151697400441,62.998182525680775,0.0,88.39366358855045
|
| 7 |
+
eyeglasses_100_FAST ICP,83.25933052880839,83.6455549234024,0.0,138.441164617354,87.91565392206297,120.19252811407915,120.16229024965043,123.9274740368411,89.38070150013246,73.34182900709585,48.162544052730524,6.053365129834639,115.53112399921835,77.99800519479543,88.0234213204532,43.043867230841535,96.24409415789599,117.31152962984014,122.72661087779578,32.81601486837901,0.0,0.0,0.0,0.0,0.0,87.79879491374795
|
| 8 |
+
eyeglasses_75_FAST ICP,87.59348591652028,87.95490632510855,86.88875913091353,44.470836569747924,43.71061852053137,46.793998928083326,83.05447322760746,86.9346022982755,119.08036872456668,118.66576501373137,127.2266874783626,89.04371831256802,25.655750332101828,76.2550865148604,116.57063585495702,78.51019338414193,121.65809080630324,92.39735930197293,92.94996323126686,45.527628716770565,0.0,0.0,0.0,0.0,0.0,83.54714642941956
|
| 9 |
+
eyeglasses_50_FAST ICP,86.0763549694485,49.68189483541356,56.20684361842143,39.659207297431045,55.97000886332607,85.66180046090125,81.99508192645266,86.29719309174237,125.03048057649723,120.94070231940039,119.33314210743859,93.55507599220287,53.09451237091268,52.152240233865484,95.84602760694249,82.64703660623049,85.4847207515356,120.06227292950956,3.4602165949774615,90.99547399152466,0.0,0.0,0.0,0.0,0.0,79.20751435720872
|
| 10 |
+
eyeglasses_25_FAST ICP,88.43695786583005,91.355310607797,47.27542703372179,42.120320913797464,43.676167099741996,44.4985451099317,50.61097931810664,88.28400421737079,121.91519957430303,121.43038402556962,117.9204313500966,89.29297217263692,77.58177854517152,45.975742439891036,43.446218031717024,84.11027273071637,94.56049542373195,119.7796612581059,121.24848419679245,94.96569231273946,0.0,0.0,0.0,0.0,0.0,81.42425221138846
|
| 11 |
+
eyeglasses_0_FAST ICP,115.42655952850498,129.84498477775207,44.73693072210608,42.58000493333838,43.4188689874817,83.21615895179785,29.491718184127247,123.05886311431154,118.61935249506642,117.28836436147213,123.34530824484305,114.66968992422467,47.98326775052566,136.483018755102,41.52386553001874,43.033533759041056,84.91339922070132,120.41386622508821,133.49774824244366,81.56017477839804,117.7771468485543,115.44267056618092,91.83404140006382,63.00817377845625,0.0,90.13198796165
|
| 12 |
+
eyeglasses_100_Robust ICP,86.7066479705238,87.55012209547141,0.0,163.05902509452125,88.65716154166327,122.1680785583144,122.87628842880657,124.31604559750879,2.024246520883091,46.97136293004783,48.60116705472443,16.50283887635228,3.776908897688169,86.07974635425529,71.63026948684329,163.0533152664762,89.67216607377028,85.70942015233359,124.5840695676283,39.20787567956067,0.0,0.0,0.0,0.0,0.0,82.79719769196699
|
| 13 |
+
eyeglasses_75_Robust ICP,151.41523960749677,150.31776014678826,1.8107102879669095,51.252232982779475,46.85608080337175,90.63247674647141,87.76671691334438,88.13912441714463,120.1706060704092,121.48972047501763,121.42183729775677,3.0400867813622217,0.6339512365285208,92.45014091630793,85.34547804290855,84.34054076763374,124.2597246884901,3.8885597233421216,47.404646252979504,50.859014378653406,0.0,0.0,0.0,0.0,0.0,76.17473242683766
|
| 14 |
+
eyeglasses_50_Robust ICP,1.5646396083486813,0.7517041733763252,52.26880699234589,42.88469790905038,50.52956528672817,88.66648573278594,85.29264509936135,85.09095482747925,123.34319536178069,123.68879936065457,123.17220367511057,11.93718347991646,58.305112979828294,46.75801192489096,85.42505342317124,82.88776957004617,83.14976387682698,121.56202184115082,5.581632177525955,108.90269359538092,0.0,0.0,0.0,0.0,0.0,69.08814704478799
|
| 15 |
+
eyeglasses_25_Robust ICP,45.422566455623766,60.67576487285533,44.99186608199595,48.96448042615409,47.907548103137344,49.81540798069544,86.96136381912397,88.40622994799939,123.51821414252255,123.86992634239387,120.11875982226809,2.261041129221705,4.281067336092791,51.38157440768348,49.7849937440697,88.15123451007855,109.0115226471195,120.46009571560536,123.82728232097722,92.88234463063829,0.0,0.0,0.0,0.0,0.0,74.13466422181281
|
| 16 |
+
eyeglasses_0_Robust ICP,123.23435121891893,121.44591175895162,53.0254481266743,43.83627999991385,52.8877585552124,87.0035916828804,51.652271230158135,136.03095760976038,120.70291936078122,119.6344976909571,122.65242013978416,112.31681510033143,57.56273207066243,134.23088063506245,49.14440770442545,47.88912496228431,83.13567035357237,121.12682087418904,134.68297473868637,88.37743123125612,119.87281144471238,117.80349960266126,84.550520464908,26.76555227696616,0.0,92.06523536807127
|
| 17 |
+
eyeglasses_100_Sparse ICP,78.8810086912939,79.1325418477661,0.0,161.7028175083206,88.47349194782349,122.74541812265606,119.39469173428428,80.69224845877164,18.865159582534055,36.500560185094635,45.53884624027995,8.924245168251684,107.0296528873712,43.537395080498875,88.65285230062719,69.19722052009814,92.27113006058877,84.33813723792366,95.19842508372773,21.550317946954976,0.0,0.0,0.0,0.0,0.0,75.92769266341405
|
| 18 |
+
eyeglasses_75_Sparse ICP,2.7604454612085885,2.500605749267673,5.7018790212237525,48.87751943719046,42.806559404136586,52.169592375551765,87.08746768754162,88.49679658409723,118.09202033677094,117.06641055025895,4.6641921780474185,4.772973974434927,5.393413692206664,85.53338617197232,88.0414035897964,81.56911019785555,115.74125981030363,3.1039412157518758,47.33293357064003,43.20884558633797,0.0,0.0,0.0,0.0,0.0,52.24603782972972
|
| 19 |
+
eyeglasses_50_Sparse ICP,2.6624888716656145,8.362011269583268,57.043530879901965,42.9630016732886,49.17665687669085,83.1428928978546,86.21254803240139,84.75726782618939,91.5907108476903,117.7664744704949,118.94451314930882,14.286125464297012,57.324034387329306,122.92677018158575,83.1437795547307,80.07910783091864,95.3136696136579,115.93538772329832,4.063045598805345,103.01560861833316,0.0,0.0,0.0,0.0,0.0,70.93548128840129
|
| 20 |
+
eyeglasses_25_Sparse ICP,129.7118638101871,110.97633992402636,49.67124987100319,41.61350170549567,42.03778634019483,48.47134374394434,87.54293806970988,88.77044024724003,123.06414903305127,118.95345840022419,115.80902943668991,5.0837724121877255,3.3686777745541314,45.859338329893205,79.97394025028154,83.04799797058858,111.94444778050168,90.69001417810635,111.13600985242536,1.4436218850590348,0.0,0.0,0.0,0.0,0.0,74.45849605076822
|
| 21 |
+
eyeglasses_0_Sparse ICP,115.18971151856663,119.83591584311813,60.6757444125914,43.585937207755016,47.50325334329502,47.55699323350378,93.67566694135215,123.23775294522568,120.54542957227446,116.71702919691299,115.94541069002054,109.50117689362996,44.063481639195906,94.24746659642413,41.22577053843914,46.0064570088857,79.14628546589906,124.16091995053786,136.3151176040749,85.65408696480347,117.70961052333362,115.31598199207728,87.3173560231305,37.10002321073114,0.0,88.42635747149076
|
| 22 |
+
ICP,85.34135366702805,88.8873971784716,47.02313930144262,61.453319946023456,54.934998307369085,76.07134417163272,73.06051464054298,101.69779251837248,114.80684805698482,110.32651956582879,107.36593681226259,78.51984425303854,63.96749868397624,69.40921733700279,77.08083233889809,72.75224949013092,96.57752739040818,113.9951420061735,94.78189701474203,69.1706723337333,23.55613352920723,23.08887049197965,18.382303394800882,12.599636505136155,0.0,84.04901048737767
|
| 23 |
+
FAST ICP,92.15853776182243,88.49653029389472,47.02159210103257,61.454306866333766,54.938263478628826,76.07260631295865,73.06290858118888,101.70042735170826,114.80522057411315,110.33340894545385,107.19762264669427,78.52296430629343,63.969286599586006,77.77281862770288,77.08203366881769,66.26898074219427,96.57216007203361,113.99293786890334,94.77660462865524,69.17299693356236,23.555429369710858,23.088534113236186,18.366808280012766,12.60163475569125,0.0,84.42193917468293
|
| 24 |
+
FAST AND ROBUST ICP,81.6686889721824,84.1482526094886,30.419366297796607,69.9993432824838,57.367622858022585,87.65720814022953,86.90985709815888,104.39666247997847,97.95183629127534,107.13086135981419,107.1932775979288,29.21159307343682,24.911954504160043,82.18007084764001,68.26604048028364,93.2643970153038,97.84576952795584,90.54938366132419,87.21612101155947,76.04587190309789,23.974562288942476,23.56069992053225,16.9101040929816,5.3531104553932325,0.0,78.85199535069535
|
| 25 |
+
SPARSE ICP,65.84110367058437,64.16148292675231,34.61848083694406,67.74855550641007,53.99954958242815,70.8172480747021,94.78266249305787,93.1909012123048,94.4314938744642,101.40078656059714,80.18039833886932,28.513658782560263,43.43585207613144,78.42087127207486,76.20754924677499,71.97997870566932,98.8833585461902,83.64568006112361,78.80910634193467,50.97449620029772,23.541922104666725,23.063196398415457,17.4634712046261,7.420004642146227,0.0,72.39881306076082
|
data/glasses/eyeglasses_data_num.csv
ADDED
|
@@ -0,0 +1,6 @@
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|
| 1 |
+
,Counts
|
| 2 |
+
eyeglasses_100_ICP,19
|
| 3 |
+
eyeglasses_75_ICP,20
|
| 4 |
+
eyeglasses_50_ICP,20
|
| 5 |
+
eyeglasses_25_ICP,20
|
| 6 |
+
eyeglasses_0_ICP,24
|
data/glasses/filename.txt
ADDED
|
@@ -0,0 +1 @@
|
|
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|
| 1 |
+
100_7
|
data/glasses/filter_tea .ipynb
ADDED
|
@@ -0,0 +1,474 @@
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|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 63,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"import open3d as o3d\n",
|
| 10 |
+
"import numpy as np\n",
|
| 11 |
+
"\n",
|
| 12 |
+
"GT = False\n",
|
| 13 |
+
"if GT==True:\n",
|
| 14 |
+
" mesh = o3d.io.read_triangle_mesh(\"./source.stl\")\n",
|
| 15 |
+
" pointcloud = mesh.sample_points_poisson_disk(50000)\n",
|
| 16 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 17 |
+
" mesh.compute_vertex_normals()\n",
|
| 18 |
+
" mesh_triangles = np.asarray(mesh.triangles)\n",
|
| 19 |
+
" vertex_positions = np.asarray(mesh.vertices)\n",
|
| 20 |
+
" triangle_normals = np.asarray(mesh.triangle_normals)\n",
|
| 21 |
+
" # ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ\n",
|
| 22 |
+
" centroid = mesh.get_center()\n",
|
| 23 |
+
"\n",
|
| 24 |
+
" \n",
|
| 25 |
+
" # n_points๋ ์ํ๋งํ ํฌ์ธํธ ๊ฐ์\n",
|
| 26 |
+
" pcd = mesh.sample_points_uniformly(number_of_points=50000)\n",
|
| 27 |
+
" # ๊ฒฐ๊ณผ ์๊ฐํ\n",
|
| 28 |
+
" o3d.visualization.draw_geometries([pcd,coord_frame ])\n",
|
| 29 |
+
"\n",
|
| 30 |
+
"\n",
|
| 31 |
+
"\n",
|
| 32 |
+
"\n",
|
| 33 |
+
" pcd_array = np.asarray(pcd.points)"
|
| 34 |
+
]
|
| 35 |
+
},
|
| 36 |
+
{
|
| 37 |
+
"cell_type": "code",
|
| 38 |
+
"execution_count": 70,
|
| 39 |
+
"metadata": {},
|
| 40 |
+
"outputs": [
|
| 41 |
+
{
|
| 42 |
+
"name": "stdout",
|
| 43 |
+
"output_type": "stream",
|
| 44 |
+
"text": [
|
| 45 |
+
"0_23\n",
|
| 46 |
+
"(896000, 3)\n"
|
| 47 |
+
]
|
| 48 |
+
}
|
| 49 |
+
],
|
| 50 |
+
"source": [
|
| 51 |
+
"import open3d as o3d\n",
|
| 52 |
+
"import numpy as np\n",
|
| 53 |
+
"GT = False\n",
|
| 54 |
+
"file_names = []\n",
|
| 55 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 56 |
+
" for line in f:\n",
|
| 57 |
+
" file_names.append(line.strip())\n",
|
| 58 |
+
"filename = file_names[0]\n",
|
| 59 |
+
"print(filename)\n",
|
| 60 |
+
"\n",
|
| 61 |
+
"\n",
|
| 62 |
+
"if GT==False:\n",
|
| 63 |
+
"\n",
|
| 64 |
+
" if GT: ply_path = \"gt_filtered.ply\"\n",
|
| 65 |
+
" else: ply_path = f\"./dataset/{filename}.ply\"\n",
|
| 66 |
+
" \n",
|
| 67 |
+
" pcd = o3d.io.read_point_cloud(ply_path)\n",
|
| 68 |
+
"\n",
|
| 69 |
+
"\n",
|
| 70 |
+
"\n",
|
| 71 |
+
"pcd_array = np.asarray(pcd.points)\n",
|
| 72 |
+
"print(pcd_array.shape)\n",
|
| 73 |
+
"\n",
|
| 74 |
+
"coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 75 |
+
"o3d.visualization.draw_geometries([pcd, coord_frame])"
|
| 76 |
+
]
|
| 77 |
+
},
|
| 78 |
+
{
|
| 79 |
+
"cell_type": "code",
|
| 80 |
+
"execution_count": 65,
|
| 81 |
+
"metadata": {},
|
| 82 |
+
"outputs": [
|
| 83 |
+
{
|
| 84 |
+
"name": "stdout",
|
| 85 |
+
"output_type": "stream",
|
| 86 |
+
"text": [
|
| 87 |
+
"[ 17.76671151 -35.99837303 579.24060685]\n"
|
| 88 |
+
]
|
| 89 |
+
}
|
| 90 |
+
],
|
| 91 |
+
"source": [
|
| 92 |
+
"if GT==False:\n",
|
| 93 |
+
" \n",
|
| 94 |
+
" new_pcd_array = np.unique(pcd_array, axis=0)\n",
|
| 95 |
+
"\n",
|
| 96 |
+
" # new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 580]\n",
|
| 97 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 1000]\n",
|
| 98 |
+
" # new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100] \n",
|
| 99 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -1000] #diagonal\n",
|
| 100 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 120]\n",
|
| 101 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 0] > -1000]\n",
|
| 102 |
+
" new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 1000] #diagonal\n",
|
| 103 |
+
" print(np.mean(new_pcd_array, axis=0))\n",
|
| 104 |
+
"\n",
|
| 105 |
+
" new_pcd = o3d.geometry.PointCloud()\n",
|
| 106 |
+
" new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)\n",
|
| 107 |
+
"\n",
|
| 108 |
+
" theta = np.radians(90)\n",
|
| 109 |
+
" # theta = np.radians(-90)\n",
|
| 110 |
+
"\n",
|
| 111 |
+
"\n",
|
| 112 |
+
" rotation_y = np.array([\n",
|
| 113 |
+
" [np.cos(theta), 0, np.sin(theta)],\n",
|
| 114 |
+
" [0, 1, 0 ],\n",
|
| 115 |
+
" [-np.sin(theta),0, np.cos(theta)]\n",
|
| 116 |
+
" ])\n",
|
| 117 |
+
"\n",
|
| 118 |
+
" rotation_x = np.array([\n",
|
| 119 |
+
" [1, 0, 0 ],\n",
|
| 120 |
+
" [0, np.cos(theta), -np.sin(theta)],\n",
|
| 121 |
+
" [0, np.sin(theta), np.cos(theta)]\n",
|
| 122 |
+
"\n",
|
| 123 |
+
" ])\n",
|
| 124 |
+
" rotation_z = np.array([\n",
|
| 125 |
+
" [np.cos(theta), -np.sin(theta), 0],\n",
|
| 126 |
+
" [np.sin(theta), np.cos(theta), 0],\n",
|
| 127 |
+
" [0, 0, 1]\n",
|
| 128 |
+
"\n",
|
| 129 |
+
" ])\n",
|
| 130 |
+
"\n",
|
| 131 |
+
"\n",
|
| 132 |
+
" new_pcd_array = np.asarray(new_pcd.points)\n",
|
| 133 |
+
"\n",
|
| 134 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 135 |
+
" o3d.visualization.draw_geometries([new_pcd, coord_frame])"
|
| 136 |
+
]
|
| 137 |
+
},
|
| 138 |
+
{
|
| 139 |
+
"cell_type": "markdown",
|
| 140 |
+
"metadata": {},
|
| 141 |
+
"source": [
|
| 142 |
+
"## Delete ground plane "
|
| 143 |
+
]
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"cell_type": "code",
|
| 147 |
+
"execution_count": 66,
|
| 148 |
+
"metadata": {},
|
| 149 |
+
"outputs": [
|
| 150 |
+
{
|
| 151 |
+
"name": "stdout",
|
| 152 |
+
"output_type": "stream",
|
| 153 |
+
"text": [
|
| 154 |
+
"Plane equation: -0.01x + -0.00y + 1.00z + -579.39 = 0\n"
|
| 155 |
+
]
|
| 156 |
+
}
|
| 157 |
+
],
|
| 158 |
+
"source": [
|
| 159 |
+
" \n",
|
| 160 |
+
"if GT==False:\n",
|
| 161 |
+
" \n",
|
| 162 |
+
" plane_model, inliers = new_pcd.segment_plane(distance_threshold=2,\n",
|
| 163 |
+
" ransac_n=100,\n",
|
| 164 |
+
" num_iterations=1000)\n",
|
| 165 |
+
" [a, b, c, d] = plane_model\n",
|
| 166 |
+
" print(f\"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0\")\n",
|
| 167 |
+
" \n",
|
| 168 |
+
" \n",
|
| 169 |
+
" \n",
|
| 170 |
+
" inlier_cloud = new_pcd.select_by_index(inliers)\n",
|
| 171 |
+
" inlier_cloud.paint_uniform_color([1.0, 0, 1.0])\n",
|
| 172 |
+
" outlier_cloud = new_pcd.select_by_index(inliers, invert=True)\n",
|
| 173 |
+
" o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],\n",
|
| 174 |
+
" zoom=0.8,\n",
|
| 175 |
+
" front=[-0.4999, -0.1659, -0.8499],\n",
|
| 176 |
+
" lookat=[2.1813, 2.0619, 2.0999],\n",
|
| 177 |
+
" up=[0.1204, -0.9852, 0.1215])\n",
|
| 178 |
+
" \n",
|
| 179 |
+
" new_pcd = outlier_cloud\n",
|
| 180 |
+
"\n",
|
| 181 |
+
" new_pcd_array = np.asarray(new_pcd.points)\n",
|
| 182 |
+
" \n",
|
| 183 |
+
" "
|
| 184 |
+
]
|
| 185 |
+
},
|
| 186 |
+
{
|
| 187 |
+
"cell_type": "markdown",
|
| 188 |
+
"metadata": {},
|
| 189 |
+
"source": [
|
| 190 |
+
"### Changing the source position \"gt_filtered\"\n"
|
| 191 |
+
]
|
| 192 |
+
},
|
| 193 |
+
{
|
| 194 |
+
"cell_type": "code",
|
| 195 |
+
"execution_count": 67,
|
| 196 |
+
"metadata": {},
|
| 197 |
+
"outputs": [],
|
| 198 |
+
"source": [
|
| 199 |
+
"\n",
|
| 200 |
+
"CHECK_PERTURB = GT\n",
|
| 201 |
+
"\n",
|
| 202 |
+
"def random_rotation_matrix():\n",
|
| 203 |
+
" \"\"\"\n",
|
| 204 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 205 |
+
" \n",
|
| 206 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 207 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 208 |
+
" 2. Generate a random angle\n",
|
| 209 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 210 |
+
" \n",
|
| 211 |
+
" Returns:\n",
|
| 212 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 213 |
+
" \"\"\"\n",
|
| 214 |
+
" ## for ground target\n",
|
| 215 |
+
" # Generate random angle ฯ/2\n",
|
| 216 |
+
" theta = 0\n",
|
| 217 |
+
"\n",
|
| 218 |
+
" \n",
|
| 219 |
+
" # axis is -y\n",
|
| 220 |
+
" axis = np.array([\n",
|
| 221 |
+
" 0,\n",
|
| 222 |
+
" 1,\n",
|
| 223 |
+
" 0,\n",
|
| 224 |
+
" ])\n",
|
| 225 |
+
" \n",
|
| 226 |
+
" # for lying target\n",
|
| 227 |
+
" # theta will be pi/2\n",
|
| 228 |
+
" # theta = -np.pi/2\n",
|
| 229 |
+
" # axis = np.array([\n",
|
| 230 |
+
" # 1,\n",
|
| 231 |
+
" # 0,\n",
|
| 232 |
+
" # 0,\n",
|
| 233 |
+
" # ])\n",
|
| 234 |
+
" \n",
|
| 235 |
+
"\n",
|
| 236 |
+
"\n",
|
| 237 |
+
"\n",
|
| 238 |
+
" # Normalize to ensure it's a unit vector\n",
|
| 239 |
+
" axis = axis / np.linalg.norm(axis)\n",
|
| 240 |
+
" \n",
|
| 241 |
+
"\n",
|
| 242 |
+
"\n",
|
| 243 |
+
" # Create the cross-product matrix K skew-symmetric\n",
|
| 244 |
+
" K = np.array([\n",
|
| 245 |
+
" [0, -axis[2], axis[1]],\n",
|
| 246 |
+
" [axis[2], 0, -axis[0]],\n",
|
| 247 |
+
" [-axis[1], axis[0], 0]\n",
|
| 248 |
+
" ])\n",
|
| 249 |
+
" \n",
|
| 250 |
+
" # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 251 |
+
" R = (np.eye(3) + \n",
|
| 252 |
+
" np.sin(theta) * K + \n",
|
| 253 |
+
" (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 254 |
+
" \n",
|
| 255 |
+
" return R\n",
|
| 256 |
+
"\n",
|
| 257 |
+
"if CHECK_PERTURB:\n",
|
| 258 |
+
" R_pert = random_rotation_matrix()\n",
|
| 259 |
+
" print(R_pert)\n",
|
| 260 |
+
" t_pert = np.array([\n",
|
| 261 |
+
" 0,\n",
|
| 262 |
+
" 0,\n",
|
| 263 |
+
" 0\n",
|
| 264 |
+
" ])\n",
|
| 265 |
+
"\n",
|
| 266 |
+
" \n",
|
| 267 |
+
" perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T\n",
|
| 268 |
+
"\n",
|
| 269 |
+
"\n",
|
| 270 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 271 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 272 |
+
" coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 273 |
+
" o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])"
|
| 274 |
+
]
|
| 275 |
+
},
|
| 276 |
+
{
|
| 277 |
+
"cell_type": "markdown",
|
| 278 |
+
"metadata": {},
|
| 279 |
+
"source": [
|
| 280 |
+
"### Rotate randomly in Target \"noisy filtered\""
|
| 281 |
+
]
|
| 282 |
+
},
|
| 283 |
+
{
|
| 284 |
+
"cell_type": "code",
|
| 285 |
+
"execution_count": 68,
|
| 286 |
+
"metadata": {},
|
| 287 |
+
"outputs": [],
|
| 288 |
+
"source": [
|
| 289 |
+
"CHECK_PERTURB = not GT\n",
|
| 290 |
+
"\n",
|
| 291 |
+
"def random_rotation_matrix():\n",
|
| 292 |
+
" \"\"\"\n",
|
| 293 |
+
" Generate a random 3x3 rotation matrix (SO(3) matrix).\n",
|
| 294 |
+
" \n",
|
| 295 |
+
" Uses the method described by James Arvo in \"Fast Random Rotation Matrices\" (1992):\n",
|
| 296 |
+
" 1. Generate a random unit vector for rotation axis\n",
|
| 297 |
+
" 2. Generate a random angle\n",
|
| 298 |
+
" 3. Create rotation matrix using Rodriguez rotation formula\n",
|
| 299 |
+
" \n",
|
| 300 |
+
" Returns:\n",
|
| 301 |
+
" numpy.ndarray: A 3x3 random rotation matrix\n",
|
| 302 |
+
" \"\"\"\n",
|
| 303 |
+
"# # Generate random angle between 0 and 2ฯ\n",
|
| 304 |
+
"# theta = np.random.uniform(0, 2 * np.pi)/4\n",
|
| 305 |
+
" \n",
|
| 306 |
+
"\n",
|
| 307 |
+
"# # Generate random unit vector for rotation axis\n",
|
| 308 |
+
"# phi = np.random.uniform(0, 2 * np.pi)/3\n",
|
| 309 |
+
"# cos_theta = np.random.uniform(-1, 1)/5\n",
|
| 310 |
+
"# sin_theta = np.sqrt(1 - cos_theta**2)\n",
|
| 311 |
+
" \n",
|
| 312 |
+
"# axis = np.array([\n",
|
| 313 |
+
"# sin_theta * np.cos(phi),\n",
|
| 314 |
+
"# sin_theta * np.sin(phi),\n",
|
| 315 |
+
"# cos_theta\n",
|
| 316 |
+
"# ])\n",
|
| 317 |
+
" \n",
|
| 318 |
+
"# # Normalize to ensure it's a unit vector\n",
|
| 319 |
+
"# axis = axis / np.linalg.norm(axis)\n",
|
| 320 |
+
" \n",
|
| 321 |
+
"\n",
|
| 322 |
+
"\n",
|
| 323 |
+
"# # Create the cross-product matrix K skew-symmetric\n",
|
| 324 |
+
"# K = np.array([\n",
|
| 325 |
+
"# [0, -axis[2], axis[1]],\n",
|
| 326 |
+
"# [axis[2], 0, -axis[0]],\n",
|
| 327 |
+
"# [-axis[1], axis[0], 0]\n",
|
| 328 |
+
"# ])\n",
|
| 329 |
+
" \n",
|
| 330 |
+
"# # Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ\n",
|
| 331 |
+
"# R = (np.eye(3) + \n",
|
| 332 |
+
"# np.sin(theta) * K + \n",
|
| 333 |
+
"# (1 - np.cos(theta)) * np.dot(K, K))\n",
|
| 334 |
+
" \n",
|
| 335 |
+
"# return R\n",
|
| 336 |
+
"\n",
|
| 337 |
+
"if CHECK_PERTURB:\n",
|
| 338 |
+
" # R_pert = random_rotation_matrix()\n",
|
| 339 |
+
" # print(R_pert)\n",
|
| 340 |
+
" # t_pert = np.random.rand(3, 1)*3 #* 10\n",
|
| 341 |
+
"\n",
|
| 342 |
+
" \n",
|
| 343 |
+
" # perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T\n",
|
| 344 |
+
" perturbed_pcd_array = new_pcd_array\n",
|
| 345 |
+
" perturbed_pcd = o3d.geometry.PointCloud()\n",
|
| 346 |
+
" perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)\n",
|
| 347 |
+
" \n",
|
| 348 |
+
" # # ๊ฐ์ฒด์ ์ค์ฌ์ (0, 0, 0)์ผ๋ก ๋ฐ๋ก ์ด๋\n",
|
| 349 |
+
" # perturbed_pcd.translate((0, 0, 0), relative=False)\n",
|
| 350 |
+
" # perturbed_pcd_array = np.asarray(perturbed_pcd.points)\n",
|
| 351 |
+
" # coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 352 |
+
"\n",
|
| 353 |
+
"\n",
|
| 354 |
+
"\n",
|
| 355 |
+
"\n",
|
| 356 |
+
" o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])\n"
|
| 357 |
+
]
|
| 358 |
+
},
|
| 359 |
+
{
|
| 360 |
+
"cell_type": "code",
|
| 361 |
+
"execution_count": 69,
|
| 362 |
+
"metadata": {},
|
| 363 |
+
"outputs": [
|
| 364 |
+
{
|
| 365 |
+
"name": "stdout",
|
| 366 |
+
"output_type": "stream",
|
| 367 |
+
"text": [
|
| 368 |
+
"True\n"
|
| 369 |
+
]
|
| 370 |
+
}
|
| 371 |
+
],
|
| 372 |
+
"source": [
|
| 373 |
+
"def write_ply(points, output_path):\n",
|
| 374 |
+
" \"\"\"\n",
|
| 375 |
+
" Write points and parameters to a PLY file\n",
|
| 376 |
+
" \n",
|
| 377 |
+
" Parameters:\n",
|
| 378 |
+
" points: numpy array of shape (N, 3) containing point coordinates\n",
|
| 379 |
+
" output_path: path to save the PLY file\n",
|
| 380 |
+
" \"\"\"\n",
|
| 381 |
+
" with open(output_path, 'w') as f:\n",
|
| 382 |
+
" # Write header\n",
|
| 383 |
+
" f.write(\"ply\\n\")\n",
|
| 384 |
+
" f.write(\"format ascii 1.0\\n\")\n",
|
| 385 |
+
" \n",
|
| 386 |
+
" # Write vertex element\n",
|
| 387 |
+
" f.write(f\"element vertex {len(points)}\\n\")\n",
|
| 388 |
+
" f.write(\"property float x\\n\")\n",
|
| 389 |
+
" f.write(\"property float y\\n\")\n",
|
| 390 |
+
" f.write(\"property float z\\n\")\n",
|
| 391 |
+
" \n",
|
| 392 |
+
" # Write camera element\n",
|
| 393 |
+
" f.write(\"element camera 1\\n\")\n",
|
| 394 |
+
" f.write(\"property float view_px\\n\")\n",
|
| 395 |
+
" f.write(\"property float view_py\\n\")\n",
|
| 396 |
+
" f.write(\"property float view_pz\\n\")\n",
|
| 397 |
+
" f.write(\"property float x_axisx\\n\")\n",
|
| 398 |
+
" f.write(\"property float x_axisy\\n\")\n",
|
| 399 |
+
" f.write(\"property float x_axisz\\n\")\n",
|
| 400 |
+
" f.write(\"property float y_axisx\\n\")\n",
|
| 401 |
+
" f.write(\"property float y_axisy\\n\")\n",
|
| 402 |
+
" f.write(\"property float y_axisz\\n\")\n",
|
| 403 |
+
" f.write(\"property float z_axisx\\n\")\n",
|
| 404 |
+
" f.write(\"property float z_axisy\\n\")\n",
|
| 405 |
+
" f.write(\"property float z_axisz\\n\")\n",
|
| 406 |
+
" \n",
|
| 407 |
+
" # Write phoxi frame parameters\n",
|
| 408 |
+
" f.write(\"element phoxi_frame_params 1\\n\")\n",
|
| 409 |
+
" f.write(\"property uint32 frame_width\\n\")\n",
|
| 410 |
+
" f.write(\"property uint32 frame_height\\n\")\n",
|
| 411 |
+
" f.write(\"property uint32 frame_index\\n\")\n",
|
| 412 |
+
" f.write(\"property float frame_start_time\\n\")\n",
|
| 413 |
+
" f.write(\"property float frame_duration\\n\")\n",
|
| 414 |
+
" f.write(\"property float frame_computation_duration\\n\")\n",
|
| 415 |
+
" f.write(\"property float frame_transfer_duration\\n\")\n",
|
| 416 |
+
" f.write(\"property int32 total_scan_count\\n\")\n",
|
| 417 |
+
" \n",
|
| 418 |
+
" # Write camera matrix\n",
|
| 419 |
+
" f.write(\"element camera_matrix 1\\n\")\n",
|
| 420 |
+
" for i in range(9):\n",
|
| 421 |
+
" f.write(f\"property float cm{i}\\n\")\n",
|
| 422 |
+
" \n",
|
| 423 |
+
" # Write distortion matrix\n",
|
| 424 |
+
" f.write(\"element distortion_matrix 1\\n\")\n",
|
| 425 |
+
" for i in range(14):\n",
|
| 426 |
+
" f.write(f\"property float dm{i}\\n\")\n",
|
| 427 |
+
" \n",
|
| 428 |
+
" # Write camera resolution\n",
|
| 429 |
+
" f.write(\"element camera_resolution 1\\n\")\n",
|
| 430 |
+
" f.write(\"property float width\\n\")\n",
|
| 431 |
+
" f.write(\"property float height\\n\")\n",
|
| 432 |
+
" \n",
|
| 433 |
+
" # Write frame binning\n",
|
| 434 |
+
" f.write(\"element frame_binning 1\\n\")\n",
|
| 435 |
+
" f.write(\"property float horizontal\\n\")\n",
|
| 436 |
+
" f.write(\"property float vertical\\n\")\n",
|
| 437 |
+
" \n",
|
| 438 |
+
" # End header\n",
|
| 439 |
+
" f.write(\"end_header\\n\")\n",
|
| 440 |
+
" \n",
|
| 441 |
+
" # Write vertex data\n",
|
| 442 |
+
" for point in points:\n",
|
| 443 |
+
" f.write(f\"{point[0]} {point[1]} {point[2]}\\n\")\n",
|
| 444 |
+
"\n",
|
| 445 |
+
" print(True)\n",
|
| 446 |
+
"\n",
|
| 447 |
+
"if GT: write_ply(perturbed_pcd_array, f\"gt_filtered.ply\")\n",
|
| 448 |
+
"else: write_ply(perturbed_pcd_array, f\"./noisy_result/noisy_filtered_{filename}.ply\")\n",
|
| 449 |
+
"# write_ply(new_pcd_array, \"gt_filtered.ply\")"
|
| 450 |
+
]
|
| 451 |
+
}
|
| 452 |
+
],
|
| 453 |
+
"metadata": {
|
| 454 |
+
"kernelspec": {
|
| 455 |
+
"display_name": "Python 3",
|
| 456 |
+
"language": "python",
|
| 457 |
+
"name": "python3"
|
| 458 |
+
},
|
| 459 |
+
"language_info": {
|
| 460 |
+
"codemirror_mode": {
|
| 461 |
+
"name": "ipython",
|
| 462 |
+
"version": 3
|
| 463 |
+
},
|
| 464 |
+
"file_extension": ".py",
|
| 465 |
+
"mimetype": "text/x-python",
|
| 466 |
+
"name": "python",
|
| 467 |
+
"nbconvert_exporter": "python",
|
| 468 |
+
"pygments_lexer": "ipython3",
|
| 469 |
+
"version": "3.10.12"
|
| 470 |
+
}
|
| 471 |
+
},
|
| 472 |
+
"nbformat": 4,
|
| 473 |
+
"nbformat_minor": 2
|
| 474 |
+
}
|
data/glasses/gt_Raw.ipynb
ADDED
|
@@ -0,0 +1,834 @@
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| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "7d7011e4",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## load gt and translate"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 1,
|
| 14 |
+
"id": "878f605d",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [
|
| 17 |
+
{
|
| 18 |
+
"name": "stdout",
|
| 19 |
+
"output_type": "stream",
|
| 20 |
+
"text": [
|
| 21 |
+
"=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 100\n",
|
| 24 |
+
"100_19\n",
|
| 25 |
+
"<class 'numpy.ndarray'>\n",
|
| 26 |
+
"[ 98.59357157 -6.32276816 -341.62408849] [[ 6.26526415e-01 -1.82541218e-02 -7.79186368e-01 1.39500000e+02]\n",
|
| 27 |
+
" [-7.73697793e-01 -1.35277703e-01 -6.18943989e-01 1.87500000e+02]\n",
|
| 28 |
+
" [-9.41082612e-02 9.90639567e-01 -9.88782272e-02 5.00000000e+01]\n",
|
| 29 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 30 |
+
"100_10\n",
|
| 31 |
+
"<class 'numpy.ndarray'>\n",
|
| 32 |
+
"[ 50.19192299 -58.71015024 -264.5484671 ] [[ 9.27836180e-01 1.56168127e-02 3.72660846e-01 -3.50000000e+00]\n",
|
| 33 |
+
" [-2.13101976e-06 -9.99122858e-01 4.18747813e-02 8.85000000e+01]\n",
|
| 34 |
+
" [ 3.72987926e-01 -3.88537347e-02 -9.27022338e-01 2.18500000e+02]\n",
|
| 35 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 36 |
+
"100_1\n",
|
| 37 |
+
"<class 'numpy.ndarray'>\n",
|
| 38 |
+
"[ 88.12391552 -58.39092539 -316.82673925] [[-5.53394675e-01 4.35812259e-03 8.32907736e-01 1.30000000e+01]\n",
|
| 39 |
+
" [ 1.66683515e-06 -9.99986291e-01 5.23345498e-03 9.40000000e+01]\n",
|
| 40 |
+
" [ 8.32919180e-01 2.89755454e-03 5.53387105e-01 -2.25000000e+01]\n",
|
| 41 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 42 |
+
"100_4\n",
|
| 43 |
+
"<class 'numpy.ndarray'>\n",
|
| 44 |
+
"[ 104.15825554 -60.33874486 -369.14952375] [[-9.99602556e-01 -8.95392802e-03 2.67306473e-02 1.41000000e+02]\n",
|
| 45 |
+
" [ 1.04742153e-02 -9.98302817e-01 5.72870970e-02 8.55000000e+01]\n",
|
| 46 |
+
" [ 2.61723343e-02 5.75443096e-02 9.97999847e-01 1.15000000e+01]\n",
|
| 47 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 48 |
+
"100_6\n",
|
| 49 |
+
"<class 'numpy.ndarray'>\n",
|
| 50 |
+
"[ 134.15064934 -59.17156509 -304.83953668] [[-4.57408637e-01 -5.83576597e-03 -8.89237463e-01 2.35500000e+02]\n",
|
| 51 |
+
" [-3.35887671e-02 -9.99151468e-01 2.38345843e-02 9.05000000e+01]\n",
|
| 52 |
+
" [-8.88622046e-01 4.07705344e-02 4.56824511e-01 9.35000000e+01]\n",
|
| 53 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 54 |
+
"100_5\n",
|
| 55 |
+
"<class 'numpy.ndarray'>\n",
|
| 56 |
+
"[ 131.89516322 -60.2417556 -352.05552997] [[-8.99081409e-01 -2.95190793e-02 -4.36785132e-01 1.97000000e+02]\n",
|
| 57 |
+
" [ 4.71208766e-02 -9.98453021e-01 -2.95158681e-02 9.55000000e+01]\n",
|
| 58 |
+
" [-4.35238153e-01 -4.71188650e-02 8.99081528e-01 4.05000000e+01]\n",
|
| 59 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 60 |
+
"100_17\n",
|
| 61 |
+
"<class 'numpy.ndarray'>\n",
|
| 62 |
+
"[ 121.09198072 -62.03935281 -352.47422775] [[-9.99876618e-01 -8.21960624e-04 1.56857967e-02 1.59000000e+02]\n",
|
| 63 |
+
" [ 1.57073177e-02 -5.23288921e-02 9.98506367e-01 -7.05000000e+01]\n",
|
| 64 |
+
" [ 8.74227766e-08 9.98629570e-01 5.23353480e-02 8.00000000e+01]\n",
|
| 65 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 66 |
+
"100_15\n",
|
| 67 |
+
"<class 'numpy.ndarray'>\n",
|
| 68 |
+
"[ 73.34256178 -29.90910132 -413.87732065] [[ 9.62318420e-01 2.23134970e-03 2.71915883e-01 -9.00000000e+00]\n",
|
| 69 |
+
" [ 2.68681288e-01 -1.61794901e-01 -9.49543476e-01 1.59000000e+02]\n",
|
| 70 |
+
" [ 4.18758392e-02 9.86821890e-01 -1.56297728e-01 7.10000000e+01]\n",
|
| 71 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 72 |
+
"100_12\n",
|
| 73 |
+
"<class 'numpy.ndarray'>\n",
|
| 74 |
+
"[ 125.69436664 -60.83634681 -371.48473793] [[ 9.99506593e-01 -2.55091935e-02 1.83273889e-02 1.00000000e+01]\n",
|
| 75 |
+
" [-2.29562966e-06 -5.83541214e-01 -8.12083542e-01 1.85000000e+02]\n",
|
| 76 |
+
" [ 3.14103812e-02 8.11682820e-01 -5.83253324e-01 1.71500000e+02]\n",
|
| 77 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 78 |
+
"100_16\n",
|
| 79 |
+
"<class 'numpy.ndarray'>\n",
|
| 80 |
+
"[ 142.86966267 -16.62744349 -391.25003021] [[-6.25872910e-01 -3.21382843e-02 7.79262602e-01 3.35000000e+01]\n",
|
| 81 |
+
" [ 7.72888660e-01 -1.59467459e-01 6.14176869e-01 -7.40000000e+01]\n",
|
| 82 |
+
" [ 1.04528435e-01 9.86679912e-01 1.24645680e-01 1.75000000e+01]\n",
|
| 83 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 84 |
+
"100_14\n",
|
| 85 |
+
"<class 'numpy.ndarray'>\n",
|
| 86 |
+
"[ 71.60248918 -30.41133971 -392.86407208] [[ 9.25410628e-01 -5.97307906e-02 3.74229133e-01 -1.35000000e+01]\n",
|
| 87 |
+
" [ 3.16840649e-01 -4.19823438e-01 -8.50505888e-01 1.57000000e+02]\n",
|
| 88 |
+
" [ 2.07911551e-01 9.05638158e-01 -3.69583935e-01 9.35000000e+01]\n",
|
| 89 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 90 |
+
"100_7\n",
|
| 91 |
+
"<class 'numpy.ndarray'>\n",
|
| 92 |
+
"[ 95.88993614 -58.09814223 -283.45344633] [[ 1.04437985e-01 -1.01815052e-02 -9.94479299e-01 2.16500000e+02]\n",
|
| 93 |
+
" [ 4.37764870e-03 -9.99933183e-01 1.06970733e-02 9.60000000e+01]\n",
|
| 94 |
+
" [-9.94521737e-01 -5.47066191e-03 -1.04386441e-01 1.55500000e+02]\n",
|
| 95 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 96 |
+
"100_13\n",
|
| 97 |
+
"<class 'numpy.ndarray'>\n",
|
| 98 |
+
"[ 145.73667176 -47.08891079 -393.46364069] [[ 9.23676431e-01 -2.07779948e-02 -3.82609576e-01 4.80000000e+01]\n",
|
| 99 |
+
" [-3.82601708e-01 -1.04534402e-01 -9.17980671e-01 2.06000000e+02]\n",
|
| 100 |
+
" [-2.09220648e-02 9.94304180e-01 -1.04505658e-01 8.55000000e+01]\n",
|
| 101 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 102 |
+
"100_9\n",
|
| 103 |
+
"<class 'numpy.ndarray'>\n",
|
| 104 |
+
"[ 164.97914298 -60.50444111 -248.16947062] [[ 9.80012476e-01 -6.11204542e-02 -1.89314187e-01 -5.00000000e+00]\n",
|
| 105 |
+
" [-6.68118149e-02 -9.97481167e-01 -2.38223728e-02 1.00000000e+02]\n",
|
| 106 |
+
" [-1.87381297e-01 3.59946452e-02 -9.81627524e-01 2.78000000e+02]\n",
|
| 107 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 108 |
+
"100_18\n",
|
| 109 |
+
"<class 'numpy.ndarray'>\n",
|
| 110 |
+
"[ 104.98714587 -4.08525447 -348.89350193] [[-7.33996809e-01 -4.92214896e-02 -6.77366912e-01 2.09000000e+02]\n",
|
| 111 |
+
" [-6.60891116e-01 -1.77965611e-01 7.29075551e-01 2.30000000e+01]\n",
|
| 112 |
+
" [-1.56434208e-01 9.82804894e-01 9.80961472e-02 2.75000000e+01]\n",
|
| 113 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 114 |
+
"100_2\n",
|
| 115 |
+
"<class 'numpy.ndarray'>\n",
|
| 116 |
+
"[ 87.98107134 -58.40312697 -316.64902162] [[-5.53325236e-01 1.57047901e-02 8.32817256e-01 1.20000000e+01]\n",
|
| 117 |
+
" [-8.69092811e-03 -9.99876678e-01 1.30808335e-02 9.40000000e+01]\n",
|
| 118 |
+
" [ 8.32919955e-01 8.06319955e-10 5.53393483e-01 -2.35000000e+01]\n",
|
| 119 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 120 |
+
"100_11\n",
|
| 121 |
+
"<class 'numpy.ndarray'>\n",
|
| 122 |
+
"[ 64.44118133 -57.9869393 -300.9743561 ] [[ 6.84547305e-01 -3.05283237e-02 7.28328884e-01 -5.30000000e+01]\n",
|
| 123 |
+
" [-1.57224292e-06 -9.99122739e-01 -4.18773256e-02 9.75000000e+01]\n",
|
| 124 |
+
" [ 7.28968441e-01 2.86658667e-02 -6.83946848e-01 1.16500000e+02]\n",
|
| 125 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 126 |
+
"100_20\n",
|
| 127 |
+
"<class 'numpy.ndarray'>\n",
|
| 128 |
+
"[ 85.41922326 -25.33664865 -371.38923199] [[ 8.56643319e-01 -1.43507272e-02 5.15709519e-01 -1.55000000e+01]\n",
|
| 129 |
+
" [ 5.12687683e-01 -8.78546461e-02 -8.54068458e-01 1.16500000e+02]\n",
|
| 130 |
+
" [ 5.75639792e-02 9.96029913e-01 -6.79026544e-02 5.30000000e+01]\n",
|
| 131 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 132 |
+
"100_3\n",
|
| 133 |
+
"100_8\n",
|
| 134 |
+
"<class 'numpy.ndarray'>\n",
|
| 135 |
+
"[ 159.65836874 -59.75579578 -280.65958206] [[ 7.60312915e-01 -4.96730916e-02 -6.47654891e-01 9.30000000e+01]\n",
|
| 136 |
+
" [-1.19455205e-02 -9.97972369e-01 6.25179261e-02 8.80000000e+01]\n",
|
| 137 |
+
" [-6.49447083e-01 -3.97966132e-02 -7.59364665e-01 2.66500000e+02]\n",
|
| 138 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 139 |
+
"\n",
|
| 140 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 75\n",
|
| 141 |
+
"75_6\n",
|
| 142 |
+
"<class 'numpy.ndarray'>\n",
|
| 143 |
+
"[ 97.13165139 -58.31263565 -314.62474149] [[-5.48994720e-01 1.04693333e-02 8.35760236e-01 1.10000000e+01]\n",
|
| 144 |
+
" [-5.74792363e-03 -9.99945223e-01 8.75033159e-03 9.55000000e+01]\n",
|
| 145 |
+
" [ 8.35806072e-01 0.00000000e+00 5.49024820e-01 -3.35000000e+01]\n",
|
| 146 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 147 |
+
"75_12\n",
|
| 148 |
+
"<class 'numpy.ndarray'>\n",
|
| 149 |
+
"[ 126.93695474 -65.73688892 -353.4484599 ] [[ 9.99945164e-01 6.58894656e-03 8.13681073e-03 5.00000000e+00]\n",
|
| 150 |
+
" [ 1.04700476e-02 -6.29285872e-01 -7.77103364e-01 1.78000000e+02]\n",
|
| 151 |
+
" [ 8.74227766e-08 7.77145982e-01 -6.29320383e-01 1.88000000e+02]\n",
|
| 152 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 153 |
+
"75_9\n",
|
| 154 |
+
"<class 'numpy.ndarray'>\n",
|
| 155 |
+
"[ 83.075614 -59.45950321 -299.97387383] [[-2.07864061e-01 2.09459476e-02 -9.77933407e-01 2.33500000e+02]\n",
|
| 156 |
+
" [-4.35486529e-03 -9.99780595e-01 -2.04882380e-02 9.75000000e+01]\n",
|
| 157 |
+
" [-9.78148043e-01 0.00000000e+00 2.07909673e-01 1.05500000e+02]\n",
|
| 158 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 159 |
+
"75_4\n",
|
| 160 |
+
"<class 'numpy.ndarray'>\n",
|
| 161 |
+
"[ 92.95012853 -59.04869088 -291.74401717] [[ 6.88345969e-01 4.69864905e-02 7.23859191e-01 -7.70000000e+01]\n",
|
| 162 |
+
" [ 3.60720884e-03 -9.98109281e-01 6.13581277e-02 8.85000000e+01]\n",
|
| 163 |
+
" [ 7.25373566e-01 -3.96245085e-02 -6.87214017e-01 1.50000000e+02]\n",
|
| 164 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 165 |
+
"75_11\n",
|
| 166 |
+
"<class 'numpy.ndarray'>\n",
|
| 167 |
+
"[ 138.01304835 -59.82580012 -277.86206363] [[ 8.31779957e-01 3.20479311e-02 -5.54179609e-01 8.00000000e+01]\n",
|
| 168 |
+
" [ 4.35897745e-02 -9.99020219e-01 7.65197631e-03 8.85000000e+01]\n",
|
| 169 |
+
" [-5.53391397e-01 -3.05213258e-02 -8.32361937e-01 2.75000000e+02]\n",
|
| 170 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 171 |
+
"75_7\n",
|
| 172 |
+
"<class 'numpy.ndarray'>\n",
|
| 173 |
+
"[ 98.3507269 -60.24520538 -358.93410354] [[-9.37168419e-01 1.02299070e-02 3.48727226e-01 9.80000000e+01]\n",
|
| 174 |
+
" [-1.47211887e-02 -9.99839306e-01 -1.02314092e-02 9.70000000e+01]\n",
|
| 175 |
+
" [ 3.48566502e-01 -1.47222327e-02 9.37168419e-01 -1.50000000e+01]\n",
|
| 176 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 177 |
+
"75_14\n",
|
| 178 |
+
"<class 'numpy.ndarray'>\n",
|
| 179 |
+
"[ 184.81895417 -61.75061707 -371.32786333] [[-9.92757320e-01 -2.49779616e-02 1.17511526e-01 1.69500000e+02]\n",
|
| 180 |
+
" [ 1.20136827e-01 -2.06407487e-01 9.71062839e-01 -7.00000000e+01]\n",
|
| 181 |
+
" [ 8.74227766e-08 9.78147268e-01 2.07913324e-01 6.05000000e+01]\n",
|
| 182 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 183 |
+
"75_8\n",
|
| 184 |
+
"<class 'numpy.ndarray'>\n",
|
| 185 |
+
"[ 146.38192024 -60.81455952 -353.00143029] [[-9.38268304e-01 -4.18780446e-02 -3.43364030e-01 1.99000000e+02]\n",
|
| 186 |
+
" [ 3.93273421e-02 -9.99122739e-01 1.43920397e-02 8.70000000e+01]\n",
|
| 187 |
+
" [-3.43665510e-01 3.80894656e-11 9.39092100e-01 2.30000000e+01]\n",
|
| 188 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 189 |
+
"75_16\n",
|
| 190 |
+
"<class 'numpy.ndarray'>\n",
|
| 191 |
+
"[ 107.62420136 -0.65789312 -293.25539163] [[ 3.64422388e-02 -4.84764427e-02 -9.98159289e-01 2.45000000e+02]\n",
|
| 192 |
+
" [-9.93854046e-01 -1.06232673e-01 -3.11257783e-02 1.30500000e+02]\n",
|
| 193 |
+
" [-1.04528263e-01 9.93158937e-01 -5.20498641e-02 3.75000000e+01]\n",
|
| 194 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 195 |
+
"75_17\n",
|
| 196 |
+
"<class 'numpy.ndarray'>\n",
|
| 197 |
+
"[ 100.3282339 1.99509957 -330.99240063] [[ 7.49083698e-01 1.01699512e-02 -6.62397265e-01 1.12000000e+02]\n",
|
| 198 |
+
" [-6.58009350e-01 -1.04484066e-01 -7.45725691e-01 1.97000000e+02]\n",
|
| 199 |
+
" [-7.67939612e-02 9.94474590e-01 -7.15753883e-02 3.70000000e+01]\n",
|
| 200 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 201 |
+
"75_2\n",
|
| 202 |
+
"<class 'numpy.ndarray'>\n",
|
| 203 |
+
"[ 106.88182212 -59.08352114 -260.733522 ] [[ 9.99109209e-01 -2.09451225e-02 -3.66350412e-02 9.50000000e+00]\n",
|
| 204 |
+
" [-2.09310558e-02 -9.99780655e-01 7.67493795e-04 9.60000000e+01]\n",
|
| 205 |
+
" [-3.66430804e-02 0.00000000e+00 -9.99328434e-01 2.72000000e+02]\n",
|
| 206 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 207 |
+
"75_3\n",
|
| 208 |
+
"<class 'numpy.ndarray'>\n",
|
| 209 |
+
"[ 84.00080954 -58.70668798 -255.95883811] [[ 9.78134215e-01 -9.59114917e-03 2.07753405e-01 -1.80000000e+01]\n",
|
| 210 |
+
" [-5.12371631e-03 -9.99744177e-01 -2.20309906e-02 9.90000000e+01]\n",
|
| 211 |
+
" [ 2.07911551e-01 2.04847958e-02 -9.77933109e-01 2.45000000e+02]\n",
|
| 212 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 213 |
+
"75_1\n",
|
| 214 |
+
"<class 'numpy.ndarray'>\n",
|
| 215 |
+
"[ 107.05398145 -59.11664399 -260.7118368 ] [[ 9.97684240e-01 -1.58160640e-06 -6.80156276e-02 1.30000000e+01]\n",
|
| 216 |
+
" [-1.57784496e-06 -1.00000000e+00 1.09024256e-07 9.35000000e+01]\n",
|
| 217 |
+
" [-6.80156276e-02 -1.45367218e-09 -9.97684240e-01 2.75000000e+02]\n",
|
| 218 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 219 |
+
"75_21\n",
|
| 220 |
+
"75_15\n",
|
| 221 |
+
"<class 'numpy.ndarray'>\n",
|
| 222 |
+
"[ 123.01193826 9.01241278 -327.55938679] [[-6.68128192e-01 -7.56965019e-03 -7.44007647e-01 2.31019608e+02]\n",
|
| 223 |
+
" [-7.33459830e-01 -1.61378995e-01 6.60297990e-01 2.86654854e+01]\n",
|
| 224 |
+
" [-1.25065431e-01 9.86863494e-01 1.02269821e-01 9.51398277e+00]\n",
|
| 225 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 226 |
+
"75_20\n",
|
| 227 |
+
"<class 'numpy.ndarray'>\n",
|
| 228 |
+
"[ 106.16859885 -3.4345633 -332.2763217 ] [[ 6.03132010e-01 -4.09756824e-02 7.96588242e-01 -7.15000000e+01]\n",
|
| 229 |
+
" [ 7.86011279e-01 -1.39386699e-01 -6.02293611e-01 7.55000000e+01]\n",
|
| 230 |
+
" [ 1.35713190e-01 9.89389896e-01 -5.18612303e-02 2.35000000e+01]\n",
|
| 231 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 232 |
+
"75_10\n",
|
| 233 |
+
"<class 'numpy.ndarray'>\n",
|
| 234 |
+
"[ 84.40147158 -60.1314631 -298.96081679] [[ 4.11493093e-01 -3.72422487e-02 -9.10651684e-01 1.76000000e+02]\n",
|
| 235 |
+
" [ 4.30799974e-03 -9.99074161e-01 4.28050384e-02 8.90000000e+01]\n",
|
| 236 |
+
" [-9.11402702e-01 -2.15370655e-02 -4.10951674e-01 1.85500000e+02]\n",
|
| 237 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 238 |
+
"75_13\n",
|
| 239 |
+
"<class 'numpy.ndarray'>\n",
|
| 240 |
+
"[ 83.97847951 -65.22786958 -355.08547679] [[ 9.99780655e-01 -1.23108840e-02 -1.69443302e-02 1.05000000e+01]\n",
|
| 241 |
+
" [-2.09444072e-02 -5.87656319e-01 -8.08839560e-01 1.82000000e+02]\n",
|
| 242 |
+
" [ 8.74227766e-08 8.09017003e-01 -5.87785244e-01 1.80500000e+02]\n",
|
| 243 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 244 |
+
"75_5\n",
|
| 245 |
+
"<class 'numpy.ndarray'>\n",
|
| 246 |
+
"[ 121.65774538 -58.7456104 -293.77395804] [[ 1.56433567e-01 -2.29532384e-06 9.87688482e-01 -8.20000000e+01]\n",
|
| 247 |
+
" [-3.59290851e-07 -1.00000000e+00 -2.26702923e-06 9.35000000e+01]\n",
|
| 248 |
+
" [ 9.87688482e-01 -2.27930966e-10 -1.56433567e-01 1.50000000e+01]\n",
|
| 249 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 250 |
+
"75_19\n",
|
| 251 |
+
"<class 'numpy.ndarray'>\n",
|
| 252 |
+
"[ 84.57886723 -23.3033718 -343.00200068] [[ 8.85626972e-01 -6.66689649e-02 4.59586889e-01 -2.25000000e+01]\n",
|
| 253 |
+
" [ 4.51246351e-01 -1.10305823e-01 -8.85555983e-01 1.34000000e+02]\n",
|
| 254 |
+
" [ 1.09734207e-01 9.91659164e-01 -6.76056892e-02 4.90000000e+01]\n",
|
| 255 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 256 |
+
"75_18\n",
|
| 257 |
+
"<class 'numpy.ndarray'>\n",
|
| 258 |
+
"[ 102.59570405 -53.61587272 -328.15585112] [[ 9.87566173e-01 -9.22811101e-04 -1.57201275e-01 2.95000000e+01]\n",
|
| 259 |
+
" [-1.56417266e-01 -1.05685741e-01 -9.82020438e-01 1.96500000e+02]\n",
|
| 260 |
+
" [-1.57077126e-02 9.94399130e-01 -1.04516000e-01 9.20000000e+01]\n",
|
| 261 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 262 |
+
"\n",
|
| 263 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 50\n",
|
| 264 |
+
"50_18\n",
|
| 265 |
+
"<class 'numpy.ndarray'>\n",
|
| 266 |
+
"[ 97.02771775 7.90541457 -313.3694054 ] [[-1.45360082e-01 -1.96876694e-02 -9.89182889e-01 2.28000000e+02]\n",
|
| 267 |
+
" [-9.84381080e-01 -9.74880531e-02 1.46594748e-01 1.03000000e+02]\n",
|
| 268 |
+
" [-9.93196219e-02 9.95041966e-01 -5.20929834e-03 2.25000000e+01]\n",
|
| 269 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 270 |
+
"50_8\n",
|
| 271 |
+
"<class 'numpy.ndarray'>\n",
|
| 272 |
+
"[ 123.47655071 -60.62404184 -352.99999071] [[-9.65923846e-01 -1.58150726e-06 -2.58826524e-01 1.76000000e+02]\n",
|
| 273 |
+
" [ 1.52761550e-06 -1.00000000e+00 4.09336053e-07 9.35000000e+01]\n",
|
| 274 |
+
" [-2.58826524e-01 0.00000000e+00 9.65923846e-01 2.65000000e+01]\n",
|
| 275 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 276 |
+
"50_13\n",
|
| 277 |
+
"<class 'numpy.ndarray'>\n",
|
| 278 |
+
"[ 87.76706939 -5.60973591 -404.27853164] [[ 8.05209517e-01 8.05786904e-03 5.92935622e-01 -4.65000000e+01]\n",
|
| 279 |
+
" [ 5.53403795e-01 -3.69425267e-01 -7.46504664e-01 1.19500000e+02]\n",
|
| 280 |
+
" [ 2.13030174e-01 9.29225504e-01 -3.01923990e-01 5.50000000e+01]\n",
|
| 281 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 282 |
+
"50_15\n",
|
| 283 |
+
"<class 'numpy.ndarray'>\n",
|
| 284 |
+
"[ 139.54934924 -46.88021771 -355.007513 ] [[-9.57505703e-01 5.13407821e-03 2.88368672e-01 1.42500000e+02]\n",
|
| 285 |
+
" [ 2.83626020e-01 -1.64673179e-01 9.44689929e-01 -8.25000000e+01]\n",
|
| 286 |
+
" [ 5.23367003e-02 9.86334801e-01 1.56219348e-01 4.85000000e+01]\n",
|
| 287 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 288 |
+
"50_7\n",
|
| 289 |
+
"<class 'numpy.ndarray'>\n",
|
| 290 |
+
"[ 94.82717336 -60.06908795 -373.08367637] [[-9.80218709e-01 1.04700476e-02 1.97640181e-01 1.19500000e+02]\n",
|
| 291 |
+
" [-1.02634998e-02 -9.99945164e-01 2.06941552e-03 9.25000000e+01]\n",
|
| 292 |
+
" [ 1.97651014e-01 -3.97597337e-11 9.80272472e-01 6.00000000e+00]\n",
|
| 293 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 294 |
+
"50_4\n",
|
| 295 |
+
"<class 'numpy.ndarray'>\n",
|
| 296 |
+
"[ 133.30369188 -60.16087193 -309.40942882] [[ 3.63204628e-01 2.05822811e-02 9.31482017e-01 -6.85000000e+01]\n",
|
| 297 |
+
" [ 5.70475589e-03 -9.99786377e-01 1.98671464e-02 8.95000000e+01]\n",
|
| 298 |
+
" [ 9.31691945e-01 -1.90196210e-03 -3.63244444e-01 3.45000000e+01]\n",
|
| 299 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 300 |
+
"50_5\n",
|
| 301 |
+
"<class 'numpy.ndarray'>\n",
|
| 302 |
+
"[ 104.32812486 -59.58836693 -330.54955718] [[-2.43282974e-01 5.23336045e-02 9.68542516e-01 1.15000000e+01]\n",
|
| 303 |
+
" [-1.27493460e-02 -9.98629630e-01 5.07568754e-02 8.95000000e+01]\n",
|
| 304 |
+
" [ 9.69871581e-01 0.00000000e+00 2.43616819e-01 -1.75000000e+01]\n",
|
| 305 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 306 |
+
"50_19\n",
|
| 307 |
+
"<class 'numpy.ndarray'>\n",
|
| 308 |
+
"[ 96.29982131 -58.37303979 -309.44228953] [[ 9.79412377e-01 1.91123132e-02 -2.00963020e-01 3.60000000e+01]\n",
|
| 309 |
+
" [-1.97478727e-01 -1.15796909e-01 -9.73443985e-01 2.02500000e+02]\n",
|
| 310 |
+
" [-4.18756641e-02 9.93089020e-01 -1.09638646e-01 1.00000000e+02]\n",
|
| 311 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 312 |
+
"50_16\n",
|
| 313 |
+
"<class 'numpy.ndarray'>\n",
|
| 314 |
+
"[ 149.88589896 -62.58094567 -338.11825748] [[-9.99986291e-01 5.20800240e-03 5.47378964e-04 1.89000000e+02]\n",
|
| 315 |
+
" [ 0.00000000e+00 -1.04527682e-01 9.94521976e-01 -7.05000000e+01]\n",
|
| 316 |
+
" [ 5.23668900e-03 9.94508326e-01 1.04526244e-01 7.30000000e+01]\n",
|
| 317 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 318 |
+
"50_20\n",
|
| 319 |
+
"<class 'numpy.ndarray'>\n",
|
| 320 |
+
"[ 93.57373451 2.93145736 -322.77012248] [[ 7.49273777e-01 6.80260768e-04 -6.62260056e-01 1.09500000e+02]\n",
|
| 321 |
+
" [-6.60582542e-01 -7.03702793e-02 -7.47448146e-01 1.95500000e+02]\n",
|
| 322 |
+
" [-4.71118838e-02 9.97520685e-01 -5.22772335e-02 3.00000000e+01]\n",
|
| 323 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 324 |
+
"50_14\n",
|
| 325 |
+
"<class 'numpy.ndarray'>\n",
|
| 326 |
+
"[ 88.28171183 3.8557878 -316.08167637] [[-7.27535933e-02 8.41864012e-03 9.97314394e-01 -9.00000000e+01]\n",
|
| 327 |
+
" [ 9.90704834e-01 -1.14629664e-01 7.32390508e-02 -2.55000000e+01]\n",
|
| 328 |
+
" [ 1.14938386e-01 9.93372619e-01 -6.67093786e-07 1.10000000e+01]\n",
|
| 329 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 330 |
+
"50_12\n",
|
| 331 |
+
"<class 'numpy.ndarray'>\n",
|
| 332 |
+
"[ 134.5258508 -64.54356876 -364.5357726 ] [[ 9.94030952e-01 -3.07329632e-02 -1.04680270e-01 2.30000000e+01]\n",
|
| 333 |
+
" [-1.04479052e-01 -5.44408977e-01 -8.32287788e-01 1.97000000e+02]\n",
|
| 334 |
+
" [-3.14102061e-02 8.38256717e-01 -5.44370353e-01 1.75500000e+02]\n",
|
| 335 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 336 |
+
"50_11\n",
|
| 337 |
+
"<class 'numpy.ndarray'>\n",
|
| 338 |
+
"[ 111.82756603 -60.11317851 -295.97915591] [[ 5.83470404e-01 -1.57093816e-02 -8.11982453e-01 1.40000000e+02]\n",
|
| 339 |
+
" [-9.16709006e-03 -9.99876618e-01 1.27573172e-02 9.30000000e+01]\n",
|
| 340 |
+
" [-8.12082708e-01 0.00000000e+00 -5.83542407e-01 2.20000000e+02]\n",
|
| 341 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 342 |
+
"50_9\n",
|
| 343 |
+
"<class 'numpy.ndarray'>\n",
|
| 344 |
+
"[ 120.33967845 -58.72811894 -301.49061707] [[-5.87581933e-01 -2.61756964e-02 -8.08741212e-01 2.26000000e+02]\n",
|
| 345 |
+
" [ 1.53856371e-02 -9.99657333e-01 2.11766195e-02 9.30000000e+01]\n",
|
| 346 |
+
" [-8.09018433e-01 0.00000000e+00 5.87783277e-01 7.65000000e+01]\n",
|
| 347 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 348 |
+
"50_6\n",
|
| 349 |
+
"<class 'numpy.ndarray'>\n",
|
| 350 |
+
"[ 76.86469562 -58.39298473 -322.38737147] [[-7.06140757e-01 2.64230575e-02 7.07578301e-01 4.10000000e+01]\n",
|
| 351 |
+
" [-3.70055996e-02 -9.99314964e-01 3.86947155e-04 9.95000000e+01]\n",
|
| 352 |
+
" [ 7.07103848e-01 -2.59111207e-02 7.06634820e-01 -3.80000000e+01]\n",
|
| 353 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 354 |
+
"50_17\n",
|
| 355 |
+
"<class 'numpy.ndarray'>\n",
|
| 356 |
+
"[ 106.66903344 8.51464058 -325.99066538] [[-8.22996676e-01 1.63101032e-02 -5.67811966e-01 2.05000000e+02]\n",
|
| 357 |
+
" [-5.65630078e-01 -1.15624942e-01 8.16513002e-01 -5.00000000e+00]\n",
|
| 358 |
+
" [-5.23358099e-02 9.93159056e-01 1.04384430e-01 6.00000000e+00]\n",
|
| 359 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 360 |
+
"50_1\n",
|
| 361 |
+
"<class 'numpy.ndarray'>\n",
|
| 362 |
+
"[ 131.06557723 -59.04872746 -246.8889367 ] [[ 9.96027470e-01 -6.16875989e-03 8.88328403e-02 -8.00000000e+00]\n",
|
| 363 |
+
" [-5.21744601e-03 -9.99926567e-01 -1.09372567e-02 9.50000000e+01]\n",
|
| 364 |
+
" [ 8.88937861e-02 1.04303276e-02 -9.95986521e-01 2.61000000e+02]\n",
|
| 365 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 366 |
+
"50_10\n",
|
| 367 |
+
"<class 'numpy.ndarray'>\n",
|
| 368 |
+
"[ 88.67637631 -59.51173395 -276.81028318] [[-8.88927057e-02 2.29531179e-06 -9.96041179e-01 2.26500000e+02]\n",
|
| 369 |
+
" [-2.04165474e-07 -1.00000000e+00 -2.28621366e-06 9.45000000e+01]\n",
|
| 370 |
+
" [-9.96041179e-01 1.29520797e-10 8.88927057e-02 1.18000000e+02]\n",
|
| 371 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 372 |
+
"50_3\n",
|
| 373 |
+
"<class 'numpy.ndarray'>\n",
|
| 374 |
+
"[ 80.04639924 -58.97716693 -277.66383223] [[ 8.73723686e-01 1.49904843e-02 4.86191481e-01 -2.55000000e+01]\n",
|
| 375 |
+
" [-9.15306993e-03 -9.98841345e-01 4.72455211e-02 8.90000000e+01]\n",
|
| 376 |
+
" [ 4.86336410e-01 -4.57296781e-02 -8.72574210e-01 2.03500000e+02]\n",
|
| 377 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 378 |
+
"50_2\n",
|
| 379 |
+
"<class 'numpy.ndarray'>\n",
|
| 380 |
+
"[ 131.09489789 -59.10852482 -246.68631843] [[ 9.97465611e-01 -2.34160740e-02 6.71865344e-02 -3.00000000e+00]\n",
|
| 381 |
+
" [-2.08898988e-02 -9.99057114e-01 -3.80588211e-02 9.90000000e+01]\n",
|
| 382 |
+
" [ 6.80143759e-02 3.65588441e-02 -9.97014284e-01 2.61500000e+02]\n",
|
| 383 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 384 |
+
"\n",
|
| 385 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 25\n",
|
| 386 |
+
"25_6\n",
|
| 387 |
+
"<class 'numpy.ndarray'>\n",
|
| 388 |
+
"[ 80.44698406 -58.86941603 -307.62318872] [[-4.20945942e-01 2.09405292e-02 9.06843960e-01 -1.20000000e+01]\n",
|
| 389 |
+
" [-8.81676469e-03 -9.99780715e-01 1.89939588e-02 9.25000000e+01]\n",
|
| 390 |
+
" [ 9.07042861e-01 0.00000000e+00 4.21038270e-01 -3.25000000e+01]\n",
|
| 391 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 392 |
+
"25_19\n",
|
| 393 |
+
"<class 'numpy.ndarray'>\n",
|
| 394 |
+
"[ 80.65639095 17.39362121 -287.09742084] [[ 3.47024798e-01 -6.53519258e-02 -9.35576260e-01 1.94000000e+02]\n",
|
| 395 |
+
" [-9.33123171e-01 -1.24151573e-01 -3.37442666e-01 1.64000000e+02]\n",
|
| 396 |
+
" [-9.41007286e-02 9.90108848e-01 -1.04065068e-01 2.70000000e+01]\n",
|
| 397 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 398 |
+
"25_17\n",
|
| 399 |
+
"<class 'numpy.ndarray'>\n",
|
| 400 |
+
"[ 99.71884906 6.74202689 -305.66555647] [[-9.32300985e-01 7.80264940e-03 -3.61599207e-01 1.89500000e+02]\n",
|
| 401 |
+
" [-3.57876837e-01 -1.64567947e-01 9.19152617e-01 -2.55000000e+01]\n",
|
| 402 |
+
" [-5.23358099e-02 9.86334860e-01 1.56219244e-01 1.50000000e+00]\n",
|
| 403 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 404 |
+
"25_9\n",
|
| 405 |
+
"<class 'numpy.ndarray'>\n",
|
| 406 |
+
"[ 104.80421096 -58.69354154 -296.89232101] [[-6.29284024e-01 -1.04732122e-02 -7.77104855e-01 2.27000000e+02]\n",
|
| 407 |
+
" [ 6.59098569e-03 -9.99945164e-01 8.13923124e-03 9.45000000e+01]\n",
|
| 408 |
+
" [-7.77147472e-01 9.16946241e-10 6.29318535e-01 5.60000000e+01]\n",
|
| 409 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 410 |
+
"25_11\n",
|
| 411 |
+
"<class 'numpy.ndarray'>\n",
|
| 412 |
+
"[ 91.27540426 -60.33721246 -302.61452953] [[ 4.99315888e-01 -5.23343198e-02 -8.64838004e-01 1.59500000e+02]\n",
|
| 413 |
+
" [-2.61672158e-02 -9.98629630e-01 4.53228168e-02 9.05000000e+01]\n",
|
| 414 |
+
" [-8.66024792e-01 0.00000000e+00 -5.00001073e-01 1.90000000e+02]\n",
|
| 415 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 416 |
+
"25_20\n",
|
| 417 |
+
"<class 'numpy.ndarray'>\n",
|
| 418 |
+
"[ 114.82699231 -25.31891421 -300.70985536] [[ 9.33597624e-01 2.52183285e-02 -3.57434571e-01 3.55000000e+01]\n",
|
| 419 |
+
" [-3.52777362e-01 -1.10131755e-01 -9.29203510e-01 2.04000000e+02]\n",
|
| 420 |
+
" [-6.27978593e-02 9.93597031e-01 -9.39222947e-02 6.50000000e+01]\n",
|
| 421 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 422 |
+
"25_14\n",
|
| 423 |
+
"<class 'numpy.ndarray'>\n",
|
| 424 |
+
"[ 148.56061006 -1.40141385 -312.41566071] [[ 3.90189588e-01 7.18589965e-03 9.20706511e-01 -8.85000000e+01]\n",
|
| 425 |
+
" [ 9.14781868e-01 -1.16549321e-01 -3.86769146e-01 3.55000000e+01]\n",
|
| 426 |
+
" [ 1.04528435e-01 9.93158937e-01 -5.20498641e-02 2.25000000e+01]\n",
|
| 427 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 428 |
+
"25_4\n",
|
| 429 |
+
"<class 'numpy.ndarray'>\n",
|
| 430 |
+
"[ 120.44283288 -58.59740419 -317.57804464] [[ 4.53990668e-01 -2.29676311e-06 8.91006470e-01 -8.50000000e+01]\n",
|
| 431 |
+
" [-1.04270896e-06 -1.00000000e+00 -2.04643061e-06 9.50000000e+01]\n",
|
| 432 |
+
" [ 8.91006470e-01 0.00000000e+00 -4.53990668e-01 6.70000000e+01]\n",
|
| 433 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 434 |
+
"25_16\n",
|
| 435 |
+
"<class 'numpy.ndarray'>\n",
|
| 436 |
+
"[ 159.49744903 -57.19121116 -330.51883012] [[-9.93359208e-01 -1.71818491e-02 1.13764346e-01 1.64500000e+02]\n",
|
| 437 |
+
" [ 1.14935547e-01 -1.03237145e-01 9.87993896e-01 -7.05000000e+01]\n",
|
| 438 |
+
" [-5.23085613e-03 9.94508386e-01 1.04526371e-01 7.00000000e+01]\n",
|
| 439 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 440 |
+
"25_5\n",
|
| 441 |
+
"<class 'numpy.ndarray'>\n",
|
| 442 |
+
"[ 115.56783081 -59.11134083 -292.91888734] [[ 5.23346961e-02 -2.29821808e-06 9.98629630e-01 -7.25000000e+01]\n",
|
| 443 |
+
" [-1.20200397e-07 -1.00000000e+00 -2.29507259e-06 9.55000000e+01]\n",
|
| 444 |
+
" [ 9.98629630e-01 7.62540794e-11 -5.23346961e-02 5.00000000e-01]\n",
|
| 445 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 446 |
+
"25_2\n",
|
| 447 |
+
"<class 'numpy.ndarray'>\n",
|
| 448 |
+
"[ 65.19512695 -58.32690877 -267.20007307] [[ 9.73424971e-01 -2.20275789e-01 6.26292452e-02 5.60000000e+01]\n",
|
| 449 |
+
" [-2.22945705e-01 -9.74036455e-01 3.93469594e-02 1.05000000e+02]\n",
|
| 450 |
+
" [ 5.23359850e-02 -5.22642322e-02 -9.97260928e-01 2.38500000e+02]\n",
|
| 451 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 452 |
+
"25_10\n",
|
| 453 |
+
"<class 'numpy.ndarray'>\n",
|
| 454 |
+
"[ 86.87625692 -59.3844832 -283.22515827] [[-1.04383260e-01 -5.23381904e-02 -9.93158996e-01 2.39500000e+02]\n",
|
| 455 |
+
" [ 5.47072943e-03 -9.98629391e-01 5.20514883e-02 8.75000000e+01]\n",
|
| 456 |
+
" [-9.94522095e-01 0.00000000e+00 1.04526527e-01 1.18500000e+02]\n",
|
| 457 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 458 |
+
"25_3\n",
|
| 459 |
+
"<class 'numpy.ndarray'>\n",
|
| 460 |
+
"[ 77.45646053 -58.64582287 -296.38477592] [[ 7.99148023e-01 -5.19353012e-03 6.01111889e-01 -4.30000000e+01]\n",
|
| 461 |
+
" [-2.93054041e-02 -9.99110281e-01 3.03278770e-02 9.40000000e+01]\n",
|
| 462 |
+
" [ 6.00419581e-01 -4.18522879e-02 -7.98589230e-01 1.75000000e+02]\n",
|
| 463 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 464 |
+
"25_8\n",
|
| 465 |
+
"<class 'numpy.ndarray'>\n",
|
| 466 |
+
"[ 101.9149352 -60.84140124 -342.26717733] [[-9.54185784e-01 8.90346896e-03 -2.99082369e-01 1.89000000e+02]\n",
|
| 467 |
+
" [-9.99023579e-03 -9.99947906e-01 2.10488937e-03 9.45000000e+01]\n",
|
| 468 |
+
" [-2.99048066e-01 4.99635888e-03 9.54224944e-01 1.00000000e+01]\n",
|
| 469 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 470 |
+
"25_13\n",
|
| 471 |
+
"<class 'numpy.ndarray'>\n",
|
| 472 |
+
"[ 131.00695707 -58.44608371 -374.02159395] [[ 9.96972859e-01 2.28012120e-03 7.77167380e-02 -4.00000000e+00]\n",
|
| 473 |
+
" [ 7.32111558e-02 -3.64062160e-01 -9.28492785e-01 1.86500000e+02]\n",
|
| 474 |
+
" [ 2.61766482e-02 9.31371868e-01 -3.63127053e-01 1.37000000e+02]\n",
|
| 475 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 476 |
+
"25_7\n",
|
| 477 |
+
"<class 'numpy.ndarray'>\n",
|
| 478 |
+
"[ 59.77029242 -59.54275923 -340.63904432] [[-8.54943454e-01 3.14080007e-02 5.17769456e-01 6.95000000e+01]\n",
|
| 479 |
+
" [-2.68653184e-02 -9.99506652e-01 1.62701309e-02 9.35000000e+01]\n",
|
| 480 |
+
" [ 5.18025041e-01 1.24630717e-09 8.55365455e-01 -2.80000000e+01]\n",
|
| 481 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 482 |
+
"25_12\n",
|
| 483 |
+
"<class 'numpy.ndarray'>\n",
|
| 484 |
+
"[ 144.97275845 -64.34651138 -362.24389231] [[ 1.00000000e+00 -1.22409199e-06 -1.94534368e-06 -2.00000000e+00]\n",
|
| 485 |
+
" [-2.29676311e-06 -5.00000060e-01 -8.66025388e-01 1.85000000e+02]\n",
|
| 486 |
+
" [ 8.74227766e-08 8.66025388e-01 -5.00000060e-01 1.65000000e+02]\n",
|
| 487 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 488 |
+
"25_1\n",
|
| 489 |
+
"<class 'numpy.ndarray'>\n",
|
| 490 |
+
"[ 65.0172194 -58.27853298 -266.42403184] [[ 9.76925731e-01 -4.49775578e-03 2.13532060e-01 2.65000000e+01]\n",
|
| 491 |
+
" [-1.53073696e-02 -9.98681664e-01 4.89965640e-02 8.75000000e+01]\n",
|
| 492 |
+
" [ 2.13030174e-01 -5.11346199e-02 -9.75706637e-01 2.25500000e+02]\n",
|
| 493 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 494 |
+
"25_15\n",
|
| 495 |
+
"<class 'numpy.ndarray'>\n",
|
| 496 |
+
"[ 138.90248059 7.98469512 -315.09307969] [[-4.51511025e-01 7.55800982e-04 8.92265201e-01 -1.20000000e+01]\n",
|
| 497 |
+
" [ 8.86122406e-01 -1.16762400e-01 4.48501498e-01 -6.80000000e+01]\n",
|
| 498 |
+
" [ 1.04522012e-01 9.93159592e-01 5.20497821e-02 5.00000000e-01]\n",
|
| 499 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 500 |
+
"25_18\n",
|
| 501 |
+
"<class 'numpy.ndarray'>\n",
|
| 502 |
+
"[ 100.5978645 11.61267872 -289.4689573 ] [[-2.57401466e-01 -4.22233492e-02 -9.65381622e-01 2.33000000e+02]\n",
|
| 503 |
+
" [-9.60634351e-01 -9.68885496e-02 2.60373354e-01 8.75000000e+01]\n",
|
| 504 |
+
" [-1.04528263e-01 9.94399190e-01 -1.56219453e-02 1.95000000e+01]\n",
|
| 505 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 506 |
+
"\n",
|
| 507 |
+
"--- [์นดํ
๊ณ ๋ฆฌ: 0\n",
|
| 508 |
+
"0_12\n",
|
| 509 |
+
"<class 'numpy.ndarray'>\n",
|
| 510 |
+
"[ 103.98670458 10.31890896 -425.69700743] [[-9.99122858e-01 2.09379010e-02 3.62653285e-02 1.28500000e+02]\n",
|
| 511 |
+
" [ 4.18756455e-02 4.99561489e-01 8.65265727e-01 -9.40000000e+01]\n",
|
| 512 |
+
" [ 8.74227766e-08 8.66025388e-01 -5.00000060e-01 8.10000000e+01]\n",
|
| 513 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 514 |
+
"0_17\n",
|
| 515 |
+
"<class 'numpy.ndarray'>\n",
|
| 516 |
+
"[ 186.05504476 4.50040927 -361.21430778] [[-9.33529258e-01 -4.64050137e-02 3.55485231e-01 1.37000000e+02]\n",
|
| 517 |
+
" [ 3.58348310e-01 -1.49750009e-01 9.21499550e-01 -8.80000000e+01]\n",
|
| 518 |
+
" [ 1.04717165e-02 9.87634301e-01 1.56425163e-01 -2.00000000e+00]\n",
|
| 519 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 520 |
+
"0_16\n",
|
| 521 |
+
"<class 'numpy.ndarray'>\n",
|
| 522 |
+
"[ 158.99122356 8.0566992 -340.93300553] [[-4.88214135e-01 5.64623578e-03 8.72705638e-01 -1.95000000e+01]\n",
|
| 523 |
+
" [ 8.66441429e-01 -1.16634279e-01 4.85464394e-01 -7.30000000e+01]\n",
|
| 524 |
+
" [ 1.04528435e-01 9.93158877e-01 5.20503707e-02 -1.50000000e+00]\n",
|
| 525 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 526 |
+
"0_15\n",
|
| 527 |
+
"<class 'numpy.ndarray'>\n",
|
| 528 |
+
"[ 151.76130894 6.88293335 -339.65456933] [[ 5.68182707e-01 -1.06598921e-02 8.22833419e-01 -7.10000000e+01]\n",
|
| 529 |
+
" [ 8.17503452e-01 -1.07040912e-01 -5.65889001e-01 6.60000000e+01]\n",
|
| 530 |
+
" [ 9.41091478e-02 9.94197488e-01 -5.21042943e-02 1.60000000e+01]\n",
|
| 531 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 532 |
+
"0_2\n",
|
| 533 |
+
"<class 'numpy.ndarray'>\n",
|
| 534 |
+
"[ 119.27855029 -60.61229654 -325.9406945 ] [[ 9.55740631e-01 1.04759438e-02 -2.94023991e-01 2.70000000e+01]\n",
|
| 535 |
+
" [ 1.00128353e-02 -9.99945104e-01 -3.08034662e-03 9.50000000e+01]\n",
|
| 536 |
+
" [-2.94040143e-01 -1.39263479e-09 -9.55793083e-01 2.49500000e+02]\n",
|
| 537 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 538 |
+
"0_5\n",
|
| 539 |
+
"<class 'numpy.ndarray'>\n",
|
| 540 |
+
"[ 136.77738072 -60.90191438 -315.37774331] [[-3.97150248e-01 -2.29542570e-06 9.17753637e-01 -2.45000000e+01]\n",
|
| 541 |
+
" [ 9.12159976e-07 -1.00000000e+00 -2.10640542e-06 9.50000000e+01]\n",
|
| 542 |
+
" [ 9.17753637e-01 5.78666337e-10 3.97150248e-01 -4.45000000e+01]\n",
|
| 543 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 544 |
+
"0_14\n",
|
| 545 |
+
"<class 'numpy.ndarray'>\n",
|
| 546 |
+
"[ 130.19622093 -66.73083028 -374.77296817] [[-1.00000000e+00 -1.51899189e-06 -4.20105425e-06 1.45500000e+02]\n",
|
| 547 |
+
" [-4.46637978e-06 3.58368099e-01 9.33580399e-01 -7.90000000e+01]\n",
|
| 548 |
+
" [ 8.74227766e-08 9.33580399e-01 -3.58368099e-01 1.39500000e+02]\n",
|
| 549 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 550 |
+
"0_9\n",
|
| 551 |
+
"<class 'numpy.ndarray'>\n",
|
| 552 |
+
"[ 100.51432843 -60.43930286 -326.22722644] [[-2.98776299e-01 -1.19095705e-02 -9.54248846e-01 2.26000000e+02]\n",
|
| 553 |
+
" [ 1.25229396e-02 -9.99884963e-01 8.55818950e-03 9.20000000e+01]\n",
|
| 554 |
+
" [-9.54240978e-01 -9.39301681e-03 2.98891068e-01 1.00000000e+02]\n",
|
| 555 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 556 |
+
"0_22\n",
|
| 557 |
+
"<class 'numpy.ndarray'>\n",
|
| 558 |
+
"[ 90.11145968 -59.88932663 -330.74943687] [[ 1.58813998e-01 3.70090269e-02 -9.86614645e-01 2.09500000e+02]\n",
|
| 559 |
+
" [-5.41530224e-03 -9.99249518e-01 -3.83546688e-02 9.90000000e+01]\n",
|
| 560 |
+
" [-9.87293661e-01 1.14340745e-02 -1.58494398e-01 1.49000000e+02]\n",
|
| 561 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 562 |
+
"0_4\n",
|
| 563 |
+
"<class 'numpy.ndarray'>\n",
|
| 564 |
+
"[ 129.59008078 -59.41288647 -321.76365361] [[ 1.87378153e-01 -2.29676311e-06 9.82287824e-01 -8.05000000e+01]\n",
|
| 565 |
+
" [-4.30363201e-07 -1.00000000e+00 -2.25608233e-06 9.50000000e+01]\n",
|
| 566 |
+
" [ 9.82287824e-01 0.00000000e+00 -1.87378153e-01 1.70000000e+01]\n",
|
| 567 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 568 |
+
"0_18\n",
|
| 569 |
+
"<class 'numpy.ndarray'>\n",
|
| 570 |
+
"[ 117.75412539 8.82554891 -361.16436266] [[-6.63363695e-01 -6.60083592e-02 -7.45380104e-01 2.24500000e+02]\n",
|
| 571 |
+
" [-7.41532445e-01 -7.56485090e-02 6.66638553e-01 2.05000000e+01]\n",
|
| 572 |
+
" [-1.00390613e-01 9.94947314e-01 1.23513013e-03 2.40000000e+01]\n",
|
| 573 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 574 |
+
"0_8\n",
|
| 575 |
+
"<class 'numpy.ndarray'>\n",
|
| 576 |
+
"[ 110.02384139 -61.09827171 -328.36819013] [[-7.89987803e-01 1.13137672e-02 -6.13018155e-01 2.12500000e+02]\n",
|
| 577 |
+
" [-1.65469553e-02 -9.99858975e-01 2.87058577e-03 9.55000000e+01]\n",
|
| 578 |
+
" [-6.12899244e-01 1.24113122e-02 7.90063560e-01 1.40000000e+01]\n",
|
| 579 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 580 |
+
"0_7\n",
|
| 581 |
+
"<class 'numpy.ndarray'>\n",
|
| 582 |
+
"[ 92.96316227 -63.94471886 -386.71919589] [[ 9.98287320e-01 5.84970973e-02 7.16124545e-04 -1.85000000e+01]\n",
|
| 583 |
+
" [-5.75553216e-02 9.84260798e-01 -1.67086974e-01 2.80000000e+01]\n",
|
| 584 |
+
" [-1.04789566e-02 1.66759595e-01 9.85941887e-01 7.50000000e+00]\n",
|
| 585 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 586 |
+
"0_11\n",
|
| 587 |
+
"<class 'numpy.ndarray'>\n",
|
| 588 |
+
"[ 134.82824558 -57.71182197 -351.24485975] [[ 7.60406315e-01 -2.29581678e-06 -6.49447680e-01 9.10000000e+01]\n",
|
| 589 |
+
" [-1.74647312e-06 -1.00000000e+00 1.49017035e-06 9.60000000e+01]\n",
|
| 590 |
+
" [-6.49447680e-01 1.10794729e-09 -7.60406315e-01 2.20500000e+02]\n",
|
| 591 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 592 |
+
"0_24\n",
|
| 593 |
+
"<class 'numpy.ndarray'>\n",
|
| 594 |
+
"[ 92.14853232 -59.684016 -407.77398695] [[ 8.29673052e-01 1.59621481e-02 5.58021367e-01 -8.50000000e+00]\n",
|
| 595 |
+
" [ 3.19075515e-03 -9.99710381e-01 2.38525681e-02 9.05000000e+01]\n",
|
| 596 |
+
" [ 5.58240473e-01 -1.80093236e-02 -8.29483747e-01 1.22000000e+02]\n",
|
| 597 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 598 |
+
"0_13\n",
|
| 599 |
+
"<class 'numpy.ndarray'>\n",
|
| 600 |
+
"[ 83.5096802 12.12103845 -395.35193727] [[ 9.45313692e-01 -7.14921653e-02 3.18230808e-01 1.00000000e+01]\n",
|
| 601 |
+
" [ 3.07148278e-01 -1.33137226e-01 -9.42302704e-01 1.58500000e+02]\n",
|
| 602 |
+
" [ 1.09735630e-01 9.88515735e-01 -1.03897743e-01 2.30000000e+01]\n",
|
| 603 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 604 |
+
"0_23\n",
|
| 605 |
+
"<class 'numpy.ndarray'>\n",
|
| 606 |
+
"[ 136.76550203 27.0555942 -430.0643254 ] [[-9.98189986e-01 -2.57201474e-02 -5.43616228e-02 1.48000000e+02]\n",
|
| 607 |
+
" [ 5.96299879e-02 -5.40660322e-01 -8.39124918e-01 1.72500000e+02]\n",
|
| 608 |
+
" [-7.80875701e-03 -8.40847731e-01 5.41215420e-01 8.05000000e+01]\n",
|
| 609 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 610 |
+
"0_10\n",
|
| 611 |
+
"<class 'numpy.ndarray'>\n",
|
| 612 |
+
"[ 102.17519885 -59.22792918 -337.21559805] [[ 2.27911219e-01 1.18501671e-02 -9.73609805e-01 1.94500000e+02]\n",
|
| 613 |
+
" [ 1.43385483e-02 -9.99858379e-01 -8.81315395e-03 9.60000000e+01]\n",
|
| 614 |
+
" [-9.73576307e-01 -1.19515341e-02 -2.28048846e-01 1.67500000e+02]\n",
|
| 615 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 616 |
+
"0_19\n",
|
| 617 |
+
"<class 'numpy.ndarray'>\n",
|
| 618 |
+
"[ 107.91231556 6.92816017 -312.97961018] [[-2.22880378e-01 -1.14671409e-01 -9.68077898e-01 2.77000000e+02]\n",
|
| 619 |
+
" [-9.73144829e-01 -3.24667208e-02 2.27892697e-01 8.95000000e+01]\n",
|
| 620 |
+
" [-5.75630888e-02 9.92872775e-01 -1.04355693e-01 3.25000000e+01]\n",
|
| 621 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 622 |
+
"0_1\n",
|
| 623 |
+
"<class 'numpy.ndarray'>\n",
|
| 624 |
+
"[ 152.59134089 -58.9903571 -331.92222143] [[ 8.99942935e-01 1.59255236e-01 -4.05882418e-01 -4.00000000e+00]\n",
|
| 625 |
+
" [ 1.57062009e-01 -9.86820340e-01 -3.89508605e-02 8.90000000e+01]\n",
|
| 626 |
+
" [-4.06736135e-01 -2.86951587e-02 -9.13094878e-01 2.21500000e+02]\n",
|
| 627 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 628 |
+
"0_6\n",
|
| 629 |
+
"<class 'numpy.ndarray'>\n",
|
| 630 |
+
"[ 139.06240714 -60.5229287 -314.24943205] [[-7.76765287e-01 -1.16402414e-02 6.29682660e-01 3.90000000e+01]\n",
|
| 631 |
+
" [ 2.44128127e-02 -9.99634266e-01 1.16360858e-02 9.00000000e+01]\n",
|
| 632 |
+
" [ 6.29316866e-01 2.44108327e-02 7.76765347e-01 -8.25000000e+01]\n",
|
| 633 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 634 |
+
"0_21\n",
|
| 635 |
+
"<class 'numpy.ndarray'>\n",
|
| 636 |
+
"[ 90.29181622 -60.02695941 -330.61546664] [[ 1.56436101e-01 -2.29820216e-06 -9.87688065e-01 2.13500000e+02]\n",
|
| 637 |
+
" [-3.59296649e-07 -1.00000000e+00 2.26994257e-06 9.45000000e+01]\n",
|
| 638 |
+
" [-9.87688065e-01 -2.27934657e-10 -1.56436101e-01 1.54000000e+02]\n",
|
| 639 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 640 |
+
"0_20\n",
|
| 641 |
+
"<class 'numpy.ndarray'>\n",
|
| 642 |
+
"[ 92.08817382 13.77533275 -374.63659039] [[-8.94484162e-01 -1.44307400e-04 -4.47099596e-01 1.73500000e+02]\n",
|
| 643 |
+
" [-4.44025934e-01 -1.16769537e-01 8.88372600e-01 -3.10000000e+01]\n",
|
| 644 |
+
" [-5.23358099e-02 9.93159056e-01 1.04384430e-01 -7.50000000e+00]\n",
|
| 645 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n",
|
| 646 |
+
"0_3\n",
|
| 647 |
+
"<class 'numpy.ndarray'>\n",
|
| 648 |
+
"[ 118.33043626 -57.39900045 -344.31088082] [[ 7.53564060e-01 -2.29676311e-06 6.57374501e-01 -6.30000000e+01]\n",
|
| 649 |
+
" [-1.73075796e-06 -1.00000000e+00 -1.50983351e-06 9.75000000e+01]\n",
|
| 650 |
+
" [ 6.57374501e-01 0.00000000e+00 -7.53564060e-01 1.31500000e+02]\n",
|
| 651 |
+
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n"
|
| 652 |
+
]
|
| 653 |
+
}
|
| 654 |
+
],
|
| 655 |
+
"source": [
|
| 656 |
+
"import json\n",
|
| 657 |
+
"import numpy as np\n",
|
| 658 |
+
"\n",
|
| 659 |
+
"name = \"bottle2\"\n",
|
| 660 |
+
"folder = \"./dataset\"\n",
|
| 661 |
+
"json_path = \"ply_files.json\"\n",
|
| 662 |
+
"\n",
|
| 663 |
+
"try:\n",
|
| 664 |
+
" with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
|
| 665 |
+
" categorized_files = json.load(f)\n",
|
| 666 |
+
"except FileNotFoundError:\n",
|
| 667 |
+
" print(f\"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.\")\n",
|
| 668 |
+
" exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ\n",
|
| 669 |
+
"\n",
|
| 670 |
+
"# 3. ๋ชจ๋ ์นดํ
๊ณ ๋ฆฌ์ ํ์ผ์ ์ํํ๋ ๋ฐ๋ณต๋ฌธ\n",
|
| 671 |
+
"print(\"=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===\")\n",
|
| 672 |
+
"categories = [\"100\", \"75\", \"50\", \"25\", \"0\"]\n",
|
| 673 |
+
"\n",
|
| 674 |
+
"# resolutions ๋์
๋๋ฆฌ๋ฅผ ๊ธฐ์ค์ผ๋ก ์ธ๋ถ ๋ฃจํ๋ฅผ ์คํํฉ๋๋ค.\n",
|
| 675 |
+
"for category in categories:\n",
|
| 676 |
+
" \n",
|
| 677 |
+
" print(f\"\\n--- [์นดํ
๊ณ ๋ฆฌ: {category}\")\n",
|
| 678 |
+
" \n",
|
| 679 |
+
" # JSON์์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ ํ์ผ ๋ฆฌ์คํธ๋ฅผ ๊ฐ์ ธ์ต๋๋ค.\n",
|
| 680 |
+
" # .get(category, [])๋ฅผ ์ฌ์ฉํ๋ฉด JSON์ ํด๋น ์นดํ
๊ณ ๋ฆฌ๊ฐ ์์ด๋ ์ค๋ฅ ์์ด ๋น ๋ฆฌ์คํธ๋ฅผ ๋ฐํํฉ๋๋ค.\n",
|
| 681 |
+
" filenames_in_category = categorized_files.get(category, [])\n",
|
| 682 |
+
" \n",
|
| 683 |
+
" if not filenames_in_category:\n",
|
| 684 |
+
" print(\"์ฒ๋ฆฌํ ํ์ผ์ด ์์ต๋๋ค.\")\n",
|
| 685 |
+
" continue # ํ์ผ์ด ์์ผ๋ฉด ๋ค์ ์นดํ
๊ณ ๋ฆฌ๋ก ๋์ด๊ฐ\n",
|
| 686 |
+
"\n",
|
| 687 |
+
" # ๋ด๋ถ ๋ฃจํ์์ ํด๋น ์นดํ
๊ณ ๋ฆฌ์ ๋ชจ๋ ํ์ผ์ ํ๋์ฉ ์ฒ๋ฆฌํฉ๋๋ค.\n",
|
| 688 |
+
" for filename in filenames_in_category:\n",
|
| 689 |
+
" gt_path =f\"./gt/noisy_filtered_{filename}.json\"\n",
|
| 690 |
+
" print(filename)\n",
|
| 691 |
+
" try:\n",
|
| 692 |
+
" with open(gt_path, \"r\", encoding='utf-8') as f:\n",
|
| 693 |
+
" gt_processed = json.load(f)\n",
|
| 694 |
+
" gt = np.array(gt_processed[f\"noisy_filtered_{filename}\"][\"matrix_world\"])\n",
|
| 695 |
+
"\n",
|
| 696 |
+
" print(type(gt))\n",
|
| 697 |
+
" ## get translted \n",
|
| 698 |
+
" center_path = f\"./centroid/{filename}.txt\"\n",
|
| 699 |
+
" translated = np.loadtxt(center_path) \n",
|
| 700 |
+
" print(translated, gt)\n",
|
| 701 |
+
" ## generate translate T\n",
|
| 702 |
+
" tran_T = np.eye(4)\n",
|
| 703 |
+
" tran_T[0:3,3] = translated\n",
|
| 704 |
+
" \n",
|
| 705 |
+
"\n",
|
| 706 |
+
" final_T = gt @ tran_T\n",
|
| 707 |
+
" real_final_T = np.linalg.inv(final_T)\n",
|
| 708 |
+
"\n",
|
| 709 |
+
" gt_list = real_final_T.tolist()\n",
|
| 710 |
+
" gt_processed[f\"noisy_filtered_{filename}\"][\"matrix_world\"] = gt_list\n",
|
| 711 |
+
"\n",
|
| 712 |
+
" with open(f'./gt_raw/noisy_filtered_{filename}.json', 'w', encoding='utf-8') as f:\n",
|
| 713 |
+
" json.dump(gt_processed, f, ensure_ascii=False, indent=4)\n",
|
| 714 |
+
"\n",
|
| 715 |
+
"\n",
|
| 716 |
+
" except FileNotFoundError:\n",
|
| 717 |
+
" continue"
|
| 718 |
+
]
|
| 719 |
+
},
|
| 720 |
+
{
|
| 721 |
+
"cell_type": "markdown",
|
| 722 |
+
"id": "a0277328",
|
| 723 |
+
"metadata": {},
|
| 724 |
+
"source": [
|
| 725 |
+
"## verify"
|
| 726 |
+
]
|
| 727 |
+
},
|
| 728 |
+
{
|
| 729 |
+
"cell_type": "code",
|
| 730 |
+
"execution_count": 1,
|
| 731 |
+
"id": "463b3159",
|
| 732 |
+
"metadata": {},
|
| 733 |
+
"outputs": [
|
| 734 |
+
{
|
| 735 |
+
"name": "stdout",
|
| 736 |
+
"output_type": "stream",
|
| 737 |
+
"text": [
|
| 738 |
+
"Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
|
| 739 |
+
"[Open3D INFO] WebRTC GUI backend enabled.\n",
|
| 740 |
+
"[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
|
| 741 |
+
"100_7\n",
|
| 742 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./gt_filtered.ply\u001b[0;m\n"
|
| 743 |
+
]
|
| 744 |
+
},
|
| 745 |
+
{
|
| 746 |
+
"name": "stderr",
|
| 747 |
+
"output_type": "stream",
|
| 748 |
+
"text": [
|
| 749 |
+
"RPly: Unexpected end of file\n",
|
| 750 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 751 |
+
]
|
| 752 |
+
}
|
| 753 |
+
],
|
| 754 |
+
"source": [
|
| 755 |
+
"import json\n",
|
| 756 |
+
"import numpy as np\n",
|
| 757 |
+
"import open3d as o3d\n",
|
| 758 |
+
"\n",
|
| 759 |
+
"\n",
|
| 760 |
+
"def get_T(file_path):\n",
|
| 761 |
+
" with open(file_path, 'r') as f:\n",
|
| 762 |
+
" T_matrix = np.loadtxt(file_path)\n",
|
| 763 |
+
" print(T_matrix)\n",
|
| 764 |
+
" return T_matrix\n",
|
| 765 |
+
" \n",
|
| 766 |
+
"filenames = []\n",
|
| 767 |
+
"with open(\"filename.txt\", \"r\") as f:\n",
|
| 768 |
+
" for line in f:\n",
|
| 769 |
+
" filenames.append(line.strip())\n",
|
| 770 |
+
"\n",
|
| 771 |
+
"\n",
|
| 772 |
+
"filename = filenames[0]\n",
|
| 773 |
+
"print(filename)\n",
|
| 774 |
+
"\n",
|
| 775 |
+
"with open(f\"./gt_raw/noisy_filtered_{filename}.json\", 'r') as f:\n",
|
| 776 |
+
" loaded_data = json.load(f)\n",
|
| 777 |
+
"\n",
|
| 778 |
+
"\n",
|
| 779 |
+
"\n",
|
| 780 |
+
"noisy_data = loaded_data[f'noisy_filtered_{filename}']\n",
|
| 781 |
+
"T_matrix = noisy_data['matrix_world']\n",
|
| 782 |
+
"\n",
|
| 783 |
+
"\n",
|
| 784 |
+
"##Translated\n",
|
| 785 |
+
"\n",
|
| 786 |
+
"gt_path = \"./gt_filtered.ply\"\n",
|
| 787 |
+
"noisy_path = f\"./dataset/{filename}.ply\"\n",
|
| 788 |
+
"translated_path = f\"./result3/result_{filename}.ply\"\n",
|
| 789 |
+
"\n",
|
| 790 |
+
"\n",
|
| 791 |
+
"\n",
|
| 792 |
+
"gt_pcd = o3d.io.read_point_cloud(gt_path)\n",
|
| 793 |
+
"gt_pcd.paint_uniform_color([0,0,1])\n",
|
| 794 |
+
"noisy_pcd = o3d.io.read_point_cloud(noisy_path)\n",
|
| 795 |
+
"noisy_pcd.paint_uniform_color([1,0,0])\n",
|
| 796 |
+
"\n",
|
| 797 |
+
"translated_noisy_pcd = o3d.io.read_point_cloud(translated_path)\n",
|
| 798 |
+
"translated_noisy_pcd.paint_uniform_color([0,1,0])\n",
|
| 799 |
+
"\n",
|
| 800 |
+
"\n",
|
| 801 |
+
"gt = np.array(T_matrix)\n",
|
| 802 |
+
"\n",
|
| 803 |
+
"## move and check gt and noisy\n",
|
| 804 |
+
"\n",
|
| 805 |
+
"o3d.visualization.draw_geometries([gt_pcd, noisy_pcd, translated_noisy_pcd])\n",
|
| 806 |
+
"# noisy_pcd.transform(tran_T)\n",
|
| 807 |
+
"gt_pcd.transform(gt)\n",
|
| 808 |
+
"\n",
|
| 809 |
+
"o3d.visualization.draw_geometries([noisy_pcd, translated_noisy_pcd])\n"
|
| 810 |
+
]
|
| 811 |
+
}
|
| 812 |
+
],
|
| 813 |
+
"metadata": {
|
| 814 |
+
"kernelspec": {
|
| 815 |
+
"display_name": "Python 3",
|
| 816 |
+
"language": "python",
|
| 817 |
+
"name": "python3"
|
| 818 |
+
},
|
| 819 |
+
"language_info": {
|
| 820 |
+
"codemirror_mode": {
|
| 821 |
+
"name": "ipython",
|
| 822 |
+
"version": 3
|
| 823 |
+
},
|
| 824 |
+
"file_extension": ".py",
|
| 825 |
+
"mimetype": "text/x-python",
|
| 826 |
+
"name": "python",
|
| 827 |
+
"nbconvert_exporter": "python",
|
| 828 |
+
"pygments_lexer": "ipython3",
|
| 829 |
+
"version": "3.10.12"
|
| 830 |
+
}
|
| 831 |
+
},
|
| 832 |
+
"nbformat": 4,
|
| 833 |
+
"nbformat_minor": 5
|
| 834 |
+
}
|
data/glasses/gt_filtered.ply
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/glasses/inference_ICP.ipynb
ADDED
|
@@ -0,0 +1,482 @@
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|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1271,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"# conda activate vision\n",
|
| 10 |
+
"# cd build\n",
|
| 11 |
+
"# cmake -DCMAKE_BUILD_TYPE=Release ..\n",
|
| 12 |
+
"# make\n",
|
| 13 |
+
"# ./FRICP ./data/bottle/tea_gt_filtered.ply ./data/bottle/tea_noisy_filtered.ply ./data/bottle/res/ 3\n",
|
| 14 |
+
"\n",
|
| 15 |
+
"\n",
|
| 16 |
+
"# 100_16 is the best thing. "
|
| 17 |
+
]
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"cell_type": "code",
|
| 21 |
+
"execution_count": 1272,
|
| 22 |
+
"metadata": {},
|
| 23 |
+
"outputs": [
|
| 24 |
+
{
|
| 25 |
+
"name": "stdout",
|
| 26 |
+
"output_type": "stream",
|
| 27 |
+
"text": [
|
| 28 |
+
"75_11\n"
|
| 29 |
+
]
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"source": [
|
| 33 |
+
"import open3d as o3d\n",
|
| 34 |
+
"import numpy as np\n",
|
| 35 |
+
"\n",
|
| 36 |
+
"file_names = []\n",
|
| 37 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 38 |
+
" for line in f:\n",
|
| 39 |
+
" file_names.append(line.strip())\n",
|
| 40 |
+
"filename = file_names[0]\n",
|
| 41 |
+
"print(filename)\n",
|
| 42 |
+
"\n"
|
| 43 |
+
]
|
| 44 |
+
},
|
| 45 |
+
{
|
| 46 |
+
"cell_type": "markdown",
|
| 47 |
+
"metadata": {},
|
| 48 |
+
"source": [
|
| 49 |
+
"# Modify initial file"
|
| 50 |
+
]
|
| 51 |
+
},
|
| 52 |
+
{
|
| 53 |
+
"cell_type": "code",
|
| 54 |
+
"execution_count": 1273,
|
| 55 |
+
"metadata": {},
|
| 56 |
+
"outputs": [
|
| 57 |
+
{
|
| 58 |
+
"name": "stdout",
|
| 59 |
+
"output_type": "stream",
|
| 60 |
+
"text": [
|
| 61 |
+
"\n",
|
| 62 |
+
"์์
์๋ฃ!\n",
|
| 63 |
+
"'./initialized_result/initial_75_11.ply' ํ์ผ์ด ์ฑ๊ณต์ ์ผ๋ก ์์ฑ๋์์ต๋๋ค.\n"
|
| 64 |
+
]
|
| 65 |
+
}
|
| 66 |
+
],
|
| 67 |
+
"source": [
|
| 68 |
+
"\n",
|
| 69 |
+
"output_filename = f'./initialized_result/initial_{filename}.ply'\n",
|
| 70 |
+
"\n",
|
| 71 |
+
"# 1. read file\n",
|
| 72 |
+
"\n",
|
| 73 |
+
"with open(f'./initialized_result/initial_{filename}.ply', 'r') as f:\n",
|
| 74 |
+
" lines = f.readlines()\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"# 2. seperate data and header \n",
|
| 77 |
+
"header_lines = []\n",
|
| 78 |
+
"data_lines = []\n",
|
| 79 |
+
"is_header = True\n",
|
| 80 |
+
"\n",
|
| 81 |
+
"for line in lines:\n",
|
| 82 |
+
" if \"end_header\" in line:\n",
|
| 83 |
+
" is_header = False\n",
|
| 84 |
+
" continue\n",
|
| 85 |
+
" \n",
|
| 86 |
+
" if is_header:\n",
|
| 87 |
+
" header_lines.append(line)\n",
|
| 88 |
+
" \n",
|
| 89 |
+
" else: \n",
|
| 90 |
+
" parts = line.strip().split()\n",
|
| 91 |
+
" if len(parts) >= 3:\n",
|
| 92 |
+
" data_lines.append(f\"{parts[0]} {parts[1]} {parts[2]}\\n\")\n",
|
| 93 |
+
"\n",
|
| 94 |
+
"\n",
|
| 95 |
+
"# 3. modify header\n",
|
| 96 |
+
"# vertex\n",
|
| 97 |
+
"num_points = len(data_lines)\n",
|
| 98 |
+
"\n",
|
| 99 |
+
"# generate new header\n",
|
| 100 |
+
"\n",
|
| 101 |
+
"new_header = f\"\"\"ply\n",
|
| 102 |
+
"format ascii 1.0\n",
|
| 103 |
+
"element vertex {num_points}\n",
|
| 104 |
+
"property float x\n",
|
| 105 |
+
"property float y\n",
|
| 106 |
+
"property float z\n",
|
| 107 |
+
"element camera 1\n",
|
| 108 |
+
"property float view_px\n",
|
| 109 |
+
"property float view_py\n",
|
| 110 |
+
"property float view_pz\n",
|
| 111 |
+
"property float x_axisx\n",
|
| 112 |
+
"property float x_axisy\n",
|
| 113 |
+
"property float x_axisz\n",
|
| 114 |
+
"property float y_axisx\n",
|
| 115 |
+
"property float y_axisy\n",
|
| 116 |
+
"property float y_axisz\n",
|
| 117 |
+
"property float z_axisx\n",
|
| 118 |
+
"property float z_axisy\n",
|
| 119 |
+
"property float z_axisz\n",
|
| 120 |
+
"element phoxi_frame_params 1\n",
|
| 121 |
+
"property uint32 frame_width\n",
|
| 122 |
+
"property uint32 frame_height\n",
|
| 123 |
+
"property uint32 frame_index\n",
|
| 124 |
+
"property float frame_start_time\n",
|
| 125 |
+
"property float frame_duration\n",
|
| 126 |
+
"property float frame_computation_duration\n",
|
| 127 |
+
"property float frame_transfer_duration\n",
|
| 128 |
+
"property int32 total_scan_count\n",
|
| 129 |
+
"element camera_matrix 1\n",
|
| 130 |
+
"property float cm0\n",
|
| 131 |
+
"property float cm1\n",
|
| 132 |
+
"property float cm2\n",
|
| 133 |
+
"property float cm3\n",
|
| 134 |
+
"property float cm4\n",
|
| 135 |
+
"property float cm5\n",
|
| 136 |
+
"property float cm6\n",
|
| 137 |
+
"property float cm7\n",
|
| 138 |
+
"property float cm8\n",
|
| 139 |
+
"element distortion_matrix 1\n",
|
| 140 |
+
"property float dm0\n",
|
| 141 |
+
"property float dm1\n",
|
| 142 |
+
"property float dm2\n",
|
| 143 |
+
"property float dm3\n",
|
| 144 |
+
"property float dm4\n",
|
| 145 |
+
"property float dm5\n",
|
| 146 |
+
"property float dm6\n",
|
| 147 |
+
"property float dm7\n",
|
| 148 |
+
"property float dm8\n",
|
| 149 |
+
"property float dm9\n",
|
| 150 |
+
"property float dm10\n",
|
| 151 |
+
"property float dm11\n",
|
| 152 |
+
"property float dm12\n",
|
| 153 |
+
"property float dm13\n",
|
| 154 |
+
"element camera_resolution 1\n",
|
| 155 |
+
"property float width\n",
|
| 156 |
+
"property float height\n",
|
| 157 |
+
"element frame_binning 1\n",
|
| 158 |
+
"property float horizontal\n",
|
| 159 |
+
"property float vertical\n",
|
| 160 |
+
"end_header\n",
|
| 161 |
+
"\"\"\"\n",
|
| 162 |
+
"\n",
|
| 163 |
+
"#4. write 4file \n",
|
| 164 |
+
"\n",
|
| 165 |
+
"with open(output_filename,'w') as f:\n",
|
| 166 |
+
" f.write(new_header)\n",
|
| 167 |
+
"\n",
|
| 168 |
+
" for line in data_lines:\n",
|
| 169 |
+
" f.write(line)\n",
|
| 170 |
+
"\n",
|
| 171 |
+
"\n",
|
| 172 |
+
"print(\"\\n์์
์๋ฃ!\")\n",
|
| 173 |
+
"print(f\"'{output_filename}' ํ์ผ์ด ์ฑ๊ณต์ ์ผ๋ก ์์ฑ๋์์ต๋๋ค.\")\n"
|
| 174 |
+
]
|
| 175 |
+
},
|
| 176 |
+
{
|
| 177 |
+
"cell_type": "markdown",
|
| 178 |
+
"metadata": {},
|
| 179 |
+
"source": [
|
| 180 |
+
"### Source PCD"
|
| 181 |
+
]
|
| 182 |
+
},
|
| 183 |
+
{
|
| 184 |
+
"cell_type": "code",
|
| 185 |
+
"execution_count": null,
|
| 186 |
+
"metadata": {},
|
| 187 |
+
"outputs": [],
|
| 188 |
+
"source": []
|
| 189 |
+
},
|
| 190 |
+
{
|
| 191 |
+
"cell_type": "code",
|
| 192 |
+
"execution_count": 1274,
|
| 193 |
+
"metadata": {},
|
| 194 |
+
"outputs": [
|
| 195 |
+
{
|
| 196 |
+
"name": "stdout",
|
| 197 |
+
"output_type": "stream",
|
| 198 |
+
"text": [
|
| 199 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./initialized_result/initial_75_11.ply\u001b[0;m\n",
|
| 200 |
+
"Source shape: (6651, 3)\n"
|
| 201 |
+
]
|
| 202 |
+
},
|
| 203 |
+
{
|
| 204 |
+
"name": "stderr",
|
| 205 |
+
"output_type": "stream",
|
| 206 |
+
"text": [
|
| 207 |
+
"RPly: Unexpected end of file\n",
|
| 208 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 209 |
+
]
|
| 210 |
+
}
|
| 211 |
+
],
|
| 212 |
+
"source": [
|
| 213 |
+
"\n",
|
| 214 |
+
"\n",
|
| 215 |
+
"\n",
|
| 216 |
+
"source_path = f\"./initialized_result/initial_{filename}.ply\"\n",
|
| 217 |
+
"\n",
|
| 218 |
+
"source_pcd = o3d.io.read_point_cloud(source_path)\n",
|
| 219 |
+
"\n",
|
| 220 |
+
"\n",
|
| 221 |
+
"\n",
|
| 222 |
+
"source_pcd_array = np.asarray(source_pcd.points)\n",
|
| 223 |
+
"print(\"Source shape:\", source_pcd_array.shape)\n",
|
| 224 |
+
"\n",
|
| 225 |
+
"coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])\n",
|
| 226 |
+
"o3d.visualization.draw_geometries([source_pcd,coord_frame])"
|
| 227 |
+
]
|
| 228 |
+
},
|
| 229 |
+
{
|
| 230 |
+
"cell_type": "code",
|
| 231 |
+
"execution_count": null,
|
| 232 |
+
"metadata": {},
|
| 233 |
+
"outputs": [],
|
| 234 |
+
"source": []
|
| 235 |
+
},
|
| 236 |
+
{
|
| 237 |
+
"cell_type": "markdown",
|
| 238 |
+
"metadata": {},
|
| 239 |
+
"source": [
|
| 240 |
+
"### Target PCD"
|
| 241 |
+
]
|
| 242 |
+
},
|
| 243 |
+
{
|
| 244 |
+
"cell_type": "code",
|
| 245 |
+
"execution_count": 1275,
|
| 246 |
+
"metadata": {},
|
| 247 |
+
"outputs": [
|
| 248 |
+
{
|
| 249 |
+
"name": "stderr",
|
| 250 |
+
"output_type": "stream",
|
| 251 |
+
"text": [
|
| 252 |
+
"RPly: Unexpected end of file\n",
|
| 253 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 254 |
+
]
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"name": "stdout",
|
| 258 |
+
"output_type": "stream",
|
| 259 |
+
"text": [
|
| 260 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: gt_filtered.ply\u001b[0;m\n",
|
| 261 |
+
"Target shape: (50000, 3)\n"
|
| 262 |
+
]
|
| 263 |
+
}
|
| 264 |
+
],
|
| 265 |
+
"source": [
|
| 266 |
+
"target_path = f\"gt_filtered.ply\"\n",
|
| 267 |
+
"target_pcd = o3d.io.read_point_cloud(target_path)\n",
|
| 268 |
+
"\n",
|
| 269 |
+
"target_pcd_array = np.asarray(target_pcd.points)\n",
|
| 270 |
+
"print(\"Target shape:\", target_pcd_array.shape)\n",
|
| 271 |
+
"\n",
|
| 272 |
+
"o3d.visualization.draw_geometries([target_pcd, coord_frame])"
|
| 273 |
+
]
|
| 274 |
+
},
|
| 275 |
+
{
|
| 276 |
+
"cell_type": "markdown",
|
| 277 |
+
"metadata": {},
|
| 278 |
+
"source": [
|
| 279 |
+
"## Execute termianl"
|
| 280 |
+
]
|
| 281 |
+
},
|
| 282 |
+
{
|
| 283 |
+
"cell_type": "code",
|
| 284 |
+
"execution_count": 1276,
|
| 285 |
+
"metadata": {},
|
| 286 |
+
"outputs": [
|
| 287 |
+
{
|
| 288 |
+
"name": "stdout",
|
| 289 |
+
"output_type": "stream",
|
| 290 |
+
"text": [
|
| 291 |
+
"/home/cam/Fast-Robust-ICP/data/glasses\n",
|
| 292 |
+
"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\n",
|
| 293 |
+
"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\n",
|
| 294 |
+
"source: 3x6651\n",
|
| 295 |
+
"target: 3x50000\n",
|
| 296 |
+
"scale = 607.904\n",
|
| 297 |
+
"begin registration...\n",
|
| 298 |
+
"Registration done!\n",
|
| 299 |
+
"\n"
|
| 300 |
+
]
|
| 301 |
+
}
|
| 302 |
+
],
|
| 303 |
+
"source": [
|
| 304 |
+
"# ./FRICP ./data/bottle_2/gt_filtered.ply ./data/bottle_2/result/noisy_filtered_100_1.ply ./data/bottle_2/res 3 execute\n",
|
| 305 |
+
"import os\n",
|
| 306 |
+
"print(os.getcwd())\n",
|
| 307 |
+
"\n",
|
| 308 |
+
"import subprocess\n",
|
| 309 |
+
"\n",
|
| 310 |
+
"cmd = [\n",
|
| 311 |
+
" '../../FRICP',\n",
|
| 312 |
+
" './gt_filtered.ply',\n",
|
| 313 |
+
" f'./initialized_result/initial_{filename}.ply',\n",
|
| 314 |
+
" './res',\n",
|
| 315 |
+
" '3'\n",
|
| 316 |
+
"]\n",
|
| 317 |
+
"\n",
|
| 318 |
+
"try:\n",
|
| 319 |
+
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
| 320 |
+
"\n",
|
| 321 |
+
" print(\"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\")\n",
|
| 322 |
+
" print(\"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\")\n",
|
| 323 |
+
" print(result.stdout)\n",
|
| 324 |
+
"\n",
|
| 325 |
+
"except FileNotFoundError:\n",
|
| 326 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 327 |
+
" print(f\"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.\")\n",
|
| 328 |
+
" print(\"๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.\")\n",
|
| 329 |
+
"\n",
|
| 330 |
+
"except subprocess.CalledProcessError as e:\n",
|
| 331 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 332 |
+
" print(f\"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})\")\n",
|
| 333 |
+
" print(\"\\n--- STDERR (์๋ฌ ์์ธ) ---\")\n",
|
| 334 |
+
" print(e.stderr)\n"
|
| 335 |
+
]
|
| 336 |
+
},
|
| 337 |
+
{
|
| 338 |
+
"cell_type": "markdown",
|
| 339 |
+
"metadata": {},
|
| 340 |
+
"source": [
|
| 341 |
+
"### Change the path for result\n"
|
| 342 |
+
]
|
| 343 |
+
},
|
| 344 |
+
{
|
| 345 |
+
"cell_type": "code",
|
| 346 |
+
"execution_count": 1277,
|
| 347 |
+
"metadata": {},
|
| 348 |
+
"outputs": [
|
| 349 |
+
{
|
| 350 |
+
"name": "stdout",
|
| 351 |
+
"output_type": "stream",
|
| 352 |
+
"text": [
|
| 353 |
+
"Successfully moved and renamed 'resm3reg_pc.ply' to './result/final_result_75_11.ply'\n",
|
| 354 |
+
"Successfully moved and renamed 'resm3trans.txt' to './result/final_result_75_11.txt'\n"
|
| 355 |
+
]
|
| 356 |
+
}
|
| 357 |
+
],
|
| 358 |
+
"source": [
|
| 359 |
+
"import shutil\n",
|
| 360 |
+
"import os\n",
|
| 361 |
+
"\n",
|
| 362 |
+
"transformed_path = \"resm3reg_pc.ply\"\n",
|
| 363 |
+
"destination_path = f\"./result/final_result_{filename}.ply\"\n",
|
| 364 |
+
"transformed_path2 = \"resm3trans.txt\"\n",
|
| 365 |
+
"destination_path2 = f\"./result/final_result_{filename}.txt\"\n",
|
| 366 |
+
"\n",
|
| 367 |
+
"shutil.move(transformed_path, destination_path)\n",
|
| 368 |
+
"print(f\"Successfully moved and renamed '{transformed_path}' to '{destination_path}'\")\n",
|
| 369 |
+
"\n",
|
| 370 |
+
"\n",
|
| 371 |
+
"\n",
|
| 372 |
+
"shutil.move(transformed_path2, destination_path2)\n",
|
| 373 |
+
"print(f\"Successfully moved and renamed '{transformed_path2}' to '{destination_path2}'\")\n",
|
| 374 |
+
"\n",
|
| 375 |
+
"\n"
|
| 376 |
+
]
|
| 377 |
+
},
|
| 378 |
+
{
|
| 379 |
+
"cell_type": "markdown",
|
| 380 |
+
"metadata": {},
|
| 381 |
+
"source": [
|
| 382 |
+
"### Transformed Source PCD"
|
| 383 |
+
]
|
| 384 |
+
},
|
| 385 |
+
{
|
| 386 |
+
"cell_type": "code",
|
| 387 |
+
"execution_count": 1278,
|
| 388 |
+
"metadata": {},
|
| 389 |
+
"outputs": [
|
| 390 |
+
{
|
| 391 |
+
"name": "stdout",
|
| 392 |
+
"output_type": "stream",
|
| 393 |
+
"text": [
|
| 394 |
+
"Transformed shape: (6651, 3)\n"
|
| 395 |
+
]
|
| 396 |
+
}
|
| 397 |
+
],
|
| 398 |
+
"source": [
|
| 399 |
+
"\n",
|
| 400 |
+
"transformed_pcd = o3d.io.read_point_cloud(destination_path)\n",
|
| 401 |
+
"\n",
|
| 402 |
+
"transformed_pcd_array = np.asarray(transformed_pcd.points)\n",
|
| 403 |
+
"print(\"Transformed shape:\", transformed_pcd_array.shape)\n",
|
| 404 |
+
"\n",
|
| 405 |
+
"o3d.visualization.draw_geometries([transformed_pcd, coord_frame])"
|
| 406 |
+
]
|
| 407 |
+
},
|
| 408 |
+
{
|
| 409 |
+
"cell_type": "markdown",
|
| 410 |
+
"metadata": {},
|
| 411 |
+
"source": [
|
| 412 |
+
"### Source (Original) + Target"
|
| 413 |
+
]
|
| 414 |
+
},
|
| 415 |
+
{
|
| 416 |
+
"cell_type": "code",
|
| 417 |
+
"execution_count": 1279,
|
| 418 |
+
"metadata": {},
|
| 419 |
+
"outputs": [],
|
| 420 |
+
"source": [
|
| 421 |
+
"source_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 422 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 423 |
+
"\n",
|
| 424 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 425 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 426 |
+
"vis.add_geometry(source_pcd)\n",
|
| 427 |
+
"vis.add_geometry(target_pcd)\n",
|
| 428 |
+
"vis.add_geometry(coord_frame)\n",
|
| 429 |
+
"vis.run()\n",
|
| 430 |
+
"\n",
|
| 431 |
+
"\n",
|
| 432 |
+
"vis.destroy_window()"
|
| 433 |
+
]
|
| 434 |
+
},
|
| 435 |
+
{
|
| 436 |
+
"cell_type": "markdown",
|
| 437 |
+
"metadata": {},
|
| 438 |
+
"source": [
|
| 439 |
+
"### Transformed + Target"
|
| 440 |
+
]
|
| 441 |
+
},
|
| 442 |
+
{
|
| 443 |
+
"cell_type": "code",
|
| 444 |
+
"execution_count": 1280,
|
| 445 |
+
"metadata": {},
|
| 446 |
+
"outputs": [],
|
| 447 |
+
"source": [
|
| 448 |
+
"transformed_pcd.paint_uniform_color([1, 0, 0])\n",
|
| 449 |
+
"target_pcd.paint_uniform_color([0, 1, 0])\n",
|
| 450 |
+
"\n",
|
| 451 |
+
"vis = o3d.visualization.Visualizer()\n",
|
| 452 |
+
"vis.create_window(window_name=\"Point Cloud Viewer\", width=1200, height=800, visible=True)\n",
|
| 453 |
+
"vis.add_geometry(transformed_pcd)\n",
|
| 454 |
+
"vis.add_geometry(target_pcd)\n",
|
| 455 |
+
"vis.add_geometry(coord_frame)\n",
|
| 456 |
+
"vis.run()\n",
|
| 457 |
+
"vis.destroy_window()"
|
| 458 |
+
]
|
| 459 |
+
}
|
| 460 |
+
],
|
| 461 |
+
"metadata": {
|
| 462 |
+
"kernelspec": {
|
| 463 |
+
"display_name": "Python 3",
|
| 464 |
+
"language": "python",
|
| 465 |
+
"name": "python3"
|
| 466 |
+
},
|
| 467 |
+
"language_info": {
|
| 468 |
+
"codemirror_mode": {
|
| 469 |
+
"name": "ipython",
|
| 470 |
+
"version": 3
|
| 471 |
+
},
|
| 472 |
+
"file_extension": ".py",
|
| 473 |
+
"mimetype": "text/x-python",
|
| 474 |
+
"name": "python",
|
| 475 |
+
"nbconvert_exporter": "python",
|
| 476 |
+
"pygments_lexer": "ipython3",
|
| 477 |
+
"version": "3.10.12"
|
| 478 |
+
}
|
| 479 |
+
},
|
| 480 |
+
"nbformat": 4,
|
| 481 |
+
"nbformat_minor": 2
|
| 482 |
+
}
|
data/glasses/initial_guess(kiss_match).ipynb
ADDED
|
@@ -0,0 +1,238 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"id": "c97d9003",
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"source": [
|
| 8 |
+
"## PCD file transformation"
|
| 9 |
+
]
|
| 10 |
+
},
|
| 11 |
+
{
|
| 12 |
+
"cell_type": "code",
|
| 13 |
+
"execution_count": 29,
|
| 14 |
+
"id": "57266b06",
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [
|
| 17 |
+
{
|
| 18 |
+
"name": "stdout",
|
| 19 |
+
"output_type": "stream",
|
| 20 |
+
"text": [
|
| 21 |
+
"0_24\n",
|
| 22 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./gt_filtered.ply\u001b[0;m\n",
|
| 23 |
+
"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\n"
|
| 24 |
+
]
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"name": "stderr",
|
| 28 |
+
"output_type": "stream",
|
| 29 |
+
"text": [
|
| 30 |
+
"RPly: Unexpected end of file\n",
|
| 31 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 32 |
+
]
|
| 33 |
+
}
|
| 34 |
+
],
|
| 35 |
+
"source": [
|
| 36 |
+
"import open3d as o3d\n",
|
| 37 |
+
"import numpy as np\n",
|
| 38 |
+
"\n",
|
| 39 |
+
"file_names = []\n",
|
| 40 |
+
"with open('filename.txt', 'r') as f:\n",
|
| 41 |
+
" for line in f:\n",
|
| 42 |
+
" file_names.append(line.strip())\n",
|
| 43 |
+
"filename = file_names[0]\n",
|
| 44 |
+
"print(filename)\n",
|
| 45 |
+
"\n",
|
| 46 |
+
"\n",
|
| 47 |
+
"# PLY ํ์ผ ์ฝ๊ธฐ\n",
|
| 48 |
+
"pcd = o3d.io.read_point_cloud(\"./gt_filtered.ply\")\n",
|
| 49 |
+
"\n",
|
| 50 |
+
"# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)\n",
|
| 51 |
+
"o3d.io.write_point_cloud(\"./initialize_pcdfile/gt_filtered.pcd\", pcd)\n",
|
| 52 |
+
"\n",
|
| 53 |
+
"# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:\n",
|
| 54 |
+
"# o3d.io.write_point_cloud(\"output_ascii.pcd\", pcd, write_ascii=True)\n",
|
| 55 |
+
"\n",
|
| 56 |
+
"print(\"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\")"
|
| 57 |
+
]
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"cell_type": "code",
|
| 61 |
+
"execution_count": 30,
|
| 62 |
+
"id": "8b0bc642",
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"outputs": [
|
| 65 |
+
{
|
| 66 |
+
"name": "stdout",
|
| 67 |
+
"output_type": "stream",
|
| 68 |
+
"text": [
|
| 69 |
+
"\u001b[1;33m[Open3D WARNING] Read PLY failed: unable to read file: ./noisy_result/noisy_filtered_0_24.ply\u001b[0;m\n",
|
| 70 |
+
"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\n"
|
| 71 |
+
]
|
| 72 |
+
},
|
| 73 |
+
{
|
| 74 |
+
"name": "stderr",
|
| 75 |
+
"output_type": "stream",
|
| 76 |
+
"text": [
|
| 77 |
+
"RPly: Unexpected end of file\n",
|
| 78 |
+
"RPly: Error reading 'view_px' of 'camera' number 0\n"
|
| 79 |
+
]
|
| 80 |
+
}
|
| 81 |
+
],
|
| 82 |
+
"source": [
|
| 83 |
+
"# PLY ํ์ผ ์ฝ๊ธฐ\n",
|
| 84 |
+
"pcd = o3d.io.read_point_cloud(f\"./noisy_result/noisy_filtered_{filename}.ply\")\n",
|
| 85 |
+
"\n",
|
| 86 |
+
"# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)\n",
|
| 87 |
+
"o3d.io.write_point_cloud(f\"./initialize_pcdfile/first_{filename}.pcd\", pcd)\n",
|
| 88 |
+
"\n",
|
| 89 |
+
"# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:\n",
|
| 90 |
+
"# o3d.io.write_point_cloud(\"output_ascii.pcd\", pcd, write_ascii=True)\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"print(\"PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.\")"
|
| 93 |
+
]
|
| 94 |
+
},
|
| 95 |
+
{
|
| 96 |
+
"cell_type": "markdown",
|
| 97 |
+
"id": "fcdc0f5e",
|
| 98 |
+
"metadata": {},
|
| 99 |
+
"source": [
|
| 100 |
+
"## Execute initial Guess"
|
| 101 |
+
]
|
| 102 |
+
},
|
| 103 |
+
{
|
| 104 |
+
"cell_type": "code",
|
| 105 |
+
"execution_count": 31,
|
| 106 |
+
"id": "5d191e44",
|
| 107 |
+
"metadata": {},
|
| 108 |
+
"outputs": [
|
| 109 |
+
{
|
| 110 |
+
"name": "stdout",
|
| 111 |
+
"output_type": "stream",
|
| 112 |
+
"text": [
|
| 113 |
+
"/home/cam/Fast-Robust-ICP/data/glasses\n",
|
| 114 |
+
"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\n",
|
| 115 |
+
"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\n",
|
| 116 |
+
"Loaded source point cloud: (2537, 3)\n",
|
| 117 |
+
"Loaded target point cloud: (50000, 3)\n",
|
| 118 |
+
"Resolution: 0.5\n",
|
| 119 |
+
"Yaw Augmentation Angle: None\n",
|
| 120 |
+
"============== Time ==============\n",
|
| 121 |
+
"Voxelization: 0.00200241 sec\n",
|
| 122 |
+
"Extraction : 0.0745216 sec\n",
|
| 123 |
+
"Pruning : 0.011958 sec\n",
|
| 124 |
+
"Matching : 0.0433352 sec\n",
|
| 125 |
+
"Solving : 2.9214e-05 sec\n",
|
| 126 |
+
"----------------------------------\n",
|
| 127 |
+
"\u001b[1;32mTotal : 0.131847 sec\u001b[0m\n",
|
| 128 |
+
"====== # of correspondences ======\n",
|
| 129 |
+
"# initial pairs : 46\n",
|
| 130 |
+
"# pruned pairs : 13\n",
|
| 131 |
+
"----------------------------------\n",
|
| 132 |
+
"\u001b[1;36m# rot inliers : 4\n",
|
| 133 |
+
"# trans inliers : 4\u001b[0m\n",
|
| 134 |
+
"==================================\n",
|
| 135 |
+
"\u001b[1;33m=> Registration might have failed :(\u001b[0m\n",
|
| 136 |
+
"\n",
|
| 137 |
+
"<_kiss_matcher.RegistrationSolution object at 0x71aad89217b0>\n",
|
| 138 |
+
"ply complete.\n",
|
| 139 |
+
"1.0์ด ๋์ ์๊ฐํ ์ฐฝ์ ํ์ํฉ๋๋ค...\n",
|
| 140 |
+
"Visualization complete.\n",
|
| 141 |
+
"\n"
|
| 142 |
+
]
|
| 143 |
+
}
|
| 144 |
+
],
|
| 145 |
+
"source": [
|
| 146 |
+
"import os\n",
|
| 147 |
+
"print(os.getcwd())\n",
|
| 148 |
+
"\n",
|
| 149 |
+
"import subprocess\n",
|
| 150 |
+
"\n",
|
| 151 |
+
"cmd = [\n",
|
| 152 |
+
" 'python3',\n",
|
| 153 |
+
" '../../../KISS-Matcher/python/examples/run_kiss_matcher.py',\n",
|
| 154 |
+
" '--src_path',\n",
|
| 155 |
+
" f'./initialize_pcdfile/first_{filename}.pcd',\n",
|
| 156 |
+
" '--tgt_path',\n",
|
| 157 |
+
" './initialize_pcdfile/gt_filtered.pcd',\n",
|
| 158 |
+
" '--resolution',\n",
|
| 159 |
+
" '0.5'\n",
|
| 160 |
+
"\n",
|
| 161 |
+
"]\n",
|
| 162 |
+
"try:\n",
|
| 163 |
+
" result = subprocess.run(cmd, capture_output=True, text=True, check=True)\n",
|
| 164 |
+
"\n",
|
| 165 |
+
" print(\"--- STDOUT (ํ์ค ์ถ๋ ฅ) ---\")\n",
|
| 166 |
+
" print(\"๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.\")\n",
|
| 167 |
+
" print(result.stdout)\n",
|
| 168 |
+
"\n",
|
| 169 |
+
"except FileNotFoundError:\n",
|
| 170 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 171 |
+
" print(f\"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.\")\n",
|
| 172 |
+
" print(\"๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.\")\n",
|
| 173 |
+
"\n",
|
| 174 |
+
"except subprocess.CalledProcessError as e:\n",
|
| 175 |
+
" print(\"--- ์๋ฌ ๋ฐ์! ---\")\n",
|
| 176 |
+
" print(f\"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})\")\n",
|
| 177 |
+
" print(\"\\n--- STDERR (์๋ฌ ์์ธ) ---\")\n",
|
| 178 |
+
" print(e.stderr)\n"
|
| 179 |
+
]
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"cell_type": "markdown",
|
| 183 |
+
"id": "0128f9e3",
|
| 184 |
+
"metadata": {},
|
| 185 |
+
"source": [
|
| 186 |
+
"## Saving initialized data\n"
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
{
|
| 190 |
+
"cell_type": "code",
|
| 191 |
+
"execution_count": 32,
|
| 192 |
+
"id": "63441612",
|
| 193 |
+
"metadata": {},
|
| 194 |
+
"outputs": [
|
| 195 |
+
{
|
| 196 |
+
"name": "stdout",
|
| 197 |
+
"output_type": "stream",
|
| 198 |
+
"text": [
|
| 199 |
+
"Successfully moved and renamed 'output.ply' to './initialized_result/initial_0_24.ply'\n"
|
| 200 |
+
]
|
| 201 |
+
}
|
| 202 |
+
],
|
| 203 |
+
"source": [
|
| 204 |
+
"import shutil\n",
|
| 205 |
+
"import os\n",
|
| 206 |
+
"\n",
|
| 207 |
+
"transformed_path = \"output.ply\"\n",
|
| 208 |
+
"destination_path = f\"./initialized_result/initial_{filename}.ply\"\n",
|
| 209 |
+
"\n",
|
| 210 |
+
"\n",
|
| 211 |
+
"shutil.move(transformed_path, destination_path)\n",
|
| 212 |
+
"print(f\"Successfully moved and renamed '{transformed_path}' to '{destination_path}'\")\n",
|
| 213 |
+
"\n"
|
| 214 |
+
]
|
| 215 |
+
}
|
| 216 |
+
],
|
| 217 |
+
"metadata": {
|
| 218 |
+
"kernelspec": {
|
| 219 |
+
"display_name": "Python 3",
|
| 220 |
+
"language": "python",
|
| 221 |
+
"name": "python3"
|
| 222 |
+
},
|
| 223 |
+
"language_info": {
|
| 224 |
+
"codemirror_mode": {
|
| 225 |
+
"name": "ipython",
|
| 226 |
+
"version": 3
|
| 227 |
+
},
|
| 228 |
+
"file_extension": ".py",
|
| 229 |
+
"mimetype": "text/x-python",
|
| 230 |
+
"name": "python",
|
| 231 |
+
"nbconvert_exporter": "python",
|
| 232 |
+
"pygments_lexer": "ipython3",
|
| 233 |
+
"version": "3.10.12"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"nbformat": 4,
|
| 237 |
+
"nbformat_minor": 5
|
| 238 |
+
}
|
data/glasses/merged.py
ADDED
|
@@ -0,0 +1,494 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
# coding: utf-8
|
| 3 |
+
|
| 4 |
+
# In[ ]:
|
| 5 |
+
|
| 6 |
+
import json
|
| 7 |
+
import os
|
| 8 |
+
import open3d as o3d
|
| 9 |
+
import numpy as np
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
mesh = o3d.io.read_triangle_mesh("./source.stl")
|
| 13 |
+
pointcloud = mesh.sample_points_poisson_disk(50000)
|
| 14 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 15 |
+
mesh.compute_vertex_normals()
|
| 16 |
+
mesh_triangles = np.asarray(mesh.triangles)
|
| 17 |
+
vertex_positions = np.asarray(mesh.vertices)
|
| 18 |
+
triangle_normals = np.asarray(mesh.triangle_normals)
|
| 19 |
+
# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ
|
| 20 |
+
centroid = mesh.get_center()
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
# ๋ฐ์ดํฐ์
ํด๋์ JSON ํ์ผ ๊ฒฝ๋ก
|
| 24 |
+
folder = "./dataset"
|
| 25 |
+
json_path = "ply_files.json"
|
| 26 |
+
|
| 27 |
+
# 1. ๊ฐ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ resolution ๊ฐ์ ๋์
๋๋ฆฌ๋ก ์ ์ํฉ๋๋ค.
|
| 28 |
+
# ์ด ๊ฐ์ ์กฐ์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ๋ณ ์ค์ ์ ๋ณ๊ฒฝํ ์ ์์ต๋๋ค.
|
| 29 |
+
resolutions = {
|
| 30 |
+
"100": 1.0,
|
| 31 |
+
"75": 0.8,
|
| 32 |
+
"50": 0.8,
|
| 33 |
+
"25": 0.8,
|
| 34 |
+
"0": 0.8
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
# 2. ๋ถ๋ฅ๋ ํ์ผ ๋ชฉ๋ก์ด ๋ด๊ธด JSON ํ์ผ์ ์ฝ์ด์ต๋๋ค.
|
| 38 |
+
try:
|
| 39 |
+
with open(json_path, "r", encoding="utf-8") as f:
|
| 40 |
+
categorized_files = json.load(f)
|
| 41 |
+
except FileNotFoundError:
|
| 42 |
+
print(f"์ค๋ฅ: '{json_path}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค. ๋จผ์ ํ์ผ ๋ถ๋ฅ ์ฝ๋๋ฅผ ์คํํด ์ฃผ์ธ์.")
|
| 43 |
+
exit() # ํ์ผ์ด ์์ผ๋ฉด ํ๋ก๊ทธ๋จ ์ข
๋ฃ
|
| 44 |
+
|
| 45 |
+
# 3. ๋ชจ๋ ์นดํ
๊ณ ๋ฆฌ์ ํ์ผ์ ์ํํ๋ ๋ฐ๋ณต๋ฌธ
|
| 46 |
+
print("=== ๋ฐ์ดํฐ ์ฒ๋ฆฌ ์์ ===")
|
| 47 |
+
|
| 48 |
+
# resolutions ๋์
๋๋ฆฌ๋ฅผ ๊ธฐ์ค์ผ๋ก ์ธ๋ถ ๋ฃจํ๋ฅผ ์คํํฉ๋๋ค.
|
| 49 |
+
for category, resolution in resolutions.items():
|
| 50 |
+
|
| 51 |
+
print(f"\n--- [์นดํ
๊ณ ๋ฆฌ: {category}, ํด์๋: {resolution}] ์ฒ๋ฆฌ ์์ ---")
|
| 52 |
+
|
| 53 |
+
# JSON์์ ํ์ฌ ์นดํ
๊ณ ๋ฆฌ์ ํด๋นํ๋ ํ์ผ ๋ฆฌ์คํธ๋ฅผ ๊ฐ์ ธ์ต๋๋ค.
|
| 54 |
+
# .get(category, [])๋ฅผ ์ฌ์ฉํ๋ฉด JSON์ ํด๋น ์นดํ
๊ณ ๋ฆฌ๊ฐ ์์ด๋ ์ค๋ฅ ์์ด ๋น ๋ฆฌ์คํธ๋ฅผ ๋ฐํํฉ๋๋ค.
|
| 55 |
+
filenames_in_category = categorized_files.get(category, [])
|
| 56 |
+
|
| 57 |
+
if not filenames_in_category:
|
| 58 |
+
print("์ฒ๋ฆฌํ ํ์ผ์ด ์์ต๋๋ค.")
|
| 59 |
+
continue # ํ์ผ์ด ์์ผ๋ฉด ๋ค์ ์นดํ
๊ณ ๋ฆฌ๋ก ๋์ด๊ฐ
|
| 60 |
+
|
| 61 |
+
# ๋ด๋ถ ๋ฃจํ์์ ํด๋น ์นดํ
๊ณ ๋ฆฌ์ ๋ชจ๋ ํ์ผ์ ํ๋์ฉ ์ฒ๋ฆฌํฉ๋๋ค.
|
| 62 |
+
for filename in filenames_in_category:
|
| 63 |
+
|
| 64 |
+
# ์ค์ ํ์ผ ๊ฒฝ๋ก๋ฅผ ๋ง๋ญ๋๋ค. (JSON์๋ ํ์ฅ์๊ฐ ์์ผ๋ฏ๋ก .ply๋ฅผ ๋ถ์ฌ์ค๋๋ค)
|
| 65 |
+
file_path = os.path.join(folder, f"{filename}.ply")
|
| 66 |
+
|
| 67 |
+
print(f" - ํ์ผ ์ฒ๋ฆฌ ์ค: {file_path} (ํด์๋: {resolution})")
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
filename = filename
|
| 71 |
+
# PLY ํ์ผ ๋ก๋
|
| 72 |
+
pcd = o3d.io.read_point_cloud(f"./dataset/{filename}.ply")
|
| 73 |
+
|
| 74 |
+
GT = False
|
| 75 |
+
if GT==True:
|
| 76 |
+
mesh = o3d.io.read_triangle_mesh("./bottle2.stl")
|
| 77 |
+
pointcloud = mesh.sample_points_poisson_disk(50000)
|
| 78 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 79 |
+
|
| 80 |
+
mesh.compute_vertex_normals()
|
| 81 |
+
mesh_triangles = np.asarray(mesh.triangles)
|
| 82 |
+
vertex_positions = np.asarray(mesh.vertices)
|
| 83 |
+
triangle_normals = np.asarray(mesh.triangle_normals)
|
| 84 |
+
|
| 85 |
+
# ๊ฐ์ฒด์ ์ค์ฌ์ ๊ณ์ฐ
|
| 86 |
+
centroid = mesh.get_center()
|
| 87 |
+
filtered_triangles = []
|
| 88 |
+
for i, triangle in enumerate(mesh_triangles):
|
| 89 |
+
# ์ผ๊ฐํ์ ์ค์ฌ์ ๊ณ์ฐ
|
| 90 |
+
tri_center = vertex_positions[triangle].mean(axis=0)
|
| 91 |
+
# ๊ฐ์ฒด ์ค์ฌ์์ ์ผ๊ฐํ ์ค์ฌ์ผ๋ก ํฅํ๋ ๋ฒกํฐ
|
| 92 |
+
vec_to_center = tri_center - centroid
|
| 93 |
+
# ๋ฒ์ ๋ฒกํฐ์ ๋ฐฉํฅ ๋ฒกํฐ๋ฅผ ๋ด์
|
| 94 |
+
dot_product = np.dot(triangle_normals[i], vec_to_center)
|
| 95 |
+
# ๋ด์ ๊ฐ์ด ์์์ด๋ฉด ๋ฐ๊นฅ์ชฝ ๋ฉด์ผ๋ก ํ๋จ
|
| 96 |
+
if dot_product > 0:
|
| 97 |
+
filtered_triangles.append(triangle)
|
| 98 |
+
# 3. ํํฐ๋ง๋ ๋ฉด์ผ๋ก ์๋ก์ด ๋ฉ์ฌ ์์ฑ
|
| 99 |
+
outer_mesh = o3d.geometry.TriangleMesh()
|
| 100 |
+
outer_mesh.vertices = mesh.vertices
|
| 101 |
+
outer_mesh.triangles = o3d.utility.Vector3iVector(np.array(filtered_triangles))
|
| 102 |
+
# 4. ์๋ก์ด ๋ฉ์ฌ์์ ํฌ์ธํธ ํด๋ผ์ฐ๋ ์ํ๋ง
|
| 103 |
+
# n_points๋ ์ํ๋งํ ํฌ์ธํธ ๊ฐ์
|
| 104 |
+
pcd = outer_mesh.sample_points_uniformly(number_of_points=50000)
|
| 105 |
+
# ๊ฒฐ๊ณผ ์๊ฐํ
|
| 106 |
+
# o3d.visualization.draw_geometries([pcd,coord_frame ])
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
|
| 111 |
+
pcd_array = np.asarray(pcd.points)
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
# In[160]:
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
import open3d as o3d
|
| 118 |
+
import numpy as np
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
if not GT:
|
| 122 |
+
ply_path = f"./dataset/{filename}.ply"
|
| 123 |
+
|
| 124 |
+
pcd = o3d.io.read_point_cloud(ply_path)
|
| 125 |
+
print(ply_path)
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
pcd_array = np.asarray(pcd.points)
|
| 129 |
+
print(pcd_array.shape)
|
| 130 |
+
|
| 131 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 132 |
+
# o3d.visualization.draw_geometries([pcd, coord_frame])
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
# In[161]:
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
if GT==False:
|
| 139 |
+
|
| 140 |
+
new_pcd_array = np.unique(pcd_array, axis=0)
|
| 141 |
+
|
| 142 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 580]
|
| 143 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 2] < 1000]
|
| 144 |
+
|
| 145 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -100]
|
| 146 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] > -1000] #diagonal
|
| 147 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 1] < 120]
|
| 148 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] > -1000]
|
| 149 |
+
new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 1000] #diagonal
|
| 150 |
+
# new_pcd_array = new_pcd_array[new_pcd_array[:, 0] < 100]
|
| 151 |
+
# new_pcd_array -= np.mean(new_pcd_array, axis=0)
|
| 152 |
+
print(np.mean(new_pcd_array, axis=0))
|
| 153 |
+
|
| 154 |
+
new_pcd = o3d.geometry.PointCloud()
|
| 155 |
+
new_pcd.points = o3d.utility.Vector3dVector(new_pcd_array)
|
| 156 |
+
|
| 157 |
+
theta = np.radians(90)
|
| 158 |
+
# theta = np.radians(-90)
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 162 |
+
|
| 163 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 164 |
+
# o3d.visualization.draw_geometries([new_pcd, coord_frame])
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
# ## Delete ground plane
|
| 168 |
+
|
| 169 |
+
# In[162]:
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
if GT==False:
|
| 173 |
+
|
| 174 |
+
plane_model, inliers = new_pcd.segment_plane(distance_threshold=1,
|
| 175 |
+
ransac_n=10,
|
| 176 |
+
num_iterations=1000)
|
| 177 |
+
[a, b, c, d] = plane_model
|
| 178 |
+
print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
inlier_cloud = new_pcd.select_by_index(inliers)
|
| 183 |
+
inlier_cloud.paint_uniform_color([1.0, 0, 1.0])
|
| 184 |
+
outlier_cloud = new_pcd.select_by_index(inliers, invert=True)
|
| 185 |
+
# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
|
| 186 |
+
# zoom=0.8,
|
| 187 |
+
# front=[-0.4999, -0.1659, -0.8499],
|
| 188 |
+
# lookat=[2.1813, 2.0619, 2.0999],
|
| 189 |
+
# up=[0.1204, -0.9852, 0.1215])
|
| 190 |
+
|
| 191 |
+
new_pcd = outlier_cloud
|
| 192 |
+
|
| 193 |
+
new_pcd_array = np.asarray(new_pcd.points)
|
| 194 |
+
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
# ### Changing the source position "gt_filtered"
|
| 199 |
+
#
|
| 200 |
+
|
| 201 |
+
# In[163]:
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
CHECK_PERTURB = GT
|
| 205 |
+
|
| 206 |
+
def random_rotation_matrix():
|
| 207 |
+
"""
|
| 208 |
+
Generate a random 3x3 rotation matrix (SO(3) matrix).
|
| 209 |
+
|
| 210 |
+
Uses the method described by James Arvo in "Fast Random Rotation Matrices" (1992):
|
| 211 |
+
1. Generate a random unit vector for rotation axis
|
| 212 |
+
2. Generate a random angle
|
| 213 |
+
3. Create rotation matrix using Rodriguez rotation formula
|
| 214 |
+
|
| 215 |
+
Returns:
|
| 216 |
+
numpy.ndarray: A 3x3 random rotation matrix
|
| 217 |
+
"""
|
| 218 |
+
## for ground target
|
| 219 |
+
# Generate random angle ฯ/2
|
| 220 |
+
theta = 0
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
# axis is -y
|
| 224 |
+
axis = np.array([
|
| 225 |
+
1,
|
| 226 |
+
0,
|
| 227 |
+
0,
|
| 228 |
+
])
|
| 229 |
+
|
| 230 |
+
# for lying target
|
| 231 |
+
# theta will be pi/2
|
| 232 |
+
# theta = np.pi/2
|
| 233 |
+
# axis = np.array([
|
| 234 |
+
# 0,
|
| 235 |
+
# 1,
|
| 236 |
+
# 0,
|
| 237 |
+
# ])
|
| 238 |
+
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
|
| 242 |
+
# Normalize to ensure it's a unit vector
|
| 243 |
+
axis = axis / np.linalg.norm(axis)
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
|
| 247 |
+
# Create the cross-product matrix K skew-symmetric
|
| 248 |
+
K = np.array([
|
| 249 |
+
[0, -axis[2], axis[1]],
|
| 250 |
+
[axis[2], 0, -axis[0]],
|
| 251 |
+
[-axis[1], axis[0], 0]
|
| 252 |
+
])
|
| 253 |
+
|
| 254 |
+
# Rodriguez rotation formula: R = I + sin(ฮธ)K + (1-cos(ฮธ))Kยฒ
|
| 255 |
+
R = (np.eye(3) +
|
| 256 |
+
np.sin(theta) * K +
|
| 257 |
+
(1 - np.cos(theta)) * np.dot(K, K))
|
| 258 |
+
|
| 259 |
+
return R
|
| 260 |
+
|
| 261 |
+
if CHECK_PERTURB:
|
| 262 |
+
R_pert = random_rotation_matrix()
|
| 263 |
+
print(R_pert)
|
| 264 |
+
t_pert = np.array([
|
| 265 |
+
0,
|
| 266 |
+
0,
|
| 267 |
+
0
|
| 268 |
+
])
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
perturbed_pcd_array = np.dot(R_pert, pcd_array.T).T + t_pert.T
|
| 272 |
+
|
| 273 |
+
|
| 274 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 275 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 276 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 277 |
+
# o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 278 |
+
|
| 279 |
+
|
| 280 |
+
# ### Rotate randomly in Target "noisy filtered"
|
| 281 |
+
|
| 282 |
+
# In[164]:
|
| 283 |
+
|
| 284 |
+
|
| 285 |
+
CHECK_PERTURB = not GT
|
| 286 |
+
|
| 287 |
+
|
| 288 |
+
if CHECK_PERTURB:
|
| 289 |
+
# R_pert = random_rotation_matrix()
|
| 290 |
+
# print(R_pert)
|
| 291 |
+
# t_pert = np.random.rand(3, 1)*3 #* 10
|
| 292 |
+
|
| 293 |
+
|
| 294 |
+
# perturbed_pcd_array = np.dot(R_pert, new_pcd_array.T).T + t_pert.T
|
| 295 |
+
perturbed_pcd_array = new_pcd_array
|
| 296 |
+
perturbed_pcd = o3d.geometry.PointCloud()
|
| 297 |
+
perturbed_pcd.points = o3d.utility.Vector3dVector(perturbed_pcd_array)
|
| 298 |
+
|
| 299 |
+
now_centeroid = perturbed_pcd.get_center()
|
| 300 |
+
perturbed_pcd.translate(centroid, relative=False)
|
| 301 |
+
|
| 302 |
+
## get centeroid vector
|
| 303 |
+
|
| 304 |
+
translation_vector = centroid - now_centeroid
|
| 305 |
+
|
| 306 |
+
np.savetxt(f"./centroid/{filename}.txt",translation_vector)
|
| 307 |
+
|
| 308 |
+
##### changed
|
| 309 |
+
perturbed_pcd_array = np.asarray(perturbed_pcd.points)
|
| 310 |
+
coord_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=50.0, origin=[0, 0, 0])
|
| 311 |
+
|
| 312 |
+
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
# o3d.visualization.draw_geometries([perturbed_pcd, coord_frame])
|
| 317 |
+
|
| 318 |
+
|
| 319 |
+
# In[165]:
|
| 320 |
+
|
| 321 |
+
|
| 322 |
+
def write_ply(points, output_path):
|
| 323 |
+
"""
|
| 324 |
+
Write points and parameters to a PLY file
|
| 325 |
+
|
| 326 |
+
Parameters:
|
| 327 |
+
points: numpy array of shape (N, 3) containing point coordinates
|
| 328 |
+
output_path: path to save the PLY file
|
| 329 |
+
"""
|
| 330 |
+
with open(output_path, 'w') as f:
|
| 331 |
+
# Write header
|
| 332 |
+
f.write("ply\n")
|
| 333 |
+
f.write("format ascii 1.0\n")
|
| 334 |
+
|
| 335 |
+
# Write vertex element
|
| 336 |
+
f.write(f"element vertex {len(points)}\n")
|
| 337 |
+
f.write("property float x\n")
|
| 338 |
+
f.write("property float y\n")
|
| 339 |
+
f.write("property float z\n")
|
| 340 |
+
|
| 341 |
+
# Write camera element
|
| 342 |
+
f.write("element camera 1\n")
|
| 343 |
+
f.write("property float view_px\n")
|
| 344 |
+
f.write("property float view_py\n")
|
| 345 |
+
f.write("property float view_pz\n")
|
| 346 |
+
f.write("property float x_axisx\n")
|
| 347 |
+
f.write("property float x_axisy\n")
|
| 348 |
+
f.write("property float x_axisz\n")
|
| 349 |
+
f.write("property float y_axisx\n")
|
| 350 |
+
f.write("property float y_axisy\n")
|
| 351 |
+
f.write("property float y_axisz\n")
|
| 352 |
+
f.write("property float z_axisx\n")
|
| 353 |
+
f.write("property float z_axisy\n")
|
| 354 |
+
f.write("property float z_axisz\n")
|
| 355 |
+
|
| 356 |
+
# Write phoxi frame parameters
|
| 357 |
+
f.write("element phoxi_frame_params 1\n")
|
| 358 |
+
f.write("property uint32 frame_width\n")
|
| 359 |
+
f.write("property uint32 frame_height\n")
|
| 360 |
+
f.write("property uint32 frame_index\n")
|
| 361 |
+
f.write("property float frame_start_time\n")
|
| 362 |
+
f.write("property float frame_duration\n")
|
| 363 |
+
f.write("property float frame_computation_duration\n")
|
| 364 |
+
f.write("property float frame_transfer_duration\n")
|
| 365 |
+
f.write("property int32 total_scan_count\n")
|
| 366 |
+
|
| 367 |
+
# Write camera matrix
|
| 368 |
+
f.write("element camera_matrix 1\n")
|
| 369 |
+
for i in range(9):
|
| 370 |
+
f.write(f"property float cm{i}\n")
|
| 371 |
+
|
| 372 |
+
# Write distortion matrix
|
| 373 |
+
f.write("element distortion_matrix 1\n")
|
| 374 |
+
for i in range(14):
|
| 375 |
+
f.write(f"property float dm{i}\n")
|
| 376 |
+
|
| 377 |
+
# Write camera resolution
|
| 378 |
+
f.write("element camera_resolution 1\n")
|
| 379 |
+
f.write("property float width\n")
|
| 380 |
+
f.write("property float height\n")
|
| 381 |
+
|
| 382 |
+
# Write frame binning
|
| 383 |
+
f.write("element frame_binning 1\n")
|
| 384 |
+
f.write("property float horizontal\n")
|
| 385 |
+
f.write("property float vertical\n")
|
| 386 |
+
|
| 387 |
+
# End header
|
| 388 |
+
f.write("end_header\n")
|
| 389 |
+
|
| 390 |
+
# Write vertex data
|
| 391 |
+
for point in points:
|
| 392 |
+
f.write(f"{point[0]} {point[1]} {point[2]}\n")
|
| 393 |
+
|
| 394 |
+
print(True)
|
| 395 |
+
|
| 396 |
+
if GT: write_ply(perturbed_pcd_array, f"gt_filtered.ply")
|
| 397 |
+
else:
|
| 398 |
+
write_ply(perturbed_pcd_array, f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 399 |
+
write_ply(new_pcd_array,f"./noisy_raw/noisy_filtered_{filename}.ply")
|
| 400 |
+
# write_ply(new_pcd_array, "gt_filtered.ply")
|
| 401 |
+
|
| 402 |
+
#!/usr/bin/env python
|
| 403 |
+
# coding: utf-8
|
| 404 |
+
|
| 405 |
+
# ## PCD file transformation
|
| 406 |
+
|
| 407 |
+
# In[18]:
|
| 408 |
+
|
| 409 |
+
|
| 410 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 411 |
+
pcd = o3d.io.read_point_cloud("./gt_filtered.ply")
|
| 412 |
+
|
| 413 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 414 |
+
o3d.io.write_point_cloud("./initialize_pcdfile/gt_filtered.pcd", pcd)
|
| 415 |
+
|
| 416 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 417 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 418 |
+
|
| 419 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 420 |
+
|
| 421 |
+
|
| 422 |
+
# In[19]:
|
| 423 |
+
|
| 424 |
+
|
| 425 |
+
# PLY ํ์ผ ์ฝ๊ธฐ
|
| 426 |
+
pcd = o3d.io.read_point_cloud(f"./noisy_result/noisy_filtered_{filename}.ply")
|
| 427 |
+
|
| 428 |
+
# PCD ํ์ผ๋ก ์ ์ฅ (๋ฐ์ด๋๋ฆฌ ํ์)
|
| 429 |
+
o3d.io.write_point_cloud(f"./initialize_pcdfile/first_{filename}.pcd", pcd)
|
| 430 |
+
|
| 431 |
+
# ๋ง์ฝ ASCII ํ์์ผ๋ก ์ ์ฅํ๊ณ ์ถ๋ค๋ฉด:
|
| 432 |
+
# o3d.io.write_point_cloud("output_ascii.pcd", pcd, write_ascii=True)
|
| 433 |
+
|
| 434 |
+
print("PLY ํ์ผ์ด PCD ํ์ผ๋ก ์ฑ๊ณต์ ์ผ๋ก ๋ณํ๋์์ต๋๋ค.")
|
| 435 |
+
|
| 436 |
+
|
| 437 |
+
# ## Execute initial Guess
|
| 438 |
+
|
| 439 |
+
# In[20]:
|
| 440 |
+
|
| 441 |
+
|
| 442 |
+
# import os
|
| 443 |
+
# print(os.getcwd())
|
| 444 |
+
|
| 445 |
+
# import subprocess
|
| 446 |
+
|
| 447 |
+
# cmd = [
|
| 448 |
+
# 'python3',
|
| 449 |
+
# '../../../KISS-Matcher/python/examples/run_kiss_matcher.py',
|
| 450 |
+
# '--src_path',
|
| 451 |
+
# f'./initialize_pcdfile/first_{filename}.pcd',
|
| 452 |
+
# '--tgt_path',
|
| 453 |
+
# './initialize_pcdfile/gt_filtered.pcd',
|
| 454 |
+
# '--resolution',
|
| 455 |
+
# '1'
|
| 456 |
+
|
| 457 |
+
# ]
|
| 458 |
+
# try:
|
| 459 |
+
# result = subprocess.run(cmd, capture_output=True, text=True, check=True)
|
| 460 |
+
|
| 461 |
+
# print("--- STDOUT (ํ์ค ์ถ๋ ฅ) ---")
|
| 462 |
+
# print("๋ช
๋ น์ด๊ฐ ์ฑ๊ณต์ ์ผ๋ก ์คํ๋์์ต๋๋ค.")
|
| 463 |
+
# print(result.stdout)
|
| 464 |
+
|
| 465 |
+
# except FileNotFoundError:
|
| 466 |
+
# print("--- ์๋ฌ ๋ฐ์! ---")
|
| 467 |
+
# print(f"'{cmd[0]}' ํ์ผ์ ์ฐพ์ ์ ์์ต๋๋ค.")
|
| 468 |
+
# print("๊ฒฝ๋ก๊ฐ ์ฌ๋ฐ๋ฅธ์ง, ํ์ผ์ด ๊ทธ ์์น์ ์กด์ฌํ๋์ง ํ์ธํด ์ฃผ์ธ์.")
|
| 469 |
+
|
| 470 |
+
# except subprocess.CalledProcessError as e:
|
| 471 |
+
# print("--- ์๋ฌ ๋ฐ์! ---")
|
| 472 |
+
# print(f"๋ช
๋ น์ด ์คํ ์ค ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค. (์ข
๋ฃ ์ฝ๋: {e.returncode})")
|
| 473 |
+
# print("\n--- STDERR (์๋ฌ ์์ธ) ---")
|
| 474 |
+
# print(e.stderr)
|
| 475 |
+
|
| 476 |
+
|
| 477 |
+
# # ## Saving initialized data
|
| 478 |
+
# #
|
| 479 |
+
|
| 480 |
+
# # In[21]:
|
| 481 |
+
|
| 482 |
+
|
| 483 |
+
# import shutil
|
| 484 |
+
# import os
|
| 485 |
+
|
| 486 |
+
# transformed_path = "output.ply"
|
| 487 |
+
# destination_path = f"./initialized_result/initial_{filename}.ply"
|
| 488 |
+
|
| 489 |
+
|
| 490 |
+
# shutil.move(transformed_path, destination_path)
|
| 491 |
+
# print(f"Successfully moved and renamed '{transformed_path}' to '{destination_path}'")
|
| 492 |
+
|
| 493 |
+
|
| 494 |
+
|
data/glasses/output_trans.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
data/glasses/ply_files.json
ADDED
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@@ -0,0 +1,117 @@
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| 1 |
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{
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"100": [
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| 3 |
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"100_19",
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| 4 |
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"100_10",
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| 5 |
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"100_1",
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"100_4",
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| 7 |
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"100_6",
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| 8 |
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"100_5",
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"100_17",
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"100_15",
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"100_12",
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"100_16",
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"100_14",
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"100_7",
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"100_13",
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"100_9",
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"100_18",
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"100_2",
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"100_11",
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"100_20",
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"100_3",
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| 22 |
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"100_8"
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],
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"75": [
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"75_6",
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"75_12",
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"75_9",
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"75_4",
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"75_11",
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"75_7",
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| 31 |
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"75_14",
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| 32 |
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"75_8",
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| 33 |
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"75_16",
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"75_17",
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| 35 |
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"75_2",
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"75_3",
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"75_1",
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"75_21",
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"75_15",
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"75_20",
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| 41 |
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"75_10",
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| 42 |
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"75_13",
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"75_5",
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| 44 |
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"75_19",
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| 45 |
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"75_18"
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| 46 |
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],
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"50": [
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"50_18",
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"50_8",
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"50_13",
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| 51 |
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"50_15",
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| 52 |
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"50_7",
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| 53 |
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"50_4",
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| 54 |
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"50_5",
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| 55 |
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"50_19",
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| 56 |
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"50_16",
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| 57 |
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"50_20",
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"50_14",
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"50_12",
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"50_11",
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"50_9",
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"50_6",
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"50_17",
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"50_1",
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| 65 |
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"50_10",
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"50_3",
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| 67 |
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"50_2"
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],
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"25": [
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"25_6",
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| 71 |
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"25_19",
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| 72 |
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"25_17",
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| 73 |
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"25_9",
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| 74 |
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"25_11",
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| 75 |
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"25_20",
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| 76 |
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"25_14",
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| 77 |
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"25_4",
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| 78 |
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"25_16",
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| 79 |
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"25_5",
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"25_2",
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| 81 |
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"25_10",
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| 82 |
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"25_3",
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"25_8",
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"25_13",
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| 85 |
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"25_7",
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"25_12",
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| 87 |
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"25_1",
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| 88 |
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"25_15",
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"25_18"
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],
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"0": [
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"0_12",
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| 93 |
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"0_17",
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| 94 |
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"0_16",
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| 95 |
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"0_15",
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| 96 |
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"0_2",
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| 97 |
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"0_5",
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| 98 |
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"0_14",
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| 99 |
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"0_9",
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| 100 |
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"0_22",
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| 101 |
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"0_4",
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| 102 |
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"0_18",
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| 103 |
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"0_8",
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| 104 |
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"0_7",
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| 105 |
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"0_11",
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| 106 |
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"0_24",
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| 107 |
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"0_13",
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| 108 |
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"0_23",
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| 109 |
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"0_10",
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| 110 |
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"0_19",
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| 111 |
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"0_1",
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| 112 |
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"0_6",
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| 113 |
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"0_21",
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| 114 |
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"0_20",
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| 115 |
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"0_3"
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| 116 |
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]
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}
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data/glasses/run_all.py
ADDED
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@@ -0,0 +1,45 @@
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| 1 |
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import os
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| 2 |
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import json
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| 3 |
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import subprocess
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| 4 |
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| 5 |
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import os
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import json
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| 7 |
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| 8 |
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# PLY ํ์ผ๋ค์ด ๋ค์ด ์๋ ํด๋ ๊ฒฝ๋ก
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| 9 |
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folder = "./dataset"
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| 10 |
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| 11 |
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# ๋ถ๋ฅํ ์นดํ
๊ณ ๋ฆฌ๋ฅผ ๋ฏธ๋ฆฌ ์ ์ํฉ๋๋ค.
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| 12 |
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categories = ["100", "75", "50", "25", "0"]
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| 13 |
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# ๊ฒฐ๊ณผ๋ฅผ ์ ์ฅํ ๋์
๋๋ฆฌ๋ฅผ ์นดํ
๊ณ ๋ฆฌ๋ณ๋ก ์ด๊ธฐํํฉ๋๋ค.
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| 15 |
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grouped_files = {cat: [] for cat in categories}
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| 16 |
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| 17 |
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# ํ์ฅ์๊ฐ .ply ์ธ ํ์ผ ๋ชฉ๋ก์ ๊ฐ์ ธ์ต๋๋ค.
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| 18 |
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try:
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| 19 |
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all_files = os.listdir(folder)
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| 20 |
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except FileNotFoundError:
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| 21 |
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print(f"์ค๋ฅ: '{folder}' ํด๋๋ฅผ ์ฐพ์ ์ ์์ต๋๋ค.")
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| 22 |
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all_files = []
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| 23 |
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| 24 |
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# ๊ฐ ํ์ผ์ ์ํํ๋ฉฐ ์ ์ ํ ์นดํ
๊ณ ๋ฆฌ์ ์ถ๊ฐํฉ๋๋ค.
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| 25 |
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for filename_with_ext in all_files:
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| 26 |
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if filename_with_ext.endswith(".ply"):
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| 27 |
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# ํ์ฅ์(.ply)๋ฅผ ์ ๊ฑฐํฉ๋๋ค.
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| 28 |
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filename = filename_with_ext.removesuffix('.ply')
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| 29 |
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| 30 |
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# ํ์ผ๋ช
์ '_' ๊ธฐ์ค์ผ๋ก ๋ถ๋ฆฌํ์ฌ ์ ๋์ด(prefix)๋ฅผ ์ป์ต๋๋ค.
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| 31 |
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prefix = filename.split('_')[0]
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| 32 |
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| 33 |
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# ์ ๋์ด๊ฐ ์ ์๋ ์นดํ
๊ณ ๋ฆฌ ์ค ํ๋๋ผ๋ฉด, ํด๋น ๋ฆฌ์คํธ์ ํ์ผ๋ช
์ ์ถ๊ฐํฉ๋๋ค.
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| 34 |
+
if prefix in grouped_files:
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| 35 |
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grouped_files[prefix].append(filename)
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| 36 |
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| 37 |
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# ๋ถ๋ฅ๋ ๋์
๋๋ฆฌ๋ฅผ JSON ํ์ผ๋ก ์ ์ฅํฉ๋๋ค.
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| 38 |
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with open("ply_files.json", "w", encoding="utf-8") as f:
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| 39 |
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json.dump(grouped_files, f, ensure_ascii=False, indent=2)
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| 40 |
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| 41 |
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print("JSON ์ ์ฅ ์๋ฃ! ์๋์ ๊ฐ์ด ํ์ผ์ด ๋ถ๋ฅ๋์์ต๋๋ค.")
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| 42 |
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print(json.dumps(grouped_files, indent=2))
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| 43 |
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| 44 |
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# merged.py ์คํ
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| 45 |
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subprocess.run(["python3", "merged.py"])
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