File size: 10,604 Bytes
f53b39e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactive demo of Cross-view Completion."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright (C) 2022-present Naver Corporation. All rights reserved.\n",
"# Licensed under CC BY-NC-SA 4.0 (non-commercial use only)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"from models.croco import CroCoNet\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"import matplotlib.pyplot as plt\n",
"import quaternion\n",
"import models.masking"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load CroCo model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ckpt = torch.load('pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth', 'cpu')\n",
"model = CroCoNet( **ckpt.get('croco_kwargs',{}))\n",
"msg = model.load_state_dict(ckpt['model'], strict=True)\n",
"use_gpu = torch.cuda.is_available() and torch.cuda.device_count()>0\n",
"device = torch.device('cuda:0' if use_gpu else 'cpu')\n",
"model = model.eval()\n",
"model = model.to(device=device)\n",
"print(msg)\n",
"\n",
"def process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches=False):\n",
" \"\"\"\n",
" Perform Cross-View completion using two input images, specified using Numpy arrays.\n",
" \"\"\"\n",
" # Replace the mask generator\n",
" model.mask_generator = models.masking.RandomMask(model.patch_embed.num_patches, masking_ratio)\n",
"\n",
" # ImageNet-1k color normalization\n",
" imagenet_mean = torch.as_tensor([0.485, 0.456, 0.406]).reshape(1,3,1,1).to(device)\n",
" imagenet_std = torch.as_tensor([0.229, 0.224, 0.225]).reshape(1,3,1,1).to(device)\n",
"\n",
" normalize_input_colors = True\n",
" is_output_normalized = True\n",
" with torch.no_grad():\n",
" # Cast data to torch\n",
" target_image = (torch.as_tensor(target_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
" ref_image = (torch.as_tensor(ref_image, dtype=torch.float, device=device).permute(2,0,1) / 255)[None]\n",
"\n",
" if normalize_input_colors:\n",
" ref_image = (ref_image - imagenet_mean) / imagenet_std\n",
" target_image = (target_image - imagenet_mean) / imagenet_std\n",
"\n",
" out, mask, _ = model(target_image, ref_image)\n",
" # # get target\n",
" if not is_output_normalized:\n",
" predicted_image = model.unpatchify(out)\n",
" else:\n",
" # The output only contains higher order information,\n",
" # we retrieve mean and standard deviation from the actual target image\n",
" patchified = model.patchify(target_image)\n",
" mean = patchified.mean(dim=-1, keepdim=True)\n",
" var = patchified.var(dim=-1, keepdim=True)\n",
" pred_renorm = out * (var + 1.e-6)**.5 + mean\n",
" predicted_image = model.unpatchify(pred_renorm)\n",
"\n",
" image_masks = model.unpatchify(model.patchify(torch.ones_like(ref_image)) * mask[:,:,None])\n",
" masked_target_image = (1 - image_masks) * target_image\n",
" \n",
" if not reconstruct_unmasked_patches:\n",
" # Replace unmasked patches by their actual values\n",
" predicted_image = predicted_image * image_masks + masked_target_image\n",
"\n",
" # Unapply color normalization\n",
" if normalize_input_colors:\n",
" predicted_image = predicted_image * imagenet_std + imagenet_mean\n",
" masked_target_image = masked_target_image * imagenet_std + imagenet_mean\n",
" \n",
" # Cast to Numpy\n",
" masked_target_image = np.asarray(torch.clamp(masked_target_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
" predicted_image = np.asarray(torch.clamp(predicted_image.squeeze(0).permute(1,2,0) * 255, 0, 255).cpu().numpy(), dtype=np.uint8)\n",
" return masked_target_image, predicted_image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the Habitat simulator to render images from arbitrary viewpoints (requires habitat_sim to be installed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"MAGNUM_LOG\"]=\"quiet\"\n",
"os.environ[\"HABITAT_SIM_LOG\"]=\"quiet\"\n",
"import habitat_sim\n",
"\n",
"scene = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.glb\"\n",
"navmesh = \"habitat-sim-data/scene_datasets/habitat-test-scenes/skokloster-castle.navmesh\"\n",
"\n",
"sim_cfg = habitat_sim.SimulatorConfiguration()\n",
"if use_gpu: sim_cfg.gpu_device_id = 0\n",
"sim_cfg.scene_id = scene\n",
"sim_cfg.load_semantic_mesh = False\n",
"rgb_sensor_spec = habitat_sim.CameraSensorSpec()\n",
"rgb_sensor_spec.uuid = \"color\"\n",
"rgb_sensor_spec.sensor_type = habitat_sim.SensorType.COLOR\n",
"rgb_sensor_spec.resolution = (224,224)\n",
"rgb_sensor_spec.hfov = 56.56\n",
"rgb_sensor_spec.position = [0.0, 0.0, 0.0]\n",
"rgb_sensor_spec.orientation = [0, 0, 0]\n",
"agent_cfg = habitat_sim.agent.AgentConfiguration(sensor_specifications=[rgb_sensor_spec])\n",
"\n",
"\n",
"cfg = habitat_sim.Configuration(sim_cfg, [agent_cfg])\n",
"sim = habitat_sim.Simulator(cfg)\n",
"if navmesh is not None:\n",
" sim.pathfinder.load_nav_mesh(navmesh)\n",
"agent = sim.initialize_agent(agent_id=0)\n",
"\n",
"def sample_random_viewpoint():\n",
" \"\"\" Sample a random viewpoint using the navmesh \"\"\"\n",
" nav_point = sim.pathfinder.get_random_navigable_point()\n",
" # Sample a random viewpoint height\n",
" viewpoint_height = np.random.uniform(1.0, 1.6)\n",
" viewpoint_position = nav_point + viewpoint_height * habitat_sim.geo.UP\n",
" viewpoint_orientation = quaternion.from_rotation_vector(np.random.uniform(-np.pi, np.pi) * habitat_sim.geo.UP)\n",
" return viewpoint_position, viewpoint_orientation\n",
"\n",
"def render_viewpoint(position, orientation):\n",
" agent_state = habitat_sim.AgentState()\n",
" agent_state.position = position\n",
" agent_state.rotation = orientation\n",
" agent.set_state(agent_state)\n",
" viewpoint_observations = sim.get_sensor_observations(agent_ids=0)\n",
" image = viewpoint_observations['color'][:,:,:3]\n",
" image = np.asarray(np.clip(1.5 * np.asarray(image, dtype=float), 0, 255), dtype=np.uint8)\n",
" return image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample a random reference view"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ref_position, ref_orientation = sample_random_viewpoint()\n",
"ref_image = render_viewpoint(ref_position, ref_orientation)\n",
"plt.clf()\n",
"fig, axes = plt.subplots(1,1, squeeze=False, num=1)\n",
"axes[0,0].imshow(ref_image)\n",
"for ax in axes.flatten():\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interactive cross-view completion using CroCo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reconstruct_unmasked_patches = False\n",
"\n",
"def show_demo(masking_ratio, x, y, z, panorama, elevation):\n",
" R = quaternion.as_rotation_matrix(ref_orientation)\n",
" target_position = ref_position + x * R[:,0] + y * R[:,1] + z * R[:,2]\n",
" target_orientation = (ref_orientation\n",
" * quaternion.from_rotation_vector(-elevation * np.pi/180 * habitat_sim.geo.LEFT) \n",
" * quaternion.from_rotation_vector(-panorama * np.pi/180 * habitat_sim.geo.UP))\n",
" \n",
" ref_image = render_viewpoint(ref_position, ref_orientation)\n",
" target_image = render_viewpoint(target_position, target_orientation)\n",
"\n",
" masked_target_image, predicted_image = process_images(ref_image, target_image, masking_ratio, reconstruct_unmasked_patches)\n",
"\n",
" fig, axes = plt.subplots(1,4, squeeze=True, dpi=300)\n",
" axes[0].imshow(ref_image)\n",
" axes[0].set_xlabel(\"Reference\")\n",
" axes[1].imshow(masked_target_image)\n",
" axes[1].set_xlabel(\"Masked target\")\n",
" axes[2].imshow(predicted_image)\n",
" axes[2].set_xlabel(\"Reconstruction\") \n",
" axes[3].imshow(target_image)\n",
" axes[3].set_xlabel(\"Target\")\n",
" for ax in axes.flatten():\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
"\n",
"interact(show_demo,\n",
" masking_ratio=widgets.FloatSlider(description='masking', value=0.9, min=0.0, max=1.0),\n",
" x=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
" y=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
" z=widgets.FloatSlider(value=0.0, min=-0.5, max=0.5, step=0.05),\n",
" panorama=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5),\n",
" elevation=widgets.FloatSlider(value=0.0, min=-20, max=20, step=0.5));"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
},
"vscode": {
"interpreter": {
"hash": "f9237820cd248d7e07cb4fb9f0e4508a85d642f19d831560c0a4b61f3e907e67"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|