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{
"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
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
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"vscode": {
"interpreter": {
"hash": "f9237820cd248d7e07cb4fb9f0e4508a85d642f19d831560c0a4b61f3e907e67"
}
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}
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