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| import gradio as gr | |
| import numpy as np | |
| import trimesh | |
| from geometry import create_triangles | |
| from functools import partial | |
| import tempfile | |
| def depth_edges_mask(depth): | |
| """Returns a mask of edges in the depth map. | |
| Args: | |
| depth: 2D numpy array of shape (H, W) with dtype float32. | |
| Returns: | |
| mask: 2D numpy array of shape (H, W) with dtype bool. | |
| """ | |
| # Compute the x and y gradients of the depth map. | |
| depth_dx, depth_dy = np.gradient(depth) | |
| # Compute the gradient magnitude. | |
| depth_grad = np.sqrt(depth_dx ** 2 + depth_dy ** 2) | |
| # Compute the edge mask. | |
| mask = depth_grad > 0.05 | |
| return mask | |
| def pano_depth_to_world_points(depth): | |
| """ | |
| 360 depth to world points | |
| given 2D depth is an equirectangular projection of a spherical image | |
| Treat depth as radius | |
| longitude : -pi to pi | |
| latitude : -pi/2 to pi/2 | |
| """ | |
| # Convert depth to radius | |
| radius = depth.flatten() | |
| lon = np.linspace(-np.pi, np.pi, depth.shape[1]) | |
| lat = np.linspace(-np.pi/2, np.pi/2, depth.shape[0]) | |
| lon, lat = np.meshgrid(lon, lat) | |
| lon = lon.flatten() | |
| lat = lat.flatten() | |
| # Convert to cartesian coordinates | |
| x = radius * np.cos(lat) * np.cos(lon) | |
| y = radius * np.cos(lat) * np.sin(lon) | |
| z = radius * np.sin(lat) | |
| pts3d = np.stack([x, y, z], axis=1) | |
| return pts3d | |
| def predict_depth(model, image): | |
| depth = model.infer_pil(image) | |
| return depth | |
| def get_mesh(model, image, keep_edges=False): | |
| image.thumbnail((1024,1024)) # limit the size of the image | |
| depth = predict_depth(model, image) | |
| pts3d = pano_depth_to_world_points(depth) | |
| # Create a trimesh mesh from the points | |
| # Each pixel is connected to its 4 neighbors | |
| # colors are the RGB values of the image | |
| verts = pts3d.reshape(-1, 3) | |
| image = np.array(image) | |
| if keep_edges: | |
| triangles = create_triangles(image.shape[0], image.shape[1]) | |
| else: | |
| triangles = create_triangles(image.shape[0], image.shape[1], mask=~depth_edges_mask(depth)) | |
| colors = image.reshape(-1, 3) | |
| mesh = trimesh.Trimesh(vertices=verts, faces=triangles, vertex_colors=colors) | |
| # Save as glb | |
| glb_file = tempfile.NamedTemporaryFile(suffix='.glb', delete=False) | |
| glb_path = glb_file.name | |
| mesh.export(glb_path) | |
| return glb_path | |
| def create_demo(model): | |
| gr.Markdown("### Panorama to 3D mesh") | |
| gr.Markdown("Convert a 360 spherical panorama to a 3D mesh") | |
| gr.Markdown("ZoeDepth was not trained on panoramic images. It doesn't know anything about panoramas or spherical projection. Here, we just treat the estimated depth as radius and some projection errors are expected. Nonetheless, ZoeDepth still works surprisingly well on 360 reconstruction.") | |
| with gr.Row(): | |
| input_image = gr.Image(label="Input Image", type='pil') | |
| result = gr.Model3D(label="3d mesh reconstruction", clear_color=[ | |
| 1.0, 1.0, 1.0, 1.0]) | |
| checkbox = gr.Checkbox(label="Keep occlusion edges", value=True) | |
| submit = gr.Button("Submit") | |
| submit.click(partial(get_mesh, model), inputs=[input_image, checkbox], outputs=[result]) | |
| examples = gr.Examples(examples=["examples/pano_1.jpeg", "examples/pano_2.jpeg", "examples/pano_3.jpeg"], | |
| inputs=[input_image]) |