import gradio as gr import numpy as np import trimesh from geometry import depth_to_points, 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 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 input image depth = predict_depth(model, image) pts3d = depth_to_points(depth[None]) pts3d = pts3d.reshape(-1, 3) # 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("### Image to 3D mesh") gr.Markdown("Convert a single 2D image to a 3D mesh") with gr.Row(): 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=False) submit = gr.Button("Submit") submit.click(partial(get_mesh, model), inputs=[image, checkbox], outputs=[result]) examples = gr.Examples(examples=["examples/aerial_beach.jpeg", "examples/mountains.jpeg", "examples/person_1.jpeg", "examples/ancient-carved.jpeg"], inputs=[image])