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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])
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