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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]) |