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import gradio as gr
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
import torch
import numpy as np
from PIL import Image
import open3d as o3d
from pathlib import Path
import os
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
def process_image(image_path):
image_path = Path(image_path)
image_raw = Image.open(image_path)
image = image_raw.resize(
(800, int(800 * image_raw.size[1] / image_raw.size[0])),
Image.Resampling.LANCZOS)
# prepare image for the model
encoding = feature_extractor(image, return_tensors="pt")
# forward pass
with torch.no_grad():
outputs = model(**encoding)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
).squeeze()
output = prediction.cpu().numpy()
depth_image = (output * 255 / np.max(output)).astype('uint8')
try:
gltf_path = create_3d_obj(np.array(image), depth_image, image_path)
img = Image.fromarray(depth_image)
return [img, gltf_path, gltf_path]
except Exception as e:
gltf_path = create_3d_obj(
np.array(image), depth_image, image_path, depth=8)
img = Image.fromarray(depth_image)
return [img, gltf_path, gltf_path]
except:
print("Error reconstructing 3D model")
raise Exception("Error reconstructing 3D model")
def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
depth_o3d = o3d.geometry.Image(depth_image)
image_o3d = o3d.geometry.Image(rgb_image)
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(
image_o3d, depth_o3d, convert_rgb_to_intensity=False)
w = int(depth_image.shape[1])
h = int(depth_image.shape[0])
camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
rgbd_image, camera_intrinsic)
print('normals')
pcd.normals = o3d.utility.Vector3dVector(
np.zeros((1, 3))) # invalidate existing normals
pcd.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))
pcd.orient_normals_towards_camera_location(
camera_location=np.array([0., 0., 1000.]))
pcd.transform([[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]])
pcd.transform([[-1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
print('run Poisson surface reconstruction')
with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:
mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
pcd, depth=depth, width=0, scale=1.1, linear_fit=True)
voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256
print(f'voxel_size = {voxel_size:e}')
mesh = mesh_raw.simplify_vertex_clustering(
voxel_size=voxel_size,
contraction=o3d.geometry.SimplificationContraction.Average)
# vertices_to_remove = densities < np.quantile(densities, 0.001)
# mesh.remove_vertices_by_mask(vertices_to_remove)
bbox = pcd.get_axis_aligned_bounding_box()
mesh_crop = mesh.crop(bbox)
gltf_path = f'./{image_path.stem}.gltf'
o3d.io.write_triangle_mesh(
gltf_path, mesh_crop, write_triangle_uvs=True)
return gltf_path
title = "LIVE: Towards Layer-wise Image Vectorization (CVPR 2022 Oral)"
description = "This demo shows the effectiveness of LIVE <a href='' target='_blank'>Paper</a>. Given the input image, LIVE is able to progressively build the SVG output with a layer-wise representation." \
"<br>NOTE: for efficiency, we resize input images to 240x240 for Huggingface Space. "
examples = [["examples/" + img] for img in os.listdir("examples/")]
iface = gr.Interface(fn=process_image,
inputs=[gr.inputs.Image(type="filepath", label="Input Image")
],
outputs=[gr.outputs.Image(label="predicted depth", type="pil"),
gr.outputs.Image3D(label="3d mesh reconstruction", clear_color=[
1.0, 1.0, 1.0, 1.0]),
gr.outputs.File(label="3d gLTF")],
title=title,
description=description,
examples=examples,
allow_flagging="never")
iface.launch(debug=True, enable_queue=False, cache_examples=True)
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