Duplicate from mattiagatti/image2mesh

Co-authored-by: Mattia Gatti <mattiagatti@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Image2mesh
+emoji: 👁
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+duplicated_from: mattiagatti/image2mesh
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import open3d as o3d
+import os
+from PIL import Image
+import tempfile
+import torch
+from transformers import GLPNImageProcessor, GLPNForDepthEstimation
+
+
+def predict_depth(image):
+    feature_extractor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu")
+    model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")
+
+    # load and resize the input image
+    new_height = 480 if image.height > 480 else image.height
+    new_height -= (new_height % 32)
+    new_width = int(new_height * image.width / image.height)
+    diff = new_width % 32
+    new_width = new_width - diff if diff < 16 else new_width + 32 - diff
+    new_size = (new_width, new_height)
+    image = image.resize(new_size)
+
+    # prepare image for the model
+    inputs = feature_extractor(images=image, return_tensors="pt")
+
+    # get the prediction from the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_depth = outputs.predicted_depth
+
+    output = predicted_depth.squeeze().cpu().numpy() * 1000.0
+
+    # remove borders
+    pad = 16
+    output = output[pad:-pad, pad:-pad]
+    image = image.crop((pad, pad, image.width - pad, image.height - pad))
+
+    return image, output
+
+
+def generate_mesh(image, depth_image, quality):
+    width, height = image.size
+
+    # depth_image = (depth_map * 255 / np.max(depth_map)).astype('uint8')
+    image = np.array(image)
+
+    # create rgbd image
+    depth_o3d = o3d.geometry.Image(depth_image)
+    image_o3d = o3d.geometry.Image(image)
+    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(image_o3d, depth_o3d,
+                                                                    convert_rgb_to_intensity=False)
+
+    # camera settings
+    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()
+    camera_intrinsic.set_intrinsics(width, height, 500, 500, width / 2, height / 2)
+
+    # create point cloud
+    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic)
+
+    # outliers removal
+    cl, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=20.0)
+    pcd = pcd.select_by_index(ind)
+
+    # estimate normals
+    pcd.estimate_normals()
+    pcd.orient_normals_to_align_with_direction(orientation_reference=(0., 0., -1.))
+
+    # surface reconstruction
+    mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=quality, n_threads=1)[0]
+
+    # rotate the mesh
+    rotation = mesh.get_rotation_matrix_from_xyz((np.pi, np.pi, 0))
+    mesh.rotate(rotation, center=(0, 0, 0))
+
+    # save the mesh
+    temp_name = next(tempfile._get_candidate_names()) + '.obj'
+    o3d.io.write_triangle_mesh(temp_name, mesh)
+
+    return temp_name
+
+
+def predict(image, quality):
+    image, depth_map = predict_depth(image)
+    depth_image = (depth_map * 255 / np.max(depth_map)).astype('uint8')
+    mesh_path = generate_mesh(image, depth_image, quality + 5)
+    colormap = plt.get_cmap('plasma')
+    depth_image = (colormap(depth_image) * 255).astype('uint8')
+    depth_image = Image.fromarray(depth_image)
+
+    return depth_image, mesh_path
+
+
+# GUI
+title = 'Image2Mesh'
+description = 'Demo based on my <a href="https://towardsdatascience.com/generate-a-3d-mesh-from-an-image-with-python' \
+              '-12210c73e5cc">article</a>. This demo predicts the depth of an image and then generates the 3D mesh. ' \
+              'Choosing a higher quality increases the time to generate the mesh. You can download the mesh by ' \
+              'clicking the top-right button on the 3D viewer. '
+examples = [[f'examples/{name}', 3] for name in sorted(os.listdir('examples'))]
+
+# example image source:
+# N. Silberman, D. Hoiem, P. Kohli, and Rob Fergus,
+# Indoor Segmentation and Support Inference from RGBD Images (2012)
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type='pil', label='Input Image'),
+        gr.Slider(1, 5, step=1, value=3, label='Mesh quality')
+    ],
+    outputs=[
+        gr.Image(label='Depth'),
+        gr.Model3D(label='3D Model', clear_color=[0.0, 0.0, 0.0, 0.0])
+    ],
+    examples=examples,
+    allow_flagging='never',
+    cache_examples=False,
+    title=title,
+    description=description
+)
+iface.launch()

requirements.txt

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+gradio
+matplotlib
+numpy
+open3d
+Pillow
+torch
+transformers