Create app.py

app.py

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+import gradio as gr
+import open3d as o3d
+import numpy as np
+import pandas as pd
+from PIL import Image
+from gradio.components import File, Slider, Image, Dataframe, Model3D
+import tempfile
+
+def point_cloud_to_image(point_cloud):
+    # Create Open3D visualization object
+    vis = o3d.visualization.Visualizer()
+    vis.create_window()
+    vis.add_geometry(point_cloud)
+
+    # Render the visualization and get the image
+    vis.poll_events()
+    vis.update_renderer()
+    img = vis.capture_screen_float_buffer()
+
+    # Convert image to PIL Image format
+    pil_img = Image.fromarray(np.uint8(np.asarray(img)*255))
+
+    return pil_img
+
+def create_file_mesh_from_pcd(pcd): 
+    
+    temp_file = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
+    file_path = temp_file.name
+    temp_file.close()
+    
+    alpha = 2
+    # print(f"alpha={alpha:.3f}")
+    mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha)
+    mesh.compute_vertex_normals()
+    o3d.io.write_triangle_mesh(file_path, mesh, write_triangle_uvs=True)
+    return file_path
+
+def plane_detection(pointcloud_path, voxel_size=0.05, distance_threshold=0.01, num_iterations=1000):
+    # Load point cloud from file
+    pcd = o3d.io.read_point_cloud(pointcloud_path.name)
+
+    # Downsample the point cloud to reduce computational load
+    pcd = pcd.voxel_down_sample(voxel_size=voxel_size)
+
+    # Find plane using RANSAC algorithm
+    plane_model, inliers = pcd.segment_plane(distance_threshold=distance_threshold,
+                                             ransac_n=3,
+                                             num_iterations=num_iterations)
+
+    # Get inlier and outlier point cloud
+    inlier_cloud = pcd.select_by_index(inliers)
+    outlier_cloud = pcd.select_by_index(inliers, invert=True)
+    # extract the coefficients of the plane
+    a, b, c, d = plane_model
+    plane_model = np.array([[a], [b], [c], [d]])
+    df = pd.DataFrame(plane_model.reshape(1, 4), columns=["a", "b", "c", "d"])
+    input_path = create_file_mesh_from_pcd(pcd)
+    inlier_path = create_file_mesh_from_pcd(inlier_cloud)
+    outlier_path = create_file_mesh_from_pcd(outlier_cloud)
+    # Return inlier point cloud, outlier point cloud, and plane model
+    # return point_cloud_to_image(inlier_cloud), point_cloud_to_image(outlier_cloud), df
+    return input_path, inlier_path, outlier_path, df
+
+outputs = [
+    # show pcd inlier point cloud
+    Model3D(label="Input Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
+    # show pcd inlier point cloud
+    Model3D(label="Inlier Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
+    # show pcd outlier point cloud
+    Model3D(label="Outlier Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
+    # show the centroids and counts, which is a numpy array
+    Dataframe(label="Coefficients of the plane model", type="pandas")
+]
+
+# Create Gradio interface
+iface = gr.Interface(plane_detection, 
+                     inputs=[
+                         File(label="Point cloud file (.ply or .pcd format)"),
+                         Slider(label="Voxel size", minimum=1, maximum=50, step=1, value=2),
+                         Slider(label="Distance threshold", minimum=0.5, maximum=50, step=0.5, value=5),
+                         Slider(label="Number of iterations", minimum=1, maximum=10000, step=1, value=100)
+                     ],
+                     outputs=outputs,
+                     title="Plane Detection using RANSAC",
+                     description="This app takes as input a point cloud file (.ply or .pcd format), voxel size, distance threshold, and number of iterations, finds a plane using RANSAC algorithm, displays the inlier and outlier point clouds, and returns the inlier point cloud, outlier point cloud, and the plane model.",
+                     allow_flagging="never"
+                     )
+
+
+# Launch the interface
+iface.launch()