3D point cloud visualizer

script/3d_point_cloud_visualizer.py

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+import open3d as o3d
+import numpy as np
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import matplotlib.pyplot as plt
+
+def apply_color_by_attribute(cloud, attribute):
+    colors = plt.get_cmap('viridis')(attribute)[:, :3]
+    cloud.colors = o3d.utility.Vector3dVector(colors)
+
+def voxel_downsample(cloud, voxel_size=0.05):
+    return cloud.voxel_down_sample(voxel_size)
+
+def compute_statistics(cloud, options):
+    results = {}
+    points = np.asarray(cloud.points)
+
+    if options['mean']:
+        results['mean'] = np.mean(points, axis=0)
+    if options['standard_deviation']:
+        results['standard_deviation'] = np.std(points, axis=0)
+
+    if options['density']:
+        bounding_box = cloud.get_axis_aligned_bounding_box()
+        volume = bounding_box.volume()
+        results['density'] = len(points) / volume if volume > 0 else 0
+
+    return results
+
+def compute_geometric_properties(cloud, options):
+    results = {}
+
+    if options['centroid']:
+        results['centroid'] = cloud.get_center()
+    if options['bounding_box']:
+        bounding_box = cloud.get_axis_aligned_bounding_box()
+        results['bounding_box'] = str(bounding_box)
+        results['volume'] = bounding_box.volume()
+
+    return results
+
+def visualize_ply(file_path, downsample=False, color_map=None, stats_options=None, geom_options=None):
+    cloud = o3d.io.read_point_cloud(file_path)
+    if downsample:
+        cloud = voxel_downsample(cloud)
+
+    if color_map == 'depth' and cloud.has_points():
+        zs = np.asarray(cloud.points)[:, 2]
+        zs_normalized = (zs - zs.min()) / (zs.max() - zs.min())
+        apply_color_by_attribute(cloud, zs_normalized)
+
+    stats_results = compute_statistics(cloud, stats_options)
+    geom_results = compute_geometric_properties(cloud, geom_options)
+
+    o3d.visualization.draw_geometries([cloud])
+
+    output_info = '\n'.join([f"{key}: {value}" for key, value in {**stats_results, **geom_results}.items()])
+    messagebox.showinfo("Point Cloud Analysis Results", output_info)
+
+def gui():
+    root = tk.Tk()
+    root.title("3D Point Cloud Visualizer")
+    root.geometry("400x400")
+
+    downsample_var = tk.BooleanVar()
+    color_map_var = tk.StringVar(value='none')
+
+    open_button = tk.Button(root, text="Open PLY File", command=lambda: open_file_dialog())
+    open_button.pack(pady=10)
+
+    downsample_check = tk.Checkbutton(root, text="Apply Downsampling", variable=downsample_var)
+    downsample_check.pack(anchor='w')
+
+    color_map_label = tk.Label(root, text="Color Map:")
+    color_map_label.pack(anchor='w')
+
+    color_map_menu = tk.OptionMenu(root, color_map_var, 'none', 'depth')
+    color_map_menu.pack(anchor='w')
+
+    # Options for statistics and geometric properties
+    stats_options = {'mean': tk.BooleanVar(), 'standard_deviation': tk.BooleanVar(), 'density': tk.BooleanVar()}
+    geom_options = {'centroid': tk.BooleanVar(), 'bounding_box': tk.BooleanVar(), 'volume': tk.BooleanVar()}
+
+    # Creating checkboxes for each option
+    for key in stats_options:
+        tk.Checkbutton(root, text=f"{key.replace('_', ' ').title()}", variable=stats_options[key]).pack(anchor='w')
+    for key in geom_options:
+        tk.Checkbutton(root, text=f" {key.replace('_', ' ').title()}", variable=geom_options[key]).pack(anchor='w')
+
+    def open_file_dialog():
+        file_path = filedialog.askopenfilename(title='Select a PLY File', filetypes=[('PLY Files', '*.ply')])
+        if file_path:
+            visualize_ply(file_path, downsample_var.get(), color_map_var.get(),
+                          {k: v.get() for k, v in stats_options.items()},
+                          {k: v.get() for k, v in geom_options.items()})
+
+    root.mainloop()
+
+if __name__ == "__main__":
+    gui()

script/README.txt

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+
+# 3D Point Cloud Visualizer
+
+This tool provides a simple yet powerful way to visualize and analyze 3D point cloud data.
+
+## Features
+
+- Load `.ply` files and visualize them in a 3D space.
+- Apply voxel downsampling to simplify point clouds.
+- Calculate and display various statistical measures such as mean, standard deviation, and point density.
+- Compute geometric properties including the centroid and volume of the bounding box.
+- Interactive GUI for easy manipulation and analysis.
+
+## Installation
+
+To set up the 3D Point Cloud Visualizer, you will need Python and Open3D library.
+
+
+pip install open3d matplotlib tkinter
+
+
+## Usage
+
+Run the application from the command line:
+
+
+python point_cloud_visualizer.py
+
+
+In the GUI:
+
+1. Click `Open PLY File` to load your point cloud data.
+2. Select options for downsampling and color mapping as needed.
+3. Choose the statistical and geometric properties you wish to calculate.
+4. View the results and visualizations immediately.
+
+## Contributing
+
+We welcome contributions to this project! Please consider the following steps:
+
+
+## License
+
+Distributed under the MIT License. See `LICENSE` for more information.