improve python tutorials

python/tutorials/tutorial_301_normal_estimtion.py

@@ -36,40 +36,51 @@
 print(f"Input file name: {file_name}")
 
 # Load the point cloud from the specified file.
-pc = easy3d.PointCloudIO.load(file_name)
+point_cloud = easy3d.PointCloudIO.load(file_name)
 
 # -------------------------------------------------------------------------------
 # Estimating Point Cloud Normals
 # -------------------------------------------------------------------------------
-# Estimate normals for the point cloud. The `16` parameter specifies the
-# number of neighbors to use for normal estimation.
-easy3d.PointCloudNormals.estimate(pc, 16)
+# Since we want to visualize both input and resulted point clouds, let's make a
+# copy of the input point cloud. The algorithm will be applied to the copy one.
+copied_point_cloud = easy3d.PointCloud(point_cloud)  # Create a copy
+easy3d.PointCloudNormals.estimate(copied_point_cloud, 16)
 
 # Reorient the normals to ensure consistency.
-easy3d.PointCloudNormals.reorient(pc, 16)
+easy3d.PointCloudNormals.reorient(copied_point_cloud, 16)
 
 # -------------------------------------------------------------------------------
 # Saving the Processed Point Cloud
 # -------------------------------------------------------------------------------
 # Save the point cloud with estimated normals to a new file in .ply format.
 output_file = file_name + "-result.ply"
-easy3d.PointCloudIO.save(output_file, pc)
+easy3d.PointCloudIO.save(output_file, copied_point_cloud)
 print(f"Processed point cloud saved to: {output_file}")
 
 # -------------------------------------------------------------------------------
-# Visualizing the Point Cloud
+# Visualizing the input and the resulted point clouds side-by-side
 # -------------------------------------------------------------------------------
-# Create a viewer instance to visualize the point cloud.
-viewer = easy3d.Viewer("Easy3D Viewer - Normal estimation")
-# Set to an empty to get rid of the lengthy usage instructions
-viewer.set_usage("")
 
-# Add the point cloud model to the viewer and run the viewer to interact with the model.
-viewer.add_model(pc)
+# Create a MultiViewer instance with 1 row and 2 columns.
+viewer = easy3d.MultiViewer(1, 2, "Easy3D Viewer - Normal estimation")
 
-# Set the camera view direction and up vector for a better view position.
+# Add the input point cloud to the viewer and assign it to the left view (row=0, column=0).
+viewer.add_model(point_cloud)
+viewer.assign(0, 0, point_cloud)
+
+# Add the resulted point cloud to the viewer and assign it to the right view (row=0, column=1).
+viewer.add_model(copied_point_cloud)
+viewer.assign(0, 1, copied_point_cloud)
+
+# Add instructions for the viewer (optional).
+viewer.set_usage(
+    "- Left: Original point cloud.\n"
+    "- Right: Resulted point cloud with normal information."
+)
+
+# # Set the camera view direction and up vector for a better view position.
 viewer.camera().setViewDirection(easy3d.vec3(0, 0, -1))   # Set the view direction.
 viewer.camera().setUpVector(easy3d.vec3(0, 1, 0))         # Set the up vector.
 
-# Running the Viewer
-viewer.run()
+# Launch the viewer.
+viewer.run()

python/tutorials/tutorial_303_plane_extraction.py

@@ -110,7 +110,10 @@
     print(f"  Index: {plane.primitive_index}")
     print(f"  Position: {plane.position}")
     print(f"  Normal: {plane.normal}")
-    print(f"  Vertices: {plane.vertices}")  # e.g., [5788, 5801, 5777, 5796, ... ]
+
+    # Print up to 20 vertex indices (for demonstration purposes)
+    vertex_count = len(plane.vertices)
+    print(f"  Vertices ({min(vertex_count, 20)} out of {vertex_count} shown): {plane.vertices[:20]}")  # e.g., [5788, 5801, 5777, 5796, ... ]
 
 # -----------------------------------------------------------------------------
 # Optional: Detecting Cylinders
@@ -128,3 +131,6 @@
 #     print(f"  Position: {cylinder.position}")
 #     print(f"  Direction: {cylinder.direction}")
 #     print(f"  Radius: {cylinder.radius}")
+#     # Print up to 20 vertex indices (for demonstration purposes)
+#     vertex_count = len(cylinder.vertices)
+#     print(f"  Vertices ({min(vertex_count, 20)} out of {vertex_count} shown): {cylinder.vertices[:20]}")  # e.g., [5788, 5801, 5777, 5796, ... ]

python/tutorials/tutorial_304_point_cloud_downsampling.py

@@ -9,6 +9,8 @@
 # 1. Load a point cloud from a file.
 # 2. Apply different downsampling techniques, including grid and uniform
 #    simplifications, and measure their effects on the point cloud size.
+# 3. Visualize the original point cloud and the sampled point cloud side-by-side
+#    using Easy3D's `MultiViewer`.
 # -----------------------------------------------------------------------------
 
 # If you built Easy3D locally, you'll need to add its Python bindings to the
@@ -30,8 +32,8 @@
 # Easy3D supports various file formats for point clouds (e.g., .bin, .xyz).
 # Here, we load a sample file provided by Easy3D. Replace the path with your
 # own file if needed.
-pointcloud_file = easy3d.directory() + "/data/polyhedron.bin"  # Update as needed
-point_cloud = easy3d.PointCloudIO.load(pointcloud_file)  # Load the point cloud
+file = easy3d.directory() + "/data/polyhedron.bin"  # Update as needed
+point_cloud = easy3d.PointCloudIO.load(file)  # Load the point cloud
 print(f"Loaded point cloud with {point_cloud.n_vertices()} points.")  # Print the initial point count
 
 # -----------------------------------------------------------------------------
@@ -57,7 +59,7 @@
 print(f"Uniform simplification (distance threshold): {len(uniform_indices_by_epsilon)} points will be removed.")
 
 # Option 2: Specify the desired number of points in the final point cloud (`num`).
-target_point_count = 50000
+target_point_count = 10000
 uniform_indices_by_number = easy3d.PointCloudSimplification.uniform_simplification(point_cloud, num=target_point_count)
 print(f"Uniform simplification (target point count): {len(uniform_indices_by_number)} points will be removed.")
 
@@ -66,10 +68,36 @@
 # -----------------------------------------------------------------------------
 # The indices returned by simplification methods indicate the points to be removed.
 # We use `delete_points()` to apply the simplifications to the point cloud.
-point_cloud.delete_points(uniform_indices_by_number)
-print(f"Final point cloud size after simplification: {point_cloud.n_vertices()} points.")
+# Since we want to visualize both the input and the downsampled point clouds, let's
+# make a copy of the original point cloud.
+copied_point_cloud = easy3d.PointCloud(point_cloud)  # Create a copy for simplification.
+copied_point_cloud.delete_points(uniform_indices_by_number)
+print(f"Final point cloud size after simplification: {copied_point_cloud.n_vertices()} points.")
 
-# Optional: Save the result to a file if needed (or visualize using the Easy3D viewer).
+# Optional: Save the result to a file if needed
 # output_file = "downsampled_point_cloud.ply"  # Specify the output file name
-# easy3d.PointCloudIO.save(output_file, point_cloud)
-# print(f"Downsampled point cloud saved to {output_file}")
+# easy3d.PointCloudIO.save(output_file, copied_point_cloud)
+# print(f"Downsampled point cloud saved to {output_file}")
+
+# -----------------------------------------------------------------------------
+# Visualize the input and downsampled point cloud side-by-side
+# -----------------------------------------------------------------------------
+# Create a MultiViewer instance with 1 row and 2 columns.
+viewer = easy3d.MultiViewer(1, 2, "Easy3D Viewer - Point Cloud Downsampling")
+
+# Add the input to the viewer and assign it to the left view (row=0, column=0).
+mesh = viewer.add_model(point_cloud)
+viewer.assign(0, 0, mesh)
+
+# Add the downsampled one to the viewer and assign it to the right view (row=0, column=1).
+viewer.add_model(copied_point_cloud)
+viewer.assign(0, 1, copied_point_cloud)
+
+# Add instructions for the viewer (optional).
+viewer.set_usage(
+    "- Left: Original point cloud.\n"
+    "- Right: Downsampled point cloud."
+)
+
+# Launch the viewer.
+viewer.run()