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Merge pull request #205 from zivid/2025-07-09-update-python-samples
Samples: Automatic updates to public repository
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‎README.md

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@@ -123,6 +123,12 @@ from the camera can be used.
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to the ArUco marker on a Zivid Calibration Board.
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- [roi\_box\_via\_checkerboard](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/roi_box_via_checkerboard.py) - Filter the point cloud based on a ROI box given relative
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to the Zivid Calibration Board.
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- [stitch\_continuously\_rotating\_object](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/stitch_continuously_rotating_object.py) - Stitch point clouds from a continuously rotating object
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without pre-alignment using Local Point Cloud Registration
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and apply Voxel Downsample.
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- [stitch\_via\_local\_point\_cloud\_registration](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/stitch_via_local_point_cloud_registration.py) - Stitch two point clouds using a transformation estimated
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by Local Point Cloud Registration and apply Voxel
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Downsample.
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- [transform\_point\_cloud\_from\_millimeters\_to\_meters](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/transform_point_cloud_from_millimeters_to_meters.py) - Transform point cloud data from millimeters to meters.
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- [transform\_point\_cloud\_via\_aruco\_marker](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/transform_point_cloud_via_aruco_marker.py) - Transform a point cloud from camera to ArUco marker
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coordinate frame by estimating the marker's pose from the

‎modules/zividsamples/display.py

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@@ -90,10 +90,12 @@ def copy_to_open3d_point_cloud(
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Returns:
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An Open3D PointCloud
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"""
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xyz = np.nan_to_num(xyz).reshape(-1, 3)
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if len(np.shape(xyz)) == 3:
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xyz = np.nan_to_num(xyz).reshape(-1, 3)
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if normals is not None:
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normals = np.nan_to_num(normals).reshape(-1, 3)
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rgb = rgb.reshape(-1, rgb.shape[-1])[:, :3]
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if len(np.shape(rgb)) == 3:
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rgb = rgb.reshape(-1, rgb.shape[-1])[:, :3]
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open3d_point_cloud = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz))
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open3d_point_cloud.colors = o3d.utility.Vector3dVector(rgb / 255)
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print(" 9: for point cloud colored by normals")
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print(" h: for all controls")
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visualizer.get_render_option().background_color = (0, 0, 0)
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visualizer.get_render_option().point_size = 1
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visualizer.get_render_option().point_size = 2
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visualizer.get_render_option().show_coordinate_frame = True
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visualizer.get_view_control().set_front([0, 0, -1])
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visualizer.get_view_control().set_up([0, -1, 0])

‎source/applications/advanced/stitch_continuously_rotating_object.py

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"""
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Stitch point clouds from a continuously rotating object without pre-alignment using Local Point Cloud Registration and apply Voxel Downsample.
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It is assumed that the object is rotating around its own axis and the camera is stationary.
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The camera settings should have defined a region of interest box that removes unnecessary points, keeping only the object to be stitched.
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Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
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"""
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import argparse
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import time
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from pathlib import Path
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import numpy as np
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import zivid
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from zivid.experimental.point_cloud_export import export_unorganized_point_cloud
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from zivid.experimental.point_cloud_export.file_format import PLY
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from zivid.experimental.toolbox.point_cloud_registration import (
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LocalPointCloudRegistrationParameters,
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local_point_cloud_registration,
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)
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from zividsamples.display import display_pointcloud
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def _options() -> argparse.Namespace:
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"""Function to read user arguments.
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Returns:
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Arguments from user
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"""
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--settings-path",
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required=True,
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type=Path,
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help="Path to the camera settings YML file",
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)
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return parser.parse_args()
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def _main() -> None:
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user_options = _options()
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app = zivid.Application()
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print("Connecting to camera")
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camera = app.connect_camera()
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settings_file = Path(user_options.settings_path)
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print(f"Loading settings from file: {settings_file}")
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settings = zivid.Settings.load(settings_file)
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previous_to_current_point_cloud_transform = np.eye(4)
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unorganized_stitched_point_cloud = zivid.UnorganizedPointCloud()
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registration_params = LocalPointCloudRegistrationParameters()
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for number_of_captures in range(20):
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time.sleep(1)
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frame = camera.capture_2d_3d(settings)
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unorganized_point_cloud = (
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frame.point_cloud().to_unorganized_point_cloud().voxel_downsampled(voxel_size=1.0, min_points_per_voxel=2)
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)
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if number_of_captures != 0:
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local_point_cloud_registration_result = local_point_cloud_registration(
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target=unorganized_stitched_point_cloud,
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source=unorganized_point_cloud,
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parameters=registration_params,
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initial_transform=previous_to_current_point_cloud_transform,
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)
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if not local_point_cloud_registration_result.converged():
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print("Registration did not converge...")
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continue
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previous_to_current_point_cloud_transform = local_point_cloud_registration_result.transform().to_matrix()
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unorganized_stitched_point_cloud.transform(np.linalg.inv(previous_to_current_point_cloud_transform))
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unorganized_stitched_point_cloud.extend(unorganized_point_cloud)
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print(f"Captures done: {number_of_captures}")
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print("Voxel-downsampling the stitched point cloud")
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unorganized_stitched_point_cloud = unorganized_stitched_point_cloud.voxel_downsampled(
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voxel_size=0.75, min_points_per_voxel=2
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)
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display_pointcloud(
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xyz=unorganized_stitched_point_cloud.copy_data("xyz"),
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rgb=unorganized_stitched_point_cloud.copy_data("rgba")[:, 0:3],
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)
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file_name = Path(__file__).parent / "StitchedPointCloudOfRotatingObject.ply"
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export_unorganized_point_cloud(unorganized_stitched_point_cloud, PLY(str(file_name), layout=PLY.Layout.unordered))
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if __name__ == "__main__":
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_main()

‎source/applications/advanced/stitch_via_local_point_cloud_registration.py

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"""
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Stitch two point clouds using a transformation estimated by Local Point Cloud Registration and apply Voxel Downsample.
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The ZDF files for this sample can be found in Zivid's Sample Data, under the main instructions for Zivid samples.
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Zivid's Sample Data can be downloaded from https://support.zivid.com/en/latest/api-reference/samples/sample-data.html.
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Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
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"""
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import zivid
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from zivid.experimental.toolbox.point_cloud_registration import (
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LocalPointCloudRegistrationParameters,
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local_point_cloud_registration,
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)
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from zividsamples.display import display_pointcloud
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from zividsamples.paths import get_sample_data_path
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def _main() -> None:
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zivid.Application()
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print("Reading point clouds from files")
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directory = get_sample_data_path() / "StitchingPointClouds" / "BlueObject"
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frame_1 = zivid.Frame(directory / "BlueObject.zdf")
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frame_2 = zivid.Frame(directory / "BlueObjectSlightlyMoved.zdf")
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print("Converting organized point clouds to unorganized point clouds and voxel downsampling")
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unorganized_point_cloud_1 = frame_1.point_cloud().to_unorganized_point_cloud()
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unorganized_point_cloud_2 = frame_2.point_cloud().to_unorganized_point_cloud()
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print("Displaying point clouds before stitching")
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unorganized_not_stitched_point_cloud = zivid.UnorganizedPointCloud()
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unorganized_not_stitched_point_cloud.extend(unorganized_point_cloud_1)
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unorganized_not_stitched_point_cloud.extend(unorganized_point_cloud_2)
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display_pointcloud(
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xyz=unorganized_not_stitched_point_cloud.copy_data("xyz"),
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rgb=unorganized_not_stitched_point_cloud.copy_data("rgba")[:, 0:3],
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)
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print("Estimating transformation between point clouds")
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unorganized_point_cloud_1_lpcr = unorganized_point_cloud_1.voxel_downsampled(voxel_size=1.0, min_points_per_voxel=3)
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unorganized_point_cloud_2_lpcr = unorganized_point_cloud_2.voxel_downsampled(voxel_size=1.0, min_points_per_voxel=3)
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registration_params = LocalPointCloudRegistrationParameters()
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local_point_cloud_registration_result = local_point_cloud_registration(
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target=unorganized_point_cloud_1_lpcr, source=unorganized_point_cloud_2_lpcr, parameters=registration_params
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)
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assert local_point_cloud_registration_result.converged(), "Registration did not converge..."
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point_cloud_1_to_point_cloud_2_transform = local_point_cloud_registration_result.transform()
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print("Displaying point clouds after stitching")
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final_point_cloud = zivid.UnorganizedPointCloud()
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final_point_cloud.extend(unorganized_point_cloud_1)
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unorganized_point_cloud_2_transformed = unorganized_point_cloud_2.transformed(
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point_cloud_1_to_point_cloud_2_transform.to_matrix()
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)
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final_point_cloud.extend(unorganized_point_cloud_2_transformed)
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display_pointcloud(
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xyz=final_point_cloud.copy_data("xyz"),
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rgb=final_point_cloud.copy_data("rgba")[:, 0:3],
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)
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print("Voxel-downsampling the stitched point cloud")
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final_point_cloud = final_point_cloud.voxel_downsampled(voxel_size=2.0, min_points_per_voxel=1)
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display_pointcloud(
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xyz=final_point_cloud.copy_data("xyz"),
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rgb=final_point_cloud.copy_data("rgba")[:, 0:3],
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)
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if __name__ == "__main__":
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_main()

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