Python

サンプルリスト

サンプルには、カメラ と アプリケーション という 2 つの主なカテゴリがあります。 カメラ カテゴリのサンプルは、カメラの使用方法のみに焦点を当てています。 アプリケーション カテゴリのサンプルは、3D 点群、2D 画像、カメラからのその他のデータなど、カメラによって生成された出力を使用します。これらのサンプルは、カメラからのデータがどのように使用されるかを示しています。

• camera

  • basic

    • capture - Capture colored point cloud, save 2D image, save 3D ZDF, and export PLY, using the Zivid camera.

    • capture_from_file_camera - Capture point clouds, with color, with the Zivid file camera.

    • capture_hdr_complete_settings - Capture point clouds, with color, from the Zivid camera with fully configured settings.

    • capture_with_settings_from_yml - Capture images and point clouds, with and without color, from the Zivid camera with settings from YML file.

    • connect - Connect to a Zivid camera using the different available methods.

    • create_file_camera_from_zdf_with_diagnostics - Capture a frame with diagnostics enabled and create a file camera from it.

  • advanced

    • capture_2d_and_3d - Capture 2D and 3D with the Zivid camera.

    • capture_and_print_normals - Capture Zivid point clouds, compute normals and print a subset.

  • info_util_other

    • adapt_settings_for_flickering_ambient_light - Adapt camera acquisition settings based on known ambient light conditions.

    • camera_info - Print version information for Python, zivid-python and Zivid SDK, then list cameras and print camera info and state for each connected camera.

    • camera_user_data - Store user data on the Zivid camera.

    • capture_with_diagnostics - Capture a 2D+3D frame and a 2D frame from the Zivid camera with diagnostics enabled.

    • check_health - Poll the camera health check from a separate thread while capturing in the main thread, printing the statuses and values every second.

    • context_manager_with_zivid - Sample showing how to use a context manager with Zivid Application and safely return processed data.

    • firmware_updater - Update firmware on the Zivid camera.

    • frame_info - Read frame info from the Zivid camera.

    • get_camera_intrinsics - Read intrinsic parameters from the Zivid camera (OpenCV model) or estimate them from the point cloud.

    • measure_scene_conditions - Measure ambient light conditions in the scene and output the measured flickering frequency of the ambient light if flickering is detected.

    • warmup - Short example of a basic way to warm up the camera with specified time and capture cycle.

    • network

      • automatic_network_configuration_for_cameras - Automatically configure the IP addresses of connected cameras to match the network of the user's PC.

      • network_configuration - Uses Zivid API to change the IP address of the Zivid camera.

  • maintenance

    • correct_camera_in_field - Correct the dimension trueness of a Zivid camera.

    • reset_camera_in_field - Reset infield correction on a camera.

    • verify_camera_in_field - Check the dimension trueness of a Zivid camera.

    • verify_camera_in_field_from_zdf - Check the dimension trueness of a Zivid camera from a ZDF file.

• applications

  • basic

    • visualization

      • capture_and_visualize_normals - Capture Zivid point clouds, compute normals and convert to color map and display.

      • capture_from_file_camera_vis_3d - Capture point clouds, with color, with the Zivid file camera.

      • capture_vis_3d - Capture point clouds, with color, from the Zivid camera, and visualize it.

      • project_image_start_and_stop - Start the Image Projection and Stop it.

      • read_zdf_vis_3d - Read point cloud data from a ZDF file and visualize it.

    • file_formats

      • convert_zdf - Convert point cloud data from a ZDF file to your preferred format

      • read_iterate_zdf - Read point cloud data from a ZDF file, iterate through it, and extract individual points.

  • advanced

    • auto_2d_settings - Automatically find 2D settings for a 2D capture by using a Zivid calibration board.

    • capture_undistort_2d - Use camera intrinsics to undistort a 2D image.

    • color_balance - Balance color of a 2D image by using a Zivid calibration board.

    • create_depth_map - Read point cloud data from a ZDF file, convert it to OpenCV format, then extract and visualize depth map.

    • downsample - Downsample point cloud from a ZDF file.

    • gamma_correction - Capture 2D image with gamma correction.

    • mask_point_cloud - Read point cloud data from a ZDF file, apply a binary mask, and visualize it.

    • visualization

      • capture_vis_3d_in_loop - Capture point clouds, with color, from the Zivid camera, and visualize them in a loop.

      • capture_vis_3d_in_loop_with_keypress_exit - Capture point clouds, with color, from the Zivid camera, and visualize them in a loop. Press 'q' to exit.

    • cuda

      • capture_and_process_image_with_cupy_on_cuda - Demonstrate GPU interop with CuPy: wrap a Zivid GPU image buffer as a CuPy array without copying it through CPU memory.

      • capture_and_render_point_cloud_with_opengl_on_cuda - Demonstrate GPU point cloud rendering with CUDA-OpenGL interop: capture a Zivid point cloud, copy it device-to-device

      • capture_and_segment_image_with_pytorch_on_cuda - Demonstrate zero-copy GPU interop between Zivid and PyTorch/CuPy by feeding a Zivid 2D image into a third-party

    • transform

      • get_checkerboard_pose_from_zdf - Read point cloud data of a Zivid calibration board from a ZDF file, estimate the

      • transform_point_cloud_from_millimeters_to_meters - Transform point cloud data from millimeters to meters.

      • transform_point_cloud_via_aruco_marker - Transform a point cloud from camera to ArUco marker coordinate frame by estimating the marker's pose from the point cloud.

      • transform_point_cloud_via_checkerboard - Transform a point cloud from camera to checkerboard (Zivid Calibration Board) coordinate frame by getting checkerboard pose from the API.

    • roi

      • roi_box_via_aruco_marker - Filter the point cloud based on a ROI box given relative to the ArUco marker on a Zivid Calibration Board.

      • roi_box_via_checkerboard - Filter the point cloud based on a ROI box given relative to the Zivid Calibration Board.

    • stitching

      • stitch_continuously_rotating_object - Stitch point clouds from a continuously rotating object without pre-alignment using Local Point Cloud Registration and apply Voxel Downsample.

      • stitch_using_robot_mounted_camera - Stitch multiple point clouds captured with a robot mounted camera.

      • stitch_via_local_point_cloud_registration - Stitch two point clouds using a transformation estimated by Local Point Cloud Registration and apply Voxel Downsample.

    • barcode

      • barcode_detector - Detect and decode linear and matrix barcodes from a 2D capture.

    • hand_eye_calibration

      • hand_eye_calibration - Perform Hand-Eye calibration.

      • hand_eye_gui - Hand-Eye Calibration GUI

      • pose_conversion_gui - Convert between different rotation formats with a GUI:

      • pose_conversions - Convert to/from Transformation Matrix (Rotation Matrix + Translation Vector).

      • utilize_hand_eye_calibration - Transform single data point or entire point cloud from camera to robot base reference frame using Hand-Eye calibration

      • verify_hand_eye_with_visualization - Verify hand-eye calibration by transforming all dataset point clouds and

      • ur_hand_eye_calibration

        • universal_robots_perform_hand_eye_calibration - Script to generate a dataset and perform hand-eye calibration using a Universal Robot UR5e robot.

      • robodk_hand_eye_calibration

        • robodk_hand_eye_calibration - Generate a dataset and perform hand-eye calibration using the Robodk interface.

        • robodk_verify_hand_eye_calibration - Perform a touch test with a robot to verify Hand-Eye Calibration using the RoboDK interface.

    • multi_camera

      • multi_camera_calibration - Use captures of a calibration object to generate transformation matrices to a single coordinate frame, from multiple connected cameras.

      • multi_camera_calibration_from_zdf - Use captures of a calibration object to generate transformation matrices to a single coordinate frame, from ZDF files captured with multiple cameras.

      • stitch_by_transformation - Use transformation matrices from Multi-Camera calibration to transform point clouds into a single coordinate frame, from connected cameras.

      • stitch_by_transformation_from_zdf - Use transformation matrices from Multi-Camera calibration to transform point clouds into single coordinate frame, from a ZDF files.

    • projector

      • project_and_find_marker - Show a marker using the projector, capture a set of 2D images to find the marker coordinates (2D and 3D).

      • read_project_and_capture_image - Read a 2D image from file and project it using the camera projector.

      • reproject_points - Illuminate checkerboard (Zivid Calibration Board) centers by getting the checkerboard feature points

    • robot_guidance

      • robodk_robot_guidance - Guide the robot to follow a path on the Zivid Calibration Board.

• modules

  • zividsamples

    • calibration_board_utils - Utility functions for the Zivid calibration board.

    • camera_verification - def capture_and_measure_from_frame(frame: zivid.Frame) -> VerificationAndState:

    • color_to_grayscale - if rgba_image.ndim != 3 or rgba_image.shape[2] != 4:

    • display - Display relevant data for Zivid Samples.

    • paths - Get relevant paths for Zivid Samples.

    • robodk_tools - Robot Control Module

    • save_load_matrix - Save and load Zivid 4x4 transformation matrices from and to YAML files.

    • save_load_transformation_matrix - assert_affine_matrix_and_save(transformation_matrix.as_matrix(), yaml_path)

    • save_residuals - per_pose_residuals = [

    • settings_utils - categories = zivid.presets.categories2d(camera.info.model)

    • transformation_matrix - Convenience functions and a class for 4x4 transformation matrices.

    • white_balance_calibration - Balance color for 2D capture using white surface as reference.

手順

注釈

これらのサンプルに推奨される Python バージョンは 3.7 - 3.9 です。

なお、Open3D は Python 3.13 以降では利用できません。

1. Zivid ソフトウェアをインストールします。

2. Zivid サンプルデータをダウンロードします。

3. Zivid の Python サンプルリポジトリを確認してください。

   git clone http://github.com/zivid/zivid-python-samples.git
   

4. IDE またはコマンドラインを使用して、ランタイム要件をインストールします。

cd zivid-python-samples
pip install -r requirements.txt

注釈

Open3D のインストールに失敗した場合は、Open3D がサポートしている Python バージョンを使用していることを確認してください。または、以下のコマンドを実行して、Open3D 以外のサンプルに必要な依存関係のみをインストールすることもできます。

pip install ./modules

5. サンプルのいずれかを開いて実行してください。

   注釈

   Windows 上で GUI サンプルを実行する場合は、 pythonw を使用してください。

• Spyder IDE を使用して Python「Hello World」アプリケーションを作成する