Samples: Automatic updates to public repository

Remember to do the following: 1. Ensure that modified/deleted/new files are correct 2. Make this commit message relevant for the changes 3. Force push 4. Delete branch after PR is merged If this commit is an update from one SDK version to another, make sure to create a release tag for previous version.
zivid · Sep 13, 2024 · 6fa70ec · 6fa70ec
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diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # Python samples
 
-This repository contains python code samples for Zivid SDK v2.13.1. For
+This repository contains python code samples for Zivid SDK v2.11.1. For
 tested compatibility with earlier SDK versions, please check out
 [accompanying
 releases](https://github.com/zivid/zivid-python-samples/tree/master/../../releases).
@@ -60,9 +60,6 @@ from the camera can be used.
           - [capture\_hdr\_print\_normals](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/advanced/capture_hdr_print_normals.py) - Capture Zivid point clouds, compute normals and print a
             subset.
       - **info\_util\_other**
-          - [automatic\_network\_configuration\_for\_cameras](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/automatic_network_configuration_for_cameras.py) - Automatically set the IP addresses of any number of
-            cameras to be in the same subnet as the provided IP address
-            of the network interface.
           - [camera\_info](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/camera_info.py) - Print version information for Python, zivid-python and
             Zivid SDK, then list cameras and print camera info and state
             for each connected camera.
@@ -72,8 +69,6 @@ from the camera can be used.
           - [firmware\_updater](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/firmware_updater.py) - Update firmware on the Zivid camera.
           - [get\_camera\_intrinsics](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/get_camera_intrinsics.py) - Read intrinsic parameters from the Zivid camera (OpenCV
             model) or estimate them from the point cloud.
-          - [network\_configuration](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/network_configuration.py) - Uses Zivid API to change the IP address of the Zivid
-            camera.
           - [warmup](https://github.com/zivid/zivid-python-samples/tree/master/source/camera/info_util_other/warmup.py) - A basic warm-up method for a Zivid camera with specified
             time and capture cycle.
       - **maintenance**
@@ -119,23 +114,15 @@ from the camera can be used.
             images to find the marker coordinates (2D and 3D).
           - [reproject\_points](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/reproject_points.py) - Illuminate checkerboard (Zivid Calibration Board) corners
             by getting checkerboard pose
-          - [roi\_box\_via\_aruco\_marker](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/roi_box_via_aruco_marker.py) - Filter the point cloud based on a ROI box given relative
-            to the ArUco marker on a Zivid Calibration Board.
           - [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
             to the Zivid Calibration Board.
-          - [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
-            coordinate frame by estimating the marker's pose from the
-            point cloud.
-          - [transform\_point\_cloud\_via\_checkerboard](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/transform_point_cloud_via_checkerboard.py) - Transform a point cloud from camera to checkerboard (Zivid
-            Calibration Board) coordinate frame by getting checkerboard
-            pose from the API.
           - **hand\_eye\_calibration**
               - [pose\_conversions](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/hand_eye_calibration/pose_conversions.py) - Convert to/from Transformation Matrix (Rotation Matrix
                 + Translation Vector).
               - [robodk\_hand\_eye\_calibration](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/hand_eye_calibration/robodk_hand_eye_calibration/robodk_hand_eye_calibration.py) - Generate a dataset and perform hand-eye calibration
                 using the Robodk interface.
               - [utilize\_hand\_eye\_calibration](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/hand_eye_calibration/utilize_hand_eye_calibration.py) - Transform single data point or entire point cloud from
-                camera to robot base reference frame using Hand-Eye
+                camera frame to robot base frame using Hand-Eye
                 calibration
               - [verify\_hand\_eye\_with\_visualization](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/hand_eye_calibration/verify_hand_eye_with_visualization.py) - Verify hand-eye calibration by transforming all
                 dataset point clouds and
@@ -147,14 +134,10 @@ from the camera can be used.
       - [display](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/display.py) - Display relevant data for Zivid Samples.
       - [paths](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/paths.py) - Get relevant paths for Zivid Samples.
       - [robodk\_tools](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/robodk_tools.py) - Robot Control Module
-      - [save\_load\_matrix](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/save_load_matrix.py) - Save and load Zivid 4x4 transformation matrices from and to
-        YAML files.
+      - [save\_load\_matrix](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/save_load_matrix.py) - try:
       - [white\_balance\_calibration](https://github.com/zivid/zivid-python-samples/tree/master/source/sample_utils/white_balance_calibration.py) - Balance color for 2D capture using white surface as reference.
   - **applications**
       - **advanced**
-          - **robot\_guidance**
-              - [robodk\_robot\_guidance](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/robot_guidance/robodk_robot_guidance.py) - Guide the robot to follow a path on the Zivid
-                Calibration Board.
           - **verify\_hand\_eye\_calibration**
               - [robodk\_verify\_hand\_eye\_calibration](https://github.com/zivid/zivid-python-samples/tree/master/source/applications/advanced/verify_hand_eye_calibration/robodk_verify_hand_eye_calibration.py) - Perform a touch test with a robot to verify Hand-Eye
                 Calibration using the RoboDK interface.

diff --git a/continuous-integration/setup.sh b/continuous-integration/setup.sh
@@ -28,7 +28,8 @@ function install_www_deb {
     rm -r $TMP_DIR || exit
 }
 
-install_www_deb "https://downloads.zivid.com/sdk/releases/2.13.1+18e79e79-1/u${VERSION_ID:0:2}/zivid_2.13.1+18e79e79-1_amd64.deb" || exit
+install_www_deb "https://downloads.zivid.com/sdk/releases/2.11.1+de9b5dae-1/u${VERSION_ID:0:2}/zivid-telicam-driver_3.0.1.1-3_amd64.deb" || exit
+install_www_deb "https://downloads.zivid.com/sdk/releases/2.11.1+de9b5dae-1/u${VERSION_ID:0:2}/zivid_2.11.1+de9b5dae-1_amd64.deb" || exit
 
 python3 -m pip install --upgrade pip || exit
 python3 -m pip install --requirement "$ROOT_DIR/requirements.txt" || exit

diff --git a/pyproject.constraints b/pyproject.constraints
@@ -0,0 +1,17 @@
+# Generated file, do not edit @ 2023-11-20 07:12:00
+#
+# Command:
+#     python infrastructure/tools/launchers/dependency_updater.py --max-processes=30
+#
+# To update all Python requirements and constraints manually, you should run:
+#
+#   python infrastructure/tools/launchers/dependency_updater.py
+#
+# This file is autogenerated by pip-compile with Python 3.11
+# by the following command:
+#
+#    pip-compile --all-extras --extra-index-url=http://se-ci-elastic-server-1.localdomain:8081/artifactory/api/pypi/zivid-pypi/simple --output-file=sdk/samples/public/python/pyproject.constraints --strip-extras --trusted-host=se-ci-elastic-server-1.localdomain sdk/samples/public/python/pyproject.toml
+#
+--extra-index-url http://se-ci-elastic-server-1.localdomain:8081/artifactory/api/pypi/zivid-pypi/simple
+--trusted-host se-ci-elastic-server-1.localdomain
+
diff --git a/source/applications/advanced/auto_2d_settings.py b/source/applications/advanced/auto_2d_settings.py
@@ -147,12 +147,12 @@ def _find_white_mask_and_distance_to_checkerboard(camera: zivid.Camera) -> Tuple
     """
     try:
         settings = _capture_assistant_settings(camera)
-        frame = camera.capture(settings)
+        point_cloud = camera.capture(settings).point_cloud()
 
-        checkerboard_pose = zivid.calibration.detect_calibration_board(frame).pose().to_matrix()
+        checkerboard_pose = zivid.calibration.detect_feature_points(point_cloud).pose().to_matrix()
         distance_to_checkerboard = checkerboard_pose[2, 3]
 
-        rgb = frame.point_cloud().copy_data("rgba")[:, :, :3]
+        rgb = point_cloud.copy_data("rgba")[:, :, :3]
         white_squares_mask = find_white_mask_from_checkerboard(rgb)
     except RuntimeError as exc:
         raise RuntimeError("Unable to find checkerboard, make sure it is in view of the camera.") from exc
@@ -175,7 +175,37 @@ def _find_lowest_acceptable_fnum(camera: zivid.Camera, image_distance_near: floa
         Lowest acceptable f-number that gives a focused image
 
     """
-    if camera.info.model == zivid.CameraInfo.Model.zividTwo:
+    if camera.info.model == zivid.CameraInfo.Model.zividOnePlusSmall:
+        focus_distance = 500
+        focal_length = 16
+        circle_of_confusion = 0.015
+        fnum_min = 1.4
+        if image_distance_near < 300 or image_distance_far > 1000:
+            print(
+                f"WARNING: Closest imaging distance ({image_distance_near:.2f}) or farthest imaging distance"
+                f"({image_distance_far:.2f}) is outside recommended working distance for camera [300, 1000]"
+            )
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusMedium:
+        focus_distance = 1000
+        focal_length = 16
+        circle_of_confusion = 0.015
+        fnum_min = 1.4
+        if image_distance_near < 500 or image_distance_far > 2000:
+            print(
+                f"WARNING: Closest imaging distance ({image_distance_near:.2f}) or farthest imaging distance"
+                f"({image_distance_far:.2f}) is outside recommended working distance for camera [500, 2000]"
+            )
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusLarge:
+        focus_distance = 1800
+        focal_length = 16
+        circle_of_confusion = 0.015
+        fnum_min = 1.4
+        if image_distance_near < 1200 or image_distance_far > 3000:
+            print(
+                f"WARNING: Closest imaging distance ({image_distance_near:.2f}) or farthest imaging distance"
+                f"({image_distance_far:.2f}) is outside recommended working distance for camera [1200, 3000]"
+            )
+    elif camera.info.model == zivid.CameraInfo.Model.zividTwo:
         focus_distance = 700
         focal_length = 8
         circle_of_confusion = 0.015
@@ -258,7 +288,13 @@ def _find_lowest_exposure_time(camera: zivid.Camera) -> float:
         Lowest exposure time [us] for given camera
 
     """
-    if camera.info.model == zivid.CameraInfo.Model.zividTwo:
+    if camera.info.model == zivid.CameraInfo.Model.zividOnePlusSmall:
+        exposure_time = 6500
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusMedium:
+        exposure_time = 6500
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusLarge:
+        exposure_time = 6500
+    elif camera.info.model == zivid.CameraInfo.Model.zividTwo:
         exposure_time = 1677
     elif camera.info.model == zivid.CameraInfo.Model.zividTwoL100:
         exposure_time = 1677
@@ -287,7 +323,13 @@ def _find_max_brightness(camera: zivid.Camera) -> float:
         Highest projector brightness value for given camera
 
     """
-    if camera.info.model == zivid.CameraInfo.Model.zividTwo:
+    if camera.info.model == zivid.CameraInfo.Model.zividOnePlusSmall:
+        brightness = 1.8
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusMedium:
+        brightness = 1.8
+    elif camera.info.model == zivid.CameraInfo.Model.zividOnePlusLarge:
+        brightness = 1.8
+    elif camera.info.model == zivid.CameraInfo.Model.zividTwo:
         brightness = 1.8
     elif camera.info.model == zivid.CameraInfo.Model.zividTwoL100:
         brightness = 1.8
@@ -517,10 +559,10 @@ def _print_poor_pixel_distribution(rgb: np.ndarray) -> None:
     black_and = np.sum(np.logical_and(np.logical_and(rgb[:, :, 0] == 0, rgb[:, :, 1] == 0), rgb[:, :, 2] == 0))
 
     print("Distribution of saturated (255) and black (0) pixels with final settings:")
-    print(f"Saturated pixels (at least one channel): {saturated_or}\t ({100 * saturated_or / total_num_pixels:.2f}%)")
-    print(f"Saturated pixels (all channels):\t {saturated_and}\t ({100 * saturated_and / total_num_pixels:.2f}%)")
-    print(f"Black pixels (at least one channel):\t {black_or}\t ({100 * black_or / total_num_pixels:.2f}%)")
-    print(f"Black pixels (all channels):\t\t {black_and}\t ({100 * black_and / total_num_pixels:.2f}%)")
+    print(f"Saturated pixels (at least one channel): {saturated_or}\t ({100*saturated_or/total_num_pixels:.2f}%)")
+    print(f"Saturated pixels (all channels):\t {saturated_and}\t ({100*saturated_and/total_num_pixels:.2f}%)")
+    print(f"Black pixels (at least one channel):\t {black_or}\t ({100*black_or/total_num_pixels:.2f}%)")
+    print(f"Black pixels (all channels):\t\t {black_and}\t ({100*black_and/total_num_pixels:.2f}%)")
 
 
 def _plot_image_with_histogram(rgb: np.ndarray, settings_2d: zivid.Settings2D) -> None:

diff --git a/source/applications/advanced/get_checkerboard_pose_from_zdf.py b/source/applications/advanced/get_checkerboard_pose_from_zdf.py
@@ -32,8 +32,8 @@ def _create_open3d_point_cloud(point_cloud: zivid.PointCloud) -> o3d.geometry.Po
     xyz = np.nan_to_num(xyz).reshape(-1, 3)
     rgb = rgba[:, :, 0:3].reshape(-1, 3)
 
-    point_cloud_open3d = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz.astype(np.float64)))
-    point_cloud_open3d.colors = o3d.utility.Vector3dVector(rgb.astype(np.float64) / 255)
+    point_cloud_open3d = o3d.geometry.PointCloud(o3d.utility.Vector3dVector(xyz))
+    point_cloud_open3d.colors = o3d.utility.Vector3dVector(rgb / 255)
 
     refined_point_cloud_open3d = o3d.geometry.PointCloud.remove_non_finite_points(
         point_cloud_open3d, remove_nan=True, remove_infinite=True
@@ -50,7 +50,7 @@ def _visualize_checkerboard_point_cloud_with_coordinate_system(
 
     Args:
         point_cloud_open3d: An Open3d point cloud of a checkerboard
-        transform: Transformation matrix (4x4)
+        transform: Transformation matrix
 
     """
     coord_system_mesh = o3d.geometry.TriangleMesh.create_coordinate_frame(size=30)
@@ -72,18 +72,18 @@ def _main() -> None:
         point_cloud = frame.point_cloud()
 
         print("Detecting checkerboard and estimating its pose in camera frame")
-        camera_to_checkerboard_transform = zivid.calibration.detect_calibration_board(frame).pose().to_matrix()
-        print(f"Camera pose in checkerboard frame:\n{camera_to_checkerboard_transform}")
+        transform_camera_to_checkerboard = zivid.calibration.detect_feature_points(point_cloud).pose().to_matrix()
+        print(f"Camera pose in checkerboard frame:\n{transform_camera_to_checkerboard}")
 
         transform_file_name = "CameraToCheckerboardTransform.yaml"
         print(f"Saving detected checkerboard pose to YAML file: {transform_file_name}")
         transform_file_path = Path(__file__).parent / transform_file_name
-        assert_affine_matrix_and_save(camera_to_checkerboard_transform, transform_file_path)
+        assert_affine_matrix_and_save(transform_camera_to_checkerboard, transform_file_path)
 
         print("Visualizing checkerboard with coordinate system")
         checkerboard_point_cloud = _create_open3d_point_cloud(point_cloud)
         _visualize_checkerboard_point_cloud_with_coordinate_system(
-            checkerboard_point_cloud, camera_to_checkerboard_transform
+            checkerboard_point_cloud, transform_camera_to_checkerboard
         )