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zivid · Jul 12, 2024 · 8d28b4d · 8d28b4d
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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 v2.12.0. 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).
@@ -122,7 +122,7 @@ from the camera can be used.
               - [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
@@ -138,9 +138,6 @@ from the camera can be used.
       - [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/2.12.0+6afd4961-1/u${VERSION_ID:0:2}/zivid_2.12.0+6afd4961-1_amd64.deb" || exit
+install_www_deb "https://downloads.zivid.com/sdk/releases/2.11.1+de9b5dae-1/u${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
@@ -16,8 +16,6 @@
 first. If you want to use your own white reference (white wall, piece of paper, etc.) instead of using the calibration
 board, you can provide your own mask in _main(). Then you will have to specify the lower limit for f-number yourself.
 
-Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
-
 """
 
 import argparse
@@ -29,7 +27,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import zivid
-import zivid.experimental.calibration
 from sample_utils.calibration_board_utils import find_white_mask_from_checkerboard
 from sample_utils.white_balance_calibration import compute_mean_rgb_from_mask, white_balance_calibration
 
@@ -150,12 +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.experimental.calibration.detect_feature_points(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
@@ -178,7 +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
@@ -261,7 +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
@@ -290,7 +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

diff --git a/source/applications/advanced/get_checkerboard_pose_from_zdf.py b/source/applications/advanced/get_checkerboard_pose_from_zdf.py
@@ -5,16 +5,13 @@
 The checkerboard point cloud is also visualized with a coordinate system.
 The ZDF file for this sample can be found under the main instructions for Zivid samples.
 
-Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
-
 """
 
 from pathlib import Path
 
 import numpy as np
 import open3d as o3d
 import zivid
-import zivid.experimental.calibration
 from sample_utils.paths import get_sample_data_path
 from sample_utils.save_load_matrix import assert_affine_matrix_and_save
 
@@ -75,9 +72,7 @@ def _main() -> None:
         point_cloud = frame.point_cloud()
 
         print("Detecting checkerboard and estimating its pose in camera frame")
-        transform_camera_to_checkerboard = (
-            zivid.experimental.calibration.detect_feature_points(frame).pose().to_matrix()
-        )
+        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"

diff --git a/source/applications/advanced/hand_eye_calibration/hand_eye_calibration.py b/source/applications/advanced/hand_eye_calibration/hand_eye_calibration.py
@@ -1,8 +1,6 @@
 """
 Perform Hand-Eye calibration.
 
-Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
-
 """
 
 import datetime
@@ -11,7 +9,6 @@
 
 import numpy as np
 import zivid
-import zivid.experimental.calibration
 from sample_utils.save_load_matrix import assert_affine_matrix_and_save
 
 
@@ -49,12 +46,10 @@ def _perform_calibration(hand_eye_input: List[zivid.calibration.HandEyeInput]) -
         calibration_type = input("Enter type of calibration, eth (for eye-to-hand) or eih (for eye-in-hand):").strip()
         if calibration_type.lower() == "eth":
             print("Performing eye-to-hand calibration")
-            print("The resulting transform is the camera pose in robot base frame")
             hand_eye_output = zivid.calibration.calibrate_eye_to_hand(hand_eye_input)
             return hand_eye_output
         if calibration_type.lower() == "eih":
             print("Performing eye-in-hand calibration")
-            print("The resulting transform is the camera pose in flange (end-effector) frame")
             hand_eye_output = zivid.calibration.calibrate_eye_in_hand(hand_eye_input)
             return hand_eye_output
         print(f"Unknown calibration type: '{calibration_type}'")
@@ -88,12 +83,6 @@ def _main() -> None:
     hand_eye_input = []
     calibrate = False
 
-    print(
-        "Zivid primarily operates with a (4x4) transformation matrix. To convert\n"
-        "from axis-angle, rotation vector, roll-pitch-yaw, or quaternion, check out\n"
-        "our pose_conversions sample."
-    )
-
     while not calibrate:
         command = input("Enter command, p (to add robot pose) or c (to perform calibration):").strip()
         if command == "p":
@@ -103,7 +92,7 @@ def _main() -> None:
                 frame = _assisted_capture(camera)
 
                 print("Detecting checkerboard in point cloud")
-                detection_result = zivid.experimental.calibration.detect_feature_points(frame)
+                detection_result = zivid.calibration.detect_feature_points(frame.point_cloud())
 
                 if detection_result.valid():
                     print("Calibration board detected")
@@ -125,12 +114,6 @@ def _main() -> None:
     transform_file_path = Path(Path(__file__).parent / "transform.yaml")
     assert_affine_matrix_and_save(transform, transform_file_path)
 
-    print(
-        "Zivid primarily operates with a (4x4) transformation matrix. To convert\n"
-        "to axis-angle, rotation vector, roll-pitch-yaw, or quaternion, check out\n"
-        "our pose_conversions sample."
-    )
-
     if calibration_result.valid():
         print("Hand-Eye calibration OK")
         print(f"Result:\n{calibration_result}")

diff --git a/.../advanced/hand_eye_calibration/robodk_hand_eye_calibration/robodk_hand_eye_calibration.py b/.../advanced/hand_eye_calibration/robodk_hand_eye_calibration/robodk_hand_eye_calibration.py
@@ -14,8 +14,6 @@
 https://support.zivid.com/latest/academy/applications/hand-eye/hand-eye-calibration-process.html
 Make sure to launch your RDK file and connect to robot through Robodk before running this script.
 
-Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
-
 """
 
 import argparse
@@ -27,7 +25,6 @@
 import cv2
 import numpy as np
 import zivid
-import zivid.experimental.calibration
 from robodk.robolink import Item
 from sample_utils.robodk_tools import connect_to_robot, get_robot_targets, set_robot_speed_and_acceleration
 from sample_utils.save_load_matrix import assert_affine_matrix_and_save, load_and_assert_affine_matrix
@@ -98,7 +95,7 @@ def _verify_good_capture(frame: zivid.Frame) -> None:
         RuntimeError: If no feature points are detected in frame
 
     """
-    detected_features = zivid.experimental.calibration.detect_feature_points(frame)
+    detected_features = zivid.calibration.detect_feature_points(frame.point_cloud())
     if not detected_features.valid():
         raise RuntimeError("Failed to detect feature points from captured frame.")
 
@@ -237,8 +234,8 @@ def perform_hand_eye_calibration(
 
         if frame_file_path.is_file() and pose_file_path.is_file():
             print(f"Detect feature points from img{pose_and_image_iterator:02d}.zdf")
-            frame = zivid.Frame(frame_file_path)
-            detected_features = zivid.experimental.calibration.detect_feature_points(frame)
+            point_cloud = zivid.Frame(frame_file_path).point_cloud()
+            detected_features = zivid.calibration.detect_feature_points(point_cloud)
             print(f"Read robot pose from pos{pose_and_image_iterator:02d}.yaml")
             transform = load_and_assert_affine_matrix(pose_file_path)
 

diff --git a/..._eye_calibration/ur_hand_eye_calibration/universal_robots_perform_hand_eye_calibration.py b/..._eye_calibration/ur_hand_eye_calibration/universal_robots_perform_hand_eye_calibration.py
@@ -15,8 +15,6 @@
 Further explanation of this sample is found in our knowledge base:
 https://support.zivid.com/latest/academy/applications/hand-eye/ur5-robot-%2B-python-generate-dataset-and-perform-hand-eye-calibration.html
 
-Note: This example uses experimental SDK features, which may be modified, moved, or deleted in the future without notice.
-
 """
 
 import argparse
@@ -28,7 +26,6 @@
 import cv2
 import numpy as np
 import zivid
-import zivid.experimental.calibration
 from rtde import rtde, rtde_config
 from sample_utils.save_load_matrix import assert_affine_matrix_and_save, load_and_assert_affine_matrix
 from scipy.spatial.transform import Rotation
@@ -269,7 +266,8 @@ def _verify_good_capture(frame: zivid.Frame) -> None:
         RuntimeError: If no feature points are detected in frame
 
     """
-    detection_result = zivid.experimental.calibration.detect_feature_points(frame)
+    point_cloud = frame.point_cloud()
+    detection_result = zivid.calibration.detect_feature_points(point_cloud)
 
     if not detection_result.valid():
         raise RuntimeError("Failed to detect feature points from captured frame.")
@@ -392,8 +390,8 @@ def perform_hand_eye_calibration(
 
         if frame_file_path.is_file() and pose_file_path.is_file():
             print(f"Detect feature points from img{idata:02d}.zdf")
-            frame = zivid.Frame(frame_file_path)
-            detection_result = zivid.experimental.calibration.detect_feature_points(frame)
+            point_cloud = zivid.Frame(frame_file_path).point_cloud()
+            detection_result = zivid.calibration.detect_feature_points(point_cloud)
 
             if not detection_result.valid():
                 raise RuntimeError(f"Failed to detect feature points from frame {frame_file_path}")