Samples: Update zivid python samples

This PR adds the following samples:
- Hand eye calibration samples based on RoboDK
- Firmware updater
- ROI box via checkerboard

It also formats and standardize all samples.

.pylintrc

@@ -3,7 +3,9 @@ disable=bad-continuation, ######## Disabled because of conflicts with bl
  consider-using-enumerate, ######## Temporary disabled ########
  import-error,
  missing-docstring,
- duplicate-code
+ no-member,
+ duplicate-code,
+
 [TYPE CHECK]
 generated-members=cv2.*
 

README.md

@@ -60,6 +60,7 @@ from the camera can be used.
  - [camera\_user\_data](https://github.com/zivid/zivid-python-samples/tree/master//source/camera/info_util_other/camera_user_data.py) - Store user data on the Zivid camera.
  - [capture\_with\_diagnostics](https://github.com/zivid/zivid-python-samples/tree/master//source/camera/info_util_other/capture_with_diagnostics.py) - Capture point clouds, with color, from the Zivid camera,
  with settings from YML file and diagnostics enabled.
+ - [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).
  - [print\_version\_info](https://github.com/zivid/zivid-python-samples/tree/master//source/camera/info_util_other/print_version_info.py) - Print version information for Python, zivid-python and
@@ -97,9 +98,13 @@ from the camera can be used.
  - [hand\_eye\_calibration](https://github.com/zivid/zivid-python-samples/tree/master//source/applications/advanced/hand_eye_calibration/hand_eye_calibration.py) - Perform Hand-Eye calibration.
  - [mask\_point\_cloud](https://github.com/zivid/zivid-python-samples/tree/master//source/applications/advanced/mask_point_cloud.py) - Read point cloud data from a ZDF file, apply a binary
  mask, and visualize it.
+ - [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.
  - **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 frame to robot base frame using Hand-Eye
  calibration
@@ -111,43 +116,44 @@ from the camera can be used.
  - **sample\_utils**
  - [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.
+ - **applications**
+ - **advanced**
+ - **hand\_eye\_calibration**
+ - **robodk\_hand\_eye\_calibration**
+ - [robot\_tools](https://github.com/zivid/zivid-python-samples/tree/master//source/applications/advanced/hand_eye_calibration/robodk_hand_eye_calibration/robot_tools.py) - Robot Control Module
 
 ## Installation
 
 1. [Install Zivid
- Software](https://support.zivid.com/latest//getting-started/software-installation.html)
+ Software](https://support.zivid.com/latest//getting-started/software-installation.html).
 
-2. [Install Zivid Python](https://github.com/zivid/zivid-python). Note:
- The recommended Python version for these samples is 3.8.
+2. [Install Zivid
+ Python](https://github.com/zivid/zivid-python#installation).
 
 3. [Download Zivid Sample
- Data](https://support.zivid.com/latest//api-reference/samples/sample-data.html)
-
-4. \[Optional\] Launch the Python IDE of your choice. Read our
- instructions on [setting up
- Python](https://support.zivid.com/latest//api-reference/samples/python/setting-up-python.html).
+ Data](https://support.zivid.com/latest//api-reference/samples/sample-data.html).
 
-5. Install the runtime requirements using IDE or command line:
+4. Install the runtime requirements using IDE or command line:
 
  ``` sourceCode 
  pip install -r requirements.txt
  ```
 
-6. Add the directory source to PYTHONPATH. Navigate to the root of the
+5. Add the directory source to PYTHONPATH. Navigate to the root of the
  repository and run:
 
 >  - PowerShell: `$env:PYTHONPATH=$env:PYTHONPATH + ";$PWD\source"`
 >  - cmd: `set PYTHONPATH="$PYTHONPATH;$PWD\source"`
 >  - bash: `export PYTHONPATH="$PYTHONPATH:$PWD/source"`
 
-7. Open and run one of the samples.
+6. Open and run one of the samples.
 
 ## Support
 
 For more information about the Zivid cameras, please visit our
 [Knowledge Base](https://support.zivid.com/latest). If you run into any
 issues please check out
-[Troubleshooting](https://support.zivid.com/latest/rst/support/troubleshooting.html).
+[Troubleshooting](https://support.zivid.com/latest/support/troubleshooting.html).
 
 ## License
 

source/.gitattributes

@@ -0,0 +1 @@
+*.rdk filter=lfs diff=lfs merge=lfs -text

source/applications/advanced/color_balance.py

@@ -5,9 +5,9 @@
 import datetime
 from dataclasses import dataclass
 
-import matplotlib.pyplot as plt
 import numpy as np
 import zivid
+from sample_utils.display import display_rgb
 
 
 @dataclass
@@ -27,22 +27,6 @@ class MeanColor:
  blue: np.float64
 
 
-def _display_rgb(rgb, title):
- """Display RGB image.
-
- Args:
- rgb: RGB image (HxWx3 darray)
- title: Image title
-
- Returns None
-
- """
- plt.figure()
- plt.imshow(rgb)
- plt.title(title)
- plt.show(block=False)
-
-
 def _compute_mean_rgb(rgb, pixels):
  """Compute mean RGB values.
 
@@ -221,7 +205,7 @@ def _main():
  settings_2d = _auto_settings_configuration(camera)
 
  rgba = camera.capture(settings_2d).image_rgba().copy_data()
- _display_rgb(rgba[:, :, 0:3], "RGB image before color balance")
+ display_rgb(rgba[:, :, 0:3], title="RGB image before color balance", block=False)
 
  [red_balance, green_balance, blue_balance] = _color_balance_calibration(camera, settings_2d)
 
@@ -231,8 +215,7 @@ def _main():
  settings_2d.processing.color.balance.blue = blue_balance
  rgba_balanced = camera.capture(settings_2d).image_rgba().copy_data()
 
- _display_rgb(rgba_balanced[:, :, 0:3], "RGB image after color balance")
- input("Press Enter to close...")
+ display_rgb(rgba_balanced[:, :, 0:3], title="RGB image after color balance", block=True)
 
 
 if __name__ == "__main__":

source/applications/advanced/downsample.py

@@ -45,8 +45,6 @@ def _main():
 
  display_pointcloud(xyz_donwsampled, rgba_downsampled[:, :, 0:3])
 
- input("Press Enter to close...")
-
 
 if __name__ == "__main__":
  # If running the script from Spyder IDE, first run '%gui qt'

source/applications/advanced/get_checkerboard_pose_from_zdf.py

@@ -65,7 +65,7 @@ def _visualize_checkerboard_point_cloud_with_coordinate_system(point_cloud_open3
  coord_system_mesh = o3d.geometry.TriangleMesh.create_coordinate_frame(size=30)
  coord_system_mesh.transform(transform)
 
- visualizer = o3d.visualization.Visualizer() # pylint: disable=no-member
+ visualizer = o3d.visualization.Visualizer()
  visualizer.create_window()
  visualizer.add_geometry(point_cloud_open3d)
  visualizer.add_geometry(coord_system_mesh)
@@ -83,9 +83,7 @@ def _main():
  point_cloud = frame.point_cloud()
 
  print("Detecting checkerboard and estimating its pose in camera frame")
- transform_camera_to_checkerboard = (
- zivid.calibration.detect_feature_points(point_cloud).pose().to_matrix()
- ) # pylint: disable=no-member
+ 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 = "CameraToCheckerboardTransform.yaml"

source/applications/advanced/hand_eye_calibration/README.md

@@ -3,9 +3,9 @@
 To fully understand Hand-Eye Calibration, please see the [tutorial][Tutorial-url] in our Knowledge Base.
 
 -----------------
-The following applications creates a **Transformation Matrix** from data provided by a user
+The following applications create a **Transformation Matrix** from data provided by a user
 
-[HandEyeCalibration][HandEyeCalibration-url]:
+[**HandEyeCalibration**][HandEyeCalibration-url]
 
 * Application which walks through the collection of calibration poses
  1. Provide robot pose to application (manual entry)
@@ -14,9 +14,26 @@ The following applications creates a **Transformation Matrix** from data provide
  4. Repeat i.-iii. until 10-20 pose pairs are collected
  5. Enter command to perform calibration and return a **Transformation Matrix**
 
-[ZividHandEyeCalibration][ZividHandEyeCalibration-url]
+[**ZividHandEyeCalibration**][ZividHandEyeCalibration-url]
 
-* [CLI application][CLI application-url] which takes a collection of pose pairs (e.g. output of steps i.-iii. in [HandEyeCalibration][HandEyeCalibration-url]) and returns a **Transformation Matrix**. This application comes with the Windows installer and is part of the tools deb for Ubuntu.
+* [CLI application][CLI application-url] which takes a collection of pose pairs (e.g. output of steps 1-3 in [HandEyeCalibration][HandEyeCalibration-url]) and returns a **Transformation Matrix**. This application comes with the Windows installer and is part of the tools deb for Ubuntu.
+
+-----------------
+
+There are two samples that show how to perform acquisition of the hand-eye calibration dataset in this repository.
+Both samples go through the process of acquiring the pose and point cloud pairs and then process them to return the resulting hand-eye **Transform Matrix**.
+
+[**UniversalRobotsPerformHandEyeCalibration**][URhandeyecalibration-url]
+
+* This sample is created to work specifically with the UR5e robot.
+* To follow the tutorial for this sample go to [**UR5e + Python Hand Eye Tutorial**][URHandEyeTutorial-url].
+
+[**RoboDKHandEyeCalibration**][RobodkHandEyeCalibration-url]
+
+The second sample uses RoboDK for robot control and can be used with any robot that the software supports.
+The list for the robots that they support can be found [**here**][robodk-robot-library-url].
+Poses must be added by the user to their personal rdk file. 
+To find best pose practice follow the instructions provided on the Zivid knowledge base for the [hand-eye calibration process][ZividHandEyeCalibration-url].
 
 -----------------
 The following applications assume that a **Transformation Matrix** has been found
@@ -31,27 +48,31 @@ The following applications assume that a **Transformation Matrix** has been foun
 
 [**PoseConversions**][PoseConversions-url]:
 
-* Zivid primarily operate with a (4x4) Transformation Matrix (Rotation Matrix + Translation Vector). This example shows how to use Eigen to convert to and from:
+* Zivid primarily operate with a (4x4) **Transformation Matrix** (Rotation Matrix + Translation Vector). This example shows how to use Eigen to convert to and from:
  * AxisAngle, Rotation Vector, Roll-Pitch-Yaw, Quaternion
 
 [**VerifyHandEyeWithVisualization**][VerifyHandEyeWithVisualization-url]:
 
 Visually demonstrates the hand-eye calibration accuracy by overlapping transformed points clouds.
+
 * The application asks the user for the hand-eye calibration type (manual entry).
-* After loading the hand-eye dataset (point clouds and robot poses) and the hand-eye output (transformation matrix), the application repeats the following process for all data pairs:
+* After loading the hand-eye dataset (point clouds and robot poses) and the hand-eye output (**transformation matrix**), the application repeats the following process for all data pairs:
  1. Transforms the point cloud
  2. Finds cartesian coordinates of the checkerboard centroid
  3. Creates a region of interest around the checkerboard and filters out points outside the region of interest
  4. Saves the point cloud to a PLY file
  5. Appends the point cloud to a list (overlapped point clouds)
-This application ends by displaying all point clouds from the list.
-
 
+This application ends by displaying all point clouds from the list.
 
 [HandEyeCalibration-url]: hand_eye_calibration.py
 [UtilizeHandEyeCalibration-url]: utilize_hand_eye_calibration.py
 [VerifyHandEyeWithVisualization-url]: verify_hand_eye_with_visualization.py
 [ZividHandEyeCalibration-url]: https://support.zivid.com/latest/academy/applications/hand-eye/hand-eye-calibration-process.html
 [Tutorial-url]: https://support.zivid.com/latest/academy/applications/hand-eye.html
 [PoseConversions-url]: pose_conversions.py
-[CLI application-url]: https://support.zivid.com/latest/academy/applications/hand-eye/zivid_CLI_tool_for_hand_eye_calibration.html
+[CLI application-url]: https://support.zivid.com/latest/academy/applications/hand-eye/zivid_CLI_tool_for_hand_eye_calibration.html
+[URhandeyecalibration-url]: ur_hand_eye_calibration/universal_robots_perform_hand_eye_calibration.py
+[URHandEyeTutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/ur5-robot-+-python-generate-dataset-and-perform-hand-eye-calibration.html
+[RobodkHandEyeCalibration-url]: robodk_hand_eye_calibration/robodk_hand_eye_calibration.py
+[robodk-robot-library-url]: https://robodk.com/supported-robots

source/applications/advanced/hand_eye_calibration/robodk_hand_eye_calibration/README.md

@@ -0,0 +1,27 @@
+# Hand Eye Calibration with RoboDK
+
+Hand-eye calibration is a necessity for any picking scenario that involves a camera and a robot.
+This sample offers an easy and adaptable method to perform hand eye with a variety of robots that are supported in RoboDK.
+
+For more on Hand-Eye Calibration, please see the [tutorial](https://support.zivid.com/latest/academy/applications/hand-eye.html) in our Knowledge Base.
+
+If you need help with the sample, visit our Knowledge Base article [here](help.zivid.com) (To be updated)
+
+This sample is made and modeled with a Universal Robots UR5e robot.
+It is a requirement that you make your own poses that suit your environment.
+If you have a different robot from a UR5e you will need to load in the corresponding robot to your rdk file.
+
+## Installation
+
+1. [Install Zivid Software](https://support.zivid.com/latest//getting-started/software-installation.html)
+
+2. [Install Zivid Python](https://github.com/zivid/zivid-python).
+Note: The recommended Python version for these samples is 3.8.
+
+3. [Install RoboDK](https://robodk.com/download)
+
+4. [Install RoboDK python](https://pypi.org/project/robodk/)
+
+Other requirements can be installed using the following command:
+
+ pip install -r requirements.txt

source/applications/advanced/hand_eye_calibration/robodk_hand_eye_calibration/requirements.txt

@@ -0,0 +1,2 @@
+robodk
+typing