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Author: csu-bot-zivid

README.md

@@ -199,10 +199,11 @@ from the camera can be used.
     - **multi_camera**
       - [multi_camera_calibration] - Use captures of a calibration
         object to generate transformation matrices to a single
-        coordinate frame, from connected cameras.
+        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.
+        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.

source/applications/README.md

@@ -0,0 +1,3 @@
+# Applications
+
+> **Tutorials:** Read the [Applications](https://support.zivid.com/en/latest/camera/academy/applications.html) tutorials on Zivid Knowledge Base.

source/applications/advanced/auto_2d_settings.py

@@ -16,6 +16,9 @@
 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.
 
+For more information about camera settings, check out this article:
+https://support.zivid.com/en/latest/camera/reference-articles/camera-settings.html
+
 """
 
 import argparse

source/applications/advanced/barcode/README.md

@@ -0,0 +1,3 @@
+# Barcode Detection
+
+> **Tutorial:** Read the full [Barcode Detection](https://support.zivid.com/en/latest/camera/academy/applications/barcode-detection.html) tutorial on Zivid Knowledge Base.

source/applications/advanced/barcode/barcode_detector.py

@@ -1,6 +1,9 @@
 """
 Detect and decode linear and matrix barcodes from a 2D capture.
 
+For more information on how to use the Zivid Barcode Detector, check out the Barcode Detection tutorial:
+https://support.zivid.com/en/latest/camera/academy/applications/barcode-detection.html
+
 """
 
 import zivid

source/applications/advanced/color_balance.py

@@ -8,6 +8,9 @@
 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().
 
+For more information on color balance, check out this article:
+https://support.zivid.com/en/latest/reference-articles/settings/2d-settings/color-balance.html
+
 """
 
 import argparse

source/applications/advanced/downsample.py

@@ -3,6 +3,9 @@
 
 The ZDF files for this sample can be found under the main instructions for Zivid samples.
 
+For more information on downsampling point clouds, check out this tutorial:
+https://support.zivid.com/en/latest/camera/academy/applications/downsampling.html
+
 """
 
 import argparse
@@ -58,6 +61,7 @@ def _main() -> None:
 
         point_cloud = frame.point_cloud()
 
+
         print(f"Before downsampling: {point_cloud.width * point_cloud.height} point cloud")
 
         visualize_point_cloud(point_cloud)

source/applications/advanced/gamma_correction.py

@@ -1,6 +1,9 @@
 """
 Capture 2D image with gamma correction.
 
+For more information about gamma correction, check out this article:
+https://support.zivid.com/en/latest/camera/reference-articles/settings/2d-settings/gamma.html
+
 """
 
 import argparse

source/applications/advanced/hand_eye_calibration/README.md

@@ -1,234 +1,3 @@
 # Hand-Eye Calibration
 
-This page provides an overview of how to **perform**, **verify**, and **use Hand–Eye Calibration** with Zivid cameras.
-
-If you are new to Hand–Eye Calibration, start with the [Hand–Eye Calibration – Concept & Theory][HandEyeTutorial-url], explaining:
-
-- What Hand–Eye Calibration is
-- The difference between **eye-in-hand** and **eye-to-hand**
-- Best practices for dataset (point clouds and robot poses) acquisition
-
-If you already know what you’re doing and just want to run calibration or check out our Hand-Eye calibration code, continue reading.
-
-<!-- Use "Markdown All in One plugin in VS code to automatically generate and update TOC". -->
-
-- [Quick Start: Just Calibrate](#quick-start-just-calibrate)
-- [Programmatic Hand–Eye Calibration](#programmatic-handeye-calibration)
-- [Dataset Acquisition Samples](#dataset-acquisition-samples)
-- [After Hand–Eye Calibration](#after-handeye-calibration)
-- [Verifying Calibration Accuracy](#verifying-calibration-accuracy)
-- [Summary: Which Tool Should I Use?](#summary-which-tool-should-i-use)
-
----
-
-## Quick Start: Just Calibrate
-
-If your goal is **only to compute the Hand–Eye Transformation Matrix**, use one of the tools below and follow Zivid’s [best-practice guide for capture poses][ZividHandEyeCalibration-url].
-
-### Hand–Eye Calibration GUI (Recommended)
-
-- Tutorial: [Hand–Eye GUI Tutorial][HandEyeCalibrationGUITutorial-url]
-- Application: [HandEyeCalibration GUI][HandEyeCalibrationGUI-url]
-
-Best choice if you:
-
-- Want a guided, no-code workflow
-
----
-
-## Programmatic Hand–Eye Calibration
-
-The following applications produce a Hand–Eye Transformation Matrix from robot poses and calibration captures.
-
-### Minimal Hand-Eye Calibration Code Example
-
-- Sample: [HandEyeCalibration][HandEyeCalibration-url]
-- Tutorial: [Integrating Zivid Hand-Eye Calibration][hand-eye-procedure-url]
-
-Workflow:
-
-1. User inputs robot pose in the form of a 4x4 transformation matrix (manual entry)
-2. Camera captures the calibration object
-3. User moves the robot to a new capture pose and enters the command to add a new pose
-4. First three steps are repeated (typically 10–20 pose pairs)
-5. User enters the command to perform calibration and the application returns a Hand-Eye Transformation Matrix
-
-Use this if you:
-
-- Want the simplest integration example
-- Are building your own calibration pipeline
-
----
-
-### Hand Eye Calibration CLI Tool
-
-- Tutorial: [Zivid CLI Tool for Hand–Eye Calibration][CLI application-url]
-- Installed with:
-  - Windows Zivid installer
-  - `tools` deb package on Ubuntu
-
-Use this if you:
-
-- Already have a dataset (robot poses + point clouds)
-- Want a command-line, batch-style workflow
-
----
-
-## Dataset Acquisition Samples
-
-The samples below show how to acquire robot poses and point clouds, then compute the Hand–Eye Transformation Matrix.
-
-### RoboDK-Based (Robot-Agnostic)
-
-- Sample: [RoboDKHandEyeCalibration][RobodkHandEyeCalibration-url]
-- Tutorial: [Any Robot + RoboDK + Python Hand–Eye Tutorial][RoboDKHandEyeTutorial-url]
-- Supported robots: [RoboDK robot library][robodk-robot-library-url]
-
-Features:
-
-- Works with any RoboDK-supported robot
-- Capture poses are manually defined in the `.rdk` file
-- Fully automated robot control
-
----
-
-### Universal Robots (e.g. UR5e)
-
-- Sample: [UniversalRobotsPerformHandEyeCalibration][URhandeyecalibration-url]
-- Tutorial: [UR5e + Python Hand–Eye Tutorial][URHandEyeTutorial-url]
-
-Features:
-
-- Designed specifically for UR robots
-- Fully automated robot control
-
----
-
-## After Hand–Eye Calibration
-
-The following applications assume that a **Hand–Eye Transformation Matrix already exists**.
-
-### Utilize Hand-Eye Calibration
-
-- Sample: [UtilizeHandEyeCalibration][UtilizeHandEyeCalibration-url]
-- Tutorial: [How To Use The Result Of Hand-Eye Calibration][UtilizeHandEyeCalibrationTutorial-url]
-
-Demonstrates how to:
-
-- Transform poses from camera coordinates to robot coordinates
-- Use the transform in real applications (e.g., bin picking)
-
-Example workflow:
-
-1. Capture a point cloud with a Zivid camera
-2. Find an object pick pose in camera coordinate system
-3. Transform the pose into robot coordinate system
-4. Plan and execute the robot motion
-
----
-
-### Pose Conversions
-
-- Sample: [PoseConversions][PoseConversions-url]
-- Application: [PoseConversions GUI][PoseConversionsGUI-url]
-- Theory: [Conversions Between Common Orientation Representations][PoseConversionsTheory-url]
-
-Zivid primarily operates with a (4x4) Transformation Matrix (Rotation Matrix + Translation Vector). This example shows how to convert to and from:
-
-- Axis–Angle
-- Rotation Vector
-- Roll–Pitch–Yaw
-- Quaternion
-
-Useful for integrating with robot controllers.
-
----
-
-## Verifying Calibration Accuracy
-
-### Verify Hand-Eye With Visualization
-
-- Sample: [VerifyHandEyeWithVisualization][VerifyHandEyeWithVisualization-url]
-
-Application validation approach:
-
-- Loads the hand-eye dataset and output (transformation matrix)
-- For each dataset pair:
-  - Transforms the point cloud to common coordinate system
-  - Finds the checkerboard centroid cartesian coordinates
-  - Removes the points outside the the checkerboard ROI
-- Overlaps transformed point clouds
-- Visualizes alignment accuracy
-
-Best for:
-
-- Visual verification
-- Detecting systematic rotation/translation errors
-
----
-
-### RoboDK Touch Test Verification
-
-- Script: [RobodkHandEyeVerification][RobodkHandEyeVerification-url]
-- Tutorial: [Verify Hand-Eye Calibration Result Via Touch Test][RobodkHandEyeVerificationTutorial-url]
-
-Verification steps:
-
-1. Robot moves to a predefined capture pose
-2. User places the calibration object in the FOV
-3. Camera estimates a touch point
-4. Robot physically touches the calibration object
-5. User repeats the test at multiple locations
-
-Best for:
-
-- Physical validation
-- High-accuracy requirement applications
-
----
-
-## Summary: Which Tool Should I Use?
-
-| Goal | Recommended Tool |
-|------|------------------|
-| Conceptual understanding | [Knowledge Base article][HandEyeTutorial-url] |
-| Guided calibration | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] |
-| Minimal integration example | [HandEyeCalibration][HandEyeCalibration-url] |
-| Existing dataset | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url]|
-| UR robots | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] or [UR Hand–Eye sample][URHandEyeTutorial-url] |
-| Any robot | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] or [RoboDK Hand–Eye sample][RoboDKHandEyeTutorial-url] |
-| Use calibration result | [UtilizeHandEyeCalibration][UtilizeHandEyeCalibrationTutorial-url] |
-| Verify visually | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] or [VerifyHandEyeWithVisualization][VerifyHandEyeWithVisualization-url] |
-| Verify physically | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] or [RoboDK Touch Test][RobodkHandEyeVerification-url] |
-
-
-[HandEyeTutorial-url]: https://support.zivid.com/latest/academy/applications/hand-eye.html
-
-[HandEyeCalibration-url]: hand_eye_calibration.py
-
-[HandEyeCalibrationGUI-url]: hand_eye_gui.py
-[HandEyeCalibrationGUITutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/hand-eye-gui.html
-
-[UtilizeHandEyeCalibration-url]: utilize_hand_eye_calibration.py
-[UtilizeHandEyeCalibrationTutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/how-to-use-the-result-of-hand-eye-calibration.html
-
-[VerifyHandEyeWithVisualization-url]: verify_hand_eye_with_visualization.py
-[ZividHandEyeCalibration-url]: https://support.zivid.com/latest/academy/applications/hand-eye/hand-eye-calibration-process.html
-[hand-eye-procedure-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/hand-eye-calibration-process.html#custom-integration
-
-[PoseConversions-url]: pose_conversions.py
-[PoseConversionsGUI-url]: pose_conversion_gui.py
-[PoseConversionsTheory-url]: https://support.zivid.com/en/latest/reference-articles/pose-conversions.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-%2B-python-generate-dataset-and-perform-hand-eye-calibration.html
-
-[RobodkHandEyeCalibration-url]: robodk_hand_eye_calibration/robodk_hand_eye_calibration.py
-[RoboDKHandEyeTutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/robodk-%2B-python-generate-dataset-and-perform-hand-eye-calibration.html
-
-[RobodkHandEyeVerification-url]: robodk_hand_eye_calibration/robodk_verify_hand_eye_calibration.py
-[RobodkHandEyeVerificationTutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/hand-eye-calibration-verification-via-touch-test.html
-
-[robodk-robot-library-url]: https://robodk.com/supported-robots
+> **Tutorial:** Read the full [Hand-Eye Calibration](https://support.zivid.com/en/latest/camera/academy/applications/hand-eye.html) tutorial on Zivid Knowledge Base.

source/applications/advanced/hand_eye_calibration/hand_eye_calibration.py

@@ -1,6 +1,9 @@
 """
 Perform Hand-Eye calibration.
 
+For more information on Hand-Eye calibration, check out this tutorial:
+https://support.zivid.com/en/latest/camera/academy/applications/hand-eye.html
+
 """
 
 import argparse