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Samples: Automatic updates to public repository
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‎README.md‎

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- [ZividBenchmark] - Zividbenchmark is a sample that will test the
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average speed of different operations on your computer.
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- **Network**
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- [AutomaticNetworkConfigurationForCameras] - \* Automatically
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- [AutomaticNetworkConfigurationForCameras] - Automatically
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configure the IP addresses of connected cameras to match the
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network of the user's PC.
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- [NetworkConfiguration] - Uses Zivid API to change the IP address
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- **MultiCamera**
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- [MultiCameraCalibration] - Use captures of a calibration object
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to generate transformation matrices to a single coordinate
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frame, from connected cameras.
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frame, from multiple connected cameras.
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- [MultiCameraCalibrationFromZDF] - Use captures of a calibration
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object to generate transformation matrices to a single
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coordinate frame, from a ZDF files.
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- [Install Zivid + HALCON for LINUX]
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- [Create a HALCON "Hello World" Program]
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- [How to Run a HALCON Sample]
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- [Capture Settings in HALCON]
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- [Debug in HALCON]
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- [HALCON Sample Videos]
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[Install Zivid + HALCON for LINUX]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/install-zivid-halcon-for-linux.html
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[Create a HALCON "Hello World" Program]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/create-a-halcon-hello-world.html
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[How to Run a HALCON Sample]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/how-to-run-a-halcon-sample.html
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[Capture Settings in HALCON]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/capture-settings-in-halcon.html
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[Debug in HALCON]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/halcon-debug.html
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[HALCON Sample Videos]: https://support.zivid.com/en/latest/camera/api-reference/samples/halcon/halcon-sample-videos.html
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[Knowledge Base]: https://support.zivid.com/en/latest

‎source/Applications/Advanced/Barcode/BarcodeDetector/BarcodeDetector.cpp‎

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/*
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Detect and decode linear and matrix barcodes from a 2D capture.
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For more information on how to use the Zivid Barcode Detector, check out the Barcode Detection tutorial:
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https://support.zivid.com/en/latest/camera/academy/applications/barcode-detection.html
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*/
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#include <Zivid/Experimental/Toolbox/Barcode.h>

‎source/Applications/Advanced/Barcode/README.md‎

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# Barcode Detection
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> **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/Downsample/Downsample.cpp‎

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Downsample point cloud from a ZDF file.
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The ZDF files for this sample can be found under the main instructions for Zivid samples.
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For more information on downsampling point clouds, check out this tutorial:
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https://support.zivid.com/en/latest/camera/academy/applications/downsampling.html
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*/
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#include <Zivid/Visualization/Visualizer.h>

‎source/Applications/Advanced/GammaCorrection/GammaCorrection.cpp‎

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/*
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Capture 2D image with gamma correction.
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For more information about gamma correction, check out this article:
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https://support.zivid.com/en/latest/camera/reference-articles/settings/2d-settings/gamma.html
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*/
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#include <Zivid/Zivid.h>

‎source/Applications/Advanced/HandEyeCalibration/HandEyeCalibration/HandEyeCalibration.cpp‎

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/*
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Perform Hand-Eye calibration.
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For more information on Hand-Eye calibration, check out this tutorial:
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https://support.zivid.com/en/latest/camera/academy/applications/hand-eye.html
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*/
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#include <Zivid/Application.h>

‎source/Applications/Advanced/HandEyeCalibration/PoseConversions/PoseConversions.cpp‎

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The convenience functions from this example can be reused in applicable applications. The YAML files for this sample can
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be found under the main instructions for Zivid samples.
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For more information on pose conversions, check out this article:
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https://support.zivid.com/en/latest/camera/reference-articles/pose-conversions.html
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*/
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#include <Zivid/Zivid.h>

‎source/Applications/Advanced/HandEyeCalibration/README.md‎

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# Hand-Eye Calibration
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This page provides an overview of how to **perform**, **verify**, and **use Hand–Eye Calibration** with Zivid cameras.
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If you are new to Hand–Eye Calibration, start with the [Hand–Eye Calibration – Concept & Theory][HandEyeTutorial-url], explaining:
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- What Hand–Eye Calibration is
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- The difference between **eye-in-hand** and **eye-to-hand**
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- Best practices for dataset (point clouds and robot poses) acquisition
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If you already know what you’re doing and just want to run calibration or check out our Hand-Eye calibration code, continue reading.
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<!-- Use "Markdown All in One plugin in VS code to automatically generate and update TOC". -->
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- [Quick Start: Just Calibrate](#quick-start-just-calibrate)
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- [Programmatic Hand–Eye Calibration](#programmatic-handeye-calibration)
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- [Using an Existing Hand–Eye Calibration](#using-an-existing-handeye-calibration)
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- [Summary: Which Tool Should I Use?](#summary-which-tool-should-i-use)
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---
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## Quick Start: Just Calibrate
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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].
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### Hand–Eye Calibration GUI (Recommended)
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**Note:** This is a **Python-based GUI application** (it is not related to the C++ samples).
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- Tutorial: [Hand–Eye GUI Tutorial][HandEyeCalibrationGUITutorial-url]
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- Application: [HandEyeCalibration GUI][HandEyeCalibrationGUI-url] (Python)
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Best choice if you:
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- Want a guided, no-code workflow
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---
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## Programmatic Hand–Eye Calibration
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The following applications produce a Hand–Eye Transformation Matrix from robot poses and calibration captures.
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### Minimal Hand-Eye Calibration Code Example
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- Sample: [HandEyeCalibration][HandEyeCalibration-url]
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- Tutorial: [Integrating Zivid Hand-Eye Calibration][hand-eye-procedure-url]
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Workflow:
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1. User inputs robot pose in the form of a 4x4 transformation matrix (manual entry)
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2. Camera captures the calibration object
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3. User moves the robot to a new capture pose and enters the command to add a new pose
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4. First three steps are repeated (typically 10–20 pose pairs)
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5. User enters the command to perform calibration and the application returns a Hand-Eye Transformation Matrix
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Use this if you:
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- Want the simplest integration example
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- Are building your own calibration pipeline
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---
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### Hand Eye Calibration CLI Tool
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- Tutorial: [Zivid CLI Tool for Hand–Eye Calibration][CLI application-url]
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- Installed with:
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- Windows Zivid installer
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- `tools` deb package on Ubuntu
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Use this if you:
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- Already have a dataset (robot poses + point clouds)
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- Want a command-line, batch-style workflow
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---
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## Using an Existing Hand–Eye Calibration
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The following applications assume that a **Hand–Eye Transformation Matrix already exists**.
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### Utilize Hand-Eye Calibration
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- Sample: [UtilizeHandEyeCalibration][UtilizeHandEyeCalibration-url]
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- Tutorial: [How To Use The Result Of Hand-Eye Calibration][UtilizeHandEyeCalibrationTutorial-url]
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Demonstrates how to:
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- Transform poses from camera coordinates to robot coordinates
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- Use the transform in real applications (e.g., bin picking)
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Example workflow:
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1. Capture a point cloud with a Zivid camera
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2. Find an object pick pose in camera coordinate system
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3. Transform the pose into robot coordinate system
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4. Plan and execute the robot motion
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---
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### Pose Conversions
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- Sample: [PoseConversions][PoseConversions-url]
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- Application: [PoseConversions GUI][PoseConversionsGUI-url] (Python)
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- Theory: [Conversions Between Common Orientation Representations][PoseConversionsTheory-url]
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Zivid primarily operates with a (4x4) Transformation Matrix (Rotation Matrix + Translation Vector). This example shows how to convert to and from:
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- Axis–Angle
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- Rotation Vector
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- Roll–Pitch–Yaw
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- Quaternion
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Useful for integrating with robot controllers.
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---
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## Summary: Which Tool Should I Use?
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| Goal | Recommended Tool |
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|------|------------------|
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| Conceptual understanding | [Knowledge Base article][HandEyeTutorial-url] |
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| Guided calibration | [Hand–Eye GUI (Python)][HandEyeCalibrationGUITutorial-url] |
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| Minimal integration example | [HandEyeCalibration][HandEyeCalibration-url] |
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| Existing dataset | [Hand–Eye GUI (Python)][HandEyeCalibrationGUITutorial-url]|
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| Use calibration result | [UtilizeHandEyeCalibration][UtilizeHandEyeCalibrationTutorial-url] |
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| Verify visually | [Hand–Eye GUI (Python)][HandEyeCalibrationGUITutorial-url] |
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| Verify physically | [Hand–Eye GUI][HandEyeCalibrationGUITutorial-url] |
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[HandEyeTutorial-url]: https://support.zivid.com/latest/academy/applications/hand-eye.html
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[HandEyeCalibration-url]: HandEyeCalibration/HandEyeCalibration.cpp
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[HandEyeCalibrationGUI-url]: https://github.com/zivid/zivid-python-samples/blob/master/source/applications/advanced/hand_eye_calibration/hand_eye_gui.py
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[HandEyeCalibrationGUITutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/hand-eye-gui.html
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[UtilizeHandEyeCalibration-url]: UtilizeHandEyeCalibration/UtilizeHandEyeCalibration.cpp
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[UtilizeHandEyeCalibrationTutorial-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/how-to-use-the-result-of-hand-eye-calibration.html
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[ZividHandEyeCalibration-url]: https://support.zivid.com/latest/academy/applications/hand-eye/hand-eye-calibration-process.html
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[hand-eye-procedure-url]: https://support.zivid.com/en/latest/academy/applications/hand-eye/hand-eye-calibration-process.html#custom-integration
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[PoseConversions-url]: PoseConversions/PoseConversions.cpp
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[PoseConversionsGUI-url]: https://github.com/zivid/zivid-python-samples/blob/master/source/applications/advanced/hand_eye_calibration/pose_conversion_gui.py
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[PoseConversionsTheory-url]: https://support.zivid.com/en/latest/reference-articles/pose-conversions.html
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[CLI application-url]: https://support.zivid.com/latest/academy/applications/hand-eye/zivid_CLI_tool_for_hand_eye_calibration.html
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> **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/HandEyeCalibration/UtilizeHandEyeCalibration/UtilizeHandEyeCalibration.cpp‎

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For verification, check that the Zivid gem centroid 3D coordinates are the same as above after the transformation.
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The YAML files for this sample can be found under the main instructions for Zivid samples.
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For more information on how to utilize Hand-Eye calibration results, check out this tutorial:
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https://support.zivid.com/en/latest/camera/academy/applications/hand-eye/how-to-use-the-result-of-hand-eye-calibration.html
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#include <Zivid/Zivid.h>

‎source/Applications/Advanced/MultiCamera/MultiCameraCalibration/MultiCameraCalibration.cpp‎

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/*
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Use captures of a calibration object to generate transformation matrices to a single coordinate frame, from connected cameras.
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Use captures of a calibration object to generate transformation matrices to a single coordinate frame, from multiple connected cameras.
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For more information on multi-camera calibration, check out this tutorial:
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https://support.zivid.com/en/latest/camera/academy/applications/multi-camera-calibration.html
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*/
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#include <clipp.h>

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