{"id":23406940,"url":"https://github.com/jshulgach/aruco_pose_estimation","last_synced_at":"2026-04-28T17:34:27.809Z","repository":{"id":268316423,"uuid":"903943068","full_name":"Jshulgach/ArUco_Pose_Estimation","owner":"Jshulgach","description":"Simple pose estimation package using ArUco markers","archived":false,"fork":false,"pushed_at":"2025-02-28T06:49:27.000Z","size":9653,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-09T00:07:16.134Z","etag":null,"topics":["aruco","opencv","pose-estimation","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Jshulgach.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-16T00:02:23.000Z","updated_at":"2025-02-28T06:49:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"cab587b1-92d8-4686-8a5a-dfa13721eaef","html_url":"https://github.com/Jshulgach/ArUco_Pose_Estimation","commit_stats":null,"previous_names":["jshulgach/aruco_pose_estimation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jshulgach%2FArUco_Pose_Estimation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jshulgach%2FArUco_Pose_Estimation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jshulgach%2FArUco_Pose_Estimation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jshulgach%2FArUco_Pose_Estimation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Jshulgach","download_url":"https://codeload.github.com/Jshulgach/ArUco_Pose_Estimation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247947859,"owners_count":21023066,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["aruco","opencv","pose-estimation","python"],"created_at":"2024-12-22T14:16:20.965Z","updated_at":"2026-04-28T17:34:22.774Z","avatar_url":"https://github.com/Jshulgach.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ArUco_Pose_Estimation\r\nThis repository contains the bare minimum code for simple pose estimation using ArUco markers in Python\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n\u003cimg src = 'assets/aruco-track.gif ' width = 700\u003e\r\n\u003c/div\u003e\r\n\r\n## Dependencies\r\n- OpenCV\r\n- Numpy\r\n\r\n## Installation\r\n\r\n1. Download the repository:\r\n    ```bash\r\n    git clone https://github.com/Jshulgach/ArUco_Pose_Estimation.git\r\n    cd ArUco_Pose_Estimation\r\n    ```\r\n2. Install dependencies according to your OS:\r\n    ### Windows\r\n    It's best to install dependencies in a virtual environment. Using either Anaconda or Python venv, prepare your environment:\r\n    ```bash\r\n    python -m venv aruco python=3.10\r\n    call aruco\\Scripts\\activate\r\n    pip install -r requirements.txt\r\n    ```\r\n    ### Linux\r\n    Install using the `apt install` command:\r\n    ```bash\r\n    sudo apt install python3-opencv python3-yaml\r\n    ```\r\n\r\n## Usage\r\n\r\n### Checkerboard\r\nMake sure to have a checkerboard printed if you want to have accurate pose calibration. You can find a multitude of patterns from [Mark Hedley Jones](https://markhedleyjones.com/projects/calibration-checkerboard-collection). Once you print out the one you like, make sure to update the `calibration_settings.yaml` file with the correct dimensions of the squares on the checkerboard.\r\n\r\n### Running the scripts\r\nThe files are named in the order of operations:\r\n\r\n- `1_save_frames.py` : Used to save frames from the camera feed.\r\n- `2_calibrate.py`   : Performs the calibration routine with a single camera, computes distortion matrix.\r\n- `3_main.py`        : Detects ArUco markers in the camera feed.\r\n\r\nYou can find more details on parameters for each script using `python my-script-to-run.py --help`. Ideally all of these should be run without much to change\r\n   \r\n1. **Save Calibration Frames**  \r\n\r\n    Run `1_save_frames.py` to initialize the camera feed. Press the space bar when prompted to start collecting images of your checkerboard. Make sure to move the checkerboard around to get different poses and orientations.\r\n    ```bash\r\n    python 1_save_frames.py\r\n    ```\r\n   \r\n2. **Calibration**  \r\n    Run `2_calibrate.py` to read the checkerboard images in your directory and generate a `calibration_matrix.npy` and `distortion_coefficients.npy` file. \r\n    ```bash\r\n    python 2_calibrate.py  \r\n    ```\r\n    * Note: If you're connected to another device via ssh (like a raspberry pi) and need to transfer the `.npy` files, you can copy them to your local WSL environment with the command:\r\n\t    ```bash\r\n\t    scp myusername@192.168.1.164:/home/myusername/github/ArUco_Pose_Estimation/distortion_coefficients.npy /home/myusername\r\n\t\t```\r\n        For copying to your host windows maching:\r\n\t    ```bash\r\n\t    scp myusername@192.168.1.164:/home/myusername/github/ArUco_Pose_Estimation/distortion_coefficients.npy /mnt/c/Users/WindowsUserName\r\n\t\t```    \t\r\n   \r\n3. **Pose Estimation**  \r\n    Run `3_main.py` to begin running the pose estimation for each ArUCo marker detected. This is done in real-time for each frame obtained from the webcam feed.  \r\n    ```bash\r\n    python 3_main.py  \r\n    ```\r\n   \r\n\r\n   \r\nFeel free to reach out to me in case of any issues.  \r\nIf you find this repo useful in any way please do star ⭐️ it so that others can reap it's benefits as well.\r\n\r\n## Acknowledgements\r\nThis repository is inspired by the work of [GSNCodes](https://github.com/GSNCodes/ArUCo-Markers-Pose-Estimation-Generation-Python)\r\n\r\n## References\r\n1. https://docs.opencv.org/4.x/d9/d6d/tutorial_table_of_content_aruco.html\r\n2. https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjshulgach%2Faruco_pose_estimation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjshulgach%2Faruco_pose_estimation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjshulgach%2Faruco_pose_estimation/lists"}