Detect faces and run OpenCV tasks on images and video frames.
npx skills add https://github.com/damionrashford/media-os --skill cv-opencv
Please help me install this Agent Skill. Skill: cv-opencv Source: https://github.com/damionrashford/media-os/tree/main/skills/cv-opencv Command: npx skills add https://github.com/damionrashford/media-os --skill cv-opencv
OpenCV-powered computer vision tasks on images and video frames using Python bindings (cv2), enabling fast prototyping of detection, tracking, and inference workflows without heavy dependencies.
Inspect an image with cv-opencv to confirm its shape and basic stats, then run a small face-detection example to illustrate YuNet usage.
High-intent search queries and answers about installing and using this skill.
Face detection in Python uses the cv-opencv wrapper to run the YuNet model via cv2, processing both still images and video streams. It requires OpenCV contrib and numpy to detect faces on CPU.
ONNX-based DNN inference with OpenCV uses the cv2 DNN module on CPU. The cv-opencv wrapper provides a script interface to load ONNX models and run inference across images or video frames.
Yes, object tracking on video frames requires the opencv-contrib-python package. The cv-opencv Skill uses these extended modules to track objects across sequential video frames.
Camera calibration and panorama stitching are supported through Python cv2 bindings. The wrapper exposes OpenCV modules to calibrate camera setups and stitch image panoramas without heavy dependencies.
A lightweight OpenCV Python wrapper enables fast prototyping of detection and inference workflows without heavy dependencies. It provides direct cv2 access for image inspection and tracking on CPU.
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