YOLOv5 Object Detection with ROI Masking and GPU Support
Implement real-time object detection using YOLOv5 constrained to a specific Region of Interest (ROI) polygon, utilizing GPU acceleration and the supervision library for annotation.
npx skills add ECNU-ICALK/AutoSkill --skill yolov5-object-detection-with-roi-masking-and-gpu-support --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# YOLOv5 Object Detection with ROI Masking and GPU Support Implement real-time object detection using YOLOv5 constrained to a specific Region of Interest (ROI) polygon, utilizing GPU acceleration and the supervision library for annotation. ## Prompt # Role & Objective Act as a Computer Vision Engineer. Write Python code to perform real-time object detection using YOLOv5, constrained to a specific Region of Interest (ROI) defined by a polygon. The code must run on GPU if available. # Operational Rules & Constraints 1. **Model Loading**: Load YOLOv5 via `torch.hub.load('ultralytics/yolov5', 'yolov5s6', device=device)`. 2. **Device Selection**: Automatically select CUDA if available: `device = 'cuda' if torch.cuda.is_available() else 'cpu'`. 3. **ROI Definition**: Define the ROI as a numpy array of integer coordinates (e.g., `np.array([[x1,y1], [x2,y2], ...], dtype=np.int32)`). 4. **Masking Logic**: - Create a black mask matching frame dimensions. - Fill the ROI polygon with white (255, 255, 255). - Apply `cv2.bitwise_and` to mask the frame. 5. **Inference**: Run model inference on the *masked* frame. 6. **Filtering**: Filter detections to keep only class ID 0 (person) with confidence
- Prompt
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What does the YOLOv5 Object Detection with ROI Masking and GPU Support skill do?
Implement real-time object detection using YOLOv5 constrained to a specific Region of Interest (ROI) polygon, utilizing GPU acceleration and the supervision library for annotation.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill yolov5-object-detection-with-roi-masking-and-gpu-support --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.