{"id":24523763,"url":"https://github.com/scorleos773/snap-scan","last_synced_at":"2026-05-08T10:32:24.839Z","repository":{"id":271965144,"uuid":"915122126","full_name":"SCORLEOs773/Snap-Scan","owner":"SCORLEOs773","description":"Object detection using YOLOv8 with sub-object identification and JSON output","archived":false,"fork":false,"pushed_at":"2025-01-13T08:36:13.000Z","size":8370,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-13T14:46:51.119Z","etag":null,"topics":["building-box","computer-vision","data-visualization","deep-learning","image-processing-python","json","machine-learning-algorithms","object-detection","opencv-python","python","python-ai","tensorflow","yolov8"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SCORLEOs773.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-01-11T03:04:20.000Z","updated_at":"2025-01-13T08:40:19.000Z","dependencies_parsed_at":"2025-01-18T03:17:09.608Z","dependency_job_id":null,"html_url":"https://github.com/SCORLEOs773/Snap-Scan","commit_stats":null,"previous_names":["scorleos773/snap-scan"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SCORLEOs773/Snap-Scan","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SCORLEOs773%2FSnap-Scan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SCORLEOs773%2FSnap-Scan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SCORLEOs773%2FSnap-Scan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SCORLEOs773%2FSnap-Scan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SCORLEOs773","download_url":"https://codeload.github.com/SCORLEOs773/Snap-Scan/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SCORLEOs773%2FSnap-Scan/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32776646,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"ssl_error","status_checked_at":"2026-05-08T08:22:45.650Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["building-box","computer-vision","data-visualization","deep-learning","image-processing-python","json","machine-learning-algorithms","object-detection","opencv-python","python","python-ai","tensorflow","yolov8"],"created_at":"2025-01-22T04:16:42.498Z","updated_at":"2026-05-08T10:32:24.815Z","avatar_url":"https://github.com/SCORLEOs773.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Snap-Scan: Object Detection with YOLOv8\n\nThis project demonstrates object detection using the YOLOv8 model. The program detects objects in an input image, identifies sub-objects (e.g., helmets for people, tires for cars, etc.), and saves the results, including cropped sub-object images and annotated visuals.\n\n---\n\n## Features\n\n- Detect objects in an image using YOLOv8.\n- Identify sub-objects for specific detected objects:\n  - Helmet for \"person\"\n  - Tire for \"car\"\n  - \"Unknown\" for other objects\n- Save annotated images with bounding boxes.\n- Generate a JSON file containing object and sub-object details.\n- Save cropped images of detected sub-objects.\n\n---\n\n## Folder Structure\n\nENV/\n├── Include/\n├── Lib/\n├── Scripts/\n├── share/\n├── sub_objects/             # Cropped sub-object images are saved here\n├── annotated_sample.jpg     # Annotated image with bounding boxes\n├── detect_objects.py        # Main Python script for object detection\n├── output.json              # JSON file with object and sub-object details\n├── README.md                # Project documentation\n├── sample.jpeg              # Input image for object detection\n├── yolov8n.pt               # Pre-trained YOLOv8 model weights\n\n\n---\n\n## Setup Instructions\n\n1. **Clone the Repository**\n   ```bash\n   git clone \u003crepository_url\u003e\n   cd \u003crepository_directory\u003e\n\n2. **Setup the Python Environment**\n  ```bash\n    python -m venv ENV\n    source ENV/Scripts/activate  # On Windows\n    source ENV/bin/activate      # On macOS/Linux\n    pip install -r requirements.txt\n  ```\n3. **Place Input Image**\n   Replace ```sample.jpeg``` with your input image.\n\n## Usage\n\n1. Run the Script\n```bash\npython detect_objects.py\n```\n\n2. Results\n   a. Annotated Image: ```annotated_sample.jpg```\n   b. JSON Output: ```output.json```\n   c. Cropped Sub-Objects: Saved in the ```sub_objects/``` folder.\n\n## Sample JSON Output\n```json\n[\n    {\n        \"object\": \"person\",\n        \"id\": 1,\n        \"bbox\": [50.34, 100.56, 200.45, 300.67],\n        \"subobject\": {\n            \"object\": \"Helmet\",\n            \"id\": 1,\n            \"bbox\": [60.34, 110.56, 190.45, 290.67]\n        }\n    }\n]\n```\n\n## Notes\n1. The yolov8n.pt model is pre-trained on the COCO dataset.\n2. Sub-object detection is simulated and depends on predefined rules.\n3. The JSON output includes bounding box coordinates for both objects and sub-objects.\n\n## Acknowledgments\n1. Ultralytics YOLO\n2. COCO Dataset\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscorleos773%2Fsnap-scan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscorleos773%2Fsnap-scan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscorleos773%2Fsnap-scan/lists"}