{"id":18160817,"url":"https://github.com/blankeos/license-plate-detection","last_synced_at":"2025-04-07T03:54:57.409Z","repository":{"id":231551960,"uuid":"781518718","full_name":"Blankeos/license-plate-detection","owner":"Blankeos","description":"🪪 Detect License Plate with YOLOV5, OpenCV, and EasyOCR. 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Git clone and go to directory\n\n```sh\ngit clone https://github.com/Blankeos/license-plate-detection\ncd license-plate-detection\n```\n\n2. Create Virtual Env:\n\n```sh\npython -m venv .venv\n```\n\n2. Activate Virtual Env:\n\n```sh\n# macOS/Linux\nsource .venv/bin/activate\n\n# Windows\n.venv\\Scripts\\activate\n```\n\n3. Install Deps\n\n```sh\n# Install deps for the project.\npip install -r requirements.txt\n\n# Install deps for the model.\ngit clone https://github.com/ultralytics/yolov5\npip install -r yolov5/requirements.txt\n```\n\n4. Create a file called `serviceAccount.json` in the root and paste your **Firebase Project Service Credentials**\n\n   \u003e This data is sensitive. Protect it or you risk getting spammed on Firebase.\n\n    \u003cdetails\u003e\n    \n    \u003csummary\u003e💡 How to generate it.\u003c/summary\u003e\n\n   1. Go to project settings in Firebase.\n      ![step 1 - go to project settings](docs/generate-key-step-1.png)\n\n   2. Generate private key.\n      ![step 2 - generate private key](docs/generate-key-step-2.png)\n\n   3. Rename it to `serviceAccount.json`\n      ```jsonc\n      // Example `serviceAccount.json`\n      {\n        \"type\": \"service_account\",\n        \"project_id\": \"project-id-here\",\n        \"private_key_id\": \"private-key-id-here\",\n        \"private_key\": \"private-key-here\",\n        \"client_email\": \"license-plate-detection-v1-ser@project-id-here.iam.gserviceaccount.com\",\n        \"client_id\": \"123456789101112131415\",\n        \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n        \"token_uri\": \"https://oauth2.googleapis.com/token\",\n        \"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n        \"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/subdomain.iam.gserviceaccount.com\",\n        \"universe_domain\": \"googleapis.com\"\n      }\n      ```\n\n    \u003c/details\u003e\n\n5. Run the app (It will take a while the first run)\n\n```sh\npython main.py\n```\n\n## Resources\n\nhttps://realpython.com/python-pyqt-gui-calculator/\n\n## Notes\n\n- We use an existing YoloV5 model `best.pt` from this repo: [here](https://github.com/KALYAN1045/Automatic-Number-Plate-Recognition-using-YOLOv5/blob/main/Weights/best.pt).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblankeos%2Flicense-plate-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblankeos%2Flicense-plate-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblankeos%2Flicense-plate-detection/lists"}