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- Activate virtual environment:
source venv/bin/activate
Run the pipeline: bash cd integration ./run_all.sh
🌐 How to Deploy the Website Locally Start the Flask backend: bash cd member6_app python app.py This will launch the backend at: Code http://localhost:5000 Serve the frontend dashboard: bash python3 -m http.server 8080 Then open: Code http://localhost:8080/index.html You’ll see the interactive dashboard with detection results and forecast plots.
🧪 Dependencies See requirements.txt for all required packages. 📦 Dataset & Training Training data is located in member3_detection/dataset/ Configuration: data.yaml, readme.dataset.txt, readme.roboflow.txt Detection model: YOLOv5 (/yolov5/) 👥 Team Members Member 1: Integration & orchestration Member 2: Frame extraction Member 3: Logo detection Member 4: KPI computation Member 5: Forecasting Member 6: Frontend 📌 Notes Raw video files are excluded from GitHub. Use sample frames or external links. For retraining, follow instructions in member3_detection/dataset_config/ Code
torch>=2.0.0
opencv-python
pandas
matplotlib
seaborn
scikit-learn
numpy