Ruhh Investigation Lab

A BCA Final Year Computer Vision project using Python Flask, OpenCV, and YOLOv3.

Upload any image and receive:

• Object detection with green bounding boxes
• Scene classification (Indoor / Outdoor)
• Investigation-style observations

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Stack

Layer	Technology
Backend	Python 3, Flask
CV Engine	OpenCV DNN, YOLOv3
Math	NumPy
Frontend	HTML, CSS, JavaScript
Dataset	COCO (80 classes)

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Setup

1. Install dependencies

pip install -r requirements.txt

2. Download YOLOv3 weights

python download_weights.py

This downloads yolov3.weights (~248 MB) into the models/ folder. The yolov3.cfg and coco.names files are already included.

3. Run the app

python app.py

Open your browser at: http://localhost:8000

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Project Structure

RuhhInvestigationLab/
├── app.py                  # Flask routes
├── requirements.txt
├── download_weights.py     # One-time weight downloader
├── static/
│   ├── css/style.css
│   ├── js/script.js
│   ├── uploads/            # Saved uploaded images
│   └── results/            # Processed output images
├── templates/
│   ├── index.html          # Landing page
│   ├── upload.html         # Upload form
│   ├── result.html         # Analysis result
│   └── about.html          # About page
├── models/
│   ├── yolov3.cfg          # YOLOv3 architecture
│   ├── yolov3.weights      # Pre-trained weights (download separately)
│   └── coco.names          # 80 COCO class labels
└── vision/
    ├── detector.py         # YOLO detection logic
    ├── analyzer.py         # Scene analysis & observations
    └── utils.py            # Model loading helpers

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Notes

• No API keys required — works fully offline after setup.
• Multiple uploads work safely — each result uses a UUID filename.
• If yolov3.weights is missing, the image will still be processed (without bounding boxes).