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🌡️ PCB Thermal Anomaly Detection System 🔍

Real-time AI-powered thermal analysis for printed circuit boards — detect hotspots before they become failures.

🔥 Identify overheated PCB components in real-time using thermal imaging, visual markers, and live graphs — before damage occurs.

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📸 Gallery

🖥️ Main Interface

🌅 Welcome Screen

🎥 Real-Time Thermal Video Mode

📊 Live Thermal Tracking Results

📈 Detection Accuracy & Intensity Graphs

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⚡ Why This Project?

PCB failures cost manufacturers millions annually. Overheated components are often invisible to the naked eye — until it's too late.

This system bridges that gap by providing:

• Instant visual feedback on thermal anomalies
• Automatic documentation of every detected hotspot
• Live performance metrics for continuous monitoring
• A slick, interactive UI — no terminal expertise needed

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🌈 Feature Highlights

🔴 Detection & Analysis Real-time hotspot detection with colored markers Customizable temperature thresholds Frame-by-frame thermal intensity tracking Automatic anomaly classification	📊 Visualization & Reporting Live accuracy graphs with matplotlib Thermal intensity trend charts Timestamped anomaly snapshots Results overlay on thermal feed
🎮 User Interface Modern Pygame-powered UI Smooth animations & transitions Interactive threshold slider One-click image upload for offline testing	💾 Storage & Export Auto-save anomaly captures with timestamp Dedicated anomaly_captures/ folder Compatible with FLIR & Seek Thermal cameras Sample images included for demo/testing

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🛒 Hardware Requirements

Component	Specification	Purpose
🖥️ Computer	Windows / Linux / macOS	Host system
🐍 Python	Version 3.6 or higher	Runtime
📷 Thermal Camera	FLIR or Seek Thermal (USB)	Thermal imaging input
🔌 USB Port	USB 2.0 / 3.0	Camera connection
💾 Storage	~500MB free space	Anomaly capture storage

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🚀 Getting Started

Step 1 — Clone the Repository

git clone https://github.com/astromanu007/PCB_Anomaly_Detection.git
cd PCB_Anomaly_Detection

Step 2 — Install Dependencies

# Option A: Install individually
pip install numpy opencv-python pygame matplotlib

# Option B: Install from requirements file
pip install -r requirements.txt

Step 3 — Connect Your Thermal Camera

🔌 Plug in your FLIR or Seek Thermal USB camera
✅ Verify it's detected by your OS

Step 4 — Launch the Application

python main.py

💡 No hardware? No problem! Use the Upload feature to test with sample images from the sample_images/ folder.

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📁 Project Structure

PCB_Anomaly_Detection/
│
├── 📂 assets/                      # Static assets
│   ├── 🖼️  thermal_icon.png         # App icon
│   └── 🧑  creator_photo.jpg        # Creator photo (About section)
│
├── 📂 sample_images/               # Demo PCB images for offline testing
│
├── 📂 anomaly_captures/            # 🔴 Auto-saved hotspot images (timestamped)
│
├── 📂 images/                      # README screenshot assets
│   ├── MAIN.jpg
│   ├── INTRO.png
│   ├── VIDEO_MODE.png
│   ├── LIVE_TRACKING_RESULTS.png
│   └── GRAPHS.jpg
│
├── 📄 main.py                      # ⭐ Core application — start here
└── 📄 README.md                    # Documentation

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🎮 UI Controls Reference

Control	Symbol	Action
Start / Stop	🟢 / 🔴	Toggle real-time thermal camera monitoring
Threshold Slider	🎚️	Fine-tune the temperature sensitivity cutoff
Record	🔴	Enable auto-capture of anomaly frames
Upload	📤	Load a PCB image manually for offline analysis
Results	📊	View processed frame with hotspot markers
Graphs	📈	Open live accuracy & intensity visualization

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🔄 How It Works

┌─────────────────────────────────────────────────────────────────┐
│                     APPLICATION FLOW                            │
└─────────────────────────────────────────────────────────────────┘

  📷 Thermal Camera Feed
         │
         ▼
  🖼️  Frame Capture (OpenCV)
         │
         ▼
  🌡️  Temperature Mapping
         │
         ▼
  ⚖️  Threshold Comparison ──── Below threshold → ✅ Normal
         │
         │ Above threshold
         ▼
  🔴 Anomaly Detected!
         │
    ┌────┴────┐
    │         │
    ▼         ▼
  📸 Auto    🖥️ Visual
  Capture   Overlay
  + Save    on UI
    │         │
    └────┬────┘
         ▼
  📊 Update Graphs
  (Accuracy + Intensity)

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📊 What the Graphs Show

Graph	Metric	Use Case
📈 Accuracy Graph	Detection confidence over time	Monitor system reliability
🌡️ Intensity Graph	Thermal intensity per frame	Spot heat spikes & trends

Both graphs update live during monitoring sessions, giving you a complete picture of PCB thermal behavior.

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🧪 Testing Without a Thermal Camera

You can fully evaluate this system using the included sample images:

1. Launch python main.py
2. Click 📤 Upload in the UI
3. Browse to sample_images/ folder
4. Select any PCB image
5. View detection results and graph outputs

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🛠️ Tech Stack

┌──────────────┬──────────────────────────────────────────┐
│  Layer        │  Technology                              │
├──────────────┼──────────────────────────────────────────┤
│  Language     │  Python 3.6+                            │
│  Vision       │  OpenCV — frame capture & processing    │
│  UI           │  Pygame — interface & animations        │
│  Graphs       │  Matplotlib — live chart rendering      │
│  Numerics     │  NumPy — thermal data computation       │
│  Hardware     │  FLIR / Seek Thermal USB camera         │
└──────────────┴──────────────────────────────────────────┘

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🤝 Contributing

Contributions, bug reports, and feature ideas are always welcome!

1. Fork the repository
2. Create your branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -m 'Add YourFeature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a Pull Request 🎉

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📞 Contact

Platform	Link
📧 Email	manishdhatrak1121@gmail.com
💼 LinkedIn	Manish Dhatrak
🐙 GitHub	@astromanu007

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📜 License

This project is licensed under the MIT License — free to use, modify, and distribute.

See the LICENSE file for full details.

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🌡️ Hot components detected. Cool solutions delivered. 🌡️

Built with ❤️ by Manish Dhatrak

⭐ Star this repo if it helped you! ⭐