CrackMap

CrackMap is a deep learning-powered image analysis tool designed for detecting cracks in materials using computer vision. It provides a complete pipeline from image preprocessing (tiling, enhancement, transformation) to crack inference using a pretrained model (e.g., ResNet18). A Streamlit interface is available for an easy-to-use demo.

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🔧 Features

• ✅ Crack detection using a PyTorch deep learning model (e.g., ResNet18)
• 🧩 Image preprocessing:
  • Tiling large images
  • CLAHE (adaptive histogram equalization)
  • Gaussian Blur
  • Skeletonization
  • Normalization & tensor transformation
• 📊 Visualization:
  • Heatmap overlays
  • Cracked region masks
• 🖼️ Streamlit-based web interface for real-time interaction

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🚀 Installation

1. Clone the Repository

git clone https://github.com/ihsan31415/crackmap.git
cd crackmap

2. Create and Activate Virtual Environment

For Linux/macOS:

python3 -m venv venv
source venv/bin/activate

For Windows:

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

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🖥️ Usage

Streamlit Web App

streamlit run app.py

Optional arguments:

• Tile Division Factor (d): Size of each tile (usually 4, ex. = 4x4)
• Minimum Crack Length (px): minimum lenght of the crack line (too smaal, too sensitive)

Upload an image through the UI, visualize Heatmap.

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🧠 Model Details

• Backbone: ResNet18
• Framework: PyTorch
• Input size: 224x224

To train your own model, refer to notebook.ipynb.

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📁 Folder Structure

crackmap/
├── app.py                               # streamlit page
├── main.py                              # main app
├── models/                              # Saved PyTorch models
|   └── concrete_crack_model_new.pth             
└── pages/
|   └── About.py
├── requirements.txt
├── assets/
├── notebook.ipynb
└── README.md

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🧪 Supported Image Formats

• .jpg, .jpeg
• .png

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📷 Sample Results

Input Image	results

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📄 License

This project is licensed under the MIT License.

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork and submit a pull request.

👇 Ways you can contribute:

• ⭐ Star this repo to show your support
• 🐛 Open an issue if you find a bug or have a suggestion
• 📥 Submit a pull request if you've made improvements
• 📣 Share it with others who might find it useful

If you're interested in collaborating or just want to say hi, feel free to connect!

📧 Email: ihsanmuhammadkhoirul@gmail.com
🔗 LinkedIn: khoirul ihsan

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🌐 Credits

Built by ihsan31415, pls contrib if u want <3. Inspired by real-world needs in infrastructure and material health monitoring.