https://github.com/ihsan31415/crackmap
Concrete crack detection heatmapping system
https://github.com/ihsan31415/crackmap
clahe cnn cnn-classification cnn-model-tunning heatmap heatmap-visualization python python3 pytorch streamlit
Last synced: 4 months ago
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Concrete crack detection heatmapping system
- Host: GitHub
- URL: https://github.com/ihsan31415/crackmap
- Owner: ihsan31415
- License: mit
- Created: 2025-06-09T10:06:38.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-12T07:45:30.000Z (4 months ago)
- Last Synced: 2025-06-12T08:39:27.945Z (4 months ago)
- Topics: clahe, cnn, cnn-classification, cnn-model-tunning, heatmap, heatmap-visualization, python, python3, pytorch, streamlit
- Language: Python
- Homepage:
- Size: 38.8 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
---
## 🔧 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
---
## 🚀 Installation
### 1. Clone the Repository
```bash
git clone https://github.com/ihsan31415/crackmap.git
cd crackmap
````
### 2. Create and Activate Virtual Environment
**For Linux/macOS:**
```bash
python3 -m venv venv
source venv/bin/activate
```
**For Windows:**
```cmd
python -m venv venv
venv\Scripts\activate
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
---
## 🖥️ Usage
### Streamlit Web App
```bash
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.
---
## 🧠 Model Details
* Backbone: ResNet18
* Framework: PyTorch
* Input size: 224x224
To train your own model, refer to `notebook.ipynb`.
---
## 📁 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
```
---
## 🧪 Supported Image Formats
* `.jpg`, `.jpeg`
* `.png`
---
## 📷 Sample Results
| Input Image | results |
|-------------|----------------|
|  |  |
---
## 📄 License
This project is licensed under the [MIT License](LICENSE).
---
## 🤝 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](mailto:ihsanmuhammadkhoirul@gmail.com)
🔗 LinkedIn: [khoirul ihsan](https://www.linkedin.com/in/khoirul-ihsan-387115288/)
---
## 🌐 Credits
Built by [ihsan31415](https://github.com/ihsan31415), pls contrib if u want <3.
Inspired by real-world needs in infrastructure and material health monitoring.