{"id":28712883,"url":"https://github.com/ihsan31415/crackmap","last_synced_at":"2026-04-15T15:37:49.806Z","repository":{"id":298666854,"uuid":"998825852","full_name":"ihsan31415/crackmap","owner":"ihsan31415","description":"Concrete crack detection heatmapping system","archived":false,"fork":false,"pushed_at":"2025-06-12T07:45:30.000Z","size":40643,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-12T08:39:27.945Z","etag":null,"topics":["clahe","cnn","cnn-classification","cnn-model-tunning","heatmap","heatmap-visualization","python","python3","pytorch","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ihsan31415.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-06-09T10:06:38.000Z","updated_at":"2025-06-12T07:45:33.000Z","dependencies_parsed_at":"2025-06-12T08:50:31.176Z","dependency_job_id":null,"html_url":"https://github.com/ihsan31415/crackmap","commit_stats":null,"previous_names":["ihsan31415/crackmap"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/ihsan31415/crackmap","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ihsan31415%2Fcrackmap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ihsan31415%2Fcrackmap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ihsan31415%2Fcrackmap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ihsan31415%2Fcrackmap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ihsan31415","download_url":"https://codeload.github.com/ihsan31415/crackmap/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ihsan31415%2Fcrackmap/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259901296,"owners_count":22929219,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clahe","cnn","cnn-classification","cnn-model-tunning","heatmap","heatmap-visualization","python","python3","pytorch","streamlit"],"created_at":"2025-06-15T00:01:02.365Z","updated_at":"2026-04-15T15:37:44.773Z","avatar_url":"https://github.com/ihsan31415.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CrackMap\n\n![Python](https://img.shields.io/badge/Python-3.8%2B-blue)\n![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)\n![PyTorch](https://img.shields.io/badge/PyTorch-Enabled-EE4C2C?logo=pytorch)\n![OpenCV](https://img.shields.io/badge/OpenCV-Enabled-5C3EE8?logo=opencv)\n![Streamlit](https://img.shields.io/badge/Streamlit-App-FF4B4B?logo=streamlit)\n![Platform](https://img.shields.io/badge/Platform-Linux%20%7C%20Windows-lightgrey)\n\nCrackMap 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.\n\n---\n\n## 🔧 Features\n\n- ✅ Crack detection using a PyTorch deep learning model (e.g., ResNet18)\n- 🧩 Image preprocessing:\n  - Tiling large images\n  - CLAHE (adaptive histogram equalization)\n  - Gaussian Blur\n  - Skeletonization\n  - Normalization \u0026 tensor transformation\n- 📊 Visualization:\n  - Heatmap overlays\n  - Cracked region masks\n- 🖼️ Streamlit-based web interface for real-time interaction\n\n---\n\n## 🚀 Installation\n\n### 1. Clone the Repository\n\n```bash\ngit clone https://github.com/ihsan31415/crackmap.git\ncd crackmap\n````\n\n### 2. Create and Activate Virtual Environment\n\n**For Linux/macOS:**\n\n```bash\npython3 -m venv venv\nsource venv/bin/activate\n```\n\n**For Windows:**\n\n```cmd\npython -m venv venv\nvenv\\Scripts\\activate\n```\n\n### 3. Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## 🖥️ Usage\n### Streamlit Web App\n```bash\nstreamlit run app.py\n```\nOptional arguments:\n\n* `Tile Division Factor (d)`: Size of each tile (usually 4, ex. = 4x4)\n* `Minimum Crack Length (px)`: minimum lenght of the crack line (too smaal, too sensitive)\n\nUpload an image through the UI, visualize Heatmap.\n\n![Alt Text](assets/page.png)\n\n---\n\n## 🧠 Model Details\n\n* Backbone: ResNet18\n* Framework: PyTorch\n* Input size: 224x224 \n\nTo train your own model, refer to `notebook.ipynb`.\n\n---\n\n## 📁 Folder Structure\n\n```\ncrackmap/\n├── app.py                               # streamlit page\n├── main.py                              # main app\n├── models/                              # Saved PyTorch models\n|   └── concrete_crack_model_new.pth             \n└── pages/\n|   └── About.py\n├── requirements.txt\n├── assets/\n├── notebook.ipynb\n└── README.md\n\n```\n\n---\n\n## 🧪 Supported Image Formats\n\n* `.jpg`, `.jpeg`\n* `.png`\n\n---\n\n## 📷 Sample Results\n| Input Image | results |\n|-------------|----------------|\n| ![](assets/input.jpeg) | ![](assets/results.jpeg) |\n\n\n---\n\n## 📄 License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n---\n\n## 🤝 Contributing\n\nContributions, issues, and feature requests are welcome!\nFeel free to fork and submit a pull request.\n\n### 👇 Ways you can contribute:\n- ⭐ Star this repo to show your support\n- 🐛 Open an issue if you find a bug or have a suggestion\n- 📥 Submit a pull request if you've made improvements\n- 📣 Share it with others who might find it useful\n\nIf you're interested in collaborating or just want to say hi, feel free to connect!\n\n📧 Email: [ihsanmuhammadkhoirul@gmail.com](mailto:ihsanmuhammadkhoirul@gmail.com)  \n🔗 LinkedIn: [khoirul ihsan](https://www.linkedin.com/in/khoirul-ihsan-387115288/)\n\n---\n\n## 🌐 Credits\n\nBuilt by [ihsan31415](https://github.com/ihsan31415), pls contrib if u want \u003c3.\nInspired by real-world needs in infrastructure and material health monitoring.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fihsan31415%2Fcrackmap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fihsan31415%2Fcrackmap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fihsan31415%2Fcrackmap/lists"}