🔬 Intelligent Sperm Image Detection and Classification System

click 【中文版本】 complete running: code.ipynb

📝 Project Overview

This project is a biomedical image analysis system based on computer vision and image processing techniques, focusing on intelligent detection, classification, and visualization of sperm. Using advanced image processing algorithms, the project can accurately identify, locate, and classify targets from complex biomedical images.

🌟 Key Features

• 🖼️ Multi-step Image Processing Workflow
• 🔍 Precise Target Detection Algorithms
• 📊 Multi-dimensional Target Classification
• 📈 Detailed Visualization of Results
• 🧩 Modular Code Architecture

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip Package Manager

Installation Steps

1. Clone the Project Repository

git clone https://github.com/yourusername/tadpole-detection.git  
cd tadpole-detection

2. Create Virtual Environment (Recommended)

python -m venv venv  
source venv/bin/activate  # Use `venv\Scripts\activate` on Windows  

3. Install Dependencies

pip install -r requirements.txt  

🔧 Project Structure

spermproject/  
│  
├── data/                # Test Images  
│   └── sperm.jpg  
│  
├── src/                 # Source Code  
│   ├── __init__.py  
│   ├── detection.py     # Core Detection Algorithms  
│   ├── utils.py         # Utility Functions  
│   └── visualization.py # Visualization Tools  
│  
├── tests/               # Unit Tests  
│   ├── conftest.py  
│   └── test_detection.py  
│  
├── visualization/       # Processing Step Visualizations  
│  
├── main.py              # Main Program Entry  
├── requirements.txt     # Dependency List  
└── README.md            # Project Documentation  

📖 User Guide

Basic Execution python main.py
Command Line Arguments (If Available) python main.py --input data/custom_image.jpg
🔬 Workflow Explanation Image Preprocessing

Color Space Conversion (BGR → HSV) Color Mask Generation Morphological Processing Target Detection

Contour Extraction Bounding Box Generation Abnormal Frame Processing Color Classification

Light/Deep Purple Ratio Analysis Multi-dimensional Classification Result Visualization

Target Frame Annotation Classification Statistical Charts 📊 Output Examples Console Output Detection Statistics:
Category One: 5 targets
Category Two: 3 targets

Pixel Ratio for Detection Frames:
Frame (x, y, w, h): Light/Deep Purple Ratio 0.3456
Visualization Output Processing step images generated in visualization/ directory Generate result_detection.jpg final detection result Generate result_detection.jpg image comparison

🧪 Unit Testing

pytest tests/
pytest tests/test_detection.py # Run specific tests
pytest --cov=src # Code coverage

🤝 Contribution Guidelines

Fork the Project Create Feature Branch (git checkout -b feature/AmazingFeature) Commit Changes (git commit -m 'Added Some Amazing Feature') Push to Branch (git push origin feature/AmazingFeature) Submit Pull Request

🛠️ Technology Stack

Python 3.8+ OpenCV Image Processing NumPy Numerical Computing Matplotlib Data Visualization Pytest Unit Testing

📌 Precautions

Ensure input images are clear with appropriate contrast Recommended to use JPG or PNG formats Large or extremely complex images may require algorithm parameter adjustments

🔒 License

This project is licensed under the MIT License - see the LICENSE file for details

🙌 Acknowledgments

OpenCV Development Team NumPy Community Matplotlib Project

Disclaimer: This project is for academic research and educational purposes only and should not be directly used for clinical diagnosis.

🌐 Contact Project Homepage: [https://github.com/cyfedu-dlut/Medical-Sperm-Detection-and-Recognition-System] Email: [email protected] Personal Blog/Homepage: [https://cyfedu-dlut.github.io/PersonalWeb/]