Opencv MCP Server

🚀 OpenCV MCP Server

The OpenCV MCP (Open Source Computer Vision Machine Learning Processing) Server is a Python-based tool for image and video processing. It combines the capabilities of the OpenCV library with machine learning models, supporting real-time processing, batch processing, and cloud deployment.

🚀 Quick Start

The OpenCV MCP Server is a powerful tool for image and video processing. It integrates the OpenCV library and machine learning models, enabling real-time and batch processing, as well as cloud deployment.

✨ Features

Core Features

• Image Processing: Includes operations such as image enhancement, filtering, and edge detection.
• Object Detection: Uses models like YOLO and Faster R-CNN for object recognition.
• Video Analysis: Enables real-time monitoring of video streams and event detection.
• Machine Learning Integration: Supports model deployment with frameworks like TensorFlow and PyTorch.
• Cloud Service Support: Provides REST API interfaces for easy integration with other systems.

📦 Installation

pip install opencv-mcp-server

💻 Usage Examples

Basic Function List

Image Processing Tools

Image Resize Tool (resize_image_tool)

Resize the width and height of an image.

Usage Example:

result = resize_image_tool(
    image_path="input.jpg",
    width=800,
    height=600
)
resized_image_path = result["output_path"]

Apply Filter Tool (apply_filter_tool)

Apply different types of image filters.

Parameters:

• image_path: Path to the input image.
• filter_type: Type of filter ("gaussian", "median", "blur", etc.).
• kernel_size: Kernel size (default is 3).
• sigma: Gaussian standard deviation (only applicable to Gaussian filters).

Machine Learning Tools

Object Detection Tool (detect_objects_tool)

Detect objects in an image using a pre-trained model.

Parameters:

• image_path: Path to the input image.
• confidence_threshold: Confidence threshold (default is 0.5).
• nms_threshold: Non-maximum suppression threshold (default is 0.4).

Video Processing Tools

Extract Video Frames Tool (extract_video_frames_tool)

Extract specific frames from a video file.

Parameters:

• video_path: Path to the input video.
• start_frame: Starting frame number (default is 0).
• step: Frame step size (default is 1).
• max_frames: Maximum number of frames to extract (default is unlimited).

Detect Video Objects Tool (detect_video_objects_tool)

Detect objects in real-time from a video stream.

Parameters:

• video_path: Path to the input video.
• model_paths: List of paths to pre-trained models.
• confidence_threshold: Confidence threshold (default is 0.5).
• frame_step: Frame processing step size (default is 1).

Other Tools

Generate Histogram Tool (generate_histogram_tool)

Generate a color histogram of an image.

Parameters:

• image_path: Path to the input image.
• color_space: Color space ("RGB", "HSV", "LUV", etc.).
• bins: Number of bins in the histogram (default is 256).

Advanced Usage Examples

Basic Image Processing

result = apply_filter_tool(
    image_path="input.jpg",
    filter_type="gaussian",
    kernel_size=5
)
filtered_image_path = result["output_path"]

Object Detection and Tracking

detections = detect_objects_tool(
    image_path="input.jpg",
    confidence_threshold=0.7,
    nms_threshold=0.3
)
for detection in detections:
    print(f"Detected object: {detection['label']}, Confidence: {detection['confidence']}")

Video Processing

frames = extract_video_frames_tool(
    video_path="input.mp4",
    start_frame=0,
    step=10,
    max_frames=50
)
for frame in frames:
    print(f"Frame {frame['index']} extracted at path: {frame['path']}")

📚 Documentation

Real - World Application Scenarios

Intelligent Transportation

• License Plate Recognition: Use object detection and OCR technology for automatic license plate recognition.
• Traffic Monitoring: Analyze video streams in real-time to count the number of vehicles and pedestrians.

Security Monitoring

• Abnormal Behavior Detection: Detect abnormal behaviors in videos through deep learning models.
• Facial Recognition: Deploy in access control systems for identity verification.

Medical Image Processing

• Tumor Detection: Use Convolutional Neural Networks (CNNs) to assist doctors in identifying lesions in medical images.
• Image Enhancement: Improve the readability of low-quality medical images.

Roadmap

Planned Features

• Statistical Analysis Tools: Integrate data visualization and statistical analysis functions to help users analyze processing results.
• Automated Model Updates: Support online updates of pre-trained models to maintain detection accuracy and speed.
• Extended Hardware Support: Optimize the server version to support more types of hardware acceleration (e.g., NVIDIA GPU).

Future Plans

• Multimodal Data Processing: Support simultaneous processing of multiple types of data such as images, text, and voice.
• Enhanced Privacy Protection: Introduce Federated Learning technology to protect user data privacy.
• Cloud - Native Deployment: Optimize the containerized deployment process to improve operational efficiency in a Kubernetes environment.

Contribution

Ways to Contribute

1. Report Issues: Submit issues on GitHub to report problems encountered during use.
2. Suggest Features: Propose new features or improvement suggestions and participate in requirement discussions.
3. Contribute Code: Fork the project repository and submit a Pull Request.

Commercial Support

For commercial use, please contact the project maintainers for authorization and related support services.