bus-opencv

🖼️ Bus OpenCV: OpenCV Integration for Java

Computer Vision and Image Processing with OpenCV

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📖 Project Introduction

Bus OpenCV provides a simplified Java interface to OpenCV (Open Source Computer Vision Library), making it easy to perform computer vision tasks, image processing, and video analysis in your Java applications.

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✨ Core Features

🎯 Image Processing

• Image Filtering: Blur, sharpen, edge detection
• Geometric Transformations: Resize, rotate, warp
• Color Space Conversion: RGB, HSV, grayscale conversions
• Histogram Operations: Equalization, manipulation
• Morphological Operations: Erosion, dilation, opening, closing

🌍 Computer Vision

• Feature Detection: SIFT, SURF, ORB, FAST
• Object Detection: Haar cascades, HOG, deep learning models
• Face Recognition: Face detection and recognition
• Motion Detection: Optical flow, background subtraction
• Camera Calibration: Intrinsic and extrinsic parameters

📊 Video Processing

• Video Capture: From camera or video files
• Video Writing: Create and save video files
• Frame Processing: Process individual video frames
• Real-time Processing: Live camera stream processing

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🚀 Quick Start

Maven Dependency

<dependency>
    <groupId>org.miaixz</groupId>
    <artifactId>bus-opencv</artifactId>
    <version>8.x.x</version>
</dependency>

Load OpenCV Library

import org.miaixz.bus.opengl.OpenCVKit;

public class Application {
    static {
        // Load OpenCV native library
        OpenCVKit.loadLib();
    }

    public static void main(String[] args) {
        // Your code here
    }
}

Basic Image Operations

import org.miaixz.bus.opengl.ImageProcessor;
import org.opencv.core.Mat;
import org.opencv.imgcodecs.Imgcodecs;

public class ImageExample {
    public void processImage(String inputPath, String outputPath) {
        // Load image
        Mat image = Imgcodecs.imread(inputPath);

        // Convert to grayscale
        Mat gray = ImageProcessor.toGray(image);

        // Apply Gaussian blur
        Mat blurred = ImageProcessor.gaussianBlur(gray, 15);

        // Detect edges using Canny
        Mat edges = ImageProcessor.canny(blurred, 50, 150);

        // Save result
        Imgcodecs.imwrite(outputPath, edges);

        // Release resources
        image.release();
        gray.release();
        blurred.release();
        edges.release();
    }
}

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💡 Use Cases

Face Detection

import org.miaixz.bus.opengl.FaceDetector;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Rect;

public class FaceDetectionExample {
    public void detectFaces(String imagePath) {
        // Load image
        Mat image = Imgcodecs.imread(imagePath);

        // Detect faces
        MatOfRect faces = new MatOfRect();
        FaceDetector.detectFaces(image, faces);

        // Draw rectangles around faces
        for (Rect rect : faces.toArray()) {
            Imgproc.rectangle(
                image,
                new Point(rect.x, rect.y),
                new Point(rect.x + rect.width, rect.y + rect.height),
                new Scalar(0, 255, 0),
                2
            );
        }

        // Save result
        Imgcodecs.imwrite("faces_detected.jpg", image);
    }
}

Image Filtering

public class ImageFilterExample {
    
    public void applyFilters(String inputPath) {
        Mat image = Imgcodecs.imread(inputPath);

        // Apply various filters
        Mat blurred = ImageProcessor.gaussianBlur(image, 21);
        Mat sharpened = ImageProcessor.sharpen(image);
        Mat edged = ImageProcessor.canny(image, 100, 200);

        // Display or save results
        // ...
    }
    
}

Video Processing

import org.miaixz.bus.opengl.VideoCapture;
import org.miaixz.bus.opengl.VideoWriter;

public class VideoExample {
    public void processVideo(String inputPath, String outputPath) {
        // Open video file
        VideoCapture capture = new VideoCapture(inputPath);

        // Get video properties
        double fps = capture.get(Videoio.CAP_PROP_FPS);
        int width = (int) capture.get(Videoio.CAP_PROP_FRAME_WIDTH);
        int height = (int) capture.get(Videoio.CAP_PROP_FRAME_HEIGHT);

        // Create video writer
        VideoWriter writer = new VideoWriter(
            outputPath,
            Videoio_fourcc('m', 'p', '4', 'v'),
            fps,
            new Size(width, height)
        );

        Mat frame = new Mat();
        while (capture.read(frame)) {
            // Process frame
            Mat processed = ImageProcessor.toGray(frame);
            Mat edges = ImageProcessor.canny(processed, 50, 150);

            // Write frame
            writer.write(edges);
        }

        // Release resources
        capture.release();
        writer.release();
    }
}

Object Detection

public class ObjectDetectionExample {

    public void detectObjects(String imagePath) {
        Mat image = Imgcodecs.imread(imagePath);

        // Detect specific objects using trained models
        List<DetectedObject> objects = ObjectDetector.detect(
            image,
            ObjectDetector.HAAR_CASCADE_FRONTALFACE
        );

        // Process detected objects
        for (DetectedObject obj : objects) {
            System.out.println("Detected: " + obj.getLabel()
                + " at " + obj.getBoundingBox());
        }
    }

}

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🔧 Configuration

OpenCV Native Library

The framework automatically loads OpenCV native libraries. You can configure the library path:

extend:
  opencv:
    library-path: /usr/local/lib
    auto-load: true

Performance Tuning

extend:
  opencv:
    use-gpu: false
    thread-count: 4
    buffer-size: 1024

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📊 Performance Tips

Memory Management

// Always release Mat resources when done
Mat image = Imgcodecs.imread("image.jpg");
try {
    // Process image
} finally {
    image.release();
}

// Or use try-with-resources pattern
try (Mat image = Imgcodecs.imread("image.jpg")) {
    // Process image
}

Parallel Processing

// Process multiple images in parallel
List<String> imagePaths = Arrays.asList("img1.jpg", "img2.jpg", "img3.jpg");
imagePaths.parallelStream().forEach(path -> {
    Mat image = Imgcodecs.imread(path);
    // Process image
    image.release();
});

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🔄 Version Compatibility

Bus OpenCV Version	OpenCV Version	JDK Version
8.x	4.x	17+
7.x	4.x	11+

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🎯 Supported Operations

Image Operations

Operation	Method	Description
Load Image	Imgcodecs.imread()	Load image from file
Save Image	Imgcodecs.imwrite()	Save image to file
Resize	Imgproc.resize()	Resize image
Rotate	Imgproc.rotate()	Rotate image
Crop	Mat.submat()	Crop image region
Flip	Imgproc.flip()	Flip image

Filter Operations

Filter	Method	Description
Gaussian Blur	GaussianBlur()	Blur image
Median Blur	medianBlur()	Median filtering
Bilateral Filter	bilateralFilter()	Edge-preserving smoothing
Box Filter	boxFilter()	Box filtering

Feature Detection

Feature	Method	Description
Corners	goodFeaturesToTrack()	Detect corner features
Edges	Canny()	Detect edges
Contours	findContours()	Find contours
Lines	HoughLines()	Detect lines

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❓ FAQ

Q: Do I need to install OpenCV separately?

A: Yes, you need to install OpenCV on your system. This module provides the Java interface.

Q: Can I use GPU acceleration?

A: Yes, if you have OpenCV compiled with CUDA support, you can enable GPU operations.

Q: How do I handle different image formats?

A: OpenCV supports common formats (JPEG, PNG, BMP, TIFF). Use appropriate file extensions.

Q: What if I get "UnsatisfiedLinkError"?

A: Make sure OpenCV native library is properly installed and accessible in your library path.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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🔗 Related Documentation

• OpenCV Official Documentation
• OpenCV Java Tutorial