javacv从入门到精通——第十一章深入理解JavaCV之JavaCV高级特性

企业开发 2023-05-04 15:10:53 阅读次数: 0

  1. 高级图像处理

JavaCV提供了许多高级图像处理功能，包括图像滤波、边缘检测、形态学操作、图像分割和特征提取等。以下是JavaCV中的一些高级图像处理特性：

  1. 图像滤波

JavaCV支持各种图像滤波算法，例如均值滤波、高斯滤波、中值滤波、双边滤波等。这些滤波算法可以有效地降噪，平滑图像并改善图像质量。

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgproc;

public class ImageFilteringExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("lena.png");
        opencv_core.Mat dst = new opencv_core.Mat();
        
        // 高斯滤波
        opencv_imgproc.GaussianBlur(src, dst, new opencv_core.Size(3, 3), 0);
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("Image Filtering Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(dst));
    }
}

  1. 边缘检测

JavaCV提供了多种边缘检测算法，例如Sobel、Canny、Laplacian等。这些算法可以用于检测图像中的边缘，以便于进一步的图像分析和处理。

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgproc;

public class EdgeDetectionExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("lena.png");
        opencv_core.Mat gray = new opencv_core.Mat();
        opencv_core.Mat edges = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
        
        // Canny边缘检测
        opencv_imgproc.Canny(gray, edges, 50, 150);
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("Edge Detection Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(edges));
    }
}

  1. 形态学操作

JavaCV支持各种形态学操作，例如腐蚀、膨胀、开操作、闭操作等。这些操作可以用于处理二值图像或灰度图像，并可以应用于图像分割、形态学重建等领域。

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgproc;

public class MorphologyExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("text.png");
        opencv_core.Mat gray = new opencv_core.Mat();
        opencv_core.Mat binary = new opencv_core.Mat();
        opencv_core.Mat closed = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
        
        // 二值化
        opencv_imgproc.threshold(gray, binary, 0, 255, opencv_imgproc.THRESH_BINARY_INV | opencv_imgproc.THRESH_OTSU);
        
        // 闭操作
        opencv_core.Mat kernel = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, new opencv_core.Size(5, 5));
        opencv_imgproc.morphologyEx(binary, closed, opencv_imgproc.MORPH_CLOSE, kernel);
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("Morphology Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(closed));
    }
}

  1. 图像分割

JavaCV支持各种图像分割算法，例如基于阈值的分割、区域生长、分水岭算法等。这些算法可以将图像分成多个不同的区域，以便于进一步的图像分析和处理。

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgproc;

public class ImageSegmentationExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("coins.jpg");
        opencv_core.Mat gray = new opencv_core.Mat();
        opencv_core.Mat binary = new opencv_core.Mat();
        opencv_core.Mat labels = new opencv_core.Mat();
        opencv_core.Mat stats = new opencv_core.Mat();
        opencv_core.Mat centroids = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
        
        // 二值化
        opencv_imgproc.threshold(gray, binary, 0, 255, opencv_imgproc.THRESH_BINARY_INV | opencv_imgproc.THRESH_OTSU);
        
        // 连通组件标记
        opencv_imgproc.connectedComponentsWithStats(binary, labels, stats, centroids);
        
        // 绘制结果
        opencv_core.Mat result = new opencv_core.Mat();
        opencv_core.cvtColor(src, result, opencv_imgproc.COLOR_BGR2GRAY);
        for (int i = 1; i < stats.rows(); i++) {
            int x = (int) stats.ptr(i, opencv_imgproc.CC_STAT_LEFT)[0];
            int y = (int) stats.ptr(i, opencv_imgproc.CC_STAT_TOP)[0];
            int w = (int) stats.ptr(i, opencv_imgproc.CC_STAT_WIDTH)[0];
            int h = (int) stats.ptr(i, opencv_imgproc.CC_STAT_HEIGHT)[0];
            opencv_core.rectangle(result, new opencv_core.Point(x, y), new opencv_core.Point(x + w, y + h), opencv_core.Scalar.RED);
        }
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("Image Segmentation Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(result));
    }
}

  1. 特征提取

JavaCV提供了各种特征提取算法，例如SIFT、SURF、ORB、HOG等。这些算法可以提取图像中的关键点和特征描述子，用于图像匹配、对象识别等领域。

ORB算法是一种基于FAST算法的特征点检测器，与SIFT和SURF算法相比，ORB算法具有速度快、精度高、鲁棒性好等优点。以下是ORB算法的代码示例：

import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_features2d;
import org.bytedeco.javacpp.opencv_imgcodecs;

public class ORBExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("lena.png");
        opencv_core.Mat gray = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
        
        // ORB特征点检测
        opencv_features2d.ORB orb = opencv_features2d.ORB.create();
        opencv_core.MatOfKeyPoint keyPoints = new opencv_core.MatOfKeyPoint();
        opencv_core.Mat descriptors = new opencv_core.Mat();
        orb.detectAndCompute(gray, new opencv_core.Mat(), keyPoints, descriptors);
        
        // 绘制特征点
        opencv_core.Mat result = new opencv_core.Mat();
        opencv_features2d.drawKeypoints(src, keyPoints, result);
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("ORB Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(result));
    }
}

BRISK算法是一种基于FAST算法的特征点检测器，与ORB算法相比，BRISK算法具有更快的速度和更好的旋转不变性，但是对于尺度变化和视角变化的鲁棒性稍弱。以下是BRISK算法的代码示例：

import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_features2d;
import org.bytedeco.javacpp.opencv_imgcodecs;

public class BRISKExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src = opencv_imgcodecs.imread("lena.png");
        opencv_core.Mat gray = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src, gray, opencv_imgproc.COLOR_BGR2GRAY);
        
        // BRISK特征点检测
        opencv_features2d.BRISK brisk = opencv_features2d.BRISK.create();
        opencv_core.MatOfKeyPoint keyPoints = new opencv_core.MatOfKeyPoint();
        opencv_core.Mat descriptors = new opencv_core.Mat();
        brisk.detectAndCompute(gray, new opencv_core.Mat(), keyPoints, descriptors);
        
        // 绘制特征点
        opencv_core.Mat result = new opencv_core.Mat();
        opencv_features2d.drawKeypoints(src, keyPoints, result);
    // 显示结果
        CanvasFrame frame = new CanvasFrame("BRISK Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(result));
    }
}

以下是使用基于SIFT算法的特征描述符进行特征匹配的代码示例：

import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_features2d;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_nonfree.SIFT;

public class SIFTExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src1 = opencv_imgcodecs.imread("lena.png");
        opencv_core.Mat src2 = opencv_imgcodecs.imread("lena_rotate.png");
        opencv_core.Mat gray1 = new opencv_core.Mat();
        opencv_core.Mat gray2 = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src1, gray1, opencv_imgproc.COLOR_BGR2GRAY);
        opencv_imgproc.cvtColor(src2, gray2, opencv_imgproc.COLOR_BGR2GRAY);
        
        // SIFT特征点检测和特征描述
        SIFT sift = new SIFT();
        opencv_core.MatOfKeyPoint keyPoints1 = new opencv_core.MatOfKeyPoint();
        opencv_core.MatOfKeyPoint keyPoints2 = new opencv_core.MatOfKeyPoint();
        opencv_core.Mat descriptors1 = new opencv_core.Mat();
        opencv_core.Mat descriptors2 = new opencv_core.Mat();
        sift.detectAndCompute(gray1, new opencv_core.Mat(), keyPoints1, descriptors1);
        sift.detectAndCompute(gray2, new opencv_core.Mat(), keyPoints2, descriptors2);
        
        // 特征匹配
        opencv_features2d.BFMatcher matcher = new opencv_features2d.BFMatcher(opencv_core.NORM_L2, true);
        opencv_core.DMatchVectorVector matches = new opencv_core.DMatchVectorVector();
        matcher.knnMatch(descriptors1, descriptors2, matches, 2);
        
        // 筛选匹配结果
        opencv_core.MatOfDMatch goodMatches = new opencv_core.MatOfDMatch();
        for (int i = 0; i < matches.size(); i++) {
            opencv_core.DMatchVector match = matches.get(i);
            if (match.size() == 2 && match.get(0).distance() < 0.7 * match.get(1).distance()) {
                goodMatches.fromArray(match.get(0));
            }
        }
        
        // 绘制匹配结果
        opencv_core.Mat result = new opencv_core.Mat();
        opencv_features2d.drawMatches(src1, keyPoints1, src2, keyPoints2, goodMatches, result);
        // 显示结果
        CanvasFrame frame = new CanvasFrame("SIFT Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(result));
    }

}

上述代码中，首先加载了两张图像并将它们转换为灰度图像，然后使用SIFT算法进行特征点检测和特征描述，接着使用Brute-Force匹配器进行特征点匹配，最后通过筛选出的好的匹配结果绘制匹配线并显示结果。

以下是使用基于SIFT算法的图像配准进行图像融合的代码示例：

import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_features2d;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_nonfree.SIFT;
import org.bytedeco.javacpp.opencv_stitching.DetailStitcher;

public class ImageStitchingExample {
    public static void main(String[] args) {
        // 加载图像
        opencv_core.Mat src1 = opencv_imgcodecs.imread("img1.jpg");
        opencv_core.Mat src2 = opencv_imgcodecs.imread("img2.jpg");
        opencv_core.Mat gray1 = new opencv_core.Mat();
        opencv_core.Mat gray2 = new opencv_core.Mat();
        
        // 灰度化
        opencv_imgproc.cvtColor(src1, gray1, opencv_imgproc.COLOR_BGR2GRAY);
        opencv_imgproc.cvtColor(src2, gray2, opencv_imgproc.COLOR_BGR2GRAY);
        
        // SIFT特征点检测和特征描述
        SIFT sift = new SIFT();
        opencv_core.MatOfKeyPoint keyPoints1 = new opencv_core.MatOfKeyPoint();
        opencv_core.MatOfKeyPoint keyPoints2 = new opencv_core.MatOfKeyPoint();
        opencv_core.Mat descriptors1 = new opencv_core.Mat();
        opencv_core.Mat descriptors2 = new opencv_core.Mat();
        sift.detectAndCompute(gray1, new opencv_core.Mat(), keyPoints1, descriptors1);
        sift.detectAndCompute(gray2, new opencv_core.Mat(), keyPoints2, descriptors2);
        
        // 特征点匹配
        opencv_features2d.BFMatcher matcher = new opencv_features2d.BFMatcher(opencv_core.NORM_L2, true);
        opencv_core.DMatchVectorVector matches = new opencv_core.DMatchVectorVector();
        matcher.knnMatch(descriptors1, descriptors2, matches, 2);
        
        // 筛选匹配结果
        opencv_core.MatOfDMatch goodMatches = new opencv_core.MatOfDMatch();
        for (int i = 0; i < matches.size(); i++) {
            opencv_core.DMatchVector match = matches.get(i);
            if (match.size() >= 2) {
                opencv_core.DMatch m1 = match.get(0);
                opencv_core.DMatch m2 = match.get(1);
                if (m1.distance() < 0.7 * m2.distance()) {
                    goodMatches.push_back(new opencv_core.MatOfDMatch(m1));
                }
            }
        }
        // 图像配准
        DetailStitcher stitcher = new DetailStitcher();
        opencv_core.Mat result = new opencv_core.Mat();
        opencv_core.MatVector src = new opencv_core.MatVector(2);
        src.put(0, src1);
        src.put(1, src2);
        opencv_core.MatOfInt indices = new opencv_core.MatOfInt(0, 1);
        opencv_core.MatOfIntVector matchesIndices = new opencv_core.MatOfIntVector();
        matchesIndices.push_back(goodMatches);
        stitcher.stitch(src, indices, result, matchesIndices);
        
        // 显示结果
        CanvasFrame frame = new CanvasFrame("Image Stitching Example");
        frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
        frame.showImage(new Java2DFrameConverter().convert(result));
    }
}

上述代码中，首先加载了两张图像并将它们转换为灰度图像，然后使用SIFT算法进行特征点检测和特征描述，接着使用Brute-Force匹配器进行特征点匹配并筛选出好的匹配结果，最后使用基于SIFT算法的图像配准算法进行图像配准和图像融合，最终得到了一张拼接后的图像。

  1. 高级视频处理

JavaCV 是一个基于 Java 平台的计算机视觉库，它是 OpenCV 在 Java 平台上的封装。JavaCV 提供了一系列高级特性，包括高级视频处理。下面，我将详细介绍 JavaCV 高级视频处理的特性。

  1. 视频捕捉和视频录制

JavaCV 提供了 VideoCapture 和 FFmpegFrameRecorder 两个类用于视频捕捉和视频录制。VideoCapture 可以从摄像头、视频文件、网络摄像头等多个来源捕捉视频，而 FFmpegFrameRecorder 可以将视频录制到视频文件、RTSP 服务器等多个目的地。

  1. 视频编解码和格式转换

JavaCV 通过 FFmpeg 和 OpenCV 库提供了一系列编解码和格式转换的功能。可以使用 FFmpegFrameGrabber 和 FFmpegFrameRecorder 类对视频进行编解码和格式转换。

  1. 视频流处理

JavaCV 提供了 FFmpegFrameGrabber 和 FFmpegFrameRecorder 类来处理视频流。可以从网络摄像头、RTSP 服务器等流媒体源抓取视频，也可以将视频流发布到 RTSP 服务器。

  1. 视频分析和处理

JavaCV 提供了一系列图像处理和分析的功能。可以使用 OpenCV 库进行视频的各种操作，如图像增强、目标检测、跟踪、运动估计等。另外，JavaCV 还提供了一个叫做 JavaCV FX 的库，它是 JavaFX 的扩展，可以用于在 Java 应用程序中实现图像和视频处理的交互式用户界面。

  1. 视频编解码优化

JavaCV 通过使用硬件加速来提高视频编解码的速度。JavaCV 使用 OpenCV 库提供的优化代码，利用 SIMD 指令来实现对视频编解码的加速。JavaCV 还支持基于 GPU 的加速，使用 CUDA 库来利用 GPU 的并行计算能力。

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