1.读取本地视频流,pom依赖

依赖于 org.bytedeco下的javacv/opencv/ffmpeg 包

                 
      
       org.bytedeco
                  
      
       javacv
                  
      
       1.4.3
              
             
                 
      
       org.bytedeco.javacpp-presets
                  
      
       opencv
                  
      
       3.4.3-1.4.3
                  
      
       linux-x86_64
              
             
                 
      
       org.bytedeco.javacpp-presets
                  
      
       ffmpeg
                  
      
       4.0.2-1.4.3
                  
      
       linux-x86_64
              
     

2.读取本地视频流并解帧为 opencv_core.Mat

File file = new File("/home/lab/javacv/t11.mp4");FFmpegFrameGrabber grabber = new FFmpegFrameGrabber(file);grabber.start();// 解帧为opencv_core.MatList
     
       mats = new ArrayList<>();for (int i = 0; i < grabber.getLengthInFrames(); i++) {    Frame frame = grabber.grabImage();    OpenCVFrameConverter.ToMat toMat = new OpenCVFrameConverter.ToMat();    opencv_core.Mat mat = toMat.convert(frame);    if (mat != null) {        mats.add(mat.clone());    }}grabber.stop();
     

3.获取32位dhash特征

dhash特征提取思路,图片Mat转为单通道的灰度图,并重置为5*5的Size,最后将其转储为长度为 25 的byte数组用以求取32位dhash特征

// 声明空的灰度图 Matopencv_core.Mat grayImg = new opencv_core.Mat(mat.rows(), mat.cols(), opencv_imgcodecs.IMREAD_GRAYSCALE);// 转储为灰度图opencv_imgproc.cvtColor(mat, grayImg, opencv_imgproc.COLOR_RGB2GRAY);// 修改Mat长宽sizeopencv_core.Mat resizedImg = new opencv_core.Mat();opencv_core.Size size = new opencv_core.Size(5,5);opencv_imgproc.resize(grayImg,resizedImg,size);// 转为 5*5 byte 数组byte[] bytePixels = new byte[5 * 5];resizedImg.data().get(bytePixels);int[] pixels = new int[bytePixels.length];for (int i=0; i
     
       pixels[(i+1)*5+j] ? 1 : 0;        feature = (feature << 1) + colBit;        int rowBit = pixels[i*5+j] > pixels[i*5+j+1] ? 1 : 0 ;        feature = (feature << 1) + rowBit;    }}
     

多线程部分,可参考该博: https://www.cnblogs.com/nyatom/p/10119306.html