JAVA调用OPENCV中DNN.Darknet接口进行目标检测测试（代码备份）

测试使用的是最新版opencv-3.3.1，dnn模块中加入了很多深度学习库的接口，可直接读取配置文件进行预测，对于面向应用的程序设计很有意义。

import org.opencv.core.*;import org.opencv.dnn.*;import org.opencv.imgproc.*;import org.opencv.imgcodecs.*;public class Main {    static String[] names = new String[]{            "aeroplane","bicycle","bird","boat","bottle",            "bus","car","cat","chair","cow",            "diningtable","dog","horse","motorbike","person",            "pottedplant","sheep","sofa","train","tvmonitor"            };    public static void main(String[] args) {                System.loadLibrary( Core.NATIVE_LIBRARY_NAME );         Net net = Dnn.readNetFromDarknet("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights");        if ( net.empty() ) {            System.out.println("Reading Net error");        }        String image_file = "dog.jpg";//IMG_9452.JPG        Mat im = Imgcodecs.imread(image_file, Imgcodecs.IMREAD_COLOR);        if( im.empty() ) {            System.out.println("Reading Image error");        }        Mat frame = new Mat();        Size sz1 = new Size(im.cols(),im.rows());        Imgproc.resize(im, frame, sz1);        Mat resized = new Mat();        Size sz = new Size(416,416);        Imgproc.resize(im, resized, sz);        float scale = 1.0F / 255.0F;        Mat inputBlob = Dnn.blobFromImage(im, scale, sz, new Scalar(0), false, false);        net.setInput(inputBlob, "data");         Mat detectionMat = net.forward("detection_out");        if( detectionMat.empty() ) {            System.out.println("No result");        }        for (int i = 0; i < detectionMat.rows(); i++)        {                       int probability_index = 5;            int size = (int) (detectionMat.cols() * detectionMat.channels());            float[] data = new float[size];            detectionMat.get(i, 0, data);            float confidence = -1;            int objectClass = -1;            for (int j=0; j < detectionMat.cols();j++)            {                   if (j>=probability_index && confidence<data[j])                {                    confidence = data[j];                    objectClass = j-probability_index;                }            }            if (confidence > 0.3)            {                System.out.println("Result Object: "+i);                for (int j=0; j < detectionMat.cols();j++)                     System.out.print(" "+j+":"+ data[j]);                System.out.println("");                float x = data[0];                float y = data[1];                float width = data[2];                float height = data[3];                float xLeftBottom = (x - width / 2) * frame.cols();                float yLeftBottom = (y - height / 2) * frame.rows();                float xRightTop = (x + width / 2) * frame.cols();                float yRightTop = (y + height / 2) * frame.rows();                System.out.println("Class: "+ names[objectClass]);                System.out.println("Confidence: "+confidence);                System.out.println("ROI: "+xLeftBottom+" "+yLeftBottom+" "+xRightTop+" "+yRightTop+"\n");                Imgproc.rectangle(frame, new Point(xLeftBottom, yLeftBottom),                        new Point(xRightTop,yRightTop),new Scalar(0, 255, 0),3);            }        }        Imgcodecs.imwrite("out.jpg", frame );    }}

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2017-10-31日更新：
备忘：若开发WEB应用程序，opencv-*.dll需放置到Tomcat运行所需JRE的BIN目录下，否则无法加载和调用相关库。