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 && confidenceif (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 );

    }

}

---

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。