javacv实现java桌面应用的人脸识别

- 日理万妓 2023-01-06 01:25 204阅读 0赞

这里有javacv实现人脸识别的demo，是Swing应用的

[https://github.com/bytedeco/javacv][https_github.com_bytedeco_javacv]

或者

[https://gitee.com/nanbowang/javacv.git][https_gitee.com_nanbowang_javacv.git]

pom.xml

<dependency>
    	<groupId>org.bytedeco</groupId>
    	<artifactId>javacv-platform</artifactId>
    	<version>1.5.4</version>
    </dependency>
    <dependency>
    	<groupId>org.bytedeco</groupId>
    	<artifactId>opencv-platform</artifactId>
    	<version>4.4.0-1.5.4</version>
    </dependency>

java代码如下：

import static org.bytedeco.opencv.global.opencv_core.CV_32F;
    import static org.bytedeco.opencv.global.opencv_dnn.blobFromImage;
    import static org.bytedeco.opencv.global.opencv_dnn.readNetFromCaffe;
    import static org.bytedeco.opencv.global.opencv_imgproc.rectangle;
    import static org.bytedeco.opencv.global.opencv_imgproc.resize;
    import static org.bytedeco.opencv.global.opencv_videoio.CAP_PROP_FRAME_HEIGHT;
    import static org.bytedeco.opencv.global.opencv_videoio.CAP_PROP_FRAME_WIDTH;
    
    import org.bytedeco.javacpp.indexer.FloatIndexer;
    import org.bytedeco.javacv.CanvasFrame;
    import org.bytedeco.javacv.OpenCVFrameConverter;
    import org.bytedeco.opencv.opencv_core.Mat;
    import org.bytedeco.opencv.opencv_core.Point;
    import org.bytedeco.opencv.opencv_core.Rect;
    import org.bytedeco.opencv.opencv_core.Scalar;
    import org.bytedeco.opencv.opencv_core.Size;
    import org.bytedeco.opencv.opencv_dnn.Net;
    import org.bytedeco.opencv.opencv_videoio.VideoCapture;
    
    public class DeepLearningFaceDetection {
    
        private static final String PROTO_FILE = "deploy.prototxt";
        private static final String CAFFE_MODEL_FILE = "res10_300x300_ssd_iter_140000.caffemodel";
        private static final OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage();
        private static Net net = null;
    
        static {
            net = readNetFromCaffe(PROTO_FILE, CAFFE_MODEL_FILE);
        }
    
        private static void detectAndDraw(Mat image) {//detect faces and draw a blue rectangle arroung each face
    
            resize(image, image, new Size(300, 300));//resize the image to match the input size of the model
    
            //create a 4-dimensional blob from image with NCHW (Number of images in the batch -for training only-, Channel, Height, Width) dimensions order,
            //for more detailes read the official docs at https://docs.opencv.org/trunk/d6/d0f/group__dnn.html#gabd0e76da3c6ad15c08b01ef21ad55dd8
            Mat blob = blobFromImage(image, 1.0, new Size(300, 300), new Scalar(104.0, 177.0, 123.0, 0), false, false, CV_32F);
    
            net.setInput(blob);//set the input to network model
            Mat output = net.forward();//feed forward the input to the netwrok to get the output matrix
    
            Mat ne = new Mat(new Size(output.size(3), output.size(2)), CV_32F, output.ptr(0, 0));//extract a 2d matrix for 4d output matrix with form of (number of detections x 7)
    
            FloatIndexer srcIndexer = ne.createIndexer(); // create indexer to access elements of the matric
    
            for (int i = 0; i < output.size(3); i++) {//iterate to extract elements
                float confidence = srcIndexer.get(i, 2);
                float f1 = srcIndexer.get(i, 3);
                float f2 = srcIndexer.get(i, 4);
                float f3 = srcIndexer.get(i, 5);
                float f4 = srcIndexer.get(i, 6);
                if (confidence > .6) {
                    float tx = f1 * 300;//top left point's x
                    float ty = f2 * 300;//top left point's y
                    float bx = f3 * 300;//bottom right point's x
                    float by = f4 * 300;//bottom right point's y
                    rectangle(image, new Rect(new Point((int) tx, (int) ty), new Point((int) bx, (int) by)), new Scalar(255, 0, 0, 0));//print blue rectangle 
                }
            }
        }
    
        public static void main(String[] args) {
            VideoCapture capture = new VideoCapture();
            capture.set(CAP_PROP_FRAME_WIDTH, 1280);
            capture.set(CAP_PROP_FRAME_HEIGHT, 720);
    
            if (!capture.open(0)) {
                System.out.println("Can not open the cam !!!");
            }
    
            Mat colorimg = new Mat();
    
            CanvasFrame mainframe = new CanvasFrame("Face Detection", CanvasFrame.getDefaultGamma() / 2.2);
            mainframe.setDefaultCloseOperation(javax.swing.JFrame.EXIT_ON_CLOSE);
    //        mainframe.setCanvasSize(600, 600);
            mainframe.setLocationRelativeTo(null);
            mainframe.setVisible(true);
    
            while (true) {
                while (capture.read(colorimg) && mainframe.isVisible()) {
                    detectAndDraw(colorimg);
                    mainframe.showImage(converter.convert(colorimg));
                    try {
                        Thread.sleep(50);
                    } catch (InterruptedException ex) {
                        System.out.println(ex.getMessage());
                    }
                }
            }
        }
    
    }

java文件中有写到需要deploy.prototxt 和 res10\_300x300\_ssd\_iter\_140000.caffemodel 两个文件，放在项目根路径下即可，这两个文件也在 [https://github.com/kingroc711/CVSample.git][https_github.com_kingroc711_CVSample.git] 中可以找到，具体路径在 [https://github.com/kingroc711/CVSample/tree/master/OpenCV/res][https_github.com_kingroc711_CVSample_tree_master_OpenCV_res]

本文到此结束。

[https_github.com_bytedeco_javacv]: https://github.com/bytedeco/javacv
[https_gitee.com_nanbowang_javacv.git]: https://gitee.com/nanbowang/javacv.git
[https_github.com_kingroc711_CVSample.git]: https://github.com/kingroc711/CVSample.git
[https_github.com_kingroc711_CVSample_tree_master_OpenCV_res]: https://github.com/kingroc711/CVSample/tree/master/OpenCV/res