使用opencv，简单的是现实人脸检测和人脸识别（java）

package com.lrw.opencv_test; import com.lrw.opencv_test.utils.OpencvFilePath; import jakarta.validation.constraints.Null; import org.junit.jupiter.api.Test; import org.opencv.core.*; import org.opencv.highgui.HighGui; import org.opencv.imgcodecs.Imgcodecs; import org.opencv.imgproc.Imgproc; import org.opencv.objdetect.CascadeClassifier; import org.opencv.videoio.VideoCapture; import org.opencv.videoio.Videoio; import org.springframework.boot.test.context.SpringBootTest; import java.net.URL; import java.util.List; import java.util.function.BiFunction; @SpringBootTest class OpencvTestApplicationTests { // 初始化人脸探测器 static CascadeClassifier faceDetector; static { URL url = ClassLoader.getSystemResource("lib.opencv/opencv_java481.dll"); System.load(url.getPath()); // System.loadLibrary(Core.NATIVE_LIBRARY_NAME); faceDetector = new CascadeClassifier(); faceDetector.load(OpencvFilePath.FACE_DETECTOR); } @Test void contextLoads() { String imageName = "test.jpg"; Mat image = reImage(imageName,null); //Saving the output image String outputName = imageName; boolean isOk = Imgcodecs.imwrite(OpencvFilePath.OUTPUT_PATH + outputName, image); System.out.println(isOk ? "人脸识别成功！！" : "人脸识别失败！！"); } public static Mat reImage(@Null String imageName, @Null Mat image){ //Input image image = image==null?Imgcodecs.imread(OpencvFilePath.INPUT_PATH + imageName):image; //Detecting faces MatOfRect faceDetections = new MatOfRect(); faceDetector.detectMultiScale(image, faceDetections); System.out.println("识别人脸个数： " + faceDetections.toArray().length); // 识别图片中的所以人脸并分别保存 int i = 1; //Creating a rectangular box showing faces detected for (Rect rect : faceDetections.toArray()) { Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0)); // 进行图片裁剪 imageCut(image, "E:\\Temp\\" + imageName.substring(0, imageName.lastIndexOf(".")) + "_" + i + ".png", rect.x, rect.y, rect.width, rect.height); i++; } return image; } @Test public void compareTest() { double comparison = faceRecognitionComparison("E:\\Temp\\test_1.jpg", "E:\\Temp\\test2_1.jpg"); System.out.println("对比结果：" + comparison); if (comparison > 0.85) { System.out.println("人脸匹配成功"); } else { System.out.println("人脸不匹配识别"); } System.exit(0); } public static boolean compareTo(String img1, String img2) { double comparison = faceRecognitionComparison(img1, img2); System.out.println("对比结果：" + comparison); if (comparison > 0.85) { System.out.println("人脸匹配成功"); return true; } else { System.out.println("人脸不匹配识别"); return false; } } /** * 裁剪人脸 * * @param image * @param outPath 写出文件路径 * @param x 坐标X * @param y 坐标Y * @param width 截图宽度 * @param height 截图长度 */ public static void imageCut(Mat image, String outPath, int x, int y, int width, int height) { // 截取的区域 Rect rect = new Rect(x, y, width, height); // Mat sub = new Mat(image,rect); Mat sub = image.submat(rect); Mat mat = new Mat(); Size size = new Size(width, height); // 人脸进行截图并保存 Imgproc.resize(sub, mat, size); Imgcodecs.imwrite(outPath, mat); } /** * 人脸识别比对 */ public static double faceRecognitionComparison(String image1, String image2) { Mat mat1 = conv_Mat(image1); Mat mat2 = conv_Mat(image2); Mat mat3 = new Mat(); Mat mat4 = new Mat(); // 颜色范围 MatOfFloat ranges = new MatOfFloat(0f, 256f); // 直方图大小， 越大匹配越精确 (越慢) MatOfInt histSize = new MatOfInt(100000); assert mat1 != null; Imgproc.calcHist(List.of(mat1), new MatOfInt(0), new Mat(), mat3, histSize, ranges); assert mat2 != null; Imgproc.calcHist(List.of(mat2), new MatOfInt(0), new Mat(), mat4, histSize, ranges); // 比较两个密集或两个稀疏直方图 return Imgproc.compareHist(mat3, mat4, Imgproc.CV_COMP_CORREL); } /** * 灰度化人脸 */ public static Mat conv_Mat(String img) { // 读取图像 Mat mat1 = Imgcodecs.imread(img); Mat mat2 = new Mat(); // 灰度化:将图像从一种颜色空间转换为另一种颜色空间 Imgproc.cvtColor(mat1, mat2, Imgproc.COLOR_BGR2GRAY); // 探测人脸:检测到的对象作为矩形列表返回 MatOfRect faceDetections = new MatOfRect(); faceDetector.detectMultiScale(mat1, faceDetections); // rect中人脸图片的范围 for (Rect rect : faceDetections.toArray()) { return new Mat(mat1, rect); } return null; } @Test public void cameraRecognition() throws Exception { cameraFaceRecognition(); // 终止当前运行的 Java 虚拟机。 System.exit(0); } public static boolean cameraLogin(String img1,String img2,BiFunction<String,String,Boolean> biFunction) { return biFunction.apply(img1, img2); } /** * 摄像头实时人脸识别 * * @throws Exception */ public static void cameraFaceRecognition() throws Exception { // 打开摄像头获取视频流，0 打开默认摄像头 VideoCapture videoCapture = new VideoCapture(0); // 检查是否支持摄像头 true：代表摄像头可以打开 false：不可以打开 System.out.println(videoCapture.isOpened()); // 获取摄像头高度 int height = (int) videoCapture.get(Videoio.CAP_PROP_FRAME_HEIGHT); // 获取摄像头宽度 int width = (int) videoCapture.get(Videoio.CAP_PROP_FRAME_WIDTH); if (height == 0 || width == 0) { throw new Exception("摄像头不存在"); } Mat video = new Mat(); int index = 0; long startTime = System.currentTimeMillis(); if (videoCapture.isOpened()) { while (true) { videoCapture.read(video); HighGui.imshow("实时人脸识别", getFace(video)); // 键盘输入 index = HighGui.waitKey(50); // 是Esc则退出，比强制退出好 if (index == 27){ return; } if (System.currentTimeMillis() - startTime > 5000) { startTime = System.currentTimeMillis(); Mat image = reImage(startTime + ".png", video); // 写入人脸 Imgcodecs.imwrite("E:\\Temp\\" + startTime + ".png", image); // videoCapture.release(); // return; boolean isOk = cameraLogin("E:\\Temp\\" + startTime + "_1"+ ".png", "E:\\Temp\\1698741419596_1.png", OpencvTestApplicationTests::compareTo); if (isOk) { return; } } } } } /** * 从视频帧中识别人脸 * * @param image 待处理Mat图片，即视频中的某一帧 * @return 处理后的图片 */ public s