本文实例为大家分享了JAVA实现人脸识别的具体代码，供大家参考，具体内容如下
官方下载 安装文件 ，以win7为例，下载opencv-2.4.13.3-vc14.exe
安装后，在build目录下 D:\opencv\build\java，获取opencv-2413.jar，copy至项目目录
同时需要dll文件 与 各 识别xml文件，进行不同特征的识别（人脸，侧脸，眼睛等）
dll目录：D:\opencv\build\java\x64\opencv_java2413.dll
xml目录：D:\opencv\sources\data\haarcascades\haarcascade_frontalface_alt.xml（目录中有各类识别文件）
项目结构：
具体代码：由于需要用到 opencv 的dll文件，故要么放在java library path 中，或放在jre lib 中，windows下可放在System32目录下，也可以在代码中动态加载，如下：
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package opencv; import com.sun.scenario.effect.ImageData; import org.opencv.core.*; import org.opencv.core.Point; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; import org.opencv.objdetect.CascadeClassifier; import javax.imageio.ImageIO; import javax.swing.*; import java.awt.*; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import java.util.Arrays; import java.util.Vector; /** * Created by Administrator on 2017/8/17. */public class Test { static{ // 导入opencv的库 String opencvpath = System.getProperty("user.dir") + "\\opencv\\x64\\"; String libPath = System.getProperty("java.library.path"); String a = opencvpath + Core.NATIVE_LIBRARY_NAME + ".dll"; System.load(opencvpath + Core.NATIVE_LIBRARY_NAME + ".dll"); } public static String getCutPath(String filePath){ String[] splitPath = filePath.split("\\."); return splitPath[0]+"Cut"+"."+splitPath[1]; } public static void process(String original,String target) throws Exception { String originalCut = getCutPath(original); String targetCut = getCutPath(target); if(detectFace(original,originalCut) && detectFace(target,targetCut)){ } } public static boolean detectFace(String imagePath,String outFile) throws Exception { System.out.println("\nRunning DetectFaceDemo"); // 从配置文件lbpcascade_frontalface.xml中创建一个人脸识别器，该文件位于opencv安装目录中 CascadeClassifier faceDetector = new CascadeClassifier( "C:\\Users\\Administrator\\Desktop\\opencv\\haarcascade_frontalface_alt.xml"); Mat image = Highgui.imread(imagePath); // 在图片中检测人脸 MatOfRect faceDetections = new MatOfRect(); faceDetector.detectMultiScale(image, faceDetections); System.out.println(String.format("Detected %s faces", faceDetections.toArray().length)); Rect[] rects = faceDetections.toArray(); if(rects != null && rects.length > 1){ throw new RuntimeException("超过一个脸"); } // 在每一个识别出来的人脸周围画出一个方框 Rect rect = rects[0]; Core.rectangle(image, new Point(rect.x-2, rect.y-2), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0)); Mat sub = image.submat(rect); Mat mat = new Mat(); Size size = new Size(300, 300); Imgproc.resize(sub, mat, size);//将人脸进行截图并保存 return Highgui.imwrite(outFile, mat); // 将结果保存到文件 // String filename = "C:\\Users\\Administrator\\Desktop\\opencv\\faceDetection.png"; // System.out.println(String.format("Writing %s", filename)); // Highgui.imwrite(filename, image); } public static void setAlpha(String imagePath,String outFile) { /** * 增加测试项 * 读取图片，绘制成半透明 */ try { ImageIcon imageIcon = new ImageIcon(imagePath); BufferedImage bufferedImage = new BufferedImage(imageIcon.getIconWidth(),imageIcon.getIconHeight() , BufferedImage.TYPE_4BYTE_ABGR); Graphics2D g2D = (Graphics2D) bufferedImage.getGraphics(); g2D.drawImage(imageIcon.getImage(), 0, 0, imageIcon.getImageObserver()); //循环每一个像素点，改变像素点的Alpha值 int alpha = 100; for (int j1 = bufferedImage.getMinY(); j1 < bufferedImage.getHeight(); j1++) { for (int j2 = bufferedImage.getMinX(); j2 < bufferedImage.getWidth(); j2++) { int rgb = bufferedImage.getRGB(j2, j1); rgb = ( (alpha + 1) << 24) | (rgb & 0x00ffffff); bufferedImage.setRGB(j2, j1, rgb); } } g2D.drawImage(bufferedImage, 0, 0, imageIcon.getImageObserver()); //生成图片为PNG ImageIO.write(bufferedImage, "png", new File(outFile)); } catch (Exception e) { e.printStackTrace(); } } private static void watermark(String a,String b,String outFile, float alpha) throws IOException { // 获取底图 BufferedImage buffImg = ImageIO.read(new File(a)); // 获取层图 BufferedImage waterImg = ImageIO.read(new File(b)); // 创建Graphics2D对象，用在底图对象上绘图 Graphics2D g2d = buffImg.createGraphics(); int waterImgWidth = waterImg.getWidth();// 获取层图的宽度 int waterImgHeight = waterImg.getHeight();// 获取层图的高度 // 在图形和图像中实现混合和透明效果 g2d.setComposite(AlphaComposite.getInstance(AlphaComposite.SRC_ATOP, alpha)); // 绘制 g2d.drawImage(waterImg, 0, 0, waterImgWidth, waterImgHeight, null); g2d.dispose();// 释放图形上下文使用的系统资源 //生成图片为PNG ImageIO.write(buffImg, "png", new File(outFile)); } public static boolean mergeSimple(BufferedImage image1, BufferedImage image2, int posw, int posh, File fileOutput) { //合并两个图像 int w1 = image1.getWidth(); int h1 = image1.getHeight(); int w2 = image2.getWidth(); int h2 = image2.getHeight(); BufferedImage imageSaved = new BufferedImage(w1, h1, BufferedImage.TYPE_INT_ARGB); Graphics2D g2d = imageSaved.createGraphics(); // 增加下面代码使得背景透明 g2d.drawImage(image1, null, 0, 0); image1 = g2d.getDeviceConfiguration().createCompatibleImage(w1, w2, Transparency.TRANSLUCENT); g2d.dispose(); g2d = image1.createGraphics(); // 背景透明代码结束 // for (int i = 0; i < w2; i++) { // for (int j = 0; j < h2; j++) { // int rgb1 = image1.getRGB(i + posw, j + posh); // int rgb2 = image2.getRGB(i, j); // // if (rgb1 != rgb2) { // //rgb2 = rgb1 & rgb2; // } // imageSaved.setRGB(i + posw, j + posh, rgb2); // } // } boolean b = false; try { b = ImageIO.write(imageSaved, "png", fileOutput); } catch (IOException ie) { ie.printStackTrace(); } return b; } public static void main(String[] args) throws Exception { String a,b,c,d; a = "C:\\Users\\Administrator\\Desktop\\opencv\\zzl.jpg"; d = "C:\\Users\\Administrator\\Desktop\\opencv\\cgx.jpg"; //process(a,d); a = "C:\\Users\\Administrator\\Desktop\\opencv\\zzlCut.jpg"; d = "C:\\Users\\Administrator\\Desktop\\opencv\\cgxCut.jpg"; CascadeClassifier faceDetector = new CascadeClassifier( "C:\\Users\\Administrator\\Desktop\\opencv\\haarcascade_frontalface_alt.xml"); CascadeClassifier eyeDetector1 = new CascadeClassifier( "C:\\Users\\Administrator\\Desktop\\opencv\\haarcascade_eye.xml"); CascadeClassifier eyeDetector2 = new CascadeClassifier( "C:\\Users\\Administrator\\Desktop\\opencv\\haarcascade_eye_tree_eyeglasses.xml"); Mat image = Highgui.imread("C:\\Users\\Administrator\\Desktop\\opencv\\gakki.jpg"); // 在图片中检测人脸 MatOfRect faceDetections = new MatOfRect(); //eyeDetector2.detectMultiScale(image, faceDetections); Vector<Rect> objects; eyeDetector1.detectMultiScale(image, faceDetections, 2.0,1,1,new Size(20,20),new Size(20,20)); Rect[] rects = faceDetections.toArray(); Rect eyea,eyeb; eyea = rects[0];eyeb = rects[1]; System.out.println("a-中心坐标 " + eyea.x + " and " + eyea.y); System.out.println("b-中心坐标 " + eyeb.x + " and " + eyeb.y); //获取两个人眼的角度 double dy=(eyeb.y-eyea.y); double dx=(eyeb.x-eyea.x); double len=Math.sqrt(dx*dx+dy*dy); System.out.println("dx is "+dx); System.out.println("dy is "+dy); System.out.println("len is "+len); double angle=Math.atan2(Math.abs(dy),Math.abs(dx))*180.0/Math.PI; System.out.println("angle is "+angle); for(Rect rect:faceDetections.toArray()) { Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0)); } String filename = "C:\\Users\\Administrator\\Desktop\\opencv\\ouput.png"; System.out.println(String.format("Writing %s", filename)); Highgui.imwrite(filename, image); // watermark(a,d,"C:\\Users\\Administrator\\Desktop\\opencv\\zzlTm2.jpg",0.7f); // // // 读取图像，不改变图像的原始信息 // Mat image1 = Highgui.imread(a); // Mat image2 = Highgui.imread(d); // Mat mat1 = new Mat();Mat mat2 = new Mat(); // Size size = new Size(300, 300); // Imgproc.resize(image1, mat1, size); // Imgproc.resize(image2, mat2, size); // Mat mat3 = new Mat(size,CvType.CV_64F); // //Core.addWeighted(mat1, 0.5, mat2, 1, 0, mat3); // // //Highgui.imwrite("C:\\Users\\Administrator\\Desktop\\opencv\\add.jpg", mat3); // // mergeSimple(ImageIO.read(new File(a)), // ImageIO.read(new File(d)),0,0, // new File("C:\\Users\\Administrator\\Desktop\\opencv\\add.jpg")); } } |
最终效果：人脸旁有绿色边框，可以将绿色边框图片截取，生成人脸图
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/z69183787/article/details/77334829
