OPENCV+JAVA实现人脸识别

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 ，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 64\\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：

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\\ 64\\\\\"; 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\// 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\ }

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\ }

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\ } 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 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\ Highgui.imwrite(filename, image);

// watermark(a,d,\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\opencv\\\\zzlTm2.jpg\//

// // 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\//

// 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。