OPENCV+JAVA实现人脸识别

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 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 http://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;

/**

UWeJifAhi * 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 http://= 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 = UWeJifAhinew 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", 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

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