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\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：

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。