用opencv做人脸识别(opencv人脸识别c++)

案例 2019-12-07 23:07:40

用opencv做人脸识别 如何识别陌生人?

如果某个识别对象和训练库中的某一个很匹配,那么就会有很高的相似度。


如果识别对象不在训练库中,那么就算是返回了结果,相似度也不会很高,只不过是数值上的最优解。


所以,设置一个相似度的阀值(THREADHOLD), 最匹配误差(leastDistSq)大于这个阀值就可以判断为不在训练库中!


...

  if ( leastDistSq > THREADHOLD ) {
      return -1;
  }
  
  return iNearest;  
}

用OpenCV开发人脸识别软件,用Java好还是用C/C++好
一般地说,用C/C++比较“主流”些,因为C/C++编译后直接生成可执行文件,不需要虚拟机,程序性能比较好。
另一方面,无论用C/C++还是JAVA,使用 OpenCV进行开发的难度和工作量,没有太大的差异。
如果你程序追求性能,建议用C或C++

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怎样使用OpenCV进行人脸识别
1.环境搭建:
  整个项目的结构图:

  2.编写DetectFaceDemo.java,代码如下:
  package com.njupt.zhb.test;
  import org.opencv.core.Core;
  import org.opencv.core.Mat;
  import org.opencv.core.MatOfRect;
  import org.opencv.core.Point;
  import org.opencv.core.Rect;
  import org.opencv.core.Scalar;
  import org.opencv.highgui.Highgui;
  import org.opencv.objdetect.CascadeClassifier;

  //
  // Detects faces in an image, draws boxes around them, and writes the results
  // to "faceDetection.png".
  //
  public class DetectFaceDemo {
  public void run() {
  System.out.println(" Running DetectFaceDemo");
  System.out.println(getClass().getResource("lbpcascade_frontalface.xml").getPath());
  // Create a face detector from the cascade file in the resources
  // directory.
  //CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("lbpcascade_frontalface.xml").getPath());
  //Mat image = Highgui.imread(getClass().getResource("lena.png").getPath());
  //注意:源程序的路径会多打印一个‘/’,因此总是出现如下错误
  /*
  * Detected 0 faces Writing faceDetection.png libpng warning: Image
  * width is zero in IHDR libpng warning: Image height is zero in IHDR
  * libpng error: Invalid IHDR data
  */
  //因此,我们将第一个字符去掉
  String xmlfilePath=getClass().getResource("lbpcascade_frontalface.xml").getPath().substring(1);
  CascadeClassifier faceDetector = new CascadeClassifier(xmlfilePath);
  Mat image = Highgui.imread(getClass().getResource("we.jpg").getPath().substring(1));
  // Detect faces in the image.
  // MatOfRect is a special container class for Rect.
  MatOfRect faceDetections = new MatOfRect();
  faceDetector.detectMultiScale(image, faceDetections);

  System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));

  // Draw a bounding box around each face.
  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));
  }

  // Save the visualized detection.
  String filename = "faceDetection.png";
  System.out.println(String.format("Writing %s", filename));
  Highgui.imwrite(filename, image);
  }
  }

  3.编写测试类:
  package com.njupt.zhb.test;
  public class TestMain {
  public static void main(String[] args) {
  System.out.println("Hello, OpenCV");
  // Load the native library.
  System.loadLibrary("opencv_java246");
  new DetectFaceDemo().run();
  }
  }
  //运行结果:
  //Hello, OpenCV
  //
  //Running DetectFaceDemo
  ///E:/eclipse_Jee/workspace/JavaOpenCV246/bin/com/njupt/zhb/test/lbpcascade_frontalface.xml
  //Detected 8 faces
  //Writing faceDetection.png