我正在尝试在Android上开发一个Face Recognition应用程序,因为我不想在项目上使用NDK(只是没有时间切换),我坚持使用Java开发整个应用程序,因此我遇到了一些问题：

>似乎Contrib模块不包含在OpenCV 2.4.2中.无论如何在项目中使用它？
>我尝试使用JavaCV来使用Contrib Module的“FaceRecognizer”类.有两个类可用,称为“FaceRecognizer”& “FaceRecognizerPtr”.有谁知道这两者之间的区别是什么？
>上面提到的类有一个叫做“Train”的方法,它在(C语言中)接收两个类型为“Mat& Integer”的矢量(模型 – >列车(图像,标签)& train(Vector< mat> theImages,Vector< ; int> theLabels).我尝试在Java中传递它们ArrayList< mat>& ArrayList< integer>和Vectors但似乎该方法显式接受了“CvArr”数据类型,我不知道如何获取…这是错误：

The method train(opencv_core.CvArr, opencv_core.CvArr) in the type
opencv_contrib.FaceRecognizer is not applicable for the arguments
(ArrayList, ArrayList)

有谁知道如何将我的ArrayList更改为CvArr？

这是我的第一篇文章,我不确定是在一个帖子还是在三个帖子中提出所有三个问题,对于给您带来的任何不便表示遗憾…如果您需要有关该项目的任何其他信息,请随时提出.

解决方法:

更新

以下文章是由Petter Christian Bjelland撰写的,所以所有的功劳都是他的.我在这里发帖,因为他的博客目前似乎处于维护模式,但我认为值得分享.

使用JavaCV进行人脸识别(自http://pcbje.com起)

我找不到任何关于如何使用OpenCV和Java进行人脸识别的教程,所以我决定在这里分享一个可行的解决方案.由于培训模型是在每次运行时构建的,因此解决方案的当前形式效率非常低,但它显示了使其工作所需的内容.

下面的类有两个参数：包含训练面的目录的路径以及要分类的图像的路径.并非所有图像都必须具有相同的尺寸,并且必须从原始图像中裁剪出面部(如果尚未进行面部检测,请查看此处).

为了简化这篇文章,该课程还要求训练图像具有文件名格式：< label> -rest_of_filename.png.例如：

1-jon_doe_1.png
1-jon_doe_2.png
2-jane_doe_1.png
2-jane_doe_2.png

… 等等.

代码：

import com.googlecode.javacv.cpp.opencv_core;
import static com.googlecode.javacv.cpp.opencv_highgui.*;
import static com.googlecode.javacv.cpp.opencv_core.*;
import static com.googlecode.javacv.cpp.opencv_imgproc.*;
import static com.googlecode.javacv.cpp.opencv_contrib.*;
import java.io.File;
import java.io.FilenameFilter;

public class OpenCVFaceRecognizer {
  public static void main(String[] args) {
    String trainingDir = args[0];
    IplImage testImage = cvLoadImage(args[1]);

    File root = new File(trainingDir);

    FilenameFilter pngFilter = new FilenameFilter() {
      public boolean accept(File dir, String name) {
        return name.toLowerCase().endsWith(".png");
      }
    };

    File[] imageFiles = root.listFiles(pngFilter);

    MatVector images = new MatVector(imageFiles.length);

    int[] labels = new int[imageFiles.length];

    int counter = 0;
    int label;

    IplImage img;
    IplImage grayImg;

    for (File image : imageFiles) {
      // Get image and label:
      img = cvLoadImage(image.getAbsolutePath());
      label = Integer.parseInt(image.getName().split("\\-")[0]);
      // Convert image to grayscale:
      grayImg = IplImage.create(img.width(), img.height(), IPL_DEPTH_8U, 1);
      cvCvtColor(img, grayImg, CV_BGR2GRAY);
      // Append it in the image list:
      images.put(counter, grayImg);
      // And in the labels list:
      labels[counter] = label;
      // Increase counter for next image:
      counter++;
    }

    FaceRecognizer faceRecognizer = createFisherFaceRecognizer();
    // FaceRecognizer faceRecognizer = createEigenFaceRecognizer();
    // FaceRecognizer faceRecognizer = createLBPHFaceRecognizer()

    faceRecognizer.train(images, labels);

    // Load the test image:
    IplImage greyTestImage = IplImage.create(testImage.width(), testImage.height(), IPL_DEPTH_8U, 1);
    cvCvtColor(testImage, greyTestImage, CV_BGR2GRAY);

    // And get a prediction:
    int predictedLabel = faceRecognizer.predict(greyTestImage);
    System.out.println("Predicted label: " + predictedLabel);
  }
}

该类需要OpenCV Java接口.如果您正在使用Maven,则可以使用以下pom.xml检索所需的库：

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
     xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.pcbje</groupId>
  <artifactId>opencvfacerecognizer</artifactId>
  <version>0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>opencvfacerecognizer</name>
  <url>http://pcbje.com</url>

  <dependencies>
    <dependency>
      <groupId>com.googlecode.javacv</groupId>
      <artifactId>javacv</artifactId>
      <version>0.3</version>
    </dependency>

    <!-- For Linux x64 environments -->
    <dependency>
      <groupId>com.googlecode.javacv</groupId>
      <artifactId>javacv</artifactId>
      <classifier>linux-x86_64</classifier>
      <version>0.3</version>
    </dependency>    

    <!-- For OSX environments -->
    <dependency>
      <groupId>com.googlecode.javacv</groupId>
      <artifactId>javacv</artifactId>
      <classifier>macosx-x86_64</classifier>
      <version>0.3</version>
    </dependency>
  </dependencies>

  <repositories>
    <repository>
      <id>javacv</id>
      <name>JavaCV</name>
      <url>http://maven2.javacv.googlecode.com/git/</url>
    </repository>
  </repositories>
</project>

原帖

引自我在http://answers.opencv.org/question/865/the-contrib-module-problem的回复.

在没有使用过javacv的情况下,让我们看看我们可以通过查看接口获得多远！该项目位于googlecode上,可以轻松浏览代码：http://code.google.com/p/javacv.

首先看看如何包装cv :: FaceRecognizer(opencv_contrib.java, line 845 at time of writing this)：

@Namespace("cv") public static class FaceRecognizer extends Algorithm {
    static { Loader.load(); }
    public FaceRecognizer() { }
    public FaceRecognizer(Pointer p) { super(p); }

    public /*abstract*/ native void train(@ByRef MatVector src, @Adapter("ArrayAdapter") CvArr labels);
    public /*abstract*/ native int predict(@Adapter("ArrayAdapter") CvArr src);
    public /*abstract*/ native void predict(@Adapter("ArrayAdapter") CvArr src, @ByRef int[] label, @ByRef double[] dist);
    public native void save(String filename);
    public native void load(String filename);
    public native void save(@Adapter("FileStorageAdapter") CvFileStorage fs);
    public native void load(@Adapter("FileStorageAdapter") CvFileStorage fs);
}

啊哈,所以你需要为图像传递MatVector！您可以在CvArr(一行或一列)中传递标签. MatVector在opencv_core, line 4629 (at time of writing this)中定义,它看起来像这样：

public static class MatVector extends Pointer {
    static { load(); }
    public MatVector()       { allocate();  }
    public MatVector(long n) { allocate(n); }
    public MatVector(Pointer p) { super(p); }
    private native void allocate();
    private native void allocate(@Cast("size_t") long n);

    public native long size();
    public native void resize(@Cast("size_t") long n);

    @Index @ValueGetter public native @Adapter("MatAdapter") CvMat getCvMat(@Cast("size_t") long i);
    @Index @ValueGetter public native @Adapter("MatAdapter") CvMatND getCvMatND(@Cast("size_t") long i);
    @Index @ValueGetter public native @Adapter("MatAdapter") IplImage getIplImage(@Cast("size_t") long i);
    @Index @ValueSetter public native MatVector put(@Cast("size_t") long i, @Adapter("MatAdapter") CvArr value);
}

再看看代码,我想它可以像这样使用：

int numberOfImages = 10;
// Allocate some memory:
MatVector images = new MatVector(numberOfImages);
// Then fill the MatVector, you probably want to do something useful instead:
for(int idx = 0; idx < numberOfImages; idx++){
   // Load an image:
   CvArr image = cvLoadImage("/path/to/your/image");
   // And put it into the MatVector:
   images.put(idx, image);
}

您可能想要自己编写一个方法来执行从Java ArrayList到MatVector的转换(如果javacv中还没有这样的函数).

现在回答你的第二个问题. FaceRecognizer等同于cv :: FaceRecognizer.本机OpenCV C类返回一个cv :: Ptr< cv :: FaceRecognizer&gt ;,这是一个指向cv :: FaceRecognizer的(智能)指针.这也必须包装好.看到这里的模式？ FaceRecognizerPtr的界面现在看起来像这样：

@Name("cv::Ptr<cv::FaceRecognizer>")
public static class FaceRecognizerPtr extends Pointer {
    static { load(); }
    public FaceRecognizerPtr()       { allocate();  }
    public FaceRecognizerPtr(Pointer p) { super(p); }
    private native void allocate();

    public native FaceRecognizer get();
    public native FaceRecognizerPtr put(FaceRecognizer value);
}

因此,您可以从此类获取FaceRecognizer或将FaceRecognizer放入其中.您应该只关注get(),因为指针由创建具体FaceRecognizer算法的方法填充：

@Namespace("cv") public static native @ByVal FaceRecognizerPtr createEigenFaceRecognizer(int num_components/*=0*/, double threshold/*=DBL_MAX*/);
@Namespace("cv") public static native @ByVal FaceRecognizerPtr createFisherFaceRecognizer(int num_components/*=0*/, double threshold/*=DBL_MAX*/);
@Namespace("cv") public static native @ByVal FaceRecognizerPtr createLBPHFaceRecognizer(int radius/*=1*/,
        int neighbors/*=8*/, int grid_x/*=8*/, int grid_y/*=8*/, double threshold/*=DBL_MAX*/);

所以,一旦你有FaceRecognizerPtr,你可以做以下事情：

// Holds your training data and labels:
MatVector images;
CvArr labels;
// Do something with the images and labels... Probably fill them?
// ...
// Then get a Pointer to a FaceRecognizer (FaceRecognizerPtr).
// Java doesn't have default parameters, so you have to add some yourself,
// if you pass 0 as num_components to the EigenFaceRecognizer, the number of
// components is determined by the data, for the threshold use the maximum possible
// value if you don't want one. I don't know the constant in Java:
FaceRecognizerPtr model = createEigenFaceRecognizer(0, 10000);
// Then train it. See how I call get(), to get the FaceRecognizer inside the FaceRecognizerPtr:
model.get().train(images, labels);

这会让你学会一个特征脸模型.就是这样！