基于Android的人脸识别

Question

我试图在Android上开发一个人脸识别应用程序，因为我不想在项目上使用NDK（根本没有时间切换），我坚持用Java开发整个应用程序，因此我遇到了一些问题：

>

似乎Contrib模块没有包含在OpenCV 2.4.2中。有没有在项目中使用它？

我尝试使用JavaCV来使用Contrib模块的“FaceRecognizer”类。有两个可用的类，称为“FaceRecognizer”和“FaceRecognizerPTR”。有人知道这两者有什么区别吗？

上面提到的类有一个名为“train”的方法，它（在C++中）接收两个向量，类型为“mat和integer”(model->train(images，labels)&train(vector theImages，vector theLabels )。我试图在Java中传递它们arraylist &arraylist 和向量，但似乎该方法显式地接受“cvarr”数据类型，我不知道如何获取...错误如下：

类型opencv_contrib.FaceRecognizer中的方法train(opencv_core.cvarr，opencv_core.cvarr)不适用于参数(ArrayList,ArrayList）

有人知道如何将ArrayList更改为CvArr吗？！

这是我的第一个帖子，我不确定是在一个帖子中还是在三个帖子中问所有的三个问题，所以很抱歉给您带来不便...如果您需要任何关于该项目的其他信息，请随时询问。

Answer 1

下面的文章是由Petter Christian Bjelland写的，所以所有的功劳都是他的。我把它贴在这里，因为他的博客此刻似乎处于维护模式，但我认为值得分享。

我找不到任何关于如何使用OpenCV和Java执行人脸识别的教程，所以我决定在这里分享一个可行的解决方案。由于训练模型是在每次运行时构建的，因此该解决方案以其当前形式非常低效，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和要分类的图像的路径。并不是说所有的图像都必须是相同的大小，而且人脸已经从他们的原始图像中剪掉了（如果你还没有做人脸检测，请看这里）。

为了简单起见，该类还要求训练图像具有文件名格式:-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

...诸如此类。

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。如果使用Maven，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>

引用我在http://answers.opencv.org/question/865/the-contrib-module-problem上的回复。

@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);
}

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);
}

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);
}

@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，您可以从该类获取FaceRecognizer，或者将FaceRecognizer放入。您应该只关心get()，因为指针是由创建具体的FaceRecognizer算法的方法填充的：

@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*/);

因此，一旦您获得了FaceRecognizerPtr，您就可以执行以下操作：

// 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);

这会让你学到一个特征脸模型。就这样！