android - Android上的人脸识别

我正在尝试在Android上开发人脸识别应用程序，并且由于我不想在该项目上使用NDK(根本没有时间进行切换)，因此我坚持使用Java开发整个应用程序，因此我遇到一些问题:

似乎Contrib模块未包含在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模块的“FaceRecognizer”类。有两个可用的类，分别称为“FaceRecognizer”和“FaceRecognizerPtr”。有人知道这两者之间有什么区别吗？

上面提到的类具有一种称为“Train”的方法，该方法(在C++中)接收两个类型为“Mat＆Integer”(model->train(images,labels) & train(Vector<mat> theImages, Vector<int> theLabels)的Vector。我尝试在Java中将它们传递给ArrayList<mat> & ArrayList<integer>和Vector，但似乎该方法明确接受了“CvArr”数据类型，我不确定该如何获取...这是错误消息:

有人知道如何将我的ArrayList更改为CvArr吗？

这是我的第一篇文章，我不确定是要在一个帖子中还是在三个帖子中问所有三个问题，因此给您带来的不便深表歉意...如果您需要有关该项目的任何其他信息，请随时提问。

最佳答案

更新资料

以下文章是由Petter Christian Bjelland撰写的，因此，所有功劳都是他的。我将其发布在这里，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(来自http://pcbje.com)

我找不到任何有关如何使用OpenCV和Java进行面部识别的教程，因此我决定在这里分享一个可行的解决方案。由于每次运行都会建立训练模型，因此该解决方案的当前形式效率很低，但是它说明了使该模型起作用的必要条件。

下面的类有两个参数:包含训练脸的目录的路径和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，并且面部必须已经从原始图像中裁剪出来(如果您尚未进行面部检测，请在此处查看)。

为了简化本文，该类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

... and so on.

The code:

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(interface)。如果您使用的是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>

原始帖子

引用我对http://answers.opencv.org/question/865/the-contrib-module-problem的答复。

无需使用javacv，让我们看看仅看接口(interface)就能走多远!该项目位于googlecode上，可以轻松浏览以下代码:http://code.google.com/p/javacv。

首先看一下如何包装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，所以您需要为图像传递MatVector!您可以以CvArr(一行或一列)传递标签。 MatVector在opencv_core, line 4629 (at time of writing this)中定义，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);
}

同样，仅通过查看代码，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);
}

您可能想编写一个方法，该方法可以完成从Java ArrayList到MatVector的转换(如果javacv中尚不存在这样的函数)。

现在是您的第二个问题。 FaceRecognizer与cv::FaceRecognizer等效。本机OpenCV C++类返回cv::Ptr<cv::FaceRecognizer>，它是cv::FaceRecognizer的(智能)指针。这也必须包装。在这里看到图案吗？
FaceRecognizerPtr的界面现在如下所示:

@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，您可以从此类中获取FaceRecognizer或将FaceRecognizer放入其中。您只需要关心get()，因为Pointer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*/);

因此，一旦获得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);

这将为您学习一个Eigenfaces模型。就是这样!