OpenCV人脸识别、图片相似度检测

1. 加载两张图片为bitmap进入内存
2. 将内存中的两张图片bitmap转换为Mat矩阵(Mat类是OpenCV最基本的一个数据类型，它可以表示一个多维的多通道的数组。Mat常用来存储图像，包括单通道二维数组——灰度图，多通道二维数组——彩色图)
3. 把Mat矩阵的type转换为Cv_8uc1(1通道8位矩阵)类型，然后转换为Cv_32F， 因为在c++代码中会判断他的类型。
4. 通过OpenCv 来进行俩个矩阵的比较（俩个矩阵必须一样大小的高宽）

1. 需要一个人脸的Haar特征分类器就是一个XML文件，该文件中会描述人脸的Haar特征值，CascadeClassifier人脸探测器将该特征值集合加载入内存
2. 加载图片为bitmap进入内存，将bitmap转换为Mat矩阵。
3. 有了Mat矩阵，然后通过调用OpenCV的Native方法，人脸探测器CascadeClassifier在该Mat矩阵中检测当前是否有人脸。
4. 如果有，我们会获取到一个Rect数组，里面会有人脸数据，然后将人脸画在屏幕上，方框或者圆形

1. 识别出人脸后会得到两个人脸的Rect数组，然后比较这两个Rect数组的相似度即可！

1. 新建Android Studio项目 OpenCVCheck

2. 导入OpenCVLibrary320

3. 在module下的build.gradle中引入OpenCVLibrary的编译：

    compile project(':openCVLibrary320') 

1. 初始化OpenCV：

   static{if (OpenCVLoader.initDebug()){Log.e(TAG, "OpenCV load success !")} else{Log.e(TAG, "OpenCV load failed !")} } 

2. 加载两张图片进入内存

   Bitmap mBitmap1 = BitmapFactory.decodeResource(getResources(), R.mipmap.pic1); Bitmap mBitmap2 = BitmapFactory.decodeResource(getResources(), R.mipmap.pic2); 

3. 将内存中的两张图片bitmap转换为Mat矩阵

   Mat mat1 = new Mat(); Mat mat2 = new Mat(); Mat mat11 = new Mat(); Mat mat22 = new Mat(); Utils.bitmapToMat(mBitmap1, mat1); Utils.bitmapToMat(mBitmap2, mat2); Imgproc.cvtColor(mat1, mat11, Imgproc.COLOR_BGR2GRAY); Imgproc.cvtColor(mat2, mat22, Imgproc.COLOR_BGR2GRAY); 

4. 把Mat矩阵的type转换为Cv_8uc1(1通道8位矩阵)类型，然后转换为Cv_32F,通过OpenCV来进行俩个矩阵的比较

   /** * 比较来个矩阵的相似度 * * @param srcMat * @param desMat */ public void comPareHist(Mat srcMat, Mat desMat){srcMat.convertTo(srcMat, CvType.CV_32F); desMat.convertTo(desMat, CvType.CV_32F); double target = Imgproc.compareHist(srcMat, desMat, Imgproc.CV_COMP_CORREL); textView.setText("相似度:" + target)} 

1. 初始化OpenCV

   if (!OpenCVLoader.initDebug()){Log.d(TAG, "Internal OpenCV library not found. Using OpenCV Manager for initialization"); OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_11, this, mLoaderCallback)} else{Log.d(TAG, "OpenCV library found inside package. Using it!"); mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS)} 

2. 编译 .so 库

   通过ndk 来编译 jni文件下的.cpp 文件，生成.so库，以备程序使用

3. 加载.so 库

    // 在Opencv初始化完成后，调用Native库 System.loadLibrary("detection_based_tracker"); 

4. 加载需要的人脸的Haar特征分类器就是一个XML文件，该文件中会描述人脸的Haar特征值

   private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this){@Override public void onManagerConnected(int status){switch (status){case LoaderCallbackInterface.SUCCESS:{Log.i(TAG, "OpenCV loaded successfully"); // Load native library after(!) OpenCV initialization System.loadLibrary("detection_based_tracker"); try{// load cascade file from application resources InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface); File cascadeDir = getDir("cascade", Context.MODE_PRIVATE); mCascadeFile = new File(cascadeDir, "lbpcascade_frontalface.xml"); FileOutputStream os = new FileOutputStream(mCascadeFile); byte[] buffer = new byte[4096]; int bytesRead; while ((bytesRead = is.read(buffer)) != -1){os.write(buffer, 0, bytesRead)} is.close(); os.close(); mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath()); if (mJavaDetector.empty()){Log.e(TAG, "Failed to load cascade classifier"); mJavaDetector = null} else Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath()); mNativeDetector = new DetectionBasedTracker(mCascadeFile.getAbsolutePath(), 0); cascadeDir.delete()} catch (IOException e){e.printStackTrace(); Log.e(TAG, "Failed to load cascade. Exception thrown: " + e)} } break; default:{super.onManagerConnected(status)} break} } }; 

5. 加载图片进入内存，得到Mat矩阵，有了Mat矩阵，然后通过调用OpenCV的Native方法，人脸探测器CascadeClassifier在该Mat矩阵中检测当前是否有人脸

    Bitmap imgtemp = BitmapFactory.decodeResource(getResources(), R.mipmap.twop); Utils.bitmapToMat(imgtemp, mRgba); Mat mat1 = new Mat(); Utils.bitmapToMat(imgtemp, mat1); Imgproc.cvtColor(mat1, mGray, Imgproc.COLOR_BGR2GRAY); if (mAbsoluteFaceSize == 0){int height = mGray.rows(); if (Math.round(height * mRelativeFaceSize) > 0){mAbsoluteFaceSize = Math.round(height * mRelativeFaceSize)} mNativeDetector.setMinFaceSize(mAbsoluteFaceSize)} MatOfRect faces = new MatOfRect(); if (mDetectorType == JAVA_DETECTOR){if (mJavaDetector != null) mJavaDetector.detectMultiScale(mGray, faces, 1.1, 2, 2, // TODO: objdetect.CV_HAAR_SCALE_IMAGE new Size(mAbsoluteFaceSize, mAbsoluteFaceSize), new Size())} else if (mDetectorType == NATIVE_DETECTOR){if (mNativeDetector != null) mNativeDetector.detect(mGray, faces)} else{Log.e(TAG, "Detection method is not selected!")} 

6. 如果有，我们会获取到一个Rect数组，里面会有人脸数据，然后将人脸画在屏幕上，方框或者圆形

   Rect[] facesArray = faces.toArray(); for (int i = 0; i < facesArray.length; i++) Imgproc.rectangle(mRgba, facesArray[i].tl(), facesArray[i].br(), FACE_RECT_COLOR, 3); Utils.matToBitmap(mRgba, imgtemp, true); imageView.setImageBitmap(imgtemp); 

   识别两张图片中的人脸是否是同一个人脸步骤

   这个功能前面的步骤跟检测人脸的一样，唯一不同的就是：检测出两个人脸的Rect数组后，进行相似度比较：

   /** * 特征对比 * * @param file1 人脸特征 * @param file2 人脸特征 * @return 相似度 */ public double CmpPic(String file1, String file2){try{int l_bins = 256; int hist_size[] ={l_bins}; float v_ranges[] ={0, 255}; float ranges[][] ={v_ranges}; opencv_core.IplImage Image1 = cvLoadImage(getFilePath(file1), CV_LOAD_IMAGE_GRAYSCALE); opencv_core.IplImage Image2 = cvLoadImage(getFilePath(file2), CV_LOAD_IMAGE_GRAYSCALE); opencv_core.IplImage imageArr1[] ={Image1}; opencv_core.IplImage imageArr2[] ={Image2}; opencv_imgproc.CvHistogram Histogram1 = opencv_imgproc.CvHistogram.create(1, hist_size, CV_HIST_ARRAY, ranges, 1); opencv_imgproc.CvHistogram Histogram2 = opencv_imgproc.CvHistogram.create(1, hist_size, CV_HIST_ARRAY, ranges, 1); cvCalcHist(imageArr1, Histogram1, 0, null); cvCalcHist(imageArr2, Histogram2, 0, null); cvNormalizeHist(Histogram1, 100.0); cvNormalizeHist(Histogram2, 100.0); double c1 = cvCompareHist(Histogram1, Histogram2, CV_COMP_CORREL) * 100; double c2 = cvCompareHist(Histogram1, Histogram2, CV_COMP_INTERSECT); return (c1 + c2) / 2} catch (Exception e){e.printStackTrace(); return -1} }