Android Opencv smile（微笑）识别开发

最近研究opencv，主要的是以Android平台的为主。废话不多说直接来干货。

一： SDK下载：https://opencv.org/ 我使用的是opencv 2.4.10的

二：IDE我是使用eclipse的，也可以使用AS进行开发，需要NDK， eclipse的opencv配置请参考：https://blog.****.net/roboman/article/details/42883399 博主的。

三：我使用的是samples文件夹的 face-detection 示例，把示例加入工程，添加 opencv libs。在 Android Opencv smile（微笑）识别开发 detectionBasedTracker.java 中添加smile本地方法->

//smile
public float data(Mat imageGray, MatOfRect faces){
   float data = 0;
   data = nativateSmileData(mNativeObj, imageGray.getNativeObjAddr(), faces.getNativeObjAddr());
   return data ;
}
private static native float   nativateSmileData(long thiz, long inputImage, long faces);
四：再在jni文件夹中的 .h 文件中添加jni接口

/*
* smile
* */
JNIEXPORT jfloat JNICALL Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativateSmileData
(JNIEnv *, jclass, jlong, jlong, jlong);
五：修改.cpp文件添加->

//smile 处理
JNIEXPORT jfloat JNICALL Java_org_opencv_samples_facedetect_DetectionBasedTracker_nativateSmileData(
JNIEnv * jenv, jclass, jlong thiz, jlong imageGray, jlong facess) {

   Mat img;
   Mat gray, smallImg;
   double scale = 1;
   bool tryflip = 0;
   float smile_max = 0;
   vector<Rect> faces, faces2;
   img = *((Mat*) imageGray);
   CascadeClassifier nestedCascade, cascade;
   if (!cascade.load(
           "/data/data/org.opencv.samples.facedetect/app_cascade/haarcascade_frontalface_alt2.xml")) { //分类器存放的位置
       return 11;
   }
   if (!nestedCascade.load(
           "/data/data/org.opencv.samples.facedetect/app_cascade/haarcascade_smile.xml")) { //分类器存放的位置
       return 11;
   }
   const static Scalar colors[] = { Scalar(255, 0, 0), Scalar(255, 128, 0),
           Scalar(255, 255, 0), Scalar(0, 255, 0), Scalar(0, 128, 255), Scalar(
                   0, 255, 255), Scalar(0, 0, 255), Scalar(255, 0, 255) };
   cvtColor(img, gray, COLOR_BGR2GRAY);

   double fx = 1 / scale;
   resize(gray, smallImg, Size(), fx, fx, INTER_LINEAR);
   equalizeHist(smallImg, smallImg);

   cascade.detectMultiScale(smallImg, faces, 1.1, 2, 0
           | CASCADE_SCALE_IMAGE, Size(30, 30));
   if (tryflip)
       {
           flip(smallImg, smallImg, 1);
           cascade.detectMultiScale(smallImg, faces2,
               1.1, 2, 0
               | CASCADE_SCALE_IMAGE,
               Size(30, 30));
           for (vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r)
           {
               faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
           }
       }
   for (size_t i = 0; i < faces.size(); i++) {
       Rect r = faces[i];
       Mat smallImgROI;
       vector<Rect> nestedObjects; //微笑处理
       Point center;
       Scalar color = colors[i % 8];
       int radius;

       double aspect_ratio = (double) r.width / r.height;
       if (0.75 < aspect_ratio && aspect_ratio < 1.3) {
           center.x = cvRound((r.x + r.width * 0.5) * scale);
           center.y = cvRound((r.y + r.height * 0.5) * scale);
           radius = cvRound((r.width + r.height) * 0.25 * scale);
           circle(img, center, radius, color, 3, 8, 0);
       } else
           rectangle(img, cvPoint(cvRound(r.x * scale), cvRound(r.y * scale)),
                   cvPoint(cvRound((r.x + r.width - 1) * scale),
                           cvRound((r.y + r.height - 1) * scale)), color, 3, 8,
                   0);

       const int half_height = cvRound((float) r.height / 2);
       r.y = r.y + half_height;
       r.height = half_height - 1;
       smallImgROI = smallImg(r);

       //微笑处理&识别度
       nestedCascade.detectMultiScale(smallImgROI, nestedObjects, 1.1, 0, 0
               | CASCADE_SCALE_IMAGE, Size(30, 30));
       // The number of detected neighbors depends on image size (and also illumination, etc.). The
       // following steps use a floating minimum and maximum of neighbors. Intensity thus estimated will be
       //accurate only after a first smile has been displayed by the user.
       const int smile_neighbors = (int) nestedObjects.size();
       static int max_neighbors = -1;
       static int min_neighbors = -1;
       if (min_neighbors == -1)
           min_neighbors = smile_neighbors;
       max_neighbors = MAX(max_neighbors, smile_neighbors);

       // Draw rectangle on the left side of the image reflecting smile intensity
       float intensityZeroOne = ((float) smile_neighbors - min_neighbors)
               / (max_neighbors - min_neighbors + 1);
       smile_max = intensityZeroOne;
   }
   LOGE("jni_smile_ok_->3");
   return (jfloat) smile_max;
}
六：Java调用

在 public Mat onCameraFrame(CvCameraViewFrame inputFrame){

   // 这里获取相机拍摄到的原图，彩色图
       mRgba = inputFrame.rgba();
       // 这里获取相机拍摄到的灰度图，用来给下面检测人脸使用。
       mGray = inputFrame.gray();

MatOfRect faces2 = new MatOfRect();
float sm = mNativeDetector.data(mRgba, faces2);
Log.e("微笑强度-->", ""+sm);

return mRgba;

}

总结：整个工程对smile识别还是不错的，cpp算法是结合windows工程加进去改进的，其Android 是通过jni方法，NDK 调用opencv算法，的出结果反馈给上层应用。

Android Opencv smile（微笑） 识别开发

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Android Opencv smile（微笑）识别开发