[Face Detection] Haar Cascade를 사용한 이미지 얼굴검출

개발환경

사용툴       : Visual Studio 2013 라이브러리 : openCV library 2.4.11 프로젝트    : Visual C++ console application 개발날짜    : 2015-04-08

출처 : stackoverflow  - opencv and C++:detect face in image [closed]

int FaceDetection::Haar_FaceDetection_Img(string filename) {              // Global variables              // Copy this file from opencv/data/haarscascades to target folder              string face_cascade_name = "haarcascade_frontalface_alt.xml";              CascadeClassifier face_cascade;              string window_name = "Capture - Face detection";              // Function main                                         // Load the cascade                            if (!face_cascade.load(face_cascade_name))                            {                                         printf("--(!)Error loading\n");                                         return (-1);                            };                              // Read the image file                            Mat frame = imread(filename);                              for (int i = 0; i < 1; i++)                            {                                         // Apply the classifier to the frame                                         if (!frame.empty())                                         {                                                      detectAndDisplay(frame, face_cascade, filename);                                         }                                         else                                         {                                                      printf(" --(!) No captured frame -- Break!");                                                      break;                                         }                                           int c = waitKey(10);                                           if (27 == char(c))                                         {                                                      break;                                         }                            }                            return 0; } // Function detectAndDisplay void FaceDetection::detectAndDisplay(Mat frame, CascadeClassifier face_cascade, string filename) {              std::vector<Rect> faces;              Mat frame_gray;              Mat res;              Mat gray;              int filenumber = 0; // Number of file to be saved                cvtColor(frame, frame_gray, COLOR_BGR2GRAY);              equalizeHist(frame_gray, frame_gray);                // Detect faces              face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));                // Set Region of Interest              cv::Rect roi_b;              cv::Rect roi_c;                size_t ic = 0; // ic is index of current element              int ac = 0; // ac is area of current element                size_t ib = 0; // ib is index of biggest element              int ab = 0; // ab is area of biggest element                for (ic = 0; ic < faces.size(); ic++) // Iterate through all current elements (detected faces)                {                    roi_c.x = faces[ic].x;                    roi_c.y = faces[ic].y;                    roi_c.width = (faces[ic].width);                    roi_c.height = (faces[ic].height);                      ac = roi_c.width * roi_c.height; // Get the area of current element (detected face)                      roi_b.x = faces[ib].x;                    roi_b.y = faces[ib].y;                    roi_b.width = (faces[ib].width);                    roi_b.height = (faces[ib].height);                      // Get the area of biggest element, at beginning it is same as "current" element                    ab = roi_b.width * roi_b.height;                       if (ac > ab)                    {                           ib = ic;                           roi_b.x = faces[ib].x;                           roi_b.y = faces[ib].y;                           roi_b.width = (faces[ib].width);                           roi_b.height = (faces[ib].height);                    }                      Point pt1(faces[ic].x, faces[ic].y); // Display detected faces on main window - live stream from camera                    Point pt2((faces[ic].x + faces[ic].height), (faces[ic].y + faces[ic].width));                    rectangle(frame, pt1, pt2, Scalar(0, 255, 0), 2, 8, 0);              }                // Show image              imshow("original", frame); }

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