使用dlib自带的面向梯度直方图(HOG)和线性分类器方法来检测人脸
之前使用opencv里面CascadeClassifier(级联分类器)来识别人脸,
下面使用dlib库来实现人脸识别。
dlib是一个开源的库,它包含了很多内容有机器学习,图像处理,数值算法等等。
#include <dlib/image_processing/frontal_face_detector.h> #include <dlib/gui_widgets.h> #include <dlib/image_io.h> #include <iostream> using namespace dlib; using namespace std; // ---------------------------------------------------------------------------------------- int main(int argc, char** argv) { try { if (argc == 1) { cout << "Give some image files as arguments to this program." << endl; return 0; } frontal_face_detector detector = get_frontal_face_detector(); image_window win; // Loop over all the images provided on the command line. for (int i = 1; i < argc; ++i) { cout << "processing image " << argv[i] << endl; array2d<unsigned char> img; load_image(img, argv[i]); // Make the image bigger by a factor of two. This is useful since // the face detector looks for faces that are about 80 by 80 pixels // or larger. Therefore, if you want to find faces that are smaller // than that then you need to upsample the image as we do here by // calling pyramid_up(). So this will allow it to detect faces that // are at least 40 by 40 pixels in size. We could call pyramid_up() // again to find even smaller faces, but note that every time we // upsample the image we make the detector run slower since it must // process a larger image. pyramid_up(img); // Now tell the face detector to give us a list of bounding boxes // around all the faces it can find in the image. std::vector<rectangle> dets = detector(img); cout << "Number of faces detected: " << dets.size() << endl; // Now we show the image on the screen and the face detections as // red overlay boxes. win.clear_overlay(); win.set_image(img); win.add_overlay(dets, rgb_pixel(255, 0, 0)); cout << "Hit enter to process the next image..." << endl; cin.get(); } } catch (exception& e) { cout << "\nexception thrown!" << endl; cout << e.what() << endl; } }
很简单只需要输入一张图片,利用dlib里自带的frontal_face_detector 来进行检测,返回没张人脸在图片里的区域(rect)