人脸旋转归一化 （根据特征点定位，dlib+opencv）

找工作好无趣，所以来写几篇博客玩一玩

惯例先放结果：

思路：

人脸定位——特征点——旋转

//具体实现看代码，还是有一定提升空间的，233

下面就是简单粗暴的代码了：

环境参考博客： Dlib提取人脸特征点（68点，opencv画图）

#include <dlib/opencv.h>  #include <opencv2/opencv.hpp>  #include <dlib/image_processing/frontal_face_detector.h>  #include <dlib/image_processing/render_face_detections.h>  #include <dlib/image_processing.h>  #include <dlib/gui_widgets.h>  #include<algorithm>using namespace dlib;using namespace std;static cv::Rect dlibRectangleToOpenCV(dlib::rectangle r){return cv::Rect(cv::Point2i(r.left(), r.top()), cv::Point2i(r.right() + 1, r.bottom() + 1));}static dlib::rectangle openCVRectToDlib(cv::Rect r){return dlib::rectangle((long)r.tl().x, (long)r.tl().y, (long)r.br().x - 1, (long)r.br().y - 1);}int main(){try{cv::VideoCapture cap(0);if (!cap.isOpened()){cerr << "Unable to connect to camera" << endl;return 1;}//image_window win;  // Load face detection and pose estimation models.  frontal_face_detector detector = get_frontal_face_detector();shape_predictor pose_model;deserialize("shape_predictor_68_face_landmarks.dat") >> pose_model;// Grab and process frames until the main window is closed by the user.  while (cv::waitKey(30) != 27){// Grab a frame  cv::Mat temp;cap >> temp;cv_image<bgr_pixel> cimg(temp);// Detect faces   std::vector<rectangle> faces = detector(cimg);// Find the pose of each face.  std::vector<full_object_detection> shapes;for (unsigned long i = 0; i < faces.size(); ++i)shapes.push_back(pose_model(cimg, faces[i]));if (!shapes.empty()) {//for (int i = 0; i < 68; i++) {//circle(temp, cvPoint(shapes[0].part(i).x(), shapes[0].part(i).y()), 2, cv::Scalar(255, 0, 0), -1);//  shapes[0].part(i).x();//68个  //}int width = temp.cols;int length = temp.rows;double x = shapes[0].part(36).x() - shapes[0].part(45).x();double y = shapes[0].part(36).y() - shapes[0].part(45).y();double angle = x == 0 ? 0 : atan(y / x);cv::Mat rotation = cv::getRotationMatrix2D(cvPoint(shapes[0].part(30).x(), shapes[0].part(30).y()), angle / 3.14 * 180, 1.0);cv::warpAffine(temp, temp, rotation, cv::Size(width, length));}cimg = cv_image<bgr_pixel>(temp);faces = detector(cimg);if (faces.size() != 0) {cv::Rect rect = dlibRectangleToOpenCV(faces[0]);full_object_detection points = pose_model(cimg, faces[0]);long xmax = points.part(0).x();long xmin = points.part(0).x();long ymin = points.part(0).y();long ymax = points.part(0).y();;for (int i = 1; i < 68; i++) {xmax = std::max(xmax, points.part(i).x());xmin = std::min(xmin, points.part(i).x());ymax = std::max(ymax, points.part(i).y());ymin = std::min(ymin, points.part(i).y());}rect.x = xmin ;rect.width = xmax - xmin  ;rect.y = ymin;rect.height = ymax - ymin ;cv::Mat imCrop = temp(rect);cv::resize(imCrop, imCrop, cv::Size(200, 200));imshow("Image", imCrop);cv::rectangle(temp, rect, cv::Scalar(255, 0, 0), 2);}imshow("人脸矫正", temp);;}}catch (serialization_error& e){cout << "You need dlib's default face landmarking model file to run this example." << endl;cout << "You can get it from the following URL: " << endl;cout << "   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;cout << endl << e.what() << endl;system("pause");}catch (exception& e){cout << e.what() << endl;system("pause");}}

还是画上特征点顺眼点，2333