找工作好無趣，所以來寫幾篇部落格玩一玩

慣例先放結果：

思路：

人臉定位——特徵點——旋轉

//具體實現看程式碼，還是有一定提升空間的，233

下面就是簡單粗暴的程式碼了：

#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個  
				//	}
				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("人臉矯正", 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");
	}
}