face alignment中opencv读取pts文件并修改系列程序

一、读取300W数据集中测试数据集pts标注文件，在原图中显示68个人脸关键点。

https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/

#include <iostream>#include <fstream>#include <opencv2/opencv.hpp>  using namespace cv;using namespace std;int main(){string Imagedir = "E:/人脸关键点检测/data/300W/afw/134212_1.jpg";Mat image = imread(Imagedir, -1);    //读取原图vector<Point2f> points;Point2f point;ifstream input;input.open("E:/人脸关键点检测/data/300W/afw/134212_1.pts");string s;for (int k = 0; k < 3; k++) {    //前三行getline(input, s);//cout << s << endl;}for (int i = 0; i < 68; i++) {input >> point.x >> point.y;//cout << point.x << " " << point.y << endl;points.push_back(point);}input.close();for (int i = 0; i < 68; i++) {//cout << points[i].x << "  " << points[i].y << endl;circle(image, points[i], 3, Scalar(0, 0, 255), CV_FILLED, CV_AA);}imshow("landmark", image);waitKey();return 0;}

      效果如下图所示：

二、将300W数据集中68个关键点转变为5个关键点，有三种方法。

方法一：

左眼中心：37,40中心点；

右眼中心：43,46中心点；

鼻尖： 31

左嘴角：49；

右嘴角：55。

方法二：

左眼中心：38,39,41,42中心点；

右眼中心：44,45,47,48中心点；

鼻尖： 31

左嘴角：49；

右嘴角：55。

方法三：

左眼中心：37,38,39,40,41,42中心点；

右眼中心：43,44,45,46,47,48中心点；

鼻尖： 31

左嘴角：49；

右嘴角：55。

300W测试数据集地址：  

https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/

      试验发现：通过随机抽象比对发现： 方法二效果更好。

#include <iostream>#include <fstream>#include <opencv2/opencv.hpp>  using namespace cv;using namespace std;int main(){ifstream infile;string image_name;string Imagedir = "E:/face_alignment/data/300W/ibug/";string Input_ptsdir = "E:/face_alignment/data/300W/ibug/";string Output_ptsdir = "E:/face_alignment/data/300W/68_5points/ibug/";infile.open("E:/face_alignment/data/300W/pts_name/ibug.txt");while (infile){infile >> image_name;string image_funame = image_name + ".jpg";cout << image_funame << endl;string Imagedir_name = Imagedir + image_funame;Mat image = imread(Imagedir_name, -1);    //读取原图vector<Point2f> points;vector<Point2f> points_5;Point2f point;points_5.resize(5);//读取和写入pts文件string Inputdir_name = Input_ptsdir + image_name + ".pts";string Outputdir_name = Output_ptsdir + image_name + ".pts";ifstream input;input.open(Inputdir_name);ofstream output;output.open(Outputdir_name);string s;getline(input, s);    //前三行output << s << endl;getline(input, s);output << "n_points:  5" << endl;getline(input, s);output << s << endl;for (int i = 0; i < 68; i++) {    //读取68个关键点input >> point.x >> point.y;//cout << point.x << " " << point.y << endl;points.push_back(point);}string end;input >> end;input.close();////method 1//points_5[0].x = (points[36].x + points[39].x) / 2.0f;//points_5[0].y = (points[36].y + points[39].y) / 2.0f;//points_5[1].x = (points[42].x + points[46].x) / 2.0f;//points_5[1].y = (points[42].y + points[46].y) / 2.0f;//points_5[2] = points[30];//points_5[3] = points[48];//points_5[4] = points[54];//method 2， 取5个新的人脸关键点points_5[0].x = (points[37].x + points[38].x + points[40].x + points[41].x) / 4.0f;points_5[0].y = (points[37].y + points[38].y + points[40].y + points[41].y) / 4.0f;points_5[1].x = (points[43].x + points[44].x + points[46].x + points[47].x) / 4.0f;points_5[1].y = (points[43].y + points[44].y + points[46].y + points[47].y) / 4.0f;points_5[2] = points[30];points_5[3] = points[48];points_5[4] = points[54];////method 3//points_5[0].x = (points[36].x + points[37].x + points[38].x + points[39].x + points[40].x + points[41].x) / 6.0f;//points_5[0].y = (points[36].y + points[37].y + points[38].y + points[39].y + points[40].y + points[41].y) / 6.0f;//points_5[1].x = (points[42].x + points[43].x + points[44].x + points[45].x + points[46].x + points[47].x) / 6.0f;//points_5[1].y = (points[42].y + points[43].y + points[44].y + points[45].y + points[46].y + points[47].y) / 6.0f;//points_5[2] = points[30];//points_5[3] = points[48];//points_5[4] = points[54];//for (int i = 0; i < 68; i++) {//cout << points[i].x << "  " << points[i].y << endl;//circle(image, points[i], 3, Scalar(0, 0, 255), CV_FILLED, CV_AA);//}//在原图中显示5个人脸关键点，并将5个关键点写入新的pts文件中for (int i = 0; i < 5; i++){//cout << points_5[i].x << "  " << points_5[i] << endl;output << points_5[i].x << " " << points_5[i].y << endl;circle(image, points_5[i], 3, Scalar(0, 0, 255), CV_FILLED, CV_AA);}output << end << endl;output.close();//imshow("landmark", image);//waitKey();}return 0;}

三、批量在原图中显示取出的5个关键点

#include <iostream>#include <fstream>#include <opencv2/opencv.hpp>  using namespace cv;using namespace std;int main(){ifstream infile;string image_name;string Imagedir = "E:/face_alignment/data/300W_test_5points/ibug/";string Input_ptsdir = "E:/face_alignment/data/300W_test_5points/ibug/";infile.open("E:/face_alignment/data/300W_test_5points/ibug.txt");while (infile){infile >> image_name;string image_funame = image_name + ".jpg";cout << image_funame << endl;string Imagedir_name = Imagedir + image_funame;Mat image = imread(Imagedir_name, -1);    //读取原图vector<Point2f> points;vector<Point2f> points_5;Point2f point;points_5.resize(5);string Inputdir_name = Input_ptsdir + image_name + ".pts";ifstream input;input.open(Inputdir_name);string s;getline(input, s);    //前三行getline(input, s);getline(input, s);for (int i = 0; i < 5; i++) {    //读取68个关键点input >> point.x >> point.y;//cout << point.x << " " << point.y << endl;points.push_back(point);}input.close();for (int i = 0; i < 5; i++) {cout << points[i].x << "  " << points[i].y << endl;circle(image, points[i], 3, Scalar(0, 0, 255), CV_FILLED, CV_AA);}imshow("landmark", image);waitKey();}return 0;}

      效果如下图所示：