300W数据集测试MTCNN的landmark效果代码
300W数据集测试MTCNN的landmark效果，用提取其中afw数据集337张图片的预测关键点并写入到txt中，再用测试程序和标注landmark做对比。
程序如下：
#include "network.h"
#include "mtcnn.h"
#include <time.h>
#include <fstream>
#include <opencv2/opencv.hpp>
#pragma comment(lib, "libopenblas.dll.a")
using namespace cv;
std::vector<std::string> split(std::string& str, std::string& pattern);
void str2int(int &int_temp, const string &string_temp);
int main()
{
//因为执行目录被设置到openblas/x64下了，保证dll能正常载入，这时候图片路径就相对要提上去2级
//Mat im = imread("…/…/test10.jpg");
int i = 0;
string img_dir = “E:/face_alignment/data/300W_test_5points/afw/”;
string name, s="_";
ifstream infile;
ofstream outfile;
infile.open(“E:/face_alignment/data/300W_test_5points/afw_mtcnn_test_V2_2.txt”);
outfile.open(“E:/face_alignment/data/300W_test_5points/MTCNN_V2_2_test/afw_test.txt”);
while (infile)
{
infile >> name;
vector<string> result = split(name, s);
cout << i << endl;
i++;
//cout << "name: " << result[0] << " s: " << result[1] << endl;
string shot_name = result[0];
int decide;
str2int(decide, result[1]);
if (decide == 1)
{
string image_name = name + ".jpg";
outfile << shot_name << endl;
string image_name_dir = img_dir + image_name;
cout << image_name_dir << endl;
Mat im = imread(image_name_dir);
vector<vector<Point2f>> key_points;
mtcnn find(im.cols, im.rows);
vector<Rect> objs = find.detectObject(im, key_points);
//outfile << objs.size() << endl;
for (int i = 0; i < objs.size(); ++i)
{
rectangle(im, objs[i], Scalar(0, 255), 2);
//outfile << objs[i].x << " " << objs[i].y << " " << objs[i].width << " " << objs[i].height << endl;
}
//cout << "num of key_points: " << key_points.size() << endl;
outfile << key_points.size() << endl;
for (int i = 0; i < key_points.size(); i++)
{
for (int j = 0; j < key_points[i].size(); j++)
{
cv::circle(im, key_points[i][j], 1, cv::Scalar(255, 255, 0), 2);
outfile << key_points[i][j].x << " " << key_points[i][j].y << " ";
}
outfile << endl;
}
string outdir = "E:/face_alignment/data/300W_test_5points/MTCNN_V2_2_test/afw/";
string out_image = outdir + shot_name + ".jpg";
imwrite(out_image, im);
//imshow("demo", im);
//waitKey(0);
}
}
infile.close();
outfile.close();
return 0;
}
//字符串分割函数
std::vector<std::string> split(std::string& str, std::string& pattern)
{
std::string::size_type pos;
std::vector<std::string> result;
//str += pattern;//扩展字符串以方便操作
int size = str.size();
pos = str.find(pattern, 0);
if (pos<size) {
std::string s1 = str.substr(0, pos);
std::string s2 = str.substr(pos + 1, size - 1);
result.push_back(s1);
result.push_back(s2);
}
return result;
}
void str2int(int &int_temp, const string &string_temp)
{
stringstream stream(string_temp);
stream >> int_temp;
}
用我们自己得到的预测txt，与作者提供的标注pts文件进行计算。 算5个landmark的欧式距离之和，除以左上角和右下角欧式距离，除以5。
#include <iostream>
#include <stdlib.h>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
std::vector<std::string> split(std::string& str, std::string& pattern);
void str2int(int &int_temp, const string &string_temp);
float computer_error(vector<float> pts_gt, vector<vector<float>> pts_pre);
int main()
{
int count = 0, pos = 0;
float acc, thread=0.1;
string name_list, s = “_”;
string pts_dir = “E:/face_alignment/data/300W_test_5points/afw/”;
ifstream infile_list, infile_pre;
infile_list.open(“E:/face_alignment/data/300W_test_5points/afw_mtcnn_test_V1.txt”);
infile_pre.open(“E:/face_alignment/data/300W_test_5points/MTCNN_V1_test/afw_test.txt”);
infile_list >> name_list;
while (infile_pre)
{
string name_pre;
int num_pre;
vector<vector<float> > pts_pre;
infile_pre >> name_pre;
infile_pre >> num_pre;
pts_pre.resize(num_pre);
for (int i = 0; i < num_pre; i++)
{
pts_pre[i].resize(10);
}
for (int j = 0; j < num_pre; j++)
{
infile_pre >> pts_pre[j][0] >> pts_pre[j][1] >> pts_pre[j][2] >> pts_pre[j][3] >> pts_pre[j][4] >> pts_pre[j][5] >> pts_pre[j][6] >> pts_pre[j][7] >> pts_pre[j][8] >> pts_pre[j][9];
}
//for (int i = 0; i < num_pre; i++)
//{
// for (int j = 0; j < 10; j++)
// {
// cout << pts_pre[i][j] << " ";
// }
// cout << endl;
//}
// read gt file
while (infile_list)
{
vector<string> result = split(name_list, s);
string name_gt = result[0];
if (name_gt.compare(name_pre) == 0)
{
count++;
cout << count << endl;
vector<float> pts_gt;
pts_gt.resize(10);
string pts_dir_name = pts_dir + name_list + ".pts";
ifstream infile_pts;
infile_pts.open(pts_dir_name);
string ss;
int yy;
infile_pts >> ss >> yy;
infile_pts >> ss >> yy;
infile_pts >> ss;
for (int i = 0; i < 5; i++)
{
infile_pts >> pts_gt[i*2] >> pts_gt[i*2+1];
}
infile_pts.close();
float error = computer_error(pts_gt, pts_pre);
error = error / 5.0;
if (error <= thread)
pos++;
cout << error << " " << endl;
infile_list >> name_list;
//cout << name_list << endl;
}
else
break;
}
}
acc = float(pos) / float(count);
cout << "accury: " << acc << endl;
infile_list.close();
infile_pre.close();
}
// computer alinment loss
float computer_error(vector<float> pts_gt, vector<vector<float>> pts_pre)
{
if (pts_pre.size() == 0)
return 10;
float RMSE, d_outer, align_loss;
d_outer = sqrt((pts_gt[0] - pts_gt[8])*(pts_gt[0] - pts_gt[8]) + (pts_gt[1] - pts_gt[9])*(pts_gt[1] - pts_gt[9]));
for (int i = 0; i < pts_pre.size(); i++)
{
RMSE = 0;
for (int j = 0; j < 5; j++)
{
RMSE += sqrt((pts_gt[2 * j] - pts_pre[i][2 * j])*(pts_gt[2 * j] - pts_pre[i][2 * j]) + (pts_gt[2 * j + 1] - pts_pre[i][2 * j + 1])*(pts_gt[2 * j + 1] - pts_pre[i][2 * j + 1]));
}
RMSE = RMSE / d_outer;
if (i == 0)
{
align_loss = RMSE;
}
else
{
if (align_loss > RMSE)
align_loss = RMSE;
}
}
return align_loss;
}
//字符串分割函数
std::vector<std::string> split(std::string& str, std::string& pattern)
{
std::string::size_type pos;
std::vector<std::string> result;
//str += pattern;//扩展字符串以方便操作
int size = str.size();
pos = str.find(pattern, 0);
if (pos<size) {
std::string s1 = str.substr(0, pos);
std::string s2 = str.substr(pos + 1, size - 1);
result.push_back(s1);
result.push_back(s2);
}
return result;
}
void str2int(int &int_temp, const string &string_temp)
{
stringstream stream(string_temp);
stream >> int_temp;
}
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