目录
1--前言
①SYSU模式识别课程作业
②配置：基于Windows11、OpenCV4.5.5、VSCode、CMake（参考OpenCV配置方式）
③原理及源码介绍：Face Recognition with OpenCV
④数据集：ORL Database of Faces
2--处理ORL数据集
①源码：
-
import sys
-
import os.path
-
-
if __name__ ==
"__main__":
-
-
BASE_PATH =
'./ORL/att_faces/orl_faces/'
-
SEPARATOR =
";"
-
dir_txt =
open(
"./dir.txt",
'w')
-
-
label =
0
-
for dirname, dirnames, filenames
in os.walk(BASE_PATH):
-
# dirname当前路径; dirnames当前路径下所有目录名（不包含子目录）;filenames当前路径下的所有文件名（不包含子目录）
-
for subdirname
in dirnames:
# 遍历每一个目录
-
subject_path = os.path.join(dirname, subdirname)
-
for filename
in os.listdir(subject_path):
-
abs_path =
"%s/%s" % (subject_path, filename)
-
print(
"%s%s%d" % (abs_path, SEPARATOR, label))
-
dir_txt.write(abs_path)
-
dir_txt.write(SEPARATOR)
-
dir_txt.write(
str(label))
-
dir_txt.write(
"\n")
-
label = label +
1
-
dir_txt.close()
②运行及结果：
python create_csv.py
3--Eigenfaces复现过程
①源码：
-
// 引用依赖
-
#include "opencv2/core.hpp"
-
#include "opencv2/face.hpp"
-
#include "opencv2/highgui.hpp"
-
#include "opencv2/imgproc.hpp"
-
#include <iostream>
-
#include <fstream>
-
#include <sstream>
-
-
// 使用相应的命名空间
-
using
namespace cv;
-
using
namespace cv::face;
-
using
namespace std;
-
-
// 标准化函数
-
static Mat norm_0_255(InputArray _src) {
-
Mat src = _src.
getMat();
-
// Create and return normalized image:
-
Mat dst;
-
switch(src.
channels()) {
-
case
1:
-
cv::
normalize(_src, dst,
0,
255, NORM_MINMAX, CV_8UC1);
-
break;
-
case
3:
-
cv::
normalize(_src, dst,
0,
255, NORM_MINMAX, CV_8UC3);
-
break;
-
default:
-
src.
copyTo(dst);
-
break;
-
}
-
return dst;
-
}
-
-
// 读取CSV文件函数
-
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
-
std::ifstream file(filename.c_str(), ifstream::in);
-
if (!file) {
-
string error_message =
"No valid input file was given, please check the given filename.";
-
CV_Error(Error::StsBadArg, error_message);
-
}
-
string line, path, classlabel;
-
while (
getline(file, line)) {
-
stringstream liness(line);
-
getline(liness, path, separator);
-
getline(liness, classlabel);
-
if(!path.
empty() && !classlabel.
empty()) {
-
images.
push_back(
imread(path,
0));
-
labels.
push_back(
atoi(classlabel.
c_str()));
-
}
-
}
-
}
-
int main(int argc, const char *argv[]) {
-
-
//检查argc是否符合要求
-
if (argc <
2) {
-
cout <<
"usage: " << argv[
0] <<
" <csv.ext> <output_folder> " << endl;
-
exit(
1);
-
}
-
string output_folder =
".";
-
if (argc ==
3) {
-
output_folder =
string(argv[
2]);
-
}
-
-
// CSV文件的路径
-
string fn_csv =
string(argv[
1]);
-
-
// 初始化存储imgs和labels的向量
-
vector<Mat> images;
-
vector<
int> labels;
-
-
// 读取CSV文件
-
try {
-
read_csv(fn_csv, images, labels);
-
}
catch (
const cv::Exception& e) {
-
cerr <<
"Error opening file \"" << fn_csv <<
"\". Reason: " << e.msg << endl;
-
exit(
1);
-
}
-
-
// 判断img数目是否符合要求
-
if(images.
size() <=
1) {
-
string error_message =
"This demo needs at least 2 images to work. Please add more images to your data set!";
-
CV_Error(Error::StsError, error_message);
-
}
-
-
// images的高度
-
int height = images[
0].rows;
-
-
// 从训练集中选择一张图片作为测试集
-
Mat testSample = images[images.
size() -
1];
-
int testLabel = labels[labels.
size() -
1];
-
images.
pop_back();
-
labels.
pop_back();
-
-
// 创建模型，使用PCA特征脸算法
-
Ptr<EigenFaceRecognizer> model = EigenFaceRecognizer::
create();
-
model->
train(images, labels);
// 训练模型
-
int predictedLabel = model->
predict(testSample);
// 使用测试集测试模型
-
-
// 打印准确率
-
string result_message = format(
"Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
-
cout << result_message << endl;
-
// 获取模型的特征值
-
Mat eigenvalues = model->
getEigenValues();
-
// 展示特征向量
-
Mat W = model->
getEigenVectors();
-
// 从训练集中获取样本均值
-
Mat mean = model->
getMean();
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(
"mean",
norm_0_255(mean.
reshape(
1, images[
0].rows)));
-
}
else {
-
imwrite(format(
"%s/mean.png", output_folder.
c_str()),
norm_0_255(mean.
reshape(
1, images[
0].rows)));
-
}
-
// 显示或保存特征脸
-
for (
int i =
0; i <
min(
10, W.cols); i++) {
-
string msg = format(
"Eigenvalue #%d = %.5f", i, eigenvalues.
at<
double>(i));
-
cout << msg << endl;
-
// 获取特征向量
-
Mat ev = W.
col(i).
clone();
-
// resize成原始大小，并归一化到0-255
-
Mat grayscale =
norm_0_255(ev.
reshape(
1, height));
-
// 显示图像并应用Jet颜色图以获得更好的观感。
-
Mat cgrayscale;
-
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(format(
"eigenface_%d", i), cgrayscale);
-
}
else {
-
imwrite(format(
"%s/eigenface_%d.png", output_folder.
c_str(), i),
norm_0_255(cgrayscale));
-
}
-
}
-
// 在一些预定义的步骤中显示或保存图像重建的过程：
-
for(
int num_components =
min(W.cols,
10); num_components <
min(W.cols,
300); num_components+=
15) {
-
// 从模型中分割特征向量
-
Mat evs =
Mat(W, Range::
all(),
Range(
0, num_components));
-
Mat projection = LDA::
subspaceProject(evs, mean, images[
0].
reshape(
1,
1));
-
Mat reconstruction = LDA::
subspaceReconstruct(evs, mean, projection);
-
// 归一化
-
reconstruction =
norm_0_255(reconstruction.
reshape(
1, images[
0].rows));
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(format(
"eigenface_reconstruction_%d", num_components), reconstruction);
-
}
else {
-
imwrite(format(
"%s/eigenface_reconstruction_%d.png", output_folder.
c_str(), num_components), reconstruction);
-
}
-
}
-
// 如果没有写入输出文件夹，则等待键盘输入
-
if(argc ==
2) {
-
waitKey(
0);
-
}
-
return
0;
-
}
②编译过程：
CMakeLists.txt如下：
-
cmake_minimum_required(VERSION
3.24)
# 指定 cmake的 最小版本
-
project(test)
# 设置项目名称
-
-
find_package(Opencv REQUIRED)
-
INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS})
-
add_executable(eigenfaces_demo eigenfaces.cpp)
# 生成可执行文件
-
target_link_libraries(eigenfaces_demo ${OpenCV_LIBS} )
# 设置target需要链接的库
-
mkdir build
-
-
cd build
-
-
cmake ..
-
-
cd ..
-
-
mingw32-make
③运行及结果展示：
./eigenfaces_demo.exe ./dir.txt ./Engenfaces_Result
特征图：（简单修改源程序生成的文件名，再按顺序进行拼接即可生成拼接图，拼接程序参考）
重建过程：
均值图：
4--Fisherfaces复现过程
①源码：
-
// 引用依赖
-
#include "opencv2/core.hpp"
-
#include "opencv2/face.hpp"
-
#include "opencv2/highgui.hpp"
-
#include "opencv2/imgproc.hpp"
-
#include <iostream>
-
#include <fstream>
-
#include <sstream>
-
-
// 使用相应的命名空间
-
using
namespace cv;
-
using
namespace cv::face;
-
using
namespace std;
-
-
// 标准化函数
-
static Mat norm_0_255(InputArray _src) {
-
Mat src = _src.
getMat();
-
Mat dst;
-
switch(src.
channels()) {
-
case
1:
-
cv::
normalize(_src, dst,
0,
255, NORM_MINMAX, CV_8UC1);
-
break;
-
case
3:
-
cv::
normalize(_src, dst,
0,
255, NORM_MINMAX, CV_8UC3);
-
break;
-
default:
-
src.
copyTo(dst);
-
break;
-
}
-
return dst;
-
}
-
-
// 读取csv文件函数
-
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
-
std::ifstream file(filename.c_str(), ifstream::in);
-
if (!file) {
-
string error_message =
"No valid input file was given, please check the given filename.";
-
CV_Error(Error::StsBadArg, error_message);
-
}
-
string line, path, classlabel;
-
while (
getline(file, line)) {
-
stringstream liness(line);
-
getline(liness, path, separator);
-
getline(liness, classlabel);
-
if(!path.
empty() && !classlabel.
empty()) {
-
images.
push_back(
imread(path,
0));
-
labels.
push_back(
atoi(classlabel.
c_str()));
-
}
-
}
-
}
-
-
int main(int argc, const char *argv[]) {
-
-
//检查argc是否符合要求
-
if (argc <
2) {
-
cout <<
"usage: " << argv[
0] <<
" <csv.ext> <output_folder> " << endl;
-
exit(
1);
-
}
-
string output_folder =
".";
-
if (argc ==
3) {
-
output_folder =
string(argv[
2]);
-
}
-
-
// CSV文件的路径
-
string fn_csv =
string(argv[
1]);
-
-
// 初始化存储imgs和labels的向量
-
vector<Mat> images;
-
vector<
int> labels;
-
-
// 读取CSV文件
-
try {
-
read_csv(fn_csv, images, labels);
-
}
catch (
const cv::Exception& e) {
-
cerr <<
"Error opening file \"" << fn_csv <<
"\". Reason: " << e.msg << endl;
-
exit(
1);
-
}
-
-
// 判断img数目是否符合要求
-
if(images.
size() <=
1) {
-
string error_message =
"This demo needs at least 2 images to work. Please add more images to your data set!";
-
CV_Error(Error::StsError, error_message);
-
}
-
-
// images的高度
-
int height = images[
0].rows;
-
-
// 从训练集中选择一张图片作为测试集
-
Mat testSample = images[images.
size() -
1];
-
int testLabel = labels[labels.
size() -
1];
-
images.
pop_back();
-
labels.
pop_back();
-
-
// 创建模型，使用LDA线性判别分析
-
Ptr<FisherFaceRecognizer> model = FisherFaceRecognizer::
create();
-
model->
train(images, labels);
// 训练模型
-
-
int predictedLabel = model->
predict(testSample);
// 使用测试集测试模型
-
-
// 打印准确率
-
string result_message = format(
"Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
-
cout << result_message << endl;
-
// 获取模型的特征值
-
Mat eigenvalues = model->
getEigenValues();
-
// 展示特征向量
-
Mat W = model->
getEigenVectors();
-
// 从训练集中获取样本均值
-
Mat mean = model->
getMean();
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(
"mean",
norm_0_255(mean.
reshape(
1, images[
0].rows)));
-
}
else {
-
imwrite(format(
"%s/mean.png", output_folder.
c_str()),
norm_0_255(mean.
reshape(
1, images[
0].rows)));
-
}
-
// 显示或保存特征脸
-
for (
int i =
0; i <
min(
16, W.cols); i++) {
-
string msg = format(
"Eigenvalue #%d = %.5f", i, eigenvalues.
at<
double>(i));
-
cout << msg << endl;
-
// 获取特征向量
-
Mat ev = W.
col(i).
clone();
-
// resize成原始大小，并归一化到0-255
-
Mat grayscale =
norm_0_255(ev.
reshape(
1, height));
-
// 显示图像并应用Jet颜色图以获得更好的观感。
-
Mat cgrayscale;
-
applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(format(
"fisherface_%d", i), cgrayscale);
-
}
else {
-
imwrite(format(
"%s/fisherface_%d.png", output_folder.
c_str(), i),
norm_0_255(cgrayscale));
-
}
-
}
-
// 在一些预定义的步骤中显示或保存图像重建的过程：
-
for(
int num_component =
0; num_component <
min(
16, W.cols); num_component++) {
-
// 从模型中分割特征向量
-
Mat ev = W.
col(num_component);
-
Mat projection = LDA::
subspaceProject(ev, mean, images[
0].
reshape(
1,
1));
-
Mat reconstruction = LDA::
subspaceReconstruct(ev, mean, projection);
-
// 归一化
-
reconstruction =
norm_0_255(reconstruction.
reshape(
1, images[
0].rows));
-
// 根据argc判断进行展示或保存操作
-
if(argc ==
2) {
-
imshow(format(
"fisherface_reconstruction_%d", num_component), reconstruction);
-
}
else {
-
imwrite(format(
"%s/fisherface_reconstruction_%d.png", output_folder.
c_str(), num_component), reconstruction);
-
}
-
}
-
// 如果没有写入输出文件夹，则等待键盘输入
-
if(argc ==
2) {
-
waitKey(
0);
-
}
-
return
0;
-
}
②编译过程：
CMakeLists.txt如下：
-
cmake_minimum_required(VERSION
3.24)
# 指定 cmake的 最小版本
-
project(test)
# 设置项目名称
-
-
find_package(Opencv REQUIRED)
-
INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS})
-
#add_executable(eigenfaces_demo eigenfaces.cpp) # 生成可执行文件
-
#target_link_libraries(eigenfaces_demo ${OpenCV_LIBS} ) # 设置target需要链接的库
-
add_executable(fisherfaces_demo fisherfaces.cpp)
# 生成可执行文件
-
target_link_libraries(fisherfaces_demo ${OpenCV_LIBS} )
# 设置target需要链接的库
-
mkdir build
-
-
cd build
-
-
cmake ..
-
-
cd ..
-
-
mingw32-make
③运行及结果展示：
./fisherfaces_demo.exe ./dir.txt ./Fisherfaces_Result
特征图：
重建过程：
均值图：
5--分析
未完待续！
转载：https://blog.csdn.net/weixin_43863869/article/details/127703544
查看评论