OpenCV人脸识别之二：模型训练

#!/usr/bin/env python
 import sys
import os.path
 # This is a tiny script to help you creating a CSV file from a face
# database with a similar hierarchie:
#
#  philipp@mango:~/facerec/data/at$ tree
#  .
#  |-- README #  |-- s1
#  |   |-- 1.pgm #  |   |-- ...
#  |   |-- 10.pgm
 #  |-- s2
#  |   |-- 1.pgm
#  |   |-- ...
#  |   |-- 10.pgm
#  ...
#  |-- s40
 #  |   |-- 1.pgm
#  |   |-- ...
#  |   |-- 10.pgm
#
 if __name__ == "__main__":      
#if len(sys.argv) != 2:     
#    print "usage: create_csv <base_path>"     
#    sys.exit(1)      
#BASE_PATH=sys.argv[1]     
BASE_PATH="C:/Users/bingbuyu/Downloads/att_faces"      
SEPARATOR=";"      
fh = open("../etc/at.txt",'w')      
label = 0     
for dirname, dirnames, filenames in os.walk(BASE_PATH):         
for subdirname in dirnames:             
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)                
 fh.write(abs_path)                 
fh.write(SEPARATOR)                
 fh.write(str(label))                
 fh.write("n")                   
label = label + 1
 fh.close()

  Ptr<FaceRecognizer> model = createEigenFaceRecognizer();     model->train(images, labels);     model->save("MyFacePCAModel.xml");      Ptr<FaceRecognizer> model1 = createFisherFaceRecognizer();     model1->train(images, labels);     model1->save("MyFaceFisherModel.xml");      Ptr<FaceRecognizer> model2 = createLBPHFaceRecognizer();     model2->train(images, labels);     model2->save("MyFaceLBPHModel.xml");
当然在这之前要先把之前图片和标签提取出来。这时候就是at.txt派上用场的时候了。
//使用CSV文件去读图像和标签，主要使用stringstream和getline方法
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(CV_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()));        
 }
    }
}

//#include "stdafx.h"
#include <opencv2/opencv.hpp>
 #include <iostream>
#include <fstream>
#include <sstream>
#include <math.h>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src) {     
Mat src = _src.getMat();     
// 创建和返回一个归一化后的图像矩阵:     
Mat dst;     
switch (src.channels()) {     
case1:         
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);         
break;     
case3:        
 cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);         
break;     
default:         
src.copyTo(dst);        
break;     
}    
 return dst; }
//使用CSV文件去读图像和标签，主要使用stringstream和getline方法
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(CV_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()
 {     
//读取你的CSV文件路径.     
//string fn_csv = string(argv[1]);     
string fn_csv = "at.txt";     
// 2个容器来存放图像数据和对应的标签     
vector<Mat> images;     
vector<int> labels;     
// 读取数据. 如果文件不合法就会出错     
// 输入的文件名已经有了.    
 try    
 {         
read_csv(fn_csv, images, labels);     
}    
 catch (cv::Exception& e)     
{         
cerr << "Error opening file "" << fn_csv << "". Reason: " << e.msg << endl;         
// 文件有问题，我们啥也做不了了，退出了         
exit(1);     
}     
// 如果没有读取到足够图片，也退出.    
 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(CV_StsError, error_message);     
}     
// 下面的几行代码仅仅是从你的数据集中移除最后一张图片     
//[gm:自然这里需要根据自己的需要修改，他这里简化了很多问题]     
Mat testSample = images[images.size() - 1];     
int testLabel = labels[labels.size() - 1];     
images.pop_back();     
labels.pop_back();     
// 下面几行创建了一个特征脸模型用于人脸识别，     
// 通过CSV文件读取的图像和标签训练它。    
 // T这里是一个完整的PCA变换     
//如果你只想保留10个主成分，使用如下代码    
 //      cv::createEigenFaceRecognizer(10);     
//     
// 如果你还希望使用置信度阈值来初始化，使用以下语句：    
 //      cv::createEigenFaceRecognizer(10, 123.0);     
//     
// 如果你使用所有特征并且使用一个阈值，使用以下语句：    
 //      cv::createEigenFaceRecognizer(0, 123.0);     
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();     
model->train(images, labels);     
model->save("MyFacePCAModel.xml");     
Ptr<FaceRecognizer> model1 = createFisherFaceRecognizer();     
model1->train(images, labels);     
model1->save("MyFaceFisherModel.xml");    
 Ptr<FaceRecognizer> model2 = createLBPHFaceRecognizer();     
model2->train(images, labels);     
model2->save("MyFaceLBPHModel.xml");    
 // 下面对测试图像进行预测，predictedLabel是预测标签结果    
int predictedLabel = model->predict(testSample);    
 int predictedLabel1 = model1->predict(testSample);     
int predictedLabel2 = model2->predict(testSample);     
// 还有一种调用方式，可以获取结果同时得到阈值:     
//      int predictedLabel = -1;     
//      double confidence = 0.0;     
//      model->predict(testSample, predictedLabel, confidence);     
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);     
string result_message1 = format("Predicted class = %d / Actual class = %d.", predictedLabel1, testLabel);     
string result_message2 = format("Predicted class = %d / Actual class = %d.", predictedLabel2, testLabel);     
cout << result_message << endl;

cout << result_message1 << endl;     
cout << result_message2 << endl;    
 waitKey(0);     
return 0;
 }