人脸检测初探

介绍

目前人脸识别比较成熟，OpenCV也已经把目前主流的人脸识别算法(PCA,Haar,LBP)集成到算法库，并且附带例程和文档。文档链接http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html 小明网友对这篇文档详细说明 http://guoming.me/face-recognition-with-opencv

数据准备

创建自己的CSV文件在官方的文档中是python程序实现的，但有点小小的不足就是只是打印出来，自己复制到文件。。。这里小改了下程序，自动保存在文件中。

import sysimport 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]    SEPARATOR=";"    fh = open("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()

简短代码

/* * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>. * Released to public domain under terms of the BSD Simplified license. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: *   * Redistributions of source code must retain the above copyright *     notice, this list of conditions and the following disclaimer. *   * Redistributions in binary form must reproduce the above copyright *     notice, this list of conditions and the following disclaimer in the *     documentation and/or other materials provided with the distribution. *   * Neither the name of the organization nor the names of its contributors *     may be used to endorse or promote products derived from this software *     without specific prior written permission. * *   See <http://www.opensource.org/licenses/bsd-license> */#include "opencv2/core/core.hpp"#include "opencv2/contrib/contrib.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>#include <fstream>#include <sstream>using namespace cv;using namespace std;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;}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(int argc, const char *argv[]) {    // Get the path to your CSV.    string fn_csv = string("at.txt");    // These vectors hold the images and corresponding labels.    vector<Mat> images;    vector<int> labels;    // Read in the data. This can fail if no valid    // input filename is given.    try {        read_csv(fn_csv, images, labels);    } catch (cv::Exception& e) {        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;        // nothing more we can do        exit(1);    }    // Quit if there are not enough images for this demo.    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);    }    // Get the height from the first image. We'll need this    // later in code to reshape the images to their original    // size:    int height = images[0].rows;    // The following lines simply get the last images from    // your dataset and remove it from the vector. This is    // done, so that the training data (which we learn the    // cv::FaceRecognizer on) and the test data we test    // the model with, do not overlap.    Mat testSample = images[images.size() - 1];    int testLabel = labels[labels.size() - 1];    images.pop_back();    labels.pop_back();    // The following lines create an Eigenfaces model for    // face recognition and train it with the images and    // labels read from the given CSV file.    // This here is a full PCA, if you just want to keep    // 10 principal components (read Eigenfaces), then call    // the factory method like this:    //    //      cv::createEigenFaceRecognizer(10);    //    // If you want to create a FaceRecognizer with a    // confidence threshold (e.g. 123.0), call it with:    //    //      cv::createEigenFaceRecognizer(10, 123.0);    //    // If you want to use _all_ Eigenfaces and have a threshold,    // then call the method like this:    //    //      cv::createEigenFaceRecognizer(0, 123.0);    //    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();    model->train(images, labels);    // The following line predicts the label of a given    // test image:    int predictedLabel = model->predict(testSample);    //    // To get the confidence of a prediction call the model with:    //    //      int predictedLabel = -1;    //      double confidence = 0.0;    //      model->predict(testSample, predictedLabel, confidence);    //    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);    cout << result_message << endl;    return 0;}