Opencv LBPH人臉識(shí)別算法詳解

2020-01-26 13:27:42

字體：大中小

供稿：網(wǎng)友

簡(jiǎn)要：

LBPH(Local Binary PatternsHistograms)局部二進(jìn)制編碼直方圖，建立在LBPH基礎(chǔ)之上的人臉識(shí)別法基本思想如下：首先以每個(gè)像素為中心，判斷與周圍像素灰度值大小關(guān)系，對(duì)其進(jìn)行二進(jìn)制編碼，從而獲得整幅圖像的LBP編碼圖像；再將LBP圖像分為個(gè)區(qū)域，獲取每個(gè)區(qū)域的LBP編碼直方圖，繼而得到整幅圖像的LBP編碼直方圖，通過(guò)比較不同人臉圖像LBP編碼直方圖達(dá)到人臉識(shí)別的目的,其優(yōu)點(diǎn)是不會(huì)受到光照、縮放、旋轉(zhuǎn)和平移的影響。

#include<opencv2/opencv.hpp>#include<opencv2/face.hpp>using namespace cv;using namespace face;using namespace std;char win_title[40] = {}; int main(int arc, char** argv) {  //namedWindow("input",CV_WINDOW_AUTOSIZE);  //讀入模型需要輸入的數(shù)據(jù)，用來(lái)訓(xùn)練的圖像vector<Mat>images和標(biāo)簽vector<int>labels string filename = string("path.txt"); ifstream file(filename); if (!file) { printf("could not load file"); } vector<Mat>images; vector<int>labels; char separator = ';'; string line,path, classlabel; while (getline(file,line)) { stringstream lines(line); getline(lines, path, separator); getline(lines, classlabel); //printf("%d/n", atoi(classlabel.c_str())); images.push_back(imread(path, 0)); labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)將字符串轉(zhuǎn)換為整數(shù)型 } int height = images[0].rows; int width = images[0].cols; printf("height:%d,width:%d/n", height, width); //將最后一個(gè)樣本作為測(cè)試樣本 Mat testSample = images[images.size() - 1]; int testLabel = labels[labels.size() - 1]; //刪除列表末尾的元素 images.pop_back(); labels.pop_back();  //加載，訓(xùn)練，預(yù)測(cè) Ptr<LBPHFaceRecognizer> model = LBPHFaceRecognizer::create(); model->train(images, labels); int predictedLabel = model->predict(testSample); printf("actual label:%d,predict label :%d/n", testLabel, predictedLabel);  int radius = model->getRadius(); int neibs = model->getNeighbors(); int grad_x = model->getGridX(); int grad_y = model->getGridY(); double t = model->getThreshold(); printf("radius:%d/n", radius); printf("neibs:%d/n", neibs); printf("grad_x:%d/n", grad_x); printf("grad_y:%d/n", grad_y); printf("threshold:%.2f/n", t);  waitKey(0); return 0;}