人脸和性别识别(基于OpenCV)
描写叙述
人脸识别包含四个步骤
- 人脸检測:定位人脸区域,仅仅关心是不是脸;
- 人脸预处理:对人脸检測出来的图片进行调整优化。
- 收集和学习人脸:收集要识别的人的预处理过的人脸,然后通过一些算法去学习怎样识别;
- 人脸识别:识别当前人脸与数据库里的哪个人脸最类似。
人脸检測
OpenCV集成了基于PCA LDA 和LBP的人脸检測器。源文件自带非常多各种训练好的检測器。下表是经常使用的XML文件
上面的XML文件能够检測正面人脸、眼睛或鼻子。检測人脸我採用的是第一个或第二个Harr人脸检測器。
识别率比較好。
第一步:载入Harr人脸检測XML文件
try{
faceCascade.load(faceCascadeFilename);
}catch(cv::Exception& e){}
if ( faceCascade.empty() ) {
cerr << "ERROR: Could not load Face Detection cascade classifier [" << faceCascadeFilename << "]!" << endl;
cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascade_frontalface_alt2') into this WebcamFaceRec folder." << endl;
exit(1);
}
cout << "Loaded the Face Detection cascade classifier [" << faceCascadeFilename << "]." << endl;
第二步:载入摄像头,从视频获取图像帧。
try{
videoCapture.open(CameraID);
}catch(cv::Exception& e){}
if(!videoCapture.isOpened()){
cerr << "ERROR: could not open Camera!" << endl;
exit(1);
}
videoCapture >> cameraFrame;
第三步:一帧图像预处理
1、 灰度转换:使用cvtColor()函数,将彩色图像转换为灰度图像。台式机是3通道的BGR。移动设备则是4通道的BGRA格式
if(srcimg.channels() ==3 ){
cvtColor(srcimg,gray_img,CV_BGR2GRAY);
}
else if(srcimg.channels() ==4 ){
cvtColor(srcimg,gray_img,CV_BGRA2GRAY);
}
else {
gray_img = srcimg;
}
2、直方图均衡化,在OpenCV函数中利用equalizeHist()函数运行直方图均衡化,提升对照度和亮度。
equalizeHist(gray_img,equalized_Img);
第四步:检測人脸
上面已经创建了级联分类器并载入好XML文件。接着使用函数Classifier::detecMultiScale()函数来检測人脸。这个函数的參数说明:
a、minFeatureSize: 该參数决定最小的人脸大小。通常能够设为20*20或30*30像素。假设使用摄像机或移动设备检測,则人脸一般非常接近摄像机,可把參数调大。80*80;
b、searchScaleFactor: 该參数决定有多少不同大小的人脸要搜索,通常设为1.1
c、minNeighbors: 该參数决定检測器怎样确定人脸已经被检測到。通常设为3
d、flags: 该參数设定是否要查找全部的人脸或最大的人脸
(CASCADE_FIND_BIGGEST_OBJECT)
int flags = CASCADE_FIND_BIGGEST_OBJECT;
//smallest object Size
Size minFeatureSize = Size(20,20);
// How detailed should the search be. Must be larger than 1.0.
float searchScaleFactor = 1.1f;
// How much the detections should be filtered out. This should depend on how bad false detections are to your system.
// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.
int minNeighbors = 6;
vector<Rect> faces;
faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,
minNeighbors,flags,minFeatureSize);
//faceCascade.detectMultiScale(equalized_Img, faces);
int i = 0;
for(i = 0; i < faces.size(); i++){
Rect face_id = faces[i];
rectangle(orginalimg,face_id,Scalar(0,255,0),1);
}
人脸识别
为了识别人脸。须要收集足够多的要识别的人的人脸图像。
收集好之后,选择适合人脸识别的机器学习算法。通过算法来学习收集的数据。从而训练出一个模型并保存。下次进来一帧图像,通过算法对模型里的參数进行匹配识别。人脸识别机器学习算法有非常多,如SVM(支持向量机),ANN(人工神经网络)还有最经常使用的是基于特征脸的算法。OpenCV提供了CV::Algorithm类,类中有基于特征脸的(PCA 主成分分析)、Fisher脸(LDA 线性判别分析)和LPBH(局部二值模式直方图)
使用里面的算法,第一步必须通过cv::Algorithm::creat< FaceRecognizer>创建一个FaceRecognizer对象。创建了FaceRecognizer对象之后。将收集的人脸数据和标签传递给FaceRecognizer::train() 函数就可以进行训练模型。
string facerecAlgorithm = "FaceRecognizer.Fisherfaces";
Ptr<FaceRecognizer> model;
// Use OpenCV's new FaceRecognizer in the "contrib" module:
model = Algorithm::create<FaceRecognizer>(facerecAlgorithm);
if (model.empty()) {
cerr << "ERROR: The FaceRecognizer [" << facerecAlgorithm;
cerr << "] is not available in your version of OpenCV. ";
cerr << "Please update to OpenCV v2.4.1 or newer." << endl;
exit(1);
}
model->train(preprocessedFaces, faceLabels);
训练好模型之后。通常是把模型保存下来,以免下次反复训练。
直接载入模型就可以。下一步就是人脸识别。相同,opencv把识别算法集成在FaceRecognizer类中。简单地调用FaceRecognizer::predict() 就能够识别。
int identity = model->predict(preprocessedFace);
測试程序
/*
* 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>
#include <direct.h>
using namespace cv;
using namespace std;
//const char *faceCascadeFilename = "C:\\opencv\\sources\\data\\lbpcascades\\lbpcascade_frontalface.xml";
const char *faceCascadeFilename = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt2.xml";
const char *eyeCascadeFilename1 = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml"; // Basic eye detector for open eyes only.
const char *eyeCascadeFilename2 = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye_tree_eyeglasses.xml";
const char *face_lib = "face_train_img//";
const int DESIRED_CAMERA_WIDTH = 640;
const int DESIRED_CAMERA_HEIGHT = 480;
const int Width = 92;
const int Height = 112;
int gender_width;
int gender_height;
int im_width;
int im_height;
string g_listname_t[]=
{
"Jack",
"William",
"huang",
"Barton"
};
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()));
}
}
}
static void InitVideoCapture(VideoCapture &videoCapture, int CameraID)
{
try{
videoCapture.open(CameraID);
}catch(cv::Exception& e){}
if(!videoCapture.isOpened()){
cerr << "ERROR: could not open Camera!" << endl;
exit(1);
}
videoCapture.set(CV_CAP_PROP_FRAME_WIDTH, DESIRED_CAMERA_WIDTH);
videoCapture.set(CV_CAP_PROP_FRAME_HEIGHT, DESIRED_CAMERA_HEIGHT);
cout << "CameraID is :" << CameraID << endl;
}
static void InitDetectors(CascadeClassifier &faceCascade, CascadeClassifier &eyeCascade1, CascadeClassifier &eyeCascade2)
{
try{
faceCascade.load(faceCascadeFilename);
}catch(cv::Exception& e){}
if ( faceCascade.empty() ) {
cerr << "ERROR: Could not load Face Detection cascade classifier [" << faceCascadeFilename << "]!" << endl;
cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascade_frontalface_alt2') into this WebcamFaceRec folder." << endl;
exit(1);
}
cout << "Loaded the Face Detection cascade classifier [" << faceCascadeFilename << "]." << endl;
// Load the Eye Detection cascade classifier xml file.
try { // Surround the OpenCV call by a try/catch block so we can give a useful error message!
eyeCascade1.load(eyeCascadeFilename1);
} catch (cv::Exception& e) {}
if ( eyeCascade1.empty() ) {
cerr << "ERROR: Could not load 1st Eye Detection cascade classifier [" << eyeCascadeFilename1 << "]!" << endl;
cerr << "Copy the file from your OpenCV data folder (eg: 'C:\\OpenCV\\data\\haarcascades') into this WebcamFaceRec folder." << endl;
exit(1);
}
cout << "Loaded the 1st Eye Detection cascade classifier [" << eyeCascadeFilename1 << "]." << endl;
// Load the Eye Detection cascade classifier xml file.
try { // Surround the OpenCV call by a try/catch block so we can give a useful error message!
eyeCascade2.load(eyeCascadeFilename2);
} catch (cv::Exception& e) {}
if ( eyeCascade2.empty() ) {
cerr << "Could not load 2nd Eye Detection cascade classifier [" << eyeCascadeFilename2 << "]." << endl;
// Dont exit if the 2nd eye detector did not load, because we have the 1st eye detector at least.
//exit(1);
}
else
cout << "Loaded the 2nd Eye Detection cascade classifier [" << eyeCascadeFilename2 << "]." << endl;
}
void readDataTraining(Ptr<FaceRecognizer> &model,vector<Mat> &images,vector<int> &labels,string &filePath )
{
// These vectors hold the images and corresponding labels.
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(filePath, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << filePath << "\". 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);
}
/* Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();*/
model->train(images, labels);
//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;*/
}
void preprocessing(Mat &srcimg, Mat &dstimg)
{
Mat gray_img;
if(srcimg.channels() ==3 ){
cvtColor(srcimg,gray_img,CV_BGR2GRAY);
}
else if(srcimg.channels() ==4 ){
cvtColor(srcimg,gray_img,CV_BGRA2GRAY);
}
else {
gray_img = srcimg;
}
/*Mat equalized_Img;
equalizeHist(gray_img,equalized_Img);*/
dstimg = gray_img;
}
void faceDectRecog(CascadeClassifier &faceCascade,Ptr<FaceRecognizer> &model,Ptr<FaceRecognizer> &gender_model,Ptr<FaceRecognizer> &fishermodel,Mat &orginalimg,Mat &dectImg)
{
static int num = 0;
int predictedLabel = 0;
int gender_predict = 0;
// Only search for just 1 object (the biggest in the image).
int flags = CASCADE_FIND_BIGGEST_OBJECT;
//smallest object Size
Size minFeatureSize = Size(20,20);
// How detailed should the search be. Must be larger than 1.0.
float searchScaleFactor = 1.1f;
// How much the detections should be filtered out. This should depend on how bad false detections are to your system.
// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.
int minNeighbors = 8;
vector<Rect> faces;
faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,
minNeighbors,flags,minFeatureSize);
//faceCascade.detectMultiScale(equalized_Img, faces);
/* pca + Lda
Mat eigenvalues = gender_model->getMat("eigenvalues");//提取model中的特征值,该特征值默认由大到小排列
Mat W = gender_model->getMat("eigenvectors");//提取model中的特征向量,特征向量的排列方式与特征值排列顺序一一相应
int xth = 121;//打算保留前121个特征向量,代码中没有体现原因,但选择121是经过斟酌的,首先,在我的实验中,"前121个特征值之和/全部特征值总和>0.97";其次,121=11^2,能够将结果表示成一个11*11的2维图像方阵,交给fisherface去计算。
//vector<Mat> reduceDemensionimages;//降维后的图像矩阵
Mat evs = Mat(W, Range::all(), Range(0, xth));//选择前xth个特征向量,其余舍弃
Mat mean = gender_model->getMat("mean"); */
int i = 0;
for(i = 0; i < faces.size(); i++){
Rect face_id = faces[i];
Mat face = dectImg(face_id);
Mat face_resized;
Mat gender_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
cv::resize(face, gender_resized, Size(gender_width, gender_height), 1.0, 1.0, INTER_CUBIC);
rectangle(orginalimg,face_id,Scalar(0,255,0),1);
predictedLabel = model->predict(face_resized);
string result_message;
/*result_message = format("Predicted = %d ", predictedLabel);
cout << result_message << endl;*/
/* PCA +LDA
Mat projection = subspaceProject(evs, mean, gender_resized.reshape(1,1));//做子空间投影
//reduceDemensionimages.push_back(projection.reshape(1,sqrt(xth*1.0)));
Mat reduceDemensionimages = projection.reshape(1,sqrt(xth*1.0));
gender_predict = fishermodel->predict(reduceDemensionimages);*/
string box_text;
box_text = format( "Prediction = " );
if ( predictedLabel >= 0 && predictedLabel <=3 )
{
box_text.append( g_listname_t[predictedLabel] );
}
else box_text.append( "Unknown" );
gender_predict = gender_model->predict(face_resized);
if(gender_predict == 0)
{
result_message = format("Predicted: female");
box_text.append( " female" );
}
else if (gender_predict == 1)
{
result_message = format("Predicted: male");
box_text.append( " male" );
}
else result_message = format("Predicted: Unknow");
cout << result_message << endl;
// Calculate the position for annotated text (make sure we don't
// put illegal values in there):
int pos_x = std::max(face_id.tl().x - 10, 0);
int pos_y = std::max(face_id.tl().y - 10, 0);
// And now put it into the image:
putText(orginalimg, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
}
}
void faceDect(CascadeClassifier &faceCascade,Mat &orginalimg,Mat &dectImg)
{
// Only search for just 1 object (the biggest in the image).
int flags = CASCADE_FIND_BIGGEST_OBJECT;
//smallest object Size
Size minFeatureSize = Size(20,20);
// How detailed should the search be. Must be larger than 1.0.
float searchScaleFactor = 1.1f;
// How much the detections should be filtered out. This should depend on how bad false detections are to your system.
// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.
int minNeighbors = 6;
vector<Rect> faces;
faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,
minNeighbors,flags,minFeatureSize);
//faceCascade.detectMultiScale(equalized_Img, faces);
int i = 0;
for(i = 0; i < faces.size(); i++){
Rect face_id = faces[i];
rectangle(orginalimg,face_id,Scalar(0,255,0),1);
}
}
void CaptureFace(CascadeClassifier &faceCascade,Ptr<FaceRecognizer> &model,Mat &orginalimg,Mat &dectImg)
{
static int num = 0;
// Only search for just 1 object (the biggest in the image).
int flags = CASCADE_FIND_BIGGEST_OBJECT;
//smallest object Size
Size minFeatureSize = Size(20,20);
// How detailed should the search be. Must be larger than 1.0.
float searchScaleFactor = 1.1f;
// How much the detections should be filtered out. This should depend on how bad false detections are to your system.
// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.
int minNeighbors = 6;
vector<Rect> faces;
faceCascade.detectMultiScale(dectImg,faces,searchScaleFactor,
minNeighbors,flags,minFeatureSize);
//faceCascade.detectMultiScale(equalized_Img, faces);
int i = 0;
for(i = 0; i < faces.size(); i++){
Rect face_id = faces[i];
Mat face = dectImg(face_id);
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
char c[4];
itoa(num,c,10);
string s = face_lib + (string)c + ".png";
imwrite(s,face_resized);
cout << "Capture the" << num << "face" << endl;
cout << s << ";" << face_id << endl;
num ++;
rectangle(orginalimg,face_id,Scalar(0,255,0),1);
}
}
int main(int argc, const char *argv[])
{
int mode;
int i;
// Get the path to your CSV.
string fn_csv = string("at.txt");
string gender_csv = string("gender.txt");
string temp_csv = string("test.txt");
CascadeClassifier faceCascade;
CascadeClassifier eyeCascade1;
CascadeClassifier eyeCascade2;
VideoCapture videoCapture;
Ptr<FaceRecognizer> model;
Ptr<FaceRecognizer> temp_model;
Ptr<FaceRecognizer> gender_model;
int CameraID = 0;
vector<Mat> images;
vector<int> labels;
vector<Mat> temp_images;
vector<int> temp_labels;
vector<Mat> gender_images;
vector<int> gender_labels;
cout << "Compiled with OpenCV version " << CV_VERSION << endl << endl;
InitDetectors(faceCascade,eyeCascade1,eyeCascade2);
InitVideoCapture(videoCapture,CameraID);
printf("\n");
printf("FaceDec and Recognition V0.1\n");
printf("Usage: mode 0 : FaceDect; 1: train your own face; 2: Recognition \n");
printf("please input mode\n");
scanf("%d",&mode);
//model = createEigenFaceRecognizer();
temp_model = createEigenFaceRecognizer();
model =createEigenFaceRecognizer();
gender_model =createEigenFaceRecognizer();
Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();
//gender_model = createEigenFaceRecognizer();
if(mode == 3)
{
readDataTraining(model,images,labels,fn_csv);
readDataTraining(gender_model,gender_images,gender_labels,gender_csv);
gender_width = gender_images[0].cols;
gender_height = gender_images[0].rows;
im_width = images[0].cols;
im_height = images[0].rows;
model->save("Face_recog.yml");
gender_model->save("gender_recog.yml");
}
//readDataTraining(temp_model,temp_images,gender_labels,temp_csv);
model->load("Face_recog.yml");
gender_width = Width;
gender_height = Height;
im_width = Width;
im_height = Height;
gender_model->load("eigenface_gender.yml");//保存训练结果,供检測时使用
fishermodel->load("fisher.yml");
printf("gender_width :%d gender_height :%d im_width: %d im_height:%d\n",gender_width,gender_height,im_width,im_height);
int num = 0;
Mat cameraFrame;
if(mode == 4)
{
read_csv(temp_csv, temp_images, temp_labels);
for(i = 0; i < temp_images.size(); i ++)
{
Mat temp_img;
preprocessing(temp_images[i],temp_img);
CaptureFace(faceCascade,temp_model,temp_images[i],temp_img);
}
}
for(;;){
videoCapture >> cameraFrame;
if( cameraFrame.empty()){
cerr << "Error : could not grap next frame " << endl;
}
Mat processFrame = cameraFrame.clone();
Mat preprocess_img;
preprocessing(processFrame,preprocess_img);
switch(mode){
case 0:
faceDect(faceCascade,processFrame,preprocess_img);
break;
case 1:
CaptureFace(faceCascade,model,processFrame,preprocess_img);
case 2:
faceDectRecog(faceCascade,model,gender_model,fishermodel,processFrame,preprocess_img);
default:
break;
}
imshow("face_recognizer",processFrame);
char key = (char) waitKey(300);
if(key == 27)
break;
}
return 0;
}
人脸和性别识别(基于OpenCV)的更多相关文章
- 模式识别hw2-------基于matconvnet,用CNN实现人脸图片性别识别
主要来源模式识别课程大作业,本文首先感谢当初的助教和一起完毕作业的队友 matconvnet在matlab下封装了CNN常见算法,网址http://www.vlfeat.org/matconvnet/ ...
- 基于深度学习的人脸性别识别系统(含UI界面,Python代码)
摘要:人脸性别识别是人脸识别领域的一个热门方向,本文详细介绍基于深度学习的人脸性别识别系统,在介绍算法原理的同时,给出Python的实现代码以及PyQt的UI界面.在界面中可以选择人脸图片.视频进行检 ...
- python手写bp神经网络实现人脸性别识别1.0
写在前面:本实验用到的图片均来自google图片,侵删! 实验介绍 用python手写一个简单bp神经网络,实现人脸的性别识别.由于本人的机器配置比较差,所以无法使用网上很红的人脸大数据数据集(如lf ...
- 基于OpenCV性别识别
叙述性说明 所谓的性别识别推断检测到的面部是男性还是女性.它是一个二值分类问题. 识别算法可以用于SVM,BP神经网络.LDA,PCA,PCA+LDA等等.OpenCV官网给出的文档是基于Fisher ...
- 基于 OpenCV 的人脸识别
基于 OpenCV 的人脸识别 一点背景知识 OpenCV 是一个开源的计算机视觉和机器学习库.它包含成千上万优化过的算法,为各种计算机视觉应用提供了一个通用工具包.根据这个项目的关于页面,OpenC ...
- 基于人脸识别+IMDB-WIFI+Caffe的性别识别
本文用记录基于Caffe的人脸性别识别过程.基于imdb-wiki模型做finetune,imdb-wiki数据集合模型可从这里下载:https://data.vision.ee.ethz.ch/cv ...
- C++开发人脸性别识别教程(3)——OpenCv配置和ImageWatch插件介绍
OpenCv是C++图像处理的重要工具.这个人脸性别识别的项目就是借助OpenCv进行开发的. 尽管网上已经有了非常多关于OpenCv的配置教程,但出于教程完整性考虑.这里还是用专门的一篇博客来介绍O ...
- 【计算机视觉】基于OpenCV的人脸识别
一点背景知识 OpenCV 是一个开源的计算机视觉和机器学习库.它包含成千上万优化过的算法,为各种计算机视觉应用提供了一个通用工具包.根据这个项目的关于页面,OpenCV 已被广泛运用在各种项目上,从 ...
- 基于Opencv快速实现人脸识别(完整版)
无耻收藏网页链接: 基于OpenCV快速实现人脸识别:https://blog.csdn.net/beyond9305/article/details/92844258 基于Opencv快速实现人脸识 ...
随机推荐
- linux内核中GNU C和标准C的区别
linux内核中GNU C和标准C的区别 今天看了一下午的linux内核编程方面的内容,发现linux 内核中GNU C与标准C有一些差别,特记录如下: linux 系统上可用的C编译器是GNU C编 ...
- No-5.变量的命名
变量的命名 目标 标识符和关键字 变量的命名规则 0.1 标识符和关键字 1.1 标识符 标示符就是程序员定义的 变量名.函数名 名字 需要有 见名知义 的效果 标示符可以由 字母.下划线 和 数字 ...
- jquery腾讯换肤的一些技术实现
//检查cookie if($.cookie("skinID")){ $("#cssSkin").attr("href","/st ...
- 【转】C语言中access函数
头文件:unistd.h 功 能: 确定文件或文件夹的访问权限.即,检查某个文件的存取方式,比如说是只读方式.只写方式等.如果指定的存取方式有效,则函数返回0,否则函数返回-1. 用 法: int a ...
- 深入React技术栈之setState详解
抛出问题 class Example extends Component { contructor () { super() this.state = { value: 0, index: 0 } } ...
- LeetCode(38) Count and Say
题目 The count-and-say sequence is the sequence of integers beginning as follows: 1, 11, 21, 1211, 111 ...
- 详解js变量声明提升
之前一直觉会认为javascript代码执行是由上到下一行行执行的.自从看了<你不知道的JS>后发现这个观点并不完全正确.先来给大家举一个书本上的的例子: var a='hello wor ...
- 2k进制数(codevs 1157)
题目描述 Description 设r是个2k进制数,并满足以下条件: (1)r至少是个2位的2k进制数. (2)作为2k进制数,除最后一位外,r的每一位严格小于它右边相邻的那一位. (3)将r转换为 ...
- [USACO5.3]巨大的牛棚Big Barn
题目背景 (USACO 5.3.4) 题目描述 农夫约翰想要在他的正方形农场上建造一座正方形大牛棚.他讨厌在他的农场中砍树,想找一个能够让他在空旷无树的地方修建牛棚的地方.我们假定,他的农场划分成 N ...
- Codeforces913E. Logical Expression
现有串x=11110000,y=11001100,z=10101010,通过这三个串只用与或非三种操作到达给定的串,优先级非>或>与,可以加括号,问表达式最短的里面字典序最小的是谁,有&l ...