本文將使用opencv3.4和SVM識別手寫數字,開發環境爲vs2013和C++。
數據集
opencv安裝文件夾的 “samples/data” 下預置了一張手寫數字的圖片,其包含了5000個 0至9 的手寫數字,每個數字大小爲20*20, 只需相應的格式進行分割就可得到相應的數據集。先在選定的文件夾裏新建10個文件夾,分別以0至9命名,方便存放圖片。以下是代碼:
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
char ad[128] = { 0 };
int filename = 0, filenum = 0;
Mat img = imread("D:/opencv-3.4.0/samples/data/digits.png");
Mat gray;
cvtColor(img, gray, CV_BGR2GRAY);
int b = 20;
int m = gray.rows / b; //原圖爲1000*2000
int n = gray.cols / b; //裁剪爲5000個20*20的小圖塊
for (int i = 0; i < m; i++)
{
int offsetRow = i*b; //行上的偏移量
if (i % 5 == 0 && i != 0)
{
filename++;
filenum = 0;
}
for (int j = 0; j < n; j++)
{
int offsetCol = j*b; //列上的偏移量
sprintf_s(ad, "D:/實習/seg/datasets/cvSamplesDigits/%d/%d.jpg", filename, filenum++);
//截取20*20的小塊
Mat tmp;
gray(Range(offsetRow, offsetRow + b), Range(offsetCol, offsetCol + b)).copyTo(tmp);
imwrite(ad, tmp);
}
}
return 0;
}
成功運行之後,就會在每個文件夾下生成500張20*20的圖片。
訓練
下面是訓練的代碼:
#include "stdafx.h"
#include <stdio.h>
#include <time.h>
#include <opencv2/opencv.hpp>
#include <opencv/cv.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <io.h>
using namespace std;
using namespace cv;
using namespace ml;
void getFiles(string path, vector<string>& files);
void get_1(Mat& trainingImages, vector<int>& trainingLabels);
void get_0(Mat& trainingImages, vector<int>& trainingLabels);
int main()
{
//獲取訓練數據
Mat classes;
Mat trainingData;
Mat trainingImages;
vector<int> trainingLabels;
get_1(trainingImages, trainingLabels);
get_0(trainingImages, trainingLabels);
Mat(trainingImages).copyTo(trainingData);
trainingData.convertTo(trainingData, CV_32FC1);
Mat(trainingLabels).copyTo(classes);
//配置SVM訓練器參數
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setDegree(0);
svm->setTermCriteria(TermCriteria(CV_TERMCRIT_ITER, 1000, 0.01));
svm->setGamma(1);
svm->setCoef0(0);
svm->setC(1);
svm->setNu(0);
svm->setP(0);
cout << "開始訓練!!!" << endl;
//訓練
svm->train(trainingData, cv::ml::ROW_SAMPLE, classes);
//保存模型
svm->save("../../svm.xml");
cout << "訓練好了!!!" << endl;
return 0;
}
void getFiles(string path, vector<string>& files)
{
long long hFile = 0;
struct _finddata_t fileinfo;
string p;
if ((hFile = _findfirst(p.assign(path).append("/*.jpg").c_str(), &fileinfo)) != -1)
{
do
{
if ((fileinfo.attrib & _A_SUBDIR))
{
if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
getFiles(p.assign(path).append("/").append(fileinfo.name), files);
}
else
{
files.push_back(p.assign(path).append("/").append(fileinfo.name));
}
} while (_findnext(hFile, &fileinfo) == 0);
_findclose(hFile);
}
}
void get_1(Mat& trainingImages, vector<int>& trainingLabels)
{
char * filePath = "D:/實習/seg/datasets/cvSamplesDigits/1";
vector<string> files;
getFiles(filePath, files);
int number = files.size();
for (int i = 0; i < number; i++)
{
Mat SrcImage = imread(files[i].c_str(), 0);
resize(SrcImage, SrcImage, Size(8, 16), (0, 0), (0, 0), INTER_AREA);
SrcImage = SrcImage.reshape(1, 1);
trainingImages.push_back(SrcImage);
trainingLabels.push_back(1);
}
}
void get_0(Mat& trainingImages, vector<int>& trainingLabels)
{
char * filePath = "D:/實習/seg/datasets/cvSamplesDigits/0";
vector<string> files;
getFiles(filePath, files);
int number = files.size();
for (int i = 0; i < number; i++)
{
Mat SrcImage = imread(files[i].c_str(), 0);
resize(SrcImage, SrcImage, Size(8, 16), (0, 0), (0, 0), INTER_AREA);
SrcImage = SrcImage.reshape(1, 1);
trainingImages.push_back(SrcImage);
trainingLabels.push_back(0);
}
}
測試
下面是測試代碼:
#include "stdafx.h"
#include <stdio.h>
#include <time.h>
#include <opencv2/opencv.hpp>
#include <opencv/cv.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <io.h>
using namespace std;
using namespace cv;
using namespace ml;
void getFiles(string path, vector<string>& files);
int main()
{
int result = 0;
char * filePath = "D:/ʵϰ/seg/datasets/test/0";
vector<string> files;
getFiles(filePath, files);
int number = files.size();
//cout << number << endl;
string modelpath = "../../svm.xml";
Ptr<SVM> svm = StatModel::load<SVM>(modelpath);
for (int i = 0; i < number; i++)
{
Mat inMat = imread(files[i].c_str(), 0);
//cout << files[i].c_str()<<endl;
resize(inMat, inMat, Size(8, 16), (0, 0), (0, 0), INTER_AREA);
Mat p = inMat.reshape(1, 1);
p.convertTo(p, CV_32FC1);
int response = (int)svm->predict(p);
cout << response << endl;
if (response == 1)
{
result++;
}
}
//cout << result << endl;
//getchar();
return 0;
}
void getFiles(string path, vector<string>& files)
{
long long hFile = 0;
struct _finddata_t fileinfo;
string p;
if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1)
{
do
{
if ((fileinfo.attrib & _A_SUBDIR))
{
if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
getFiles(p.assign(path).append("\\").append(fileinfo.name), files);
}
else
{
files.push_back(p.assign(path).append("\\").append(fileinfo.name));
}
} while (_findnext(hFile, &fileinfo) == 0);
_findclose(hFile);
}
}
以上代碼參考自:https://blog.csdn.net/chaipp0607/article/details/68067098/ ,在此表示感謝。他(她)的代碼是早期opencv版本的,我改成了opencv3.4版本的。以上代碼都經過了測試,只需修改對應路徑。值得說明的是,svm->predict()返回的直接就是分類。例如以上測試代碼中,返回的是0或1,如果返回了其他值,說明訓練或測試過程有錯誤。