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rtree.cpp 4.31 KB
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#include "stdafx.h"
/*
#include <fstream>
#include "opencv2/opencv.hpp"
#include <vector>
using namespace std;
using namespace cv;
int featureLen = 64;
void swapBuffer(char* buf)//0123->3210
{
char temp;
temp = *(buf);
*buf = *(buf + 3);
*(buf + 3) = temp;
temp = *(buf + 1);
*(buf + 1) = *(buf + 2);
*(buf + 2) = temp;
}
void GetROI(Mat& src, Mat& dst)
{
int left, right, top, bottom;
left = src.cols;
right = 0;
top = src.rows;
bottom = 0;
//Get valid area
for (int i = 0; i < src.rows; i++)
{
for (int j = 0; j < src.cols; j++)
{
if (src.at<uchar>(i, j) > 0)
{
if (j < left) left = j;
if (j > right) right = j;
if (i < top) top = i;
if (i > bottom) bottom = i;
}
}
}
//Point center;
//center.x = (left + right) / 2;
//center.y = (top + bottom) / 2;
int width = right - left;
int height = bottom - top;
int len = (width < height) ? height : width;
//Create a squre
dst = Mat::zeros(len, len, CV_8UC1);
//Copy valid data to squre center
Rect dstRect((len - width) / 2, (len - height) / 2, width, height);
Rect srcRect(left, top, width, height);
Mat dstROI = dst(dstRect);
Mat srcROI = src(srcRect);
srcROI.copyTo(dstROI);
}
void newRtStudy(vector<NumTrainData>& trainData)
{
int testCount = trainData.size();
Mat data = Mat::zeros(testCount, featureLen, CV_32FC1);
Mat res = Mat::zeros(testCount, 1, CV_32SC1);
for (int i = 0; i < testCount; i++)
{
NumTrainData td = trainData.at(i);
memcpy(data.data + i*featureLen*sizeof(float), td.data, featureLen*sizeof(float));
res.at<unsigned int>(i, 0) = td.result;
}
/////////////START RT TRAINNING//////////////////
CvRTrees forest;
CvMat* var_importance = 0;
forest.train(data, CV_ROW_SAMPLE, res, Mat(), Mat(), Mat(), Mat(),
CvRTParams(10, 10, 0, false, 15, 0, true, 4, 100, 0.01f, CV_TERMCRIT_ITER));
forest.save("res/new_rtrees.xml");
}
int newRtPredict()
{
CvRTrees forest;
forest.load("res/new_rtrees.xml");
const char fileName[] = "res/t10k-images.idx3-ubyte";
const char labelFileName[] = "res/t10k-labels.idx1-ubyte";
ifstream lab_ifs(labelFileName, ios_base::binary);
ifstream ifs(fileName, ios_base::binary);
if (ifs.fail() == true)
return -1;
if (lab_ifs.fail() == true)
return -1;
char magicNum[4], ccount[4], crows[4], ccols[4];
ifs.read(magicNum, sizeof(magicNum));
ifs.read(ccount, sizeof(ccount));
ifs.read(crows, sizeof(crows));
ifs.read(ccols, sizeof(ccols));
int count, rows, cols;
swapBuffer(ccount);
swapBuffer(crows);
swapBuffer(ccols);
memcpy(&count, ccount, sizeof(count));
memcpy(&rows, crows, sizeof(rows));
memcpy(&cols, ccols, sizeof(cols));
Mat src = Mat::zeros(rows, cols, CV_8UC1);
Mat temp = Mat::zeros(8, 8, CV_8UC1);
Mat m = Mat::zeros(1, featureLen, CV_32FC1);
Mat img, dst;
//Just skip label header
lab_ifs.read(magicNum, sizeof(magicNum));
lab_ifs.read(ccount, sizeof(ccount));
char label = 0;
Scalar templateColor(255, 0, 0);
NumTrainData rtd;
int right = 0, error = 0, total = 0;
int right_1 = 0, error_1 = 0, right_2 = 0, error_2 = 0;
while (ifs.good())
{
//Read label
lab_ifs.read(&label, 1);
label = label + '0';
//Read data
ifs.read((char*)src.data, rows * cols);
GetROI(src, dst);
//Too small to watch
img = Mat::zeros(dst.rows * 30, dst.cols * 30, CV_8UC3);
resize(dst, img, img.size());
rtd.result = label;
resize(dst, temp, temp.size());
//threshold(temp, temp, 10, 1, CV_THRESH_BINARY);
for (int i = 0; i < 8; i++)
{
for (int j = 0; j < 8; j++)
{
m.at<float>(0, j + i * 8) = temp.at<uchar>(i, j);
}
}
if (total >= count)
break;
char ret = (char)forest.predict(m);
if (ret == label)
{
right++;
if (total <= 5000)
right_1++;
else
right_2++;
}
else
{
error++;
if (total <= 5000)
error_1++;
else
error_2++;
}
total++;
#if(SHOW_PROCESS)
stringstream ss;
ss << "Number " << label << ", predict " << ret;
string text = ss.str();
putText(img, text, Point(10, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
if (waitKey(0) == 27) //ESC to quit
break;
#endif
}
ifs.close();
lab_ifs.close();
stringstream ss;
ss << "Total " << total << ", right " << right << ", error " << error;
string text = ss.str();
putText(img, text, Point(50, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
waitKey(0);
return 0;
}
*/
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SVM-ShouXieShuZiShiBie
SVM-手写数字识别
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