164 lines
4.2 KiB
C++
164 lines
4.2 KiB
C++
/* 程序名：singlefeature.c
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功能：分总程序：读入图像文件，得出单个文件的特征值
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*/
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include <cv.h>
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#include <highgui.h>
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#include "Point.h"
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#include<io.h>
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#include<vector>
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#include<iostream>
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#include <string.h>
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#include <direct.h>
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#include"Thinner.h"
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using namespace std;
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/*各种声明*/
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int* binary(IplImage* img, int bithro); //二值化图像
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int outlinefeature(IplImage* imglk, int feature[][50]);//计算图像的轮廓特征值
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IplImage* Cjbsb(IplImage* img, IplImage* imgjbsb, int jbwhite, int jbblack);//图像角标识别
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IplImage* singlefeature(char* path, int feature[][50]){
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//定义变量
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IplImage* img = 0; //原图
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IplImage* imglk = 0; //轮廓图
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IplImage* imggj = 0; //骨架图
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IplImage* imgjbsb = 0; //角标识别图
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IplImage* imgbj = 0; //只提取笔记部分的图像
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IplImage* imgbjhf = 0; //为文字区域画上方格
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IplImage* imgwzbj = 0; //为文字区域标出是否为文字（文字标记）
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int height, width, step, channels;
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uchar *data;
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int i, j;
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int *black; //用于返回图像每行黑像素的个数
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//int feature[50][50]={0}; //特征值初始化
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img = cvLoadImage(path, 0);
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/* 获取图像信息*/
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height = img->height;
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width = img->width;
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step = img->widthStep;
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channels = img->nChannels;
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data = (uchar *)img->imageData;
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/*开始处理*/
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/*图像放大*/
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IplImage* imgbig = 0; //原图的放大图
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CvSize dst_cvsize; //目标图像的大小
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float scale = 1;
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if (width<840){
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scale = (float)840 / width;
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dst_cvsize.width = 840;
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dst_cvsize.height = (int)(height*scale);
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}
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else
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{
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dst_cvsize.width = width;
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dst_cvsize.height = height;
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}
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imgbig = cvCreateImage(dst_cvsize, img->depth, img->nChannels);
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cvResize(img, imgbig, CV_INTER_LINEAR); // CV_INTER_NN - 最近邻插值,
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//CV_INTER_LINEAR - 双线性插值 (缺省使用),
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//CV_INTER_AREA - 使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现。
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//CV_INTER_CUBIC - 立方插值.
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/*二值化*/
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int bithro = 230; //输入二值化的阈值 (0--255)
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black = binary(imgbig, bithro); //二值化，并统计黑像素的个数，返回每行黑像素的个数(black)
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//cvNamedWindow("二值化结果图",CV_WINDOW_AUTOSIZE); //显示图像
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//cvShowImage("二值化结果图",img);
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//printf("二值化求解完成！！\n");
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/*角标识别*/
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int jbwhite = 5, jbblack = 4;
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imgjbsb = cvCreateImage(cvGetSize(imgbig), imgbig->depth, imgbig->nChannels);
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imgbj = Cjbsb(imgbig, imgjbsb, jbwhite, jbblack); //返回文字的笔迹部分
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/*计算骨架图*/
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imggj = cvCreateImage(cvGetSize(imgbj), imgbj->depth, imgbj->nChannels); //复制
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cvCopy(imgbj, imggj, NULL);
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uchar *gjdata = (uchar *)imggj->imageData;
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beforethin(gjdata, gjdata, imggj->width, imggj->height);
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for (j = 0; j<imggj->height; j++)
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{ //取值范围转到0--1
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for (i = 0; i<imggj->width; i++)
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{
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if (gjdata[j*imggj->widthStep + i] == 255)
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gjdata[j*imggj->widthStep + i] = 1;
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}
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}
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ThinnerRosenfeld(imggj->imageData, imggj->height, imggj->width);
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for (j = 0; j<imggj->height; j++)
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{//取值范围转到0--255,反转过来
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for (i = 0; i<imggj->width; i++)
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{
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if (gjdata[j*imggj->widthStep + i] == 1)
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gjdata[j*imggj->widthStep + i] = 0;
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else
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gjdata[j*imggj->widthStep + i] = 255;
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}
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}
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//保存图像 应先生成图像文件名
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/*
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char processPic[100]="E:/imggj/";
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char *namePic=new char[20];
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bool flag=false;
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string xuehao=path,kaoshihao=path;
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int num_iter=sizeof(path);
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for(int iter=0;iter<num_iter;iter++)
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{
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if(path[iter]=='x')
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{
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flag=true;
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break;
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}
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}
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if(flag)
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{
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xuehao=xuehao.substr(27,13);
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kaoshihao=kaoshihao.substr(40,5);
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}else
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{
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xuehao=xuehao.substr(27,12);
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kaoshihao=kaoshihao.substr(39,5);
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}
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strcat(processPic,xuehao.c_str());
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_mkdir(processPic);
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strcat(processPic,kaoshihao.c_str());
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strcat(processPic,".jpg");
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cvSaveImage(processPic,imggj);
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*/
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/*计算骨架特征徝*/
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outlinefeature(imggj, feature); //特征值占48*48的右上三角形，feature调用返回
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//cvWaitKey(0);
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/*释放内存*/
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cvReleaseImage(&imgbig);
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cvReleaseImage(&img);
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cvReleaseImage(&imgbj);
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cvReleaseImage(&imglk);
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cvReleaseImage(&imgjbsb);
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cvReleaseImage(&imgbjhf);
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cvReleaseImage(&imgwzbj);
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cvDestroyAllWindows();
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return imggj;
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}
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