刘家伟程序备份1（主要参考点：分割部分以及保存，在图片上显示数字）

备份1（主要参考点：分割部分以及保存，在图片上显示数字）"/>

刘家伟程序备份1（主要参考点：分割部分以及保存，在图片上显示数字）

今天成功代码暂存。该程序实现了轮廓检测，矩形分割出轮廓，模板匹配后，并显示数字

主函数

#include "stdafx.h"
#include <cv.h> 
#include "putText.h"#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"//-----------------------------------【命名空间声明部分】--------------------------------------
//          描述：包含程序所使用的命名空间
//-----------------------------------------------------------------------------------------------
using namespace cv;
using namespace std;
//
//-----------------------------------【宏定义部分】-------------------------------------------- 
//  描述：定义一些辅助宏 
//------------------------------------------------------------------------------------------------ 
#define WINDOW_NAME1 "【原始图窗口】"        //为窗口标题定义的宏 
#define WINDOW_NAME2 "【效果图窗口】"        //为窗口标题定义的宏 #define WINDOW_NAME11 "【原始图片】"        //为窗口标题定义的宏 
#define WINDOW_NAME21 "【匹配窗口】"        //为窗口标题定义的宏 
//-----------------------------------【全局变量声明部分】--------------------------------------
//  描述：全局变量的声明
//-----------------------------------------------------------------------------------------------
Mat g_srcImage;
Mat g_srcImage1;
Mat g_grayImage;
cv::Mat org;
cv::Mat dst;
int g_nThresh = 50;//阈值
int g_nMaxThresh = 255;//阈值最大值
RNG g_rng(12345);//随机数生成器
char strangerName[360];
int g_nMatchMethod;
int g_nMaxTrackbarNum = 5;
//-----------------------------------【全局函数声明部分】--------------------------------------
//   描述：全局函数的声明
//-----------------------------------------------------------------------------------------------
void on_ContoursChange(int, void*);
void on_Matching(int, void*);
static void ShowHelpText();
Mat g_templateImage; Mat g_resultImage;
//-----------------------------------【main( )函数】--------------------------------------------
//   描述：控制台应用程序的入口函数，我们的程序从这里开始执行
//-----------------------------------------------------------------------------------------------
int main()
{//【0】改变console字体颜色system("color 1F");//【0】显示欢迎和帮助文字ShowHelpText();//【1】载入3通道的原图像g_srcImage = imread("010.jpg", 1);g_srcImage1 = imread("777.jpg", 1);if (!g_srcImage.data) { printf("读取图片错误，请确定目录下是否有imread函数指定的图片存在~！ \n"); return false; }//【2】得到原图的灰度图像并进行平滑cvtColor(g_srcImage, g_grayImage, CV_BGR2GRAY);blur(g_grayImage, g_grayImage, Size(3, 3));//【3】创建原始图窗口并显示namedWindow(WINDOW_NAME1, CV_WINDOW_AUTOSIZE);imshow(WINDOW_NAME1, g_srcImage);namedWindow(WINDOW_NAME11, CV_WINDOW_AUTOSIZE);namedWindow(WINDOW_NAME21, CV_WINDOW_AUTOSIZE);//【4】设置滚动条并调用一次回调函数createTrackbar(" 阈值:", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ContoursChange);on_ContoursChange(0, 0);waitKey(0);return(0);
}//----------------------------【on_ContoursChange( )函数】---------------------------------
//      描述：回调函数
//-------------------------------------------------------------------------------------------------  
void on_ContoursChange(int, void*)
{//定义一些参数Mat threshold_output;vector<vector<Point>> contours;vector<Vec4i> hierarchy;g_nThresh = 150;// 使用Threshold检测边缘threshold(g_grayImage, threshold_output, g_nThresh, 255, THRESH_BINARY);// 找出轮廓findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));// 多边形逼近轮廓 + 获取矩形和圆形边界框vector<vector<Point> > contours_poly(contours.size());vector<Rect> boundRect(contours.size());vector<Point2f>center(contours.size());vector<float>radius(contours.size());//一个循环，遍历所有部分，进行本程序最核心的操作for (unsigned int i = 0; i < contours.size(); i++){approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true);//用指定精度逼近多边形曲线 boundRect[i] = boundingRect(Mat(contours_poly[i]));//计算点集的最外面（up-right）矩形边界minEnclosingCircle(contours_poly[i], center[i], radius[i]);//对给定的 2D点集，寻找最小面积的包围圆形 }// 绘制多边形轮廓 + 包围的矩形框 + 圆形框Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);Mat image_cut[100];      //从img中按照rect进行切割，此时修改image_cut时image中对应部分也会修改，因此需要copy  Mat image_copy[100];   //clone函数创建新的图片 int geshu = 0;for (int unsigned i = 0; i<contours.size(); i++){Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));//随机设置颜色drawContours(drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point());//绘制轮廓Point text_lb;//中点//circle(drawing, center[i], (int)radius[i], color, 2, 8, 0);//绘制圆//if (radius[i]<39 && radius[i]>20 && center[i].y>300 && center[i].x>200 && center[i].x<400)//if (radius[i]<39 && radius[i]>20 && center[i].y>300 && center[i].x>350 && center[i].x<400)//中间下面的点if (radius[i]<39 && radius[i]>20 && center[i].y>200 && center[i].x>100 && center[i].x<650){geshu++;rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);//绘制矩形text_lb = Point((boundRect[i].tl().x + boundRect[i].br().x)*0.5, (boundRect[i].tl().y + boundRect[i].br().y)*0.5);//putTextZH(drawing, "中点", center[i], Scalar(0, 0, 255), 10, "Arial");int width = abs(boundRect[i].tl().x - boundRect[i].br().x);int height = abs(boundRect[i].tl().y - boundRect[i].br().y);Rect rect(boundRect[i].tl().x, boundRect[i].tl().y, width, height);   //创建一个Rect框，属于cv中的类，四个参数代表x,y,width,height  image_cut[i] = Mat(g_grayImage, rect);      //从img中按照rect进行切割，此时修改image_cut时image中对应部分也会修改，因此需要copy  image_copy[i] = image_cut[i].clone();   //clone函数创建新的图片  sprintf(strangerName, "数字%d", geshu);namedWindow(strangerName, 1);//参数为零，则可以自由拖动strangerNameimshow(strangerName, image_copy[i]);sprintf(strangerName, "E://OCR_Recognition//opencv_project//project_op//opencv2_5_20//baocun_tupian//%d.jpg", geshu);  /*注意修改路径*/imwrite(strangerName, image_copy[i]);g_templateImage = imread(strangerName, 1);//【1】给局部变量初始化Mat srcImage22;g_srcImage1.copyTo(srcImage22);//【2】初始化用于结果输出的矩阵int resultImage_cols = g_srcImage1.cols - g_templateImage.cols + 1;int resultImage_rows = g_srcImage1.rows - g_templateImage.rows + 1;g_resultImage.create(resultImage_cols, resultImage_rows, CV_32FC1);//【3】进行匹配和标准化g_nMatchMethod = 3;matchTemplate(g_srcImage1, g_templateImage, g_resultImage, g_nMatchMethod);normalize(g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat());//【4】通过函数 minMaxLoc 定位最匹配的位置double minValue; double maxValue; Point minLocation; Point maxLocation;Point matchLocation;minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat());//【5】对于方法 SQDIFF 和 SQDIFF_NORMED, 越小的数值有着更高的匹配结果. 而其余的方法, 数值越大匹配效果越好if (g_nMatchMethod == CV_TM_SQDIFF || g_nMatchMethod == CV_TM_SQDIFF_NORMED){matchLocation = minLocation;}else{matchLocation = maxLocation;}//【6】绘制出矩形，并显示最终结果Point liu = Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows);rectangle(srcImage22, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0);rectangle(g_resultImage, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0);printf(" x( %d , y ：%d)\n", matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows);if (liu.x == 1128 && liu.y == 154){putTextZH(drawing, "6", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 455 && liu.y == 59){putTextZH(drawing, "1", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 736 && liu.y == 59){putTextZH(drawing, "2", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 1261 && liu.y == 154){putTextZH(drawing, "6", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 73 && liu.y == 59){putTextZH(drawing, "0", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 203 && liu.y == 154){putTextZH(drawing, "4", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 991 && liu.y == 59){putTextZH(drawing, "3", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 728 && liu.y == 154){putTextZH(drawing, "5", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 859 && liu.y == 154){putTextZH(drawing, "5", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 597 && liu.y == 59){putTextZH(drawing, "2", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 335 && liu.y == 154){putTextZH(drawing, "5", center[i], Scalar(0, 0, 255), 30, "Arial");}else if (liu.x == 997 && liu.y == 154){putTextZH(drawing, "6", center[i], Scalar(0, 0, 255), 30, "Arial");}imshow(WINDOW_NAME11, srcImage22);imshow(WINDOW_NAME21, g_resultImage);}}// 显示效果图窗口namedWindow(WINDOW_NAME2, CV_WINDOW_AUTOSIZE);imshow(WINDOW_NAME2, drawing);
}//-----------------------------------【ShowHelpText( )函数】----------------------------------  
//      描述：输出一些帮助信息  
//----------------------------------------------------------------------------------------------  
static void ShowHelpText()
{//输出欢迎信息和OpenCV版本printf("\n\n\t\t\t数字识别\n");printf("\n\n\t\t\t   当前使用的OpenCV版本为：" CV_VERSION);printf("\n\n  ----------------------------------------------------------------------------\n");//输出一些帮助信息  printf("\n\n\n\t当前阈值150~\n\n");printf("\n\n\t按键操作说明: \n\n""\t\t键盘按键【ESC】- 退出程序\n\n""\t\t毛哥2018/5/16\n\n");
}