opencv 图像检测导出子图像

 #include "stdafx.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include "windows.h"
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include
using namespace std;
using namespace cv;
#pragma warning(disable:4996)
static Mat src, gray_src,drawImg;
static Mat cutSrc;
//src.copyTo(drawImg);
static int threshold_v = 80;
static int threshold_max = 100;
static char * title = "rectAndRound";
static RNG rng(12345);
int nHeight;  //图像宽
int nWidth;   //图像高
int nBufferDist = 100;//图像中心矩形宽和高
Point photoCenterPoint;//图像中心点坐标
Rect  photoCenterRect; //图像中心矩形
string itos(int i) // 将int 转换成string
{
	stringstream s;
	s << i;
	return s.str();
}
static void m_contours(int, void *);
Mat resizeImag(cv::Mat imgSrc,float scale)
{
	int nHeight = imgSrc.rows*scale;
	int nWidth = imgSrc.cols*scale;
	Size ResImgSize = Size(nWidth, nHeight);
	Mat ResImg = Mat(ResImgSize, imgSrc.type());
	resize(imgSrc, ResImg, ResImgSize, CV_INTER_CUBIC);
	return ResImg;
}
void cutGrayImage(cv::Mat imgSrc, Rect boundRect,string str)
{
	cv::Mat image = imgSrc;
	//图像高宽
	int nHeight = image.rows;
	int nWidth = image.cols;
	Mat TargImg;
	int bufDis = 3;
	int srcRectL = boundRect.x - bufDis;
	int srcRectT = boundRect.y - bufDis;
	int srcRectR = boundRect.x + boundRect.width  + bufDis;
	int srcRectB = boundRect.y + boundRect.height + bufDis;
	//保证处理不越图像边界
	if (srcRectL>0&& srcRectT>0&& srcRectR(cv::Point(bw, bh)) = imgSrc.at(cv::Point(m, n));
			}
		}
		imwrite(str, TargImg);
		TargImg.release();
	}
}

cv::Mat filterSourceImage(cv::Mat imgSrc)
{
	cv::Mat image = imgSrc;
	//图像高宽
	int nHeight = image.rows;
	int nWidth = image.cols;
	//创建灰度图像，初始化
	Mat TargImg;
	TargImg.create(nHeight, nWidth, CV_8UC1);
	TargImg = Scalar::all(0);
	int threRelativeVal = 20; //较差阈值
	int threWhiteVal = 80; //像素绝对值阈值
	int threBlackVal = 80; //像素绝对值阈值
	for (int j = 0; j < nHeight; j++)
	{
		for (int i = 0; i < nWidth; i++)
		{
			int b = imgSrc.at(cv::Point(i, j))[0];
			int g = imgSrc.at(cv::Point(i, j))[1];
			int r = imgSrc.at(cv::Point(i, j))[2];
			//目标找到，开始遍历；双判断条件，一是像素rgb值差值、二是像素绝对值
			if (abs(b - r) < threRelativeVal&&abs(b - g) < threRelativeVal&&abs(g - b) < threRelativeVal&&b < threBlackVal
				&&r < threBlackVal&&g < threBlackVal)
			{
				TargImg.at(cv::Point(i, j)) = 255;
			}
		}
	}
	return TargImg;
}

int index = 0;
void main(int argc, char** argv)
{
	Mat rawImg;
	rawImg = imread(("000005.jpg"));
	//imshow("src31", src);
	src=resizeImag(rawImg,0.15);
	nHeight = src.rows;
	nWidth  = src.cols;
	//图像中心点坐标
	photoCenterPoint.x = (int)(nWidth / 2.0);
	photoCenterPoint.y = (int)(nHeight / 2.0);
	//图像中心左上角点坐标
	photoCenterRect.x = photoCenterPoint.x - (int)(nBufferDist / 2.0);
	photoCenterRect.y = photoCenterPoint.y - (int)(nBufferDist / 2.0);
	photoCenterRect.width  = nBufferDist;
	photoCenterRect.height = nBufferDist;
	//Mat gray_tar;
	gray_src.create(nHeight, nWidth, CV_8UC1);
	gray_src = Scalar::all(0);
	//imshow("原始_src", src);
	gray_src = filterSourceImage(src);
	gray_src.copyTo(cutSrc);
	//imshow("gray_src", gray_src);
	imwrite("gray.jpg", cutSrc);
	cutGrayImage(cutSrc, photoCenterRect, "ceshi.jpg");
	createTrackbar("rect round:", title, &threshold_v, threshold_max, m_contours);
	m_contours(0, 0);
	waitKey(0);
}
//bool GreaterSort(vector a, vector b)
//{
//	return (a.size() > b.size());
//}
void m_contours(int, void *)
{
	//Mat binary_output;
	vector> raw_contours;
	vector> contours;
	vector hierachy;
	findContours(gray_src, raw_contours, hierachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(-1, -1));//发现轮廓
	imshow("m_contours", gray_src);
	//对轮廓点进行过滤
	for (int i = 0; i < raw_contours.size();i++)
	{
		if (raw_contours[i].size()>380 && raw_contours[i].size() < 590)
		{
			contours.push_back(raw_contours[i]);
			cout<< index++<<":" << raw_contours[i].size()<30 && raw_contours[i].size() < 90)
		{
			contours.push_back(raw_contours[i]);
			cout << index++ << ":" << raw_contours[i].size() << endl;;
		}
	}
	src.copyTo(drawImg);
	vector boundRect(contours.size());
	vector> contours_ploy(contours.size());//减少点数后的轮廓的集合
	for (size_t i = 0; i < contours.size(); i++)
	{
		approxPolyDP(Mat(contours[i]), contours_ploy[i], 3, true);//减少轮廓点数，为后面的算法提高效率
		{
			boundRect[i] = boundingRect(contours_ploy[i]);
			Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
			rectangle(drawImg, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
			string str;
			str = itos(i);
			str = "./subImg/" + str + ".jpg";
			cutGrayImage(cutSrc, boundRect[i], str);
		}
	}
	// draw it
	//Point2f pts[4];
	//画出仪表中心矩形
	//rectangle(drawImg, photoCenterRect, color, 2, 8);//画矩形
	//cutGrayImage(gray_src, photoCenterRect,"single.jpg");
	//对目标链表进行排序，过滤多余链表
	//sort(contours.begin(), contours.end(), GreaterSort);
	//cutGrayImage(gray_src,boundRect);
	//将检测到圆心和指针画到图上
	//color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
	//cv::drawContours(drawImg, refine_contours2, -1, color, 1);
	imshow("", drawImg);
}