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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 RectphotoCenterRect; //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(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=https://www.it610.com/article/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 = https://www.it610.com/article/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); }

【opencv 图像检测导出子图像】opencv 图像检测导出子图像
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