环境：OpenCV3.2.0 + VS2017
61、轮廓集合重排序(按轮廓面积从小到大)
//对轮廓集合面积从大到小排序
bool compareValue_bs(const std::vector<cv::Point> & c1, const std::vector<cv::Point> & c2)
{int area1 = cv::contourArea(c1);int area2 = cv::contourArea(c2);return area1 > area2;
} std::vector<std::vector<cv::Point>> ENDcontour;std::vector<std::vector<cv::Point>> contours;std::vector<cv::Vec4i> hierarchy;std::vector<std::vector<cv::Point>>::iterator k; //迭代器，访问容器数据cv::findContours(thresholdMat, contours, hierarchy, cv::RETR_EXTERNAL, CV_RETR_LIST); //查找外轮廓，压缩存储轮廓点std::sort(contours.begin(), contours.end(), compareValue_bs);
62、只删除过小轮廓及挨边轮廓
bool deleteSmallMat(cv::Mat thresholdMat, cv::Mat &resMat, int minSize = 30, bool debug = false);bool PlaneRec::deleteSmallMat(cv::Mat thresholdMat, cv::Mat &resMat, int minSize, bool debug)
{thresholdMat.copyTo(resMat);std::vector<cv::Rect> boundRect;std::vector<cv::RotatedRect> minRect;cv::Mat visual_bR;if (debug) thresholdMat.copyTo(visual_bR);if (visual_bR.type() != CV_8UC3) cv::cvtColor(visual_bR, visual_bR, cv::COLOR_GRAY2BGR);std::vector<std::vector<cv::Point>> ENDcontour;std::vector<std::vector<cv::Point>> contours;std::vector<cv::Vec4i> hierarchy;std::vector<std::vector<cv::Point>>::iterator k; //迭代器，访问容器数据cv::findContours(thresholdMat, contours, hierarchy, cv::RETR_EXTERNAL, CV_RETR_LIST); //查找外轮廓，压缩存储轮廓点std::sort(contours.begin(), contours.end(), compareValue_bs);if (debug) printf("\n\n\n\n\n");if (contours.size() <= 0)//图为全黑时{std::cout << __FUNCTION__ << "cons.size() <= 0";//return false;}if (debug) printf("图像处理后 检测到的轮廓数 cons.size() = %d \n", contours.size());int remainNum = 0;//剩余的轮廓数（有重画出来的轮廓数）cv::Mat tmpMat = cv::Mat(thresholdMat.rows, thresholdMat.cols, CV_8UC1, cv::Scalar(0, 0, 0));//画出轮廓;int count = 0;for (k = contours.begin(); k != contours.end(); ++k, count++) //删除小连通域的{std::vector<cv::Point> curContours = *k;if (curContours.size() < minSize) {cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), 2, CV_AA, hierarchy);continue;}remainNum++;if (1) {int area1 = cv::contourArea(curContours);if (debug) cout << __FUNCTION__ << " count: " << count << ", area1=" << area1 << endl;}minRect.push_back(cv::minAreaRect(curContours));boundRect.push_back(cv::boundingRect(curContours));if (debug) cv::rectangle(visual_bR, boundRect[boundRect.size() - 1].tl(), boundRect[boundRect.size() - 1].br(), cv::Scalar(0, 255, 0), 1);if (debug) cv::putText(visual_bR, std::to_string(boundRect.size() - 1), boundRect[boundRect.size() - 1].tl(), cv::FONT_HERSHEY_COMPLEX, 0.45, cv::Scalar(255, 135, 160), 1);if (debug) cv::putText(visual_bR, std::to_string(boundRect.size() - 1), boundRect[boundRect.size() - 1].br(), cv::FONT_HERSHEY_COMPLEX, 0.45, cv::Scalar(255, 135, 160), 1);if (debug) cv::drawContours(visual_bR, contours, count, cv::Scalar(255, 135, 160), -1, CV_AA, hierarchy);if (debug) {cv::circle(visual_bR, minRect.at(minRect.size() - 1).center, 3, cv::Scalar(0, 0, 255), -1, 8); //绘制最小外接矩形的中心点cv::Point2f rect[4];minRect.at(minRect.size() - 1).points(rect); //把最小外接矩形四个端点复制给rect数组for (int j = 0; j < 4; j++) {cv::line(visual_bR, rect[j], rect[(j + 1) % 4], cv::Scalar(0, 0, 255), 1, 8); //绘制最小外接矩形每条边}}//if (boundRect.at(boundRect.size() - 1).height < src.rows / 5) continue;//轮廓是长度比图像的1/5短则直接过掉if (boundRect.size() > 0) {cv::Rect curBR = boundRect.at(boundRect.size() - 1);cv::RotatedRect curMR = minRect.at(minRect.size() - 1);double whRatio = curBR.width*1.0 / curBR.height;//宽高比double longMR = curMR.size.width;double shortMR = curMR.size.height;longMR = curMR.size.width > curMR.size.height ? curMR.size.width : curMR.size.height;shortMR = curMR.size.width < curMR.size.height ? curMR.size.width : curMR.size.height;double wh_MR_Ratio = longMR * 1.0 / shortMR;//非垂直的外接矩形框的长边短边比int area = cv::contourArea(curContours);double curS_tect = longMR * shortMR;//非垂直最小外接矩形面积double aRetio = area / curS_tect;//轮廓本身与非垂直最小外接矩形的面积比值if (shortMR <= 10) {cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), 2, CV_AA, hierarchy);continue;}if (curBR.x == 0 ||curBR.y == 0 ||curBR.x + curBR.width >= thresholdMat.cols ||curBR.y + curBR.height >= thresholdMat.rows) {cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), 2, CV_AA, hierarchy);continue;//挨边的删掉}if (debug) printf("area = %d， curS_rect=%lf， aRetio=%lf \n", area, curS_tect, aRetio);if (aRetio >= 0.9) {//continue;}if (debug) cout << "--- curBR_" << boundRect.size() - 1 << curBR << whRatio << "\t";if (debug) cout << " \tMR.angle=" << curMR.angle << " \t, MR.center=" << curMR.center << " \t, MR.points=" << curMR.size << ", wh_MR_Ratio" << wh_MR_Ratio << endl;//if (whRatio > 1) continue;//宽高比不满足要求的直接 continue}ENDcontour.push_back(curContours);cv::drawContours(tmpMat, contours, count, cv::Scalar(255, 255, 255), -1, CV_AA, hierarchy);}if (debug) cv::namedWindow("visual_bR222", cv::NORMCONV_FILTER);if (debug) cv::imshow("visual_bR222", visual_bR);if (debug) cv::imshow("tmpMat222", tmpMat);//tmpMat.copyTo(resMat);return true;
} deleteSmallMat(thresholdMat, thresholdMat, 30, debug);63、获取轮廓点集
初始版：
std::vector<cv::Point> curContours = *k;cv::Mat curMat = cv::Mat(thresholdMat.rows, thresholdMat.cols, CV_8UC1, cv::Scalar(0, 0, 0));float peri = cv::arcLength(curContours, true);cv::approxPolyDP(curContours, conPoly[count], 0.02 * peri, true);cv::drawContours(curMat, contours, count, cv::Scalar(255, 255, 255), -1,cv::LINE_8, hierarchy);if (debug)cv::namedWindow("curMat", cv::NORMCONV_FILTER);if (debug) cv::imshow("curMat", curMat);if (1) {//找白的，白占比大于 才认为是小圆角cv::Mat imgReadOriPv;//原图上轮廓的对应位置imgGray.copyTo(imgReadOriPv, curMat);if (debug) cv::imshow("imgReadOriPv", imgReadOriPv);cv::threshold(imgMark, imgMark, 105, 255, cv::THRESH_BINARY);cv::Mat open;int tempk = 3;cv::Mat imgDil;cv::dilate(curMat, imgDil, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(tempk, tempk)));if (debug)cv::namedWindow("dilate-WriteMat2", cv::NORMCONV_FILTER);if (debug) cv::imshow("dilate-WriteMat2", imgDil);//cv::erode(dil, open, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(tempk, tempk)));imgDil.copyTo(open);open = open ^ curMat;//if (debug)cv::namedWindow("open-WriteMat2", cv::NORMCONV_FILTER);//if (debug) cv::imshow("open-WriteMat2", open);cv::threshold(open, open, 254, 255, cv::THRESH_BINARY);if (debug)cv::namedWindow("imgConEdge-WriteMat3", cv::NORMCONV_FILTER);if (debug) cv::imshow("imgConEdge-WriteMat3", open);//膨胀后减原轮廓只余下轮廓外圈，以获取边缘点集cv::Point startPnt = curContours[0];//第一个点for (int row = 0; row < open.rows; row++) {for (int col = 0; col < open.cols; col++) {cv::Point curp = cv::Point(col, row);if (open.at<uchar>(curp) >= 205) {startPnt = curp;break;}}}//if (debug) cout << "----------------- open.size()=" << open.size() << ", startPnt" << startPnt << endl;//if (debug) circle(open, startPnt, 2, cv::Scalar(0, 0, 0), cv::FILLED);if (1) {//轮廓完整点集 //为了测试轮廓形态对比 先原图像素值判定0914cxlvector<cv::Point> conPntLst;cv::Mat imgFindPnt;curMat.copyTo(imgFindPnt);cv::Mat imgFound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntLst, startPnt, 140);//以顺时针沿外边缘的顺序存入//if (debug) cout << "-----------------conPntLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (0) {//可视化cv::Mat imgF111ound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);if (debug) circle(imgF111ound, startPnt, 2, cv::Scalar(255, 255, 255), cv::FILLED);for (cv::Point curp : conPntLst) {if (debug) cout << "*** startPnt=" << curp << endl;imgFindPnt.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", imgFindPnt);imgF111ound.at<uchar>(curp) = 255;if (debug) cv::imshow("img--------------ound", imgF111ound);//if (debug) cv::waitKey(51);}}double avgPv = 0;for (cv::Point curp : conPntLst) {int pv = imgReadOriPv.at<uchar>(curp);avgPv += pv;}avgPv /= conPntLst.size();if (debug) cout << "-----------------avgPv=" << avgPv << endl;if (avgPv <= 200) {//轮廓对应原图上不够白的小轮廓，认为不是小圆角cv::drawContours(thresholdMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);cv::drawContours(thresholdMat, contours, count, cv::Scalar(0, 0, 0), 3, CV_AA, hierarchy);continue;//不要了}}if (0) {//轮廓外边缘点集//cv::Mat newMat = cv::Mat(thresholdMat.rows, thresholdMat.cols, CV_8UC1, cv::Scalar(0, 0, 0));//drawContours(newMat, conPoly, count, cv::Scalar(255, 255, 255), 1);//drawContours(open, conPoly, count, cv::Scalar(255, 255, 255), 1);vector<cv::Point> conPntLst;cv::Mat imgFindPnt;open.copyTo(imgFindPnt);//newMat.copyTo(imgFindPnt);cv::Mat imgFound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntLst, startPnt, 140);//以顺时针沿外边缘的顺序存入if (debug) cout << "-----------------conPntAllLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (1) {//可视化cv::Mat imgF111ound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);if (debug) circle(imgF111ound, startPnt, 1, cv::Scalar(255, 255, 255), cv::FILLED);for (cv::Point curp : conPntLst) {if (debug) cout << "*** startPnt=" << curp << endl;imgFindPnt.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", imgFindPnt);imgF111ound.at<uchar>(curp) = 255;if (debug) cv::imshow("img--------------ound", imgF111ound);if (debug) cv::waitKey(51);}}if (1) {std::vector<double> resList;int res = getAngleChange(conPntLst, resList);for (int i = 0; i < resList.size(); ++i) {if (debug) std::cout << resList[i] / (2.0*PI) * 360 << " -> ";}std::vector<double> trend;trend = get_trendList(resList, 1, debug);resList = trend;if (1) {int smoothCnt = 2;std::vector<double> newWLst = resList;int curCnt = smoothCnt;while (curCnt--) {linearSmooth3(newWLst, newWLst, 1);}resList = newWLst;}if (1) {if (debug) printf("disList.size()= %d \n", resList.size());float mean, variance, standard_deviation;get_meanCorrelationTest(resList, mean, variance, standard_deviation);if (debug) printf("disList 均值: %f \n", mean); // 均值if (debug) printf("disList 方差: %f \n", variance); // 方差if (debug) printf("disList 标准差: %f \n\n", standard_deviation); // 标准差}if (debug) showLine(resList);}}//轮廓外边缘点集}64、获取满足阈值的连通域点集(深搜，顺时针搜索存入)
/*
//以深搜的方式，顺时针方向获取轮廓外边缘点集
#include <stack>
cv::Mat& src, cv::Mat& matDst, 搜索结果可视化标识
vector<cv::Point> &conPntLst, 获取到的轮廓边缘点集
cv::Point2i startPnt, 起始种子点
int th，像素值大于该值才被认为是轮廓部分
*/
void getPntLst_dfs_clockwise(cv::Mat& src, cv::Mat& matDst, vector<cv::Point> &conPntLst, cv::Point2i startPnt, int th)
{//cout << __FUNCTION__ << " conPntLst.size()=" << conPntLst.size() << ", startPnt" << startPnt << endl;stack<cv::Point> ptStack;//种子点队列//搜索方向顺序数据int DIR[8][2] = { { 0, -1 }, { 1, -1 }, { 1, 0 }, { 1, 1 }, { 0, 1 }, { -1, 1 }, { -1, 0 }, { -1, -1 } };//从上往右顺时针搜ptStack.push(startPnt);//起始种子点入栈conPntLst.clear();while (!ptStack.empty()) {cv::Point curp = ptStack.top();ptStack.pop();conPntLst.push_back(curp);//分别对八个方向上的点进行生长for (int i = 0; i < 8; ++i) {cv::Point tmpp;tmpp.x = curp.x + DIR[i][0];tmpp.y = curp.y + DIR[i][1];//检查是否是边缘点if (tmpp.x < 0 ||tmpp.y < 0 ||tmpp.x > (src.cols - 1) ||tmpp.y > (src.rows - 1)) {continue;}int nGrowLable = matDst.at<uchar>(tmpp.y, tmpp.x); //是否已搜过if (nGrowLable == 0) {//未搜过int nCurValue = src.at<uchar>(tmpp.y, tmpp.x);//是否属于轮廓if (nCurValue >= th) {//属于轮廓matDst.at<uchar>(tmpp.y, tmpp.x) = 255; //标记为已搜过ptStack.push(tmpp);}}}}
}调用示例：
cv::Point startPnt = curContours[0];//起始第一个点for (int row = 0; row < open.rows; row++) {for (int col = 0; col < open.cols; col++) {cv::Point curp = cv::Point(col, row);if (open.at<uchar>(curp) >= 205) {startPnt = curp;break;}}}vector<cv::Point> conPntLst;cv::Mat imgFindPnt;open.copyTo(imgFindPnt);//newMat.copyTo(imgFindPnt);cv::Mat imgFound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntLst, startPnt, 140);//以顺时针沿外边缘的顺序存入if (debug) cout << "-----------------conPntAllLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (1) {//可视化（点集及其存入顺序）cv::Mat imgF111ound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);if (debug) circle(imgF111ound, startPnt, 2, cv::Scalar(255, 255, 255), cv::FILLED);for (cv::Point curp : conPntLst) {if (debug) cout << "*** startPnt=" << curp << endl;imgFindPnt.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", imgFindPnt);imgF111ound.at<uchar>(curp) = 255;if (debug) cv::imshow("img--------------ound", imgF111ound);if (debug) cv::waitKey(51);}}65、从轮廓中获取所需点集(轮廓外边缘点集/轮廓完整点集)
/*
//从轮廓中获取所需点集(轮廓外边缘点集/轮廓完整点集)
cv::Mat imgOriginal, 提供图像尺寸大小
std::vector<cv::Point> curContour, 依据轮廓
cv::Mat& matDst, 搜索结果可视化标识
vector<cv::Point> &conPntLst, 获取到的轮廓边缘点集(第一个点是图像最上的轮廓白点)
int mode, 模式：0为获取轮廓外边缘点集(以顺时针沿外边缘的顺序存入)，1为获取轮廓完整点集
*/
void getPntLst_fromContour(cv::Mat imgOriginal, std::vector<cv::Point> curContour, cv::Mat& imgFound, vector<cv::Point> &conPntLst, int mode = 1, bool debug = false);/*
//从轮廓中获取所需点集(轮廓外边缘点集/轮廓完整点集)
cv::Mat imgOriginal, 提供图像尺寸大小
std::vector<cv::Point> curContour, 依据轮廓
cv::Mat& matDst, 搜索结果可视化标识
vector<cv::Point> &conPntLst, 获取到的轮廓边缘点集(第一个点是图像最上的轮廓白点)
int mode, 模式：0为获取轮廓外边缘点集(以顺时针沿外边缘的顺序存入)，1为获取轮廓完整点集
*/
void getPntLst_fromContour(cv::Mat imgOriginal, std::vector<cv::Point> curContour, cv::Mat& imgFound, vector<cv::Point> &conPntLst, int mode, bool debug)
{if (curContour.empty()) return;imgFound = cv::Mat::zeros(imgOriginal.size(), CV_8UC1);std::vector<cv::Point> curContours = curContour;cv::Mat curMat = cv::Mat(imgOriginal.rows, imgOriginal.cols, CV_8UC1, cv::Scalar(0, 0, 0));//当前轮廓std::vector<std::vector<cv::Point>> contours;contours.push_back(curContours);cv::drawContours(curMat, contours, 0, cv::Scalar(255, 255, 255), -1, cv::LINE_8);if (debug)cv::namedWindow("curMat", cv::NORMCONV_FILTER);if (debug) cv::imshow("curMat", curMat);cv::Mat imgFindPnt;curMat.copyTo(imgFindPnt);if (mode == 0) {cv::Mat imgConEdge;//轮廓外边缘if (0) {//实验证明用形态学梯度回导致轮廓边缘有两层点集cv::morphologyEx(curMat, imgConEdge, cv::MORPH_GRADIENT, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)));}else {//为了使轮廓边缘只余一层点集，选择用膨胀后与原图取异或cv::Mat imgDil;int tempk = 3;cv::dilate(curMat, imgDil, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(tempk, tempk)));if (debug)cv::namedWindow("dilate-ConEdge", cv::NORMCONV_FILTER);if (debug) cv::imshow("dilate-ConEdge", imgDil);imgDil.copyTo(imgConEdge);imgConEdge = imgConEdge ^ curMat;cv::threshold(imgConEdge, imgConEdge, 254, 255, cv::THRESH_BINARY);}if (debug)cv::namedWindow("imgConEdge", cv::NORMCONV_FILTER);if (debug) cv::imshow("imgConEdge", imgConEdge);//膨胀后减原轮廓只余下轮廓外圈，以获取边缘点集imgConEdge.copyTo(imgFindPnt);}else if (mode == 1) {curMat.copyTo(imgFindPnt);}cv::Point startPnt = curContours[0];//第一个点for (int row = 0; row < imgFindPnt.rows; row++) {for (int col = 0; col < imgFindPnt.cols; col++) {cv::Point curp = cv::Point(col, row);if (imgFindPnt.at<uchar>(curp) >= 205) {startPnt = curp;break;}}}//if (debug) cout << "----------------- imgFindPnt.size()=" << imgFindPnt.size() << ", startPnt" << startPnt << endl;//if (debug) circle(open, startPnt, 2, cv::Scalar(0, 0, 0), cv::FILLED);getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntLst, startPnt, 140);//以顺时针沿外边缘的顺序存入//if (debug) cout << "-----------------conPntLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (0) {//可视化（点集及其存入顺序）cv::Mat imgF111ound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);if (debug) circle(imgF111ound, startPnt, 1, cv::Scalar(255, 255, 255), cv::FILLED);for (cv::Point curp : conPntLst) {if (debug) cout << "*** startPnt=" << curp << endl;imgFindPnt.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", imgFindPnt);imgF111ound.at<uchar>(curp) = 255;if (debug) cv::imshow("img--------------ound", imgF111ound);if (debug) cv::waitKey(51);}}
}调用示例：
//轮廓完整点集vector<cv::Point> conPntLst;cv::Mat imgFound;getPntLst_fromContour(imgOriginal, curContours, imgFound, conPntLst, 1, debug);//找白的，白占比大于 才认为是小圆角cv::Mat imgReadOriPv;//原图上轮廓的对应位置imgGray.copyTo(imgReadOriPv, curMat);if (debug) cv::imshow("imgReadOriPv", imgReadOriPv);double avgPv = 0;for (cv::Point curp : conPntLst) {int pv = imgReadOriPv.at<uchar>(curp);avgPv += pv;}avgPv /= conPntLst.size();if (debug) cout << "-----------------avgPv=" << avgPv << endl;if (avgPv <= 200) {//轮廓对应原图上不够白的小轮廓，认为不是小圆角cv::drawContours(thresholdMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);cv::drawContours(thresholdMat, contours, count, cv::Scalar(0, 0, 0), 3, CV_AA, hierarchy);continue;//不要了} //轮廓外边缘点集vector<cv::Point> conPntLst;cv::Mat imgFound;getPntLst_fromContour(imgOriginal, curContours, imgFound, conPntLst, 0, debug);if (1) {std::vector<double> resList;int res = getAngleChange(conPntLst, resList);for (int i = 0; i < resList.size(); ++i) {if (debug) std::cout << resList[i] / (2.0*PI) * 360 << " -> ";}std::vector<double> trend;trend = get_trendList(resList, 1, debug);resList = trend;if (1) {int smoothCnt = 2;std::vector<double> newWLst = resList;int curCnt = smoothCnt;while (curCnt--) {linearSmooth3(newWLst, newWLst, 1);}resList = newWLst;}if (1) {if (debug) printf("disList.size()= %d \n", resList.size());float mean, variance, standard_deviation;get_meanCorrelationTest(resList, mean, variance, standard_deviation);if (debug) printf("disList 均值: %f \n", mean); // 均值if (debug) printf("disList 方差: %f \n", variance); // 方差if (debug) printf("disList 标准差: %f \n\n", standard_deviation); // 标准差}if (debug) showLine(resList); static void showLine(std::vector<double>posList, bool debug = false);void PlaneRec::showLine(std::vector<double> posList, bool debug)
{if (posList.size() < 2) {return;}int maxVar = 360 + 1;int minVar = -360 - 1;cv::Mat canva = cv::Mat::zeros(cv::Size(posList.size()*10 + 1, maxVar - minVar), CV_8UC3);cv::Point startPos(0,(int)posList[0] / (2.0*PI) * 360 + maxVar);for (int i = 1; i < posList.size(); ++i) {double tmp = posList[i] / (2.0*PI) * 360 + maxVar;cv::Point posEnd(i*10, (int)tmp);//canva.at<cv::Vec3b>(posEnd) = cv::Vec3b(255, 255, 255);cv::line(canva, startPos, posEnd, cv::Scalar(255,255,255), 3, 8);startPos = posEnd;}if (debug) cv::namedWindow("showLine", cv::NORMCONV_FILTER);if (debug) cv::imshow("showLine", canva);
}66、删除轮廓(不再会误删被包围在中间的内圈小轮廓)
前提：
直接轮廓查找后，利用cv::drawContours()涂黑。
//cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), -1, CV_AA, hierarchy);//cv::drawContours(resMat, contours, count, cv::Scalar(0, 0, 0), 2, CV_AA, hierarchy);一旦出现：需要删除的轮廓中 完整包含着 不需要删除的小轮廓 在其内圈，
则会在删除的同时将小轮廓也一起误删。
为避免这种情况，则需按连通域来进行删除。操作如下：
1）获取待删除轮廓对应的连通域，即其完整轮廓点集。
2）然后一个点一个点地去进行涂黑删除。
即可。
如此则不会误删其包含在内部的小轮廓。
for (k = contours.begin(); k != contours.end(); ++k, count++) //删除小连通域的{std::vector<cv::Point> curContours = *k;cv::Mat curMat = cv::Mat(imgOriginal.rows, imgOriginal.cols, CV_8UC1, cv::Scalar(0, 0, 0));//当前轮廓cv::drawContours(curMat, contours, count, cv::Scalar(255, 255, 255), -1, cv::LINE_8);vector<cv::Point> conPntAllLst;//轮廓完整点集 if (1) {//以便将连通域位置删除而不会误删大轮廓包含在内的小轮廓cv::Mat imgFindPnt;curMat.copyTo(imgFindPnt);cv::Mat imgFound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);cv::Point startPnt = curContours[0];//第一个点getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntAllLst, startPnt, 140);//以顺时针沿外边缘的顺序存入//if (debug) cout << "-----------------conPntLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (0) {//可视化cv::Mat imgF111ound = cv::Mat::zeros(thresholdMat.size(), CV_8UC1);for (cv::Point curp : conPntAllLst) {if (debug) cout << "*** startPnt=" << curp << endl;thresholdMat.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", thresholdMat);imgF111ound.at<uchar>(curp) = 255;if (debug) circle(imgF111ound, startPnt, 2, cv::Scalar(255, 255, 255), cv::FILLED);if (debug) cv::imshow("img--------------ound", imgF111ound);//if (debug) cv::waitKey(51);}}}//需要删除的轮廓，则轮廓对应位置涂黑for (cv::Point curp : conPntAllLst) {resMat.at<uchar>(curp) = 0;//轮廓对应位置涂黑}}拓展：改一下画轮廓的方式
if (0) {cv::drawContours(tmpMat, contours, i, cv::Scalar(255, 255, 255), -1, CV_AA, hierarchy);}else {//改一下画轮廓的方式vector<cv::Point> conPntAllLst;//轮廓完整点集 if (1) {//以便将连通域位置删除而不会误删大轮廓包含在内的小轮廓cv::Mat imgFindPnt;thresholdMat.copyTo(imgFindPnt);cv::Mat imgFound = cv::Mat::zeros(imgFindPnt.size(), CV_8UC1);cv::Point startPnt = curContours[0];//第一个点getPntLst_dfs_clockwise(imgFindPnt, imgFound, conPntAllLst, startPnt, 140);//以顺时针沿外边缘的顺序存入//if (debug) cout << "-----------------conPntLst.size()=" << conPntLst.size() << endl;if (debug) cv::imshow("imgFound", imgFound);if (0) {//可视化cv::Mat imgF111ound = cv::Mat::zeros(thresholdMat.size(), CV_8UC1);for (cv::Point curp : conPntAllLst) {if (debug) cout << "*** startPnt=" << curp << endl;thresholdMat.at<uchar>(curp) = 0;if (debug) cv::imshow("visual_conPntLst", thresholdMat);imgF111ound.at<uchar>(curp) = 255;if (debug) circle(imgF111ound, startPnt, 2, cv::Scalar(255, 255, 255), cv::FILLED);if (debug) cv::imshow("img--------------ound", imgF111ound);//if (debug) cv::waitKey(51);}}}for (cv::Point curp : conPntAllLst) tmpMat.at<uchar>(curp) = 255;//轮廓对应位置涂白}
