//显示结果 imshow("轮廓绘制结果", edge); vector<Point2f>point_0; vector<Point2f>point_1; vector<Point2f>point_2; vector<Point2f>point_3; vector<Point2f>point_4; vector<Point2f>point_5; vector<Point2f>point_6; vector<Point2f>point_7; vector<Point2f>point_8; vector<Point2f>point_9; vector<Point2f>point_10; vector<Point2f>point_11; vector<Point2f>point_12; vector<Point2f>point_13; vector<Point2f>point_14; vector<Point2f>point_15; vector<Point2f>point_16; vector<Point2f>point_17; vector<Point2f>point_18; vector<Point2f>point_19; int m, n; for (m = 0; m < contours.size(); m++) { for (n = 0; n < contours[m].size(); n++) { switch (m) { case 0: point_0.push_back(contours[m][n]); break; case 1: point_1.push_back(contours[m][n]); break; case 2: point_2.push_back(contours[m][n]); break; case 3: point_3.push_back(contours[m][n]); break; case 4: point_4.push_back(contours[m][n]); break; case 5: point_5.push_back(contours[m][n]); break; case 6: point_6.push_back(contours[m][n]); break; case 7: point_7.push_back(contours[m][n]); break; case 8: point_8.push_back(contours[m][n]); break; case 9: point_9.push_back(contours[m][n]); break; case 10: point_10.push_back(contours[m][n]); break; case 11: point_11.push_back(contours[m][n]); break; case 12: point_12.push_back(contours[m][n]); break; case 13: point_13.push_back(contours[m][n]); break; case 14: point_14.push_back(contours[m][n]); break; case 15: point_15.push_back(contours[m][n]); break; case 16: point_16.push_back(contours[m][n]); break; case 17: point_17.push_back(contours[m][n]); break; case 18: point_18.push_back(contours[m][n]); break; case 19: point_19.push_back(contours[m][n]); break; default: cout << "input error" << endl; } } } Mat img(321, 432, CV_8UC1, cv::Scalar(0));

Mat imgthreshold; Mat imgsrc = cv::imread("C:/Users/Dell/Pictures/Camera Roll/6.png",0); imshow("imgsrc", imgsrc); threshold(imgsrc, imgthreshold,50, 110, THRESH_BINARY); imshow("imgthreshold", imgthreshold); vector<vector> contours; findContours(imgthreshold, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); cv::Mat imgdraw = imgsrc.clone(); drawContours(imgdraw, contours, -1, (0, 0, 255),0); imshow("imgdraw", imgdraw); waitKey(0);为什么这块输出的是白色边框而不是红色

vector<vector<Point>> contours; findContours(imgthreshold, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); cv::Mat imgdraw = imgsrc.clone(); drawContours(imgdraw, contours, -1, Scalar(0, 0, 255), 1)...

cv::Mat imgthreshold; cv::Mat imgsrc = cv::imread("C:/Users/Dell/Pictures/Camera Roll/Yelan.jpg",0); cv::threshold(imgsrc, imgthreshold, 100, 255, THRESH_BINARY); vector<vector> contours; cv::findContours(imgthreshold, contours,cv::noArray(), RETR_LIST, CHAIN_APPROX_SIMPLE);为什么会在findContours这个函数触发断点

2. 检查 imgthreshold 的大小和数据类型是否正确，可以使用 cv::imshow() 函数在窗口中显示图像来检查。 3. 检查 threshold() 函数是否成功将 imgsrc 转换为二值图像，并将其保存在 imgthreshold 中。...

将下列代码转换成python代码 #include <opencv2/opencv.hpp> #include <vector> #include <time.h> using namespace cv; using namespace std; // 8邻域 const Point neighbors[8] = { { 0, 1 }, { 1, 1 }, { 1, 0 }, { 1, -1 }, { 0, -1 }, { -1, -1 }, { -1, 0 }, {-1, 1} }; int main() { // 生成随机数 RNG rng(time(0)); Mat src = imread("1.jpg"); Mat gray; cvtColor(src, gray, CV_BGR2GRAY); Mat edges; Canny(gray, edges, 30, 100); vector seeds; vector contour; vector<vector> contours; int i, j, k; for (i = 0; i < edges.rows; i++) for (j = 0; j < edges.cols; j++) { Point c_pt = Point(i, j); //如果当前点为轮廓点 if (edges.at<uchar>(c_pt.x, c_pt.y) == 255) { contour.clear(); // 当前点清零 edges.at<uchar>(c_pt.x, c_pt.y) = 0; // 存入种子点及轮廓 seeds.push_back(c_pt); contour.push_back(c_pt); // 区域生长 while (seeds.size() > 0) { // 遍历8邻域 for (k = 0; k < 8; k++) { // 更新当前点坐标 c_pt.x = seeds[0].x + neighbors[k].x; c_pt.y = seeds[0].y + neighbors[k].y; // 边界界定 if ((c_pt.x >= 0) && (c_pt.x <= edges.rows - 1) && (c_pt.y >= 0) && (c_pt.y <= edges.cols - 1)) { if (edges.at<uchar>(c_pt.x, c_pt.y) == 255) { // 当前点清零 edges.at<uchar>(c_pt.x, c_pt.y) = 0; // 存入种子点及轮廓 seeds.push_back(c_pt); contour.push_back(c_pt); }// end if } } // end for // 删除第一个元素 seeds.erase(seeds.begin()); }// end while contours.push_back(contour); }// end if } // 显示一下 Mat trace_edge = Mat::zeros(edges.rows, edges.cols, CV_8UC1); Mat trace_edge_color; cvtColor(trace_edge, trace_edge_color, CV_GRAY2BGR); for (i = 0; i < contours.size(); i++) { Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)); //cout << edges[i].size() << endl; // 过滤掉较小的边缘 if (contours[i].size() > 5) { for (j = 0; j < contours[i].size(); j++) { trace_edge_color.at<Vec3b>(contours[i][j].x, contours[i][j].y)[0] = color[0]; trace_edge_color.at<Vec3b>(contours[i][j].x, contours[i][j].y)[1] = color[1]; trace_edge_color.at<Vec3b>(contours[i][j].x, contours[i][j].y)[2] = color[2]; } } } imshow("edge", trace_edge_color); waitKey(); return 0; }

if ((x >= 0) and (x <= edges.shape[0] - 1) and (y >= 0) and (y <= edges.shape[1] - 1)): if edges[x, y] == 255: # 当前点清零 edges[x, y] = 0 # 存入种子点及轮廓 seeds.append((x, y)) contour....

在c++中vector<Mat> rvecs, tvecs是什么意思？如何查看？完整代码

vector<vector<Point2f>> imagePoints; vector<Point3f> obj; for (int i = 0; i < boardSize.height; i++) { for (int j = 0; j < boardSize.width; j++) { obj.push_back(Point3f(j, i, 0)); } } // ...

Mat imgthreshold; Mat imgsrc = cv::imread("C:/Users/Dell/Pictures/Camera Roll/OIP-C (1).jfif",0); imshow("imgsrc", imgsrc); threshold(imgsrc, imgthreshold,50, 110, THRESH_BINARY); imshow("imgthreshold", imgthreshold); vector<vector> contours; findContours(imgthreshold, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); // imgdraw = imgsrc.clone(); Mat imgdraw = Mat::zeros(imgthreshold.size(), CV_8UC3); drawContours(imgdraw, contours, -1, Scalar(0, 0, 255), 1); imshow("imgdraw", imgdraw); waitKey(0);这段代码中如何用qt来实现在ui上将opencv输出的轮廓根据鼠标事件删除

Mat imgsrc = cv::imread("C:/Users/Dell/Pictures/Camera Roll/OIP-C (1).jfif",0); QImage img = QImage((const unsigned char*)(imgsrc.data), imgsrc.cols, imgsrc.rows, QImage::Format_Grayscale8); ui->label...

#include <iostream> #include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> using namespace std; using namespace cv; #include <opencv2/wechat_qrcode.hpp> int main() { const std::string modelDir = R"(D:\opencv\opencv4.5.4\build_contrib\downloads\wechat_qrcode\)"; // 构造（使用异常捕获构造函数是否正常） wechat_qrcode::WeChatQRCode detector{ modelDir + "detect.prototxt", modelDir + "detect.caffemodel", modelDir + "sr.prototxt", modelDir + "sr.caffemodel" }; // 临时变量 Mat img; vector<Mat> points; // qrcode: Retangle, not RotatedBox auto camIdx = 0; // auto camIdx = R"(C:\Users\wanggao\Desktop\qrconde_test.jpg)"; VideoCapture cap(camIdx); while(cap.read(img)){ // 检测 auto res = detector->detectAndDecode(img, points); // 结果叠加 for(size_t idx = 0; idx < res.size(); idx ++){ Point pt1 = points[idx].at(0); Point pt2 = points[idx].at(2); Rect rect{pt1, pt2}; Point center = (pt1 + pt2) / 2; // 绘制矩形框 rectangle(img, rect, {0,0,255}, 2); circle(img, center, rect.height / 15, {0,0,255}, -1); // 解码字符串 putText(img, res[idx], {pt1.x, pt2.y + 16}, 1, 1, {0,0,255}); } imshow("image", img); if (waitKey(30) >= 0) break; } return 0; } 为什么detector->detectAndDecode这句话调用几次后会导致程序崩溃

Point pt1 = points[idx].at<Point2f>(0); Point pt2 = points[idx].at<Point2f>(2); Rect rect{pt1, pt2}; Point center = (pt1 + pt2) / 2; // 绘制矩形框 rectangle(img, rect, {0,0,255}, 2)...

class SR_net { public: SR_net(string path, vector<int> input_size, bool fp32, bool cuda = true); private: vector<int64_t> Gdims; int Gfp32; Env env = Env(ORT_LOGGING_LEVEL_ERROR, "RRDB"); SessionOptions session_options = SessionOptions(); Session* Gsession = nullptr; vector<const char> Ginput_names; vector<const char> Goutput_names; vector<int> Ginput_size = {}; }; SR_net::SR_net(string path, vector<int> input_size, bool fp32, bool cuda) { this->Ginput_size = input_size; this->Gfp32 = fp32; clock_t startTime_, endTime_; startTime_ = clock(); session_options.SetIntraOpNumThreads(6); if (cuda) { OrtCUDAProviderOptions cuda_option; cuda_option.device_id = 0; cuda_option.arena_extend_strategy = 0; cuda_option.cudnn_conv_algo_search = OrtCudnnConvAlgoSearchExhaustive; cuda_option.gpu_mem_limit = SIZE_MAX; cuda_option.do_copy_in_default_stream = 1; session_options.AppendExecutionProvider_CUDA(cuda_option); } wstring widestr = wstring(path.begin(), path.end()); this->Gsession = new Session(env, widestr.c_str(), this->session_options); this->session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL); AllocatorWithDefaultOptions allocator; this->Ginput_names = { "input" }; this->Goutput_names = { "output" }; endTime_ = clock(); cout << " The model loading time is:" << (double)(endTime_ - startTime_) / CLOCKS_PER_SEC << "s" << endl; } int main() { vector<int> input_shape = {}; SR_net net("E:/prj/SR_C/onnx_file/rrdb_full.onnx", input_shape, true, true); vector<String> files; glob("E:/prj/超分样本/1", files, true); size_t num = files.size(); bool Moos = true; cout << "共读取了" << num << "张图片" << endl; cout << "--------------------------------" << endl; for (int i = 0; i < num; i++) { Mat srcimg = imread(files[i]); Mat SR_image = net.Detect(srcimg, Moos); imshow("input", srcimg); imshow("result", SR_image); imwrite("./output/" + to_string(i + 1) + ".png", SR_image); waitKey(0); } }，在这段代码中，我如何把SR_net net("E:/prj/SR_C/onnx_file/rrdb_full.onnx", input_shape, true, true);这一行写到主函数的外面？

可以在 SR_net 类的定义中增加一个默认构造函数，并在这个构造函数中调用 SR_net(string path, vector<int> input_size, bool fp32, bool cuda) 构造函数，如下所示： c++ class SR_net { public: SR_net() : ...

VideoCapture cap(0); // 创建窗口 namedWindow("Color Detection", WINDOW_NORMAL); while (true) { Mat frame; cap >> frame; // 读取摄像头图像 // 红色范围的HSV值 Scalar lower_green = Scalar(0, 50, 50); Scalar upper_green = Scalar(40, 255, 255); // 将图像从BGR颜色空间转换为HSV颜色空间 Mat hsv_frame; cvtColor(frame, hsv_frame, COLOR_BGR2HSV); // 对图像进行颜色阈值处理，提取红色区域 Mat green_mask; inRange(hsv_frame, lower_green, upper_green, green_mask); // 对二值图像进行形态学操作，去除噪声 Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 5)); morphologyEx(green_mask, green_mask, MORPH_OPEN, kernel); // 寻找红色区域的轮廓 std::vector<std::vector> contours; std::vector<Vec4i> hierarchy; findContours(green_mask.clone(), contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); // 对每个轮廓进行绘制矩形框 for (size_t i = 0; i < contours.size(); i++) { Rect bounding_rect = boundingRect(contours[i]); rectangle(frame, bounding_rect, Scalar(10, 0, 255), 2); } // 显示图像 imshow("Color Detection", frame); // 按Esc键退出循环 if (waitKey(1) == 27) break; } // 释放摄像头和销毁窗口 cap.release(); destroyAllWindows();帮我用面向对象角度解释这段代码

接着，使用findContours函数找到绿色区域的轮廓，并使用矩形框（rectangle函数）绘制出每个轮廓。最后，通过imshow函数显示处理后的图像，并通过waitKey函数等待用户按下Esc键来退出循环。在退出循环后，释放...

#include<iostream> #include<opencv2/opencv.hpp> #include <opencv2/core/core.hpp> #include<opencv2/highgui/highgui_c.h> #include <opencv2/imgproc/imgproc.hpp> using namespace std; using namespace cv; int main() { //Mat img = imread("cells.jpg", CV_LOAD_IMAGE_GRAYSCALE); // 读入灰度图像 Mat img; Mat img1 = imread("G:/图像处理/实验课设/实验课设5.31/cell3.png"); //cvtColor(img1, img, CV_BGR2GRAY); Mat img_blur; cvtColor(img1, img_blur, CV_BGR2GRAY); Mat img_thresh1; threshold(img_blur, img_thresh1, 0, 255, THRESH_BINARY_INV + THRESH_OTSU); // 二值化 Mat img_thresh; Mat element = getStructuringElement(MORPH_RECT, Size(35, 35), Point(-1, -1)); erode(img_thresh1, img_thresh, element); //morphologyEx(img_thresh1, img_thresh, MORPH_OPEN, kernel); imshow("FUSHI", img_thresh); vector<vector> contours; findContours(img_thresh, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); // 寻找轮廓 //findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0)); Mat img_contours = Mat::zeros(img_thresh.size(), CV_8UC3); Scalar color(0, 0, 255); drawContours(img_contours, contours, -1, color); cout << "细胞个数：" << contours.size() << endl; imshow("去噪后的图像", img_blur); imshow("二值化后的图像", img_thresh); imshow("轮廓", img_contours); waitKey(0); return 0; } OpenCV2015版本把element函数中size（）创建滑动控件createTrackbar x64

Mat element = getStructuringElement(MORPH_RECT, Size(35, 35), Point(-1, -1)); 要在滑动条上调整 element 的大小，您需要使用 createTrackbar 函数创建滑动条，并根据滑动条的值更新 element 的大小。 ...

帮我写一个C++程序，我有一个候选框容器，std::vector<cv::Rect> bboxes;还有这些框的中心点容器std::vector<cv::Point> centers，这些我都已经计算好可以当作输入，我想使用dbscan聚类把小框融合成大框，麻烦帮我写一个程序。

vector<Point> centers; for (auto bbox : bboxes) { centers.push_back(Point(bbox.x + bbox.width / 2, bbox.y + bbox.height / 2)); } // dbscan 聚类参数 double eps = 50; // 半径 int minPts = 2; // ...

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在opencv中如何使用c++实现把一组8张vector<Mat>类型的图，用循环语句对8张图进行通道分离后，依次存储在另一个vector<Mat>里

可以使用循环语句对8张图像进行通道分离，然后将分离后的通道依次存储在另一个vector<Mat>中。下面是一个示例代码： c++ #include <opencv2/opencv.hpp> #include <vector> using namespace cv; using ...

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java opencv 绘制Point中线

1. 首先，使用OpenCV的 line() 函数绘制两个点之间的直线。这个函数需要传入三个参数：图像、起点坐标和终点坐标。 2. 然后，计算出这两个点的中心坐标。可以使用以下代码计算中心坐标： int center_x = ...

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帮我写一个C++程序，我有一个候选框容器，std::vector<cv::Rect> bboxes;还有这些框的中心点容器std::vector<cv::Point> centers，这些我都已经计算好可以当作输入，我想使用dbscan聚类把小框融合成大框，麻烦帮我写一个程序。

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