jashliao 用 VC++ 实现 fanfuhan OpenCV 教学071 ~ opencv-071-彩色转二值化图像(直接使用Canny) 后 用图像形态学击中&击不中(morphol
jashliao 用 VC++ 实现 fanfuhan OpenCV 教学071 ~ opencv-071-彩色转二值化图像(直接使用Canny) 后 用图像形态学击中&击不中(morphologyEx)实现找出绳网中绳结位置
资料来源: https://fanfuhan.github.io/
https://fanfuhan.github.io/2019/04/22/opencv-071/
GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV
https://github.com/jash-git/jashliao-implements-FANFUHAN-OPENCV-with-VC
★前言:
★主题:
形态学的击中&击不中操作,根据结构元素不同,可以提取二值图像中的一些特殊区域,得到我们想要的结果。
★C++
// VC_FANFUHAN_OPENCV071.cpp : 定义主控台应用程式的进入点。 // /* // Debug | x32 通用属性 | C/C++ | | 一般 | | 其他 Include 目录 -> ..\..\opencv411_x64\include | | 连结器 | |一一般 | | 其他程式库目录 -> ..\..\opencv411_x64\lib | | |一输入 | | 其他相依性 -> opencv_world411d.lib;%(AdditionalDependencies) // Releas | x64 组态属性 | C/C++ | | 一般 | | 其他 Include 目录 -> ..\..\opencv411_x64\include;%(AdditionalDependencies) | | 连结器 | |一般 | | 其他程式库目录 -> ..\..\opencv411_x64\lib;%(AdditionalDependencies) | | |一输入 | | 其他相依性 -> opencv_world411.lib;%(AdditionalDependencies) */ #include "stdafx.h" #include#include #include #include using namespace std; using namespace cv; void blur_demo(Mat &image, Mat &sum); void edge_demo(Mat &image, Mat &sum); int getblockSum(Mat &sum, int x1, int y1, int x2, int y2, int i); void showHistogram(InputArray src, cv::String StrTitle); void backProjection_demo(Mat &mat, Mat &model); void blur3x3(Mat &src, Mat *det); void add_salt_pepper_noise(Mat &image); void add_gaussian_noise(Mat &image); void USMImage(Mat src, Mat &usm, float fltPar); void pyramid_up(Mat &image, vector &pyramid_images, int level); void pyramid_down(vector &pyramid_images); void laplaian_demo(vector &pyramid_images, Mat &image); void connected_component_demo(Mat &image); void componentwithstats_demo(Mat &image); void contours_info(Mat &image, vector > &pts); void contours_info(Mat &image, vector > &pts, int threshold01, int threshold02); void open_demo(bool blnopen); void close_demo(); void pause() { printf("Press Enter key to continue..."); fgetc(stdin); } int main() { Mat src = imread("../../images/cross.png");//Mat src = imread("../../images/test.png"); if (src.empty()) { cout << "could not load image.." << endl; pause(); return -1; } else { imshow("input_src", src); showHistogram(src, "Histogram_input_src"); // 二值图像 Mat gray, binary, result; cvtColor(src, gray, COLOR_BGR2GRAY); threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); imshow("input_binary", binary); // 击中击不中 Mat se = getStructuringElement(MORPH_CROSS, Size(11, 11)); morphologyEx(binary, result, MORPH_HITMISS, se); imshow("bit_and_miss", result); // 轮廓绘制 vector > contours; vector hierarchy; findContours(result, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (size_t c = 0; c < contours.size(); c++) { Rect rect = boundingRect(contours[c]); /* double area = contourArea(contours[c]); if (area < 200) { continue; } int h = rect.height; int w = rect.width; if (h >(3 * w) || h < 20) { continue; } */ rectangle(src, rect, Scalar(0, 0, 255)); } imshow("result", src); waitKey(0); } return 0; } void close_demo() { //读取图像 Mat src = imread("../images/morph3.png"); imshow("close_demo_input", src); //二值图像 Mat gray, binary; cvtColor(src, gray, COLOR_BGR2GRAY); threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); imshow("close_demo_binary", binary); //闭操作// //Mat se = getStructuringElement(MORPH_ELLIPSE, Size(30, 30), Point(-1, -1)); //Mat se = getStructuringElement(MORPH_RECT, Size(30, 30), Point(-1 , -1)); Mat se = getStructuringElement(MORPH_RECT, Size(35, 35), Point(-1, -1)); morphologyEx(binary, binary, MORPH_CLOSE, se); imshow("close_demo rect=35,35 ", binary); } void open_demo(bool blnopen) { //读取图像 Mat src = imread("../images/fill.png"); imshow("open_demo_input", src); //二值图像 Mat gray, binary; cvtColor(src, gray, COLOR_BGR2GRAY); if (blnopen) { threshold(gray, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); } else { threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); } imshow("open_demo_binary", binary); //开操作 -去除黑色杂讯 强化/获取 连通元件(mask) 保留长(x=25)底线(y=1) Mat se = getStructuringElement(MORPH_RECT, Size(25, 1), Point(-1, -1)); if (blnopen) { morphologyEx(binary, binary, MORPH_OPEN, se); } else { morphologyEx(binary, binary, MORPH_CLOSE, se);// threshold(binary, binary, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); } imshow("open_op", binary); //绘制填空位置 vector < vector > contours; vector hierarhy; findContours(binary, contours, hierarhy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(-1, -1)); for (size_t t = 0; t < contours.size(); t++) { Rect roi = boundingRect(contours[t]); roi.y = roi.y - 10; roi.height = 12; rectangle(src, roi, Scalar(0, 0, 255)); } //显示结果 imshow("open_demo", src); } void contours_info(Mat &image, vector > &pts)//目标物为同类型(颜色) ~ 抓取轮廓(findContours)函数 { // 去噪声与二值化 //彩色转二值化步骤(SOP) 彩色 -> 高斯模糊(去杂讯) -> 转灰阶 -> 二值化 Mat dst, gray, binary00; GaussianBlur(image, dst, Size(3, 3), 0, 0); cvtColor(dst, gray, COLOR_BGR2GRAY); threshold(gray, binary00, 0, 255, THRESH_BINARY | THRESH_OTSU); imshow("binary00", binary00); vector hierarchy00; Scalar color = Scalar(255, 0, 0); /* void findContours(InputOutputArray image,OutputArrayOfArrays contours,OutputArray hierarchy,int mode,int method,Point offset = Point() ) 各个参数详解如下: image表示输入图像,必须是二值图像,二值图像可以threshold输出、Canny输出、inRange输出、自适应阈值输出等。 contours获取的轮廓,每个轮廓是一系列的点集合 hierarchy轮廓的层次信息,每个轮廓有四个相关信息,分别是同层下一个、前一个、第一个子节点、父节点 mode 表示轮廓寻找时候的拓扑结搆返回 -RETR_EXTERNAL表示只返回最外层轮廓 -RETR_TREE表示返回轮廓树结搆 method表示轮廓点集合取得是基于什么算法,常见的是基于CHAIN_APPROX_SIMPLE链式编码方法 */ findContours(binary00, pts, hierarchy00, RETR_TREE, CHAIN_APPROX_SIMPLE, Point()); } void contours_info(Mat &image, vector > &pts, int threshold01, int threshold02)//目标物非同类型(颜色) ~ 抓取轮廓(findContours)函数 { Mat dst, gray, binary01; //彩色转二值化步骤(直接使用Canny) /* void Canny(InputArray image, OutputArray edges, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false ) image, edges:输入和输出的图片。 threshold1, threshold2:用来区分 strong edge 和 weak edge,范围都是 0 ~ 255,会在实作过程中进一步讨论,通常选择 threshold2 / threshold1 = 1/2 ~ 1/3,例如 (70, 140), (70, 210) apertureSize:用来计算梯度的 kernel size,也就是 Sobel 的 ksize L2gradient:选择要用 L1 norm(绝对值平均)还是 L2 norm(平方根)当作梯度的大小。预设是用 L1 norm */ Canny(image, binary01, threshold01, threshold02); // 膨胀 /* OpenCV提供getStructuringElement()让我们得到要进行侵蚀或膨胀的模板 Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-1,-1)) shape:模板形状,有MORPH_RECT、MORPH_ELLIPSE、MORPH_CROSS三种可选。 ksize:模板尺寸。 */ Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1)); /* OpenCV膨胀 dilate(const Mat &src, Mat &dst, Mat kernel, Point anchor=Point(-1,-1), int iterations=1) src:输入图,可以多通道,深度可为CV_8U、CV_16U、CV_16S、CV_32F或CV_64F。 dst:输出图,和输入图尺寸、型态相同。 kernel:结构元素,如果kernel=Mat()则为预设的3×3矩形,越大膨胀效果越明显。 anchor:原点位置,预设为结构元素的中央。 iterations:执行次数,预设为1次,执行越多次膨胀效果越明显。 */ dilate(binary01, binary01, k); imshow("binary01", binary01); vector hierarchy01; Scalar color = Scalar(255, 0, 0); findContours(binary01, pts, hierarchy01, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point()); } void componentwithstats_demo(Mat &image)//八方炼码:元件标记/寻找/计算(计数)/参数:中心位置、起始座标、长、宽、面积,取得分类的所需资讯作业 + 绘制各元件的外矩形 { // extract labels //彩色转二值化步骤(SOP) 彩色 -> 高斯模糊(去杂讯) -> 转灰阶 -> 二值化 Mat gray, binary; GaussianBlur(image, image, Size(3, 3), 0); cvtColor(image, gray, COLOR_BGR2GRAY); threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); imshow("input_binary", binary); showHistogram(binary, "Histogram_input_binary"); Mat labels = Mat::zeros(image.size(), CV_32S); Mat stats, centroids; int num_labels = connectedComponentsWithStats(binary, labels, stats, centroids, 8, 4); cout << "total labels : " << num_labels - 1 << endl; vector colors(num_labels); // 背景颜色 colors[0] = Vec3b(0, 0, 0); // 目标颜色 RNG rng; for (int i = 1; i < num_labels; ++i) { colors[i] = Vec3b(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)); } // 抽取统计信息 Mat dst = image.clone(); for (int i = 1; i < num_labels; ++i) { // 中心位置 int cx = centroids.at (i, 0); int cy = centroids.at (i, 1); // 统计信息 int x = stats.at (i, CC_STAT_LEFT); int y = stats.at (i, CC_STAT_TOP); int w = stats.at (i, CC_STAT_WIDTH); int h = stats.at (i, CC_STAT_HEIGHT); int area = stats.at (i, CC_STAT_AREA); // 中心位置绘制 circle(dst, Point(cx, cy), 2, Scalar(0, 255, 0), 2); // 外接矩形 Rect rect(x, y, w, h); rectangle(dst, rect, colors[i]); putText(dst, format("num:%d", i), Point(x, y), FONT_HERSHEY_SIMPLEX, .5, Scalar(0, 0, 255), 1); printf("num : %d, rice area : %d\n", i, area); } imshow("result", dst); } void connected_component_demo(Mat &image) //八方炼码 元件 计数(计算) 数量 / 标色 { // extract labels Mat gray, binary; //彩色转二值化步骤(SOP) 彩色 -> 高斯模糊(去杂讯) -> 转灰阶 -> 二值化 GaussianBlur(image, image, Size(3, 3), 0); cvtColor(image, gray, COLOR_BGR2GRAY); threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); imshow("input_binary", binary); showHistogram(binary, "Histogram_input_binary"); //计算(计数) 元件(mask) 数量 和 所需元素颜色数量阵列 /* 参数介绍如下: image:也就是输入图像,必须是二值图,即8位单通道图像。(因此输入图像必须先进行二值化处理才能被这个函数接受) 返回值: num_labels:所有连通域的数目 labels:图像上每一像素的标记,用数字1、2、3…表示(不同的数字表示不同的连通域) */ Mat labels = Mat::zeros(image.size(), CV_32S);//背景也会被算一个区域 int num_labels = connectedComponents(binary, labels, 8, CV_32S);//数量 cout << "total labels : " << num_labels - 1 << endl; vector colors(num_labels); // 背景颜色 colors[0] = Vec3b(0, 0, 0); // 目标颜色 RNG rng; for (int i = 1; i < num_labels; ++i) { colors[i] = Vec3b(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)); } // 给结果着色 Mat dst = Mat::zeros(image.size(), image.type()); for (int row = 0; row < image.rows; ++row) { for (int col = 0; col < image.cols; ++col) { int label = labels.at (row, col); if (label == 0) continue; dst.at (row, col) = colors[label]; } } imshow("result", dst); } void laplaian_demo(vector &pyramid_images, Mat &image)//拉普拉斯金字塔 { for (int i = pyramid_images.size() - 1; i > -1; --i) { Mat dst; if (i - 1 < 0) { pyrUp(pyramid_images[i], dst, image.size()); subtract(image, dst, dst);//图像相减 dst = dst + Scalar(127, 127, 127); //调亮度, 实际中不能这么用 imshow(format("laplaian_layer_%d", i), dst); } else { pyrUp(pyramid_images[i], dst, pyramid_images[i - 1].size()); subtract(pyramid_images[i - 1], dst, dst);//图像相减 dst = dst + Scalar(127, 127, 127); //调亮度, 实际中不能这么用 imshow(format("laplaian_layer_%d", i), dst); } } } void pyramid_down(vector &pyramid_images)//高斯金字塔01 { for (int i = pyramid_images.size() - 1; i > -1; --i) { Mat dst; /* pyrUp(tmp, dst, Size(tmp.cols * 2, tmp.rows * 2)) tmp: 当前影象, 初始化为原影象 src 。 dst : 目的影象(显示影象,为输入影象的两倍) Size(tmp.cols * 2, tmp.rows * 2) : 目的影象大小, 既然我们是向上取样, pyrUp 期待一个两倍于输入影象(tmp)的大小。 */ pyrUp(pyramid_images[i], dst); imshow(format("pyramid_down_%d", i), dst); } } void pyramid_up(Mat &image, vector &pyramid_images, int level)//高斯金字塔02 { Mat temp = image.clone(); Mat dst; for (int i = 0; i < level; ++i) { /* pyrDown( tmp, dst, Size( tmp.cols/2, tmp.rows/2 )) tmp: 当前影象, 初始化为原影象 src 。 dst: 目的影象( 显示影象,为输入影象的一半) Size( tmp.cols/2, tmp.rows/2 ) :目的影象大小, 既然我们是向下取样, pyrDown 期待一个一半于输入影象( tmp)的大小。 注意输入影象的大小(在两个方向)必须是2的冥,否则,将会显示错误。 最后,将输入影象 tmp 更新为当前显示影象, 这样后续操作将作用于更新后的影象。 tmp = dst; */ pyrDown(temp, dst); imshow(format("pyramid_up_%d", i), dst); temp = dst.clone(); pyramid_images.push_back(temp); } } void USMImage(Mat src, Mat &usm, float fltPar)//图像锐化增强演算法(USM) { Mat blur_img; /* USM锐化公式表示如下: (源图像– w*高斯模糊)/(1-w);其中w表示权重(0.1~0.9),默认为0.6 OpenCV中的代码实现步骤 – 高斯模糊 – 权重叠加 – 输出结果 */ GaussianBlur(src, blur_img, Size(0, 0), 25); addWeighted(src, (1 + fltPar), blur_img, (fltPar*-1), 0, usm);//原图 : 模糊图片= 1.5 : -0.5 的比例进行混合 imshow("usm", usm); showHistogram(usm, "Histogram_input_usm"); } void blur_demo(Mat &image, Mat &sum) { int w = image.cols; int h = image.rows; Mat result = Mat::zeros(image.size(), image.type()); int x2 = 0, y2 = 0; int x1 = 0, y1 = 0; int ksize = 5; int radius = ksize / 2; int ch = image.channels(); int cx = 0, cy = 0; for (int row = 0; row < h + radius; row++) { y2 = (row + 1)>h ? h : (row + 1); y1 = (row - ksize) < 0 ? 0 : (row - ksize); for (int col = 0; col < w + radius; col++) { x2 = (col + 1)>w ? w : (col + 1); x1 = (col - ksize) < 0 ? 0 : (col - ksize); cx = (col - radius) < 0 ? 0 : col - radius; cy = (row - radius) < 0 ? 0 : row - radius; int num = (x2 - x1)*(y2 - y1); for (int i = 0; i < ch; i++) { // 积分图查找和表,计算卷积 int s = getblockSum(sum, x1, y1, x2, y2, i); result.at (cy, cx)[i] = saturate_cast (s / num); } } } imshow("blur_demo", result); } /** * 3x3 sobel 垂直边缘检测演示 */ void edge_demo(Mat &image, Mat &sum) { int w = image.cols; int h = image.rows; Mat result = Mat::zeros(image.size(), CV_32SC3); int x2 = 0, y2 = 0; int x1 = 0, y1 = 0; int ksize = 3; // 算子大小,可以修改,越大边缘效应越明显 int radius = ksize / 2; int ch = image.channels(); int cx = 0, cy = 0; for (int row = 0; row < h + radius; row++) { y2 = (row + 1)>h ? h : (row + 1); y1 = (row - ksize) < 0 ? 0 : (row - ksize); for (int col = 0; col < w + radius; col++) { x2 = (col + 1)>w ? w : (col + 1); x1 = (col - ksize) < 0 ? 0 : (col - ksize); cx = (col - radius) < 0 ? 0 : col - radius; cy = (row - radius) < 0 ? 0 : row - radius; int num = (x2 - x1)*(y2 - y1); for (int i = 0; i < ch; i++) { // 积分图查找和表,计算卷积 int s1 = getblockSum(sum, x1, y1, cx, y2, i); int s2 = getblockSum(sum, cx, y1, x2, y2, i); result.at (cy, cx)[i] = saturate_cast (s2 - s1); } } } Mat dst, gray; convertScaleAbs(result, dst); normalize(dst, dst, 0, 255, NORM_MINMAX); cvtColor(dst, gray, COLOR_BGR2GRAY); imshow("edge_demo", gray); } int getblockSum(Mat &sum, int x1, int y1, int x2, int y2, int i) { int tl = sum.at (y1, x1)[i]; int tr = sum.at (y2, x1)[i]; int bl = sum.at (y1, x2)[i]; int br = sum.at (y2, x2)[i]; int s = (br - bl - tr + tl); return s; } void add_gaussian_noise(Mat &image)//高斯杂讯 { Mat noise = Mat::zeros(image.size(), image.type()); // 产生高斯噪声 randn(noise, (15, 15, 15), (30, 30, 30)); Mat dst; add(image, noise, dst); image = dst.clone();//dst.copyTo(image);//图像复制 //imshow("gaussian_noise", dst); } void add_salt_pepper_noise(Mat &image)//白杂讯 { // 随机数产生器 RNG rng(12345); for (int i = 0; i < 1000; ++i) { int x = rng.uniform(0, image.rows); int y = rng.uniform(0, image.cols); if (i % 2 == 1) { image.at (y, x) = Vec3b(255, 255, 255); } else { image.at (y, x) = Vec3b(0, 0, 0); } } //imshow("saltp_epper", image); } void blur3x3(Mat &src, Mat *det) { // 3x3 均值模糊,自定义版本实现 for (int row = 1; row < src.rows - 1; row++) { for (int col = 1; col < src.cols - 1; col++) { Vec3b p1 = src.at (row - 1, col - 1); Vec3b p2 = src.at (row - 1, col); Vec3b p3 = src.at (row - 1, col + 1); Vec3b p4 = src.at (row, col - 1); Vec3b p5 = src.at (row, col); Vec3b p6 = src.at (row, col + 1); Vec3b p7 = src.at (row + 1, col - 1); Vec3b p8 = src.at (row + 1, col); Vec3b p9 = src.at (row + 1, col + 1); int b = p1[0] + p2[0] + p3[0] + p4[0] + p5[0] + p6[0] + p7[0] + p8[0] + p9[0]; int g = p1[1] + p2[1] + p3[1] + p4[1] + p5[1] + p6[1] + p7[1] + p8[1] + p9[1]; int r = p1[2] + p2[2] + p3[2] + p4[2] + p5[2] + p6[2] + p7[2] + p8[2] + p9[2]; det->at (row, col)[0] = saturate_cast (b / 9); det->at (row, col)[1] = saturate_cast (g / 9); det->at (row, col)[2] = saturate_cast (r / 9); } } } void backProjection_demo(Mat &image, Mat &model)//反向投影 { Mat image_hsv, model_hsv; cvtColor(image, image_hsv, COLOR_BGR2HSV);//彩色转HSV cvtColor(model, model_hsv, COLOR_BGR2HSV); // 定义直方图参数与属性 int h_bins = 32, s_bins = 32; int histSize[] = { h_bins, s_bins };//要切分的像素强度值范围,预设为256。每个channel皆可指定一个范围。例如,[32,32,32] 表示RGB三个channels皆切分为32区段 float h_ranges[] = { 0, 180 }, s_ranges[] = { 0, 256 }; const float* ranges[] = { h_ranges, s_ranges }; int channels[] = { 0, 1 }; Mat roiHist;//计算ROI的直方图 calcHist(&model_hsv, 1, channels, Mat(), roiHist, 2, histSize, ranges); normalize(roiHist, roiHist, 0, 255, NORM_MINMAX, -1, Mat()); Mat roiproj, backproj; calcBackProject(&image_hsv, 1, channels, roiHist, roiproj, ranges);//使用反向投影 产生ROI(前景)的mask bitwise_not(roiproj, backproj);//产生背景的mask imshow("ROIProj", roiproj); imshow("BackProj", backproj); } void showHistogram(InputArray src, cv::String StrTitle)//直方图 { bool blnGray = false; if (src.channels() == 1) { blnGray = true; } // 三通道/单通道 直方图 纪录阵列 vector bgr_plane; vector gray_plane; // 定义参数变量 const int channels[1] = { 0 }; const int bins[1] = { 256 }; float hranges[2] = { 0, 255 }; const float *ranges[1] = { hranges }; Mat b_hist, g_hist, r_hist, hist; // 计算三通道直方图 /* void calcHist( const Mat* images, int nimages,const int* channels, InputArray mask,OutputArray hist, int dims, const int* histSize,const float** ranges, bool uniform=true, bool accumulate=false ); 1.输入的图像数组 2.输入数组的个数 3.通道数 4.掩码 5.直方图 6.直方图维度 7.直方图每个维度的尺寸数组 8.每一维数组的范围 9.直方图是否是均匀 10.配置阶段不清零 */ if (blnGray) { split(src, gray_plane); calcHist(&gray_plane[0], 1, 0, Mat(), hist, 1, bins, ranges); } else { split(src, bgr_plane); calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges); calcHist(&bgr_plane[1], 1, 0, Mat(), g_hist, 1, bins, ranges); calcHist(&bgr_plane[2], 1, 0, Mat(), r_hist, 1, bins, ranges); } /* * 显示直方图 */ int hist_w = 512; int hist_h = 400; int bin_w = cvRound((double)hist_w / bins[0]); Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3); // 归一化直方图数据 if (blnGray) { normalize(hist, hist, 0, histImage.rows, NORM_MINMAX, -1); } else { normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1); normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1); normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1); } // 绘制直方图曲线 for (int i = 1; i < bins[0]; ++i) { if (blnGray) { line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(hist.at (i))), Scalar(255, 255, 255), 2, 8, 0); } else { line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(b_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(b_hist.at (i))), Scalar(255, 0, 0), 2, 8, 0); line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(g_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(g_hist.at (i))), Scalar(0, 255, 0), 2, 8, 0); line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(r_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(r_hist.at (i))), Scalar(0, 0, 255), 2, 8, 0); } } imshow(StrTitle, histImage); }
★Python
import cv2 as cv import numpy as np src = cv.imread("D:/images/cross.png") cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) # 图像二值化 gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY) ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU) # 击中击不中 se = cv.getStructuringElement(cv.MORPH_CROSS, (11, 11), (-1, -1)) binary = cv.morphologyEx(binary, cv.MORPH_HITMISS, se) cv.imshow("black hat", binary) cv.imwrite("D:/binary2.png", binary) cv.waitKey(0) cv.destroyAllWindows()
★结果图:
★延伸说明/重点回顾: