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jashliao 用 VC++ 實現 opencv-121 ~ 一行代碼實現灰色鉛筆劃、彩色鉛筆劃(pencilSketch)和卡通畫(stylization)效果 資料來源: https://mp.weixin.qq.com/s/GC1Az0BSkBb8KaUdU3Aj6A GitHub: https://github.com/jash-git/jashliao-implements-FANFUHAN-OPENCV-with-VC ★前言: ★主題: ★C++ // VC_FANFUHAN_OPENCV121.cpp : 定義主控台應用程式的進入點。 // /* // Debug | x32 通用屬性 | C/C++ | | 一般 | | 其他 Include 目錄 -> ..\..\opencv411_x64\include | | 連結器 | |一一般 | | 其他程式庫目錄 -> ..\..\opencv411_x64\lib | | |一輸入 | | 其他相依性 -> opencv_world411d.lib;%(AdditionalDependencies) // Releas | x64 組態屬性 | C/C++ | | 一般 | | 其他 Include 目錄 -> ..\..\opencv411_x64\include;%(AdditionalDependencies) | | 連結器 | |一般 | | 其他程式庫目錄 -> ..\..\opencv411_x64\lib;%(AdditionalDependencies) | | |一輸入 | | 其他相依性 -> opencv_world411.lib;%(AdditionalDependencies) */ //文章: https://mp.weixin.qq.com/s/GC1Az0BSkBb8KaUdU3Aj6A #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 sort_box(vector &boxes); Mat get_template(Mat &binary, vector &rects); void detect_defects(Mat &binary, vector &rects, Mat &tpl, vector &defects); void pause() { printf("Press Enter key to continue..."); fgetc(stdin); } int main() { Mat src = imread("../../images/yuanyuan.jpg");//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"); Mat imgGray, imgColor; /* src 输入8位三通道图像 dst1 输出8位单通道图像(灰色铅笔画结果图) dst2 与输入图像相同的通道数和大小(彩色铅笔画结果图) sigma_s 取值范围0~200 sigma_r 取值范围0~1 shade_factor 取值范围0~0.1 */ pencilSketch(src, imgGray, imgColor, 5, 0.1f, 0.03f);//灰色铅笔画、彩色铅笔画效果 imshow("Pencil Sketch", imgGray); imshow("Color Pencil Sketch", imgColor); Mat result; /* src 输入8位三通道图像 dst 与输入图像相同的通道数和大小(卡通画结果图) sigma_s 取值范围0~200 sigma_r 取值范围0~1 */ stylization(src, result, 5, 0.6);//卡通画效果 imshow("Stylization", result); //OilPaint_forloop //https://github.com/housewing/OpenCV-Image-Processing/blob/master/OilPaint_forloop.cpp Mat OilPaint = Mat(src.size(), CV_8UC3); int heigh = src.rows; int width = src.cols; int BrushSize = 5; int levels = 20; int* CountIntensity = new int[levels]; int* RedAverage = new int[levels]; int* GreenAverage = new int[levels]; int* BlueAverage = new int[levels]; for (int y = BrushSize; y < (heigh - BrushSize); y++) { for (int x = BrushSize; x < (width - BrushSize); x++) { //cout << "y " << y << " x " << x << endl; // initialize the array for (int i = 0; i < levels; i++) { CountIntensity[i] = 0; RedAverage[i] = 0; GreenAverage[i] = 0; BlueAverage[i] = 0; } for (int j = -BrushSize; j <= BrushSize; j++) { for (int i = -BrushSize; i <= BrushSize; i++) { //cout << "y+j " << y+j << " x+i " << x+i << endl; double b = src.at(y + j, x + i)[0]; double g = src.at(y + j, x + i)[1]; double r = src.at(y + j, x + i)[2]; //cout << "y+j " << y+j << " x+i " << x+i << " " << b << " " << g << " " << r << endl; int nCurIntensity = (((r + g + b) / 3.0) * levels) / 255; //cout << "y+j " << y+j << " x+i " << x+i << " " << nCurIntensity << endl; int tmp = nCurIntensity; CountIntensity[tmp]++; BlueAverage[tmp] = BlueAverage[tmp] + b; GreenAverage[tmp] = GreenAverage[tmp] + g; RedAverage[tmp] = RedAverage[tmp] + r; //cout << "Average " << RedAverage[tmp] << endl; } } int nCurMax = 0; int nMaxIndex = 0; for (int nI = 0; nI < levels; nI++) { if (CountIntensity[nI] > nCurMax) { nCurMax = CountIntensity[nI]; nMaxIndex = nI; } } //cout << "CurMax " << nCurMax << " MaxIndex " << nMaxIndex << endl; double tmpB = BlueAverage[nMaxIndex] / nCurMax; double tmpG = GreenAverage[nMaxIndex] / nCurMax; double tmpR = RedAverage[nMaxIndex] / nCurMax; Vec3b pixelColor(tmpB, tmpG, tmpR); OilPaint.at(y, x) = pixelColor; //dst.at(y,x)[0] = BlueAverage[nMaxIndex] / nCurMax; //dst.at(y,x)[1] = GreenAverage[nMaxIndex] / nCurMax; //dst.at(y,x)[2] = RedAverage[nMaxIndex] / nCurMax; } } imshow("OilPaint", OilPaint); waitKey(0); } return 0; } void detect_defects(Mat &binary, vector &rects, Mat &tpl, vector &defects) { int height = tpl.rows; int width = tpl.cols; int index = 1; int size = rects.size(); // 发现缺失 for (int i = 0; i < size; ++i) { Mat roi = binary(rects[i]); resize(roi, roi, tpl.size());//調整待測物的大小與模板一致 Mat mask; subtract(tpl, roi, mask);//圖像相減 Mat se = getStructuringElement(MORPH_RECT, Size(5, 5)); morphologyEx(mask, mask, MORPH_OPEN, se);//開運算-藉此去白色雜訊 threshold(mask, mask, 0, 255, THRESH_BINARY); int count = 0; for (int row = 0; row < height; ++row) { for (int col = 0; col < width; ++col) { int pv = mask.at(row, col); if (pv == 255) { ++count; } } } if (count > 0) { defects.push_back(rects[i]); } } } Mat get_template(Mat &binary, vector &rects) //以範圍最大為良品基準 { return binary(rects[0]); } void sort_box(vector &boxes) { int size = boxes.size(); for (int i = 0; i < size - 1; ++i) { for (int j = i; j < size; ++j) { if (boxes[j].y < boxes[i].y) { Rect tmp = boxes[i]; boxes[i] = boxes[j]; boxes[j] = tmp; } } } } void close_demo() { //讀取圖像 Mat src = imread("../images/morph3.png"); imshow("close_demo_input", src); //二值圖像 Mat gray, binary; cvtColor(src, gray, COLOR_BGR2GRAY); threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU); imshow("close_demo_binary", binary); //閉操作// //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) { //讀取圖像 Mat src = imread("../images/fill.png"); imshow("open_demo_input", src); //二值圖像 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); //開操作 -去除黑色雜訊 強化/獲取 連通元件(mask) 保留長（x＝25）底線（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); //繪製填空位置 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)); } //顯示結果 imshow("open_demo", src); } void contours_info(Mat &image, vector> &pts)//目標物為同類型(顏色) ~ 抓取輪廓(findContours)函數 { // 去噪声与二值化 //彩色轉二值化步驟(SOP) 彩色 -> 高斯模糊(去雜訊) -> 轉灰階 -> 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() ) 各個參數詳解如下： image表示輸入圖像，必須是二值圖像，二值圖像可以threshold輸出、Canny輸出、inRange輸出、自適應閾值輸出等。 contours獲取的輪廓，每個輪廓是一系列的點集合 hierarchy輪廓的層次信息，每個輪廓有四個相關信息，分別是同層下一個、前一個、第一個子節點、父節點 mode 表示輪廓尋找時候的拓撲結搆返回 -RETR_EXTERNAL表示只返回最外層輪廓 -RETR_TREE表示返回輪廓樹結搆 method表示輪廓點集合取得是基於什么算法，常見的是基於CHAIN_APPROX_SIMPLE鏈式編碼方法 */ findContours(binary00, pts, hierarchy00, RETR_TREE, CHAIN_APPROX_SIMPLE, Point()); } void contours_info(Mat &image, vector> &pts, int threshold01, int threshold02)//目標物非同類型(顏色) ~ 抓取輪廓(findContours)函數 { Mat dst, gray, binary01; //彩色轉二值化步驟(直接使用Canny) /* void Canny(InputArray image, OutputArray edges, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false ) image, edges：輸入和輸出的圖片。 threshold1, threshold2：用來區分 strong edge 和 weak edge，範圍都是 0 ~ 255，會在實作過程中進一步討論，通常選擇 threshold2 / threshold1 = 1/2 ~ 1/3，例如 (70, 140), (70, 210) apertureSize：用來計算梯度的 kernel size，也就是 Sobel 的 ksize L2gradient：選擇要用 L1 norm（絕對值平均）還是 L2 norm（平方根）當作梯度的大小。預設是用 L1 norm */ Canny(image, binary01, threshold01, threshold02); // 膨胀 /* OpenCV提供getStructuringElement()讓我們得到要進行侵蝕或膨脹的模板 Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-1,-1)) shape：模板形狀，有MORPH_RECT、MORPH_ELLIPSE、MORPH_CROSS三種可選。 ksize：模板尺寸。 */ Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1)); /* OpenCV膨脹 dilate(const Mat &src, Mat &dst, Mat kernel, Point anchor=Point(-1,-1), int iterations=1) src：輸入圖，可以多通道，深度可為CV_8U、CV_16U、CV_16S、CV_32F或CV_64F。 dst：輸出圖，和輸入圖尺寸、型態相同。 kernel：結構元素，如果kernel=Mat()則為預設的3×3矩形，越大膨脹效果越明顯。 anchor：原點位置，預設為結構元素的中央。 iterations：執行次數，預設為1次，執行越多次膨脹效果越明顯。 */ 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)//八方鍊碼:元件標記/尋找/計算(計數)/參數:中心位置、起始座標、長、寬、面積，取得分類的所需資訊作業 + 繪製各元件的外矩形 { // extract labels //彩色轉二值化步驟(SOP) 彩色 -> 高斯模糊(去雜訊) -> 轉灰階 -> 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) //八方鍊碼 元件 計數(計算) 數量 / 標色 { // extract labels Mat gray, binary; //彩色轉二值化步驟(SOP) 彩色 -> 高斯模糊(去雜訊) -> 轉灰階 -> 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"); //計算(計數) 元件(mask) 數量 和 所需元素顏色數量陣列 /* 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);//背景也會被算一個區域 int num_labels = connectedComponents(binary, labels, 8, CV_32S);//數量 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);//圖像相減 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);//圖像相減 dst = dst + Scalar(127, 127, 127); //調亮度， 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: 當前影象， 初始化為原影象 src 。 dst : 目的影象(顯示影象，為輸入影象的兩倍) Size(tmp.cols * 2, tmp.rows * 2) : 目的影象大小， 既然我們是向上取樣， pyrUp 期待一個兩倍於輸入影象(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: 當前影象， 初始化為原影象 src 。 dst: 目的影象( 顯示影象，為輸入影象的一半) Size( tmp.cols/2, tmp.rows/2 ) :目的影象大小， 既然我們是向下取樣， pyrDown 期待一個一半於輸入影象( tmp)的大小。 注意輸入影象的大小(在兩個方向)必須是2的冥，否則，將會顯示錯誤。 最後，將輸入影象 tmp 更新為當前顯示影象, 這樣後續操作將作用於更新後的影象。 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)//圖像銳化增强演算法(USM) { Mat blur_img; /* USM銳化公式表示如下： （源圖像– w*高斯模糊）/（1-w）；其中w表示權重（0.1～0.9），默認為0.6 OpenCV中的代碼實現步驟 – 高斯模糊 – 權重疊加 – 輸出結果 */ GaussianBlur(src, blur_img, Size(0, 0), 25); addWeighted(src, (1 + fltPar), blur_img, (fltPar*-1), 0, usm);//原圖 : 模糊圖片= 1.5 : -0.5 的比例進行混合 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)//高斯雜訊 { 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);//圖像複製 //imshow("gaussian_noise", dst); } void add_salt_pepper_noise(Mat &image)//白雜訊 { // 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);//彩色轉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 };//要切分的像素強度值範圍，預設為256。每個channel皆可指定一個範圍。例如，[32,32,32] 表示RGB三個channels皆切分為32區段 float h_ranges[] = { 0, 180 }, s_ranges[] = { 0, 256 }; const float* ranges[] = { h_ranges, s_ranges }; int channels[] = { 0, 1 }; Mat roiHist;//計算ROI的直方圖 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 產生ROI(前景)的mask bitwise_not(roiproj, backproj);//產生背景的mask imshow("ROIProj", roiproj); imshow("BackProj", backproj); } void showHistogram(InputArray src, cv::String StrTitle)//直方圖 { bool blnGray = false; if (src.channels() == 1) { blnGray = true; } // 三通道/單通道 直方圖 紀錄陣列 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.輸入的圖像數組 2.輸入數組的個數 3.通道數 4.掩碼 5.直方圖 6.直方圖維度 7.直方圖每個維度的尺寸數組 8.每一維數組的範圍 9.直方圖是否是均勻 10.配置階段不清零 */ 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); } ★結果圖: ★延伸說明/重點回顧: