毕业设计——基于OpenCV视觉检测程序设计.zip

#include "opencv.hpp" #include "imgproc.hpp" #include <opencv2/opencv.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <cstdio> #include <stdlib.h> #include <stdio.h> //sobel 算子处理后 长度选择 using namespace std; using namespace cv; Mat g_srcImage, g_dstImage0, g_dstImage1, g_dstImage2, g_dstImage3, g_dstImage4, g_dstImage5; int g_nBoxFilterValue = 6; int g_nMeanBlurValue = 10; int g_nGaussianBlurValue = 6; int g_nMedianBlurValue = 10; int g_nBilateralFilterValue = 10; static void on_BoxFilter(int, void *); static void on_MeanBlur(int, void *); static void on_GaussianBlur(int, void *); static void on_MedianBlur(int, void *); static void on_BilateralFilter(int, void *); double Otsu(Mat &img); Mat dst, src_gary, detected_edges,gard; int lowThreshold=80; int ratio = 3; int Kernel_size = 3; int main() { Mat img = imread("H:\\image\\1.jpg"); if (!img.data) { cout << "cuowu" << endl; return -1; } Mat img_gray; Mat img2; g_dstImage0 = img.clone(); g_dstImage1 = img.clone(); g_dstImage2 = img.clone(); g_dstImage3 = img.clone(); g_dstImage4 = img.clone(); g_dstImage5 = img.clone(); double thresh=100; // GaussianBlur(img, img, Size(3, 3), 0, 0, BORDER_DEFAULT); //模糊平滑 cvtColor(img, img_gray, COLOR_BGR2GRAY); //blur(img_gray, img_gray, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1), 4); //blur(img_gray, g_dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1), 4); //灰度化 //thresh = Otsu(g_dstImage2); threshold(img_gray, g_dstImage0, 100, 255, THRESH_BINARY_INV); threshold(img_gray, g_dstImage1, thresh, 255, THRESH_BINARY_INV); //二值化分割 /********/ Mat grad_x, grad_y, abs_grad_x, abs_grad_y; Sobel(img_gray, grad_x, CV_16S, 1, 0, 3,1, 0, BORDER_DEFAULT); convertScaleAbs(grad_x, abs_grad_x); Sobel(img_gray, grad_y, CV_16S, 0, 1, 3,1, 0, BORDER_DEFAULT); convertScaleAbs(grad_y, abs_grad_y); addWeighted(abs_grad_x, 0.3, abs_grad_y, 0.3, 0, gard); threshold(gard, detected_edges, 50, 255, THRESH_BINARY); imshow("name" , detected_edges); //sobel算子高通滤波 /****** blur(img_gray, g_dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1),4); //中值滤波-低通滤波 ******/ //equalizeHist(g_dstImage2, g_dstImage3);// //equalizeHist(img_gray, g_dstImage4);//均衡化的前提都是要经过灰度化处理 //直方图均衡化 vector<vector<Point> > contours; findContours(detected_edges, contours, RETR_TREE, CHAIN_APPROX_SIMPLE); //drawContours(img, contours, -1, Scalar(0, 255, 0), 1, 8); /****** ****/ vector<vector<Point> > contours1; for (int i = 0; i <= contours.size() - 1; i++) { float Long = arcLength(contours[i], true); if (Long > 150) { contours1.push_back(contours[i]); } } drawContours(img, contours1, -1, Scalar(0, 0, 255), 1, 8); //轮廓标注 基本做法 imshow("img", img); //imshow("g_dstImage4", g_dstImage4); waitKey(0); return 0; } /**** #include <opencv2/opencv.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <cstdio> using namespace cv; Mat g_srcImage, g_dstImage1, g_dstImage2, g_dstImage3, g_dstImage4, g_dstImage5; int g_nBoxFilterValue = 6; int g_nMeanBlurValue = 10; int g_nGaussianBlurValue = 6; int g_nMedianBlurValue = 10; int g_nBilateralFilterValue = 10; static void on_BoxFilter(int, void *); static void on_MeanBlur(int, void *); static void on_GaussianBlur(int, void *); static void on_MedianBlur(int, void *); static void on_BilateralFilter(int, void *); int main() { //====================载入原图==================== g_srcImage = imread("H:\\image\\1.jpg"); if (!g_srcImage.data) { printf("no source image!\n"); return false; } //==============复制原图到5个Mat类型中============== g_dstImage1 = g_srcImage.clone(); g_dstImage2 = g_srcImage.clone(); g_dstImage3 = g_srcImage.clone(); g_dstImage4 = g_srcImage.clone(); g_dstImage5 = g_srcImage.clone(); //===================显示原图===================== namedWindow("0:source image", WINDOW_AUTOSIZE); imshow("0:source image", g_srcImage); //================【<1>方框滤波】================== namedWindow("1:image after box filter", WINDOW_AUTOSIZE); createTrackbar("内核值：", "1:image after box filter", &g_nBoxFilterValue, 50, on_BoxFilter); on_MeanBlur(g_nBoxFilterValue, 0); //================【<2>均值滤波】================== namedWindow("2:image after mean blur", WINDOW_AUTOSIZE); createTrackbar("内核值：", "2:image after mean blur", &g_nMeanBlurValue, 50, on_MeanBlur); on_MeanBlur(g_nMeanBlurValue, 0); //================【<3>高斯滤波】================== namedWindow("3:image after gaussian blur", WINDOW_AUTOSIZE); createTrackbar("内核值：", "3:image after gaussian blur", &g_nGaussianBlurValue, 50, on_GaussianBlur); on_GaussianBlur(g_nGaussianBlurValue, 0); //================【<4>中值滤波】================== namedWindow("4:image after median filter", WINDOW_AUTOSIZE); createTrackbar("参数值：", "4:image after median filter", &g_nMedianBlurValue, 50, on_MedianBlur); on_MedianBlur(g_nMedianBlurValue, 0); //================【<5>双边滤波】================== namedWindow("5:image after bilateral filter", WINDOW_AUTOSIZE); createTrackbar("参数值：", "5:image after bilateral filter", &g_nBilateralFilterValue, 50, on_BilateralFilter); on_BilateralFilter(g_nBilateralFilterValue, 0); //==================输入'q'结束=================== while (char(waitKey(1)) != 'q') {} return 0; } **/ //==============【on_BoxFilter()函数】================ static void on_BoxFilter(int, void *) { boxFilter(g_srcImage, g_dstImage1, -1, Size(g_nBoxFilterValue + 1, g_nBoxFilterValue + 1)); imshow("1:image after box filter", g_dstImage1); } //==============【on_MeanBlur()函数】================= static void on_MeanBlur(int, void *) { blur(g_srcImage, g_dstImage2, Size(g_nMeanBlurValue + 1, g_nMeanBlurValue + 1), Point(-1, -1)); imshow("2:image after mean blur", g_dstImage2); } //============【on_GaussianBlur()函数】=============== static void on_GaussianBlur(int, void *) { GaussianBlur(g_srcImage, g_dstImage3, Size(g_nGaussianBlurValue * 2 + 1, g_nGaussianBlurValue * 2 + 1), 0, 0); imshow("3:image after gaussian blur", g_dstImage3); } //==============【on_MedianBlur()函数】=============== static void on_MedianBlur(int, void *) { medianBlur(g_srcImage, g_dstImage4, g_nMedianBlurValue * 2 + 1); imshow("4:image after median filter", g_dstImage4); } //============【on_BilateralFilter()函数】============ static void on_BilateralFilter(int, void *) { bilateralFilter(g_srcImage, g_dstImage5, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, g_nBilateralFilterValue / 2); imshow("5:image after bilateral filter", g_dstImage5); }