#include #include //#include //#include //#include //#include //#include #include //#include //#include //#include //#include //#include #include //#include using namespace cv; using namespace std; //定义灰度图像变量 IplImage *g_GrayImage = NULL; //定义二值化图片变量 IplImage *g_BinaryImage = NULL; //定义二值化窗口标题 const char *WindowBinaryTitle = "二值化图片"; //定义滑块响应函数 //创建源图像窗口标题变量 const char *WindowSrcTitle = "灰度图像"; //创建滑块标题变量 const char *TheSliderTitle = "二值化阀值"; const char *SrcPath = "G:/AA computer vision/vs_opencv_example/defect detecting 201706291343/picture/1706201246_ 38.jpg"; ////定义图片路径 IplImage *g_pGrayImage_liantong = NULL; IplImage *g_pBinralyImage_liantong = NULL; int contour_num = 0; //数字编号 char number_buf[10]; ////数字编号存入数组，puttext #define num_col 11 ////二维数组的列，每一个点缺陷信息的详细信息 long int liantong_all_area = 0; ////连通区域总面积 long int Rect_all_area = 0; //// 保存最小外接矩形总的面积 ////===================================================================== struct my_struct1{ double scale; //// 定义显示图像的比例 const int threshold_value_binaryzation; ////定义第一次二值化阀值 const int threshold_value_second_binaryzation; ////定义第一次二值化阀值 }; my_struct1 picture = { 0.3, 50, 100 }; ////===================================================================== struct my_struct2{ int Model1_k1 ; ////图像膨胀腐蚀 int Model1_k2 ; ////图像膨胀腐蚀 int Model2_k1 ; ////图像膨胀腐蚀 int Model2_k2 ; ////图像膨胀腐蚀 } ; my_struct2 value = {5,2,3,2}; ////===================================================================== struct my_struct3{ double maxarea ; ////最大缺陷面积 double minarea ; ////最小显示保留的缺陷面积 double font_scale ; ////字体大小 int font_thickness ; ////字体粗细 const int Feature_value2_number ; ////定义一个二维数组的列，即缺陷的个数 }; my_struct3 value2 = { 0, 4, 0.6, 0.8 , 100}; ////===================================================================== struct my_struct4{ const int hough_Canny_thresh1 ; const int hough_Canny_thresh2 ; const int hough_Canny_kernel ; const int cvHoughLines2_thresh ; ////像素值大于多少才显示，值越大，显示的线段越少 const int cvHoughLines2_param1 ; ////显示线段的最小长度 const int cvHoughLines2_param2 ; ////线段之间的 最小间隔 }; my_struct4 Hough = { 50, 100, 3, 50, 20, 10}; ////===================================================================== int** on_trackbar( ){ CvSeq* contour = 0; CvSeq* _contour = contour; //定义存放数组的二维数组，返回指针数组 int** Feature_value2 =0; Feature_value2 = new int*[value2.Feature_value2_number]; IplImage *SrcImage_or; CvSize src_sz; ////=============================================================================================== //载入原图 IplImage *SrcImage_origin = cvLoadImage(SrcPath, CV_LOAD_IMAGE_UNCHANGED); //resize src_sz.width = SrcImage_origin->width* picture.scale; src_sz.height = SrcImage_origin->height* picture.scale; SrcImage_or = cvCreateImage(src_sz, SrcImage_origin->depth, SrcImage_origin->nChannels); cvResize(SrcImage_origin, SrcImage_or, CV_INTER_CUBIC); //cvNamedWindow("原图", CV_WINDOW_AUTOSIZE); ////显示原图到原图窗口 //cvShowImage("原图", SrcImage); //单通道灰度化处理 g_GrayImage = cvCreateImage(cvSize(SrcImage_or->width, SrcImage_or->height), IPL_DEPTH_8U, 1); cvCvtColor(SrcImage_or, g_GrayImage, CV_BGR2GRAY); //创建二值化原图 g_BinaryImage = cvCreateImage(cvGetSize(g_GrayImage), IPL_DEPTH_8U, 1); cvThreshold(g_GrayImage, g_BinaryImage, picture.threshold_value_binaryzation, 255, CV_THRESH_BINARY); //显示二值化后的图片 //// cvShowImage(WindowBinaryTitle, g_BinaryImage); ////===============================================================================================图像膨胀腐蚀 //g_BinaryImage = cvCloneImage(g_BinaryImage); //// 膨胀腐蚀 //////先cvDilate后cvErode，先膨胀后腐蚀，这个为闭合操作，图片中断裂处会缝合。 //////利用这个操作可以填充细小空洞，连接临近物体，平滑物体边缘，同时不明显改变物体面积 IplImage* temp_cvDilate = cvCreateImage(cvGetSize(g_BinaryImage), IPL_DEPTH_8U, 1); IplImage* temp_cvErode = cvCreateImage(cvGetSize(g_BinaryImage), IPL_DEPTH_8U, 1); IplImage* temp_cvErode_cvErode = cvCreateImage(cvGetSize(g_BinaryImage), IPL_DEPTH_8U, 1); IplConvKernel * myModel1; myModel1 = cvCreateStructuringElementEx( //自定义5*5,参考点（3,3）的矩形模板 value.Model1_k1, value.Model1_k1, value.Model1_k2, value.Model1_k2, CV_SHAPE_ELLIPSE ); IplConvKernel * myModel2; myModel2 = cvCreateStructuringElementEx( //自定义5*5,参考点（3,3）的矩形模板 value.Model2_k1, value.Model2_k1, value.Model2_k2, value.Model2_k2, CV_SHAPE_RECT ); ////CV_SHAPE_RECT, 长方形元素; ////CV_SHAPE_CROSS, 交错元素 across - shaped element; ////CV_SHAPE_ELLIPSE, 椭圆元素; ////CV_SHAPE_CUSTOM, 用户自定义元素 //////先膨胀后腐蚀 cvDilate(g_BinaryImage, temp_cvDilate, myModel1, 1);//膨胀 cvErode(temp_cvDilate, temp_cvErode_cvErode, myModel2, 1);//腐蚀 //namedWindow("temp_cvErode_cvErode", CV_WINDOW_AUTOSIZE); //cvShowImage("temp_cvErode_cvErode", temp_cvErode_cvErode); g_BinaryImage = cvCloneImage(temp_cvErode_cvErode); //// 膨胀腐蚀 ///////================================================================================================检测连通区域 CvMemStorage *liantong_storage = cvCreateMemStorage(); IplImage* liantogn_dst = cvCreateImage(cvGetSize(g_BinaryImage), 8, 3); //提取轮廓 cvFindContours(g_BinaryImage, liantong_storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE); cvZero(liantogn_dst);//清空数组 int n = -1, m = 0;//n为面积最大轮廓索引，m为迭代索引 ////-----------------------------------------------------------对连通区域做处理 for (; contour != 0; contour = contour->h_next) { double tmparea = fabs(cvContourArea(contour)); if (tmparea <= value2.minarea) { cvSeqRemove(contour, 0); //删除面积小于设定值的轮廓 continue; } else { liantong_all_area = liantong_all_area + tmparea; } CvRect aRect = cvBoundingRect(contour, 0); //if ((aRect.width / aRect.height)<1) //{ // cvSeqRemove(contour, 0); //删除宽高比例小于设定值的轮廓 // continue; //} if (tmparea > value2.maxarea) { value2.maxarea = tmparea; n = m; } m++; CvScalar color = CV_RGB(0, 255, 255); cvDrawContours(liantogn_dst, contour, color, color, -1, -1, 8);//绘制外部和内部的轮廓 } long int sizeof_pic = liantogn_dst->width*liantogn_dst->height; ////获取图像大小 //cvNamedWindow("连通区域", 1); //cvShowImage("连通区域", liantogn_dst); ///------------------------------------------------------------------------------------数字标记图像类型转换 IplImage *label_liantogn_dst_origin = NULL; label_liantogn_dst_origin = cvCloneImage(liantogn_dst); //// 膨胀腐蚀 Mat label_liantogn_dst = cvarrToMat(label_liantogn_dst_origin); //cvNamedWindow("label_liantogn_dst", 1); //imshow("label_liantogn_dst", label_liantogn_dst); ///------------------------------------------------------------------------------------第二次二值化 IplImage *g_BinaryImage_fanse_origin = NULL; // 转为灰度图 g_pGrayImage_liantong = cvCreateImage(cvGetSize(liantogn_dst), IPL_DEPTH_8U, 1); cvCvtColor(liantogn_dst, g_pGrayImage_liantong, CV_BGR2GRAY); // 创建二值图 g_pBinralyImage_liantong = cvCreateImage(cvGetSize(g_pGrayImage_liantong), IPL_DEPTH_8U, 1); // 转为二值图 cvThreshold(g_pGrayImage_liantong, g_pBinralyImage_liantong, picture.threshold_value_second_binaryzation, 255, CV_THRESH_BINARY); // 显示二值图 cvNamedWindow("liantong_erzhihua_2", CV_WINDOW_AUTOSIZE); cvShowImage("liantong_erzhihua_2", g_pBinralyImage_liantong); Mat g_pBinralyImage_liantong_2 = cvarrToMat(g_pBinralyImage_liantong); imwrite("save_Binra.jpg", g_pBinralyImage_liantong_2); IplImage* fanse_origin = cvCloneImage(g_pBinralyImage_liantong); //// 为下一步反色先保存数据 //g_BinaryImage = cvCloneImage(liantogn_dst); ///////================================================================================================求最小外接矩形 CvMemStorage *storage = cvCreateMemStorage(); CvSeq *seq = NULL; int cnt = cvFindContours(g_pBinralyImage_liantong, storage, &seq); seq = seq->h_next; double length = cvArcLength(seq); double area = cvContourArea(seq); CvRect rect = cvBoundingRect(seq, 1); CvBox2D box = cvMinAreaRect2(seq, NULL); IplImage *SrcImage; CvSize sz; /////////////////////////////////////////////////////////////////////////////////////////////////////////////////// //IplImage* dst_min_rec = cvLoadImage("G:\\AA computer vision\\vs_opencv_example\\defect detecting 201706261446\\picture\\1706201245_ 33.jpg", 1); IplImage* dst_min_rec = cvLoadImage(SrcPath, 1); sz.width = dst_min_rec->width* picture.scale; sz.height = dst_min_rec->height* picture.scale; SrcImage = cvCreateImage(sz, dst_min_rec->depth, dst_min_rec->nChannels); cvResize(dst_min_rec, SrcImage, CV_INTER_CUBIC); dst_min_rec = cvCloneImage(SrcImage); //// 前面，已经事先定义 cvFindContours(g_pBinralyImage_liantong, storage, &contour, sizeof(CvContour), CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE); for (; contour != 0; contour = contour->h_next) { CvBox2D rect = cvMinAreaRect2(contour, storage); CvPoint2D32f rect_pts0[4]; cvBoxPoints(rect, rect_pts0); //因为cvPolyLine要求点集的输入类型是CvPoint** //所以要把 CvPoint2D32f 型的 rect_pts0 转换为 CvPoint 型的 rect_pts //并赋予一个对应的指针 *pt int npts = 4, k = 0; int aaa = 0, bbb = 0; CvPoint rect_pts[4], *pt = rect_pts; int sum_rect_x = 0, sum_rect_y = 0; int chang = 0, kuan = 0; //printf("编号：%4d 连通区域最小外接矩形顶点坐标分别为:\n", contour_num); //Feature_value[0] = contour_num; //特征值数组第一个数 Feature_value2[contour_num] = new int[num_col]; for (int i = 0; i<4; i++) { rect_pts[i] = cvPointFrom32f(rect_pts0[i]); // printf("%d %d\n", rect_pts[i].x, rect_pts[i].y); ////=============================================================== Feature_value2[contour_num][i] = rect_pts[i].x; //特征值数组第0-3个数 Feature_value2[contour_num][i+4] = rect_pts[i].y; //特征值数组第4-7个数 sum_rect_x += rect_pts[i].x; sum_rect_y += rect_pts[i].y; aaa = (int)sqrt((pow((rect_pts[0].x - rect_pts[1].x), 2) + pow((rect_pts[0].y - rect_pts[1].y), 2))); bbb = (int)sqrt((pow((rect_pts[0].x - rect_pts[3].x), 2) + pow((rect_pts[0].y - rect_pts[3].y), 2))); if (aaaheight*0.25), Point(dst_min_rec->width, dst_min_rec->height*0.25), Scalar(89, 90, 90), 1); line(label_liantogn_dst, Point(0, dst_min_rec->height*0.5), Point(dst_min_rec->width, dst_min_rec->height*0.5), Scalar(89, 90, 90), 1); line(label_liantogn_dst, Point(0, dst_min_rec->height*0.75), Point(dst_min_rec->width, dst_min_rec->height*0.75), Scalar(89, 90, 90), 1); line(label_liantogn_dst, Point(dst_min_rec->width*0.25, 0), Point(dst_min_rec->width*0.25, dst_min_rec->height), Scalar(89, 90, 90), 1); line(label_liantogn_dst, Point(dst_min_rec->width*0.5, 0), Point(dst_min_rec->width*0.5, dst_min_rec->height), Scalar(89, 90, 90), 1); line(label_liantogn_dst, Point(dst_min_rec->width*0.75, 0), Point(dst_min_rec->width*0.75, dst_min_rec->height), Scalar(89, 90, 90), 1); ////显示原点（0，0） Point p2; p2.x = 10; p2.y = 10; //画实心点 circle(label_liantogn_dst, p2, 5, Scalar(0, 0, 255), -1); //第五个参数我设为-1，表明这是个实点。 imshow("label_liantogn_dst_result", label_liantogn_dst); imwrite("save_Label.jpg", label_liantogn_dst); printf("连通区域个数：%4d \n", contour_num); cvNamedWindow("外接矩形", CV_WINDOW_AUTOSIZE);//分配一个用以承载图片的窗口 //cvLine(dst_min_rec, cvPoint(0, 50), cvPoint(dst_min_rec->width, 50), CV_RGB(255, 0, 0), 1); cvShowImage("外接矩形", dst_min_rec); //Mat dst_min_rec_result = cvarrToMat(dst_min_rec); //imwrite("save_rect1.jpg", dst_min_rec_result); cvSaveImage("save_Rectg.jpg", dst_min_rec); /////================================================================================================求最小外接矩形 float temp_percent = 0.0; printf("连通区域面积：%d\r\n", liantong_all_area); // 打印连通区域面积，放在前面被掩盖，所以放在后面 printf("图像矩形面积：%d\r\n", Rect_all_area); printf("整副图像面积：%d\r\n", sizeof_pic); temp_percent = (float)liantong_all_area / sizeof_pic * 100; printf("缺陷面积占比：%0.2f %%\r\n", temp_percent); /////================================================================================================hough 直线检测 //IplImage* src = cvLoadImage(g_BinaryImage, 0); //IplImage *SrcImage_origin = cvLoadImage(SrcPath, CV_LOAD_IMAGE_UNCHANGED); IplImage* lines_dst; IplImage* color_dst; CvMemStorage* lines_storage = cvCreateMemStorage(0); CvSeq* lines = 0; int hough_i; lines_dst = cvCreateImage(cvGetSize(liantogn_dst), 8, 1); color_dst = cvCreateImage(cvGetSize(liantogn_dst), 8, 3); cvCanny(liantogn_dst, lines_dst, Hough.hough_Canny_thresh1, Hough.hough_Canny_thresh2, Hough.hough_Canny_kernel); cvCvtColor(lines_dst, color_dst, CV_GRAY2BGR); #if 0 lines = cvHoughLines2(lines_dst, lines_storage, CV_HOUGH_STANDARD, 1, CV_PI / 180, 100, 0, 0); for (hough_i = 0; hough_i < MIN(lines->total, 100); hough_i++) { float* line = (float*)cvGetSeqElem(lines, hough_i); float rho = line[0]; float theta = line[1]; CvPoint pt1, pt2; double a = cos(theta), b = sin(theta); double x0 = a*rho, y0 = b*rho; pt1.x = cvRound(x0 + 1000 * (-b)); pt1.y = cvRound(y0 + 1000 * (a)); pt2.x = cvRound(x0 - 1000 * (-b)); pt2.y = cvRound(y0 - 1000 * (a)); cvLine(color_dst, pt1, pt2, CV_RGB(255, 0, 0), 3, CV_AA, 0); } #else lines = cvHoughLines2(lines_dst, lines_storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI / 180, Hough.cvHoughLines2_thresh, Hough.cvHoughLines2_param1, Hough.cvHoughLines2_param2); for (hough_i = 0; hough_i < lines->total; hough_i++) { CvPoint* line = (CvPoint*)cvGetSeqElem(lines, hough_i); cvLine(dst_min_rec, line[0], line[1], CV_RGB(0, 255, 255), 3, CV_AA, 0); } //// dst_min_rec 矩形加直线 ， color_dst 只有直线 #endif //cvNamedWindow("liantogn_dst", 1); //cvShowImage("liantogn_dst", liantogn_dst); cvNamedWindow("Hough", 1); cvShowImage("Hough", dst_min_rec); cvSaveImage("save_Hough.jpg", liantogn_dst); //// 本打算通过膨胀腐蚀的办法来填充中间，再计算面积比 ////=============================================================================================图像反色 //获取图片的一些属性 int trans_W_B_height = fanse_origin->height; // 图像高度 int trans_W_B_width = fanse_origin->width; // 图像宽度（像素为单位） int trans_W_B_step = fanse_origin->widthStep; // 相邻行的同列点之间的字节数 int trans_W_B_channels = fanse_origin->nChannels; // 颜色通道数目 (1,2,3,4) uchar *trans_W_B_data = (uchar *)fanse_origin->imageData; //反色操作 for (int i = 0; i != trans_W_B_height; ++i) { for (int j = 0; j != trans_W_B_width; ++j) { for (int k = 0; k != trans_W_B_channels; ++k) { trans_W_B_data[i*trans_W_B_step + j*trans_W_B_channels + k] = 255 - trans_W_B_data[i*trans_W_B_step + j*trans_W_B_channels + k]; } } } cvNamedWindow("fanse_origin", 1); cvShowImage("fanse_origin", fanse_origin); ////=============================================================================================图像反色 return Feature_value2; ////返回该数组 } int main(){ int **Tan_return; Tan_return = on_trackbar( ); //调用DLL for (int i = 0; i < contour_num; i++) { printf("Number%3d: ", i); for (int j = 0; j < num_col; j++) { printf("%4d ", Tan_return[i][j]); } printf("\r\n"); } ////========================= 释放内存 for (int i = 0; i < contour_num; i++) { delete[] Tan_return[i]; } delete[] Tan_return; cvWaitKey(0); ////销毁窗口，释放图片（实际运行退出时一定要销毁窗口） //cvDestroyWindow(WindowBinaryTitle); //cvDestroyWindow(WindowSrcTitle); //cvReleaseImage(&g_BinaryImage); //cvReleaseImage(&g_GrayImage); //cvReleaseImage(&SrcImage); return 0; }
