| 题目要求 |
| 程序代码 |
| 结果图片 |
| 要言妙道 |
载入一副场景丰富的图像，使用cvThreshold（）函数对其进行操作，设置阈值为128,。
a、依次用表5-5中的设置类型并显示结果
b、用函数cvAdaptiveThreshold（）设param1=5
c、先设param1=0，重复b，再设param1=-5，重复b
1 // OpenCVExerciseTesting.cpp : 定义控制台应用程序的入口点。 2 // 3 //D:\Work\Work_Programming\Source\Image\lena.jpg 4 5 6 #include "stdafx.h" 7 #include <cv.h> 8 #include <highgui.h> 9 #include <iostream> 10 11 #include <opencv2/legacy/legacy.hpp> 12 //#pragma comment(lib, "opencv_legacy2411.lib") 13 14 using namespace cv; 15 using namespace std; 16 17 //函数声明-->--->-->--->-->--->-->--->// 18 19 20 //<--<--<--<--<--<--<--<--<--函数声明// 21 22 int _tmain(int argc, _TCHAR* argv[]) 23 { 24 const char * soutceFile = "D:\Work\Work_Programming\Source\Image\OpenCVExerciseImage\第5章\山水风景.jpg"; 25 IplImage * image_Resource = cvLoadImage(soutceFile, CV_LOAD_IMAGE_UNCHANGED); 26 assert(image_Resource); 27 28 cvNamedWindow("原始图像", 0); 29 cvNamedWindow("题目_a_1", 0); 30 cvNamedWindow("题目_a_2", 0); 31 cvNamedWindow("题目_a_3", 0); 32 cvNamedWindow("题目_a_4", 0); 33 cvNamedWindow("题目_a_5", 0); 34 cvNamedWindow("题目_b_1", 0); 35 cvNamedWindow("题目_b_2", 0); 36 cvNamedWindow("题目_c_1", 0); 37 cvNamedWindow("题目_c_2", 0); 38 cvNamedWindow("题目_c_3", 0); 39 cvNamedWindow("题目_c_4", 0); 40 41 IplImage * image_Gray = cvCreateImage(cvSize(image_Resource->width, image_Resource->height), IPL_DEPTH_8U, 1); 42 43 //使用cvCvtColor和cvCopy这些函数前，都应该对参数进行验证再使用 44 if (image_Resource->nChannels != 3) 45 { 46 cout << "加载的图像必须为彩色图片" << endl; 47 return 0; 48 } 49 50 cvCvtColor(image_Resource, image_Gray, CV_RGB2GRAY); 51 52 cvShowImage("原始图像", image_Gray); 53 54 //---------------------------a：开始--------------------------------// 55 56 double max_value = 255; 57 double threshold = 128; 58 59 IplImage * image_Result_a_1 = cvCloneImage(image_Gray); 60 cvZero(image_Result_a_1); 61 cvThreshold(image_Gray, image_Result_a_1, threshold, max_value, CV_THRESH_BINARY); 62 cvShowImage("题目_a_1", image_Result_a_1); 63 64 65 IplImage * image_Result_a_2 = cvCloneImage(image_Gray); 66 cvZero(image_Result_a_2); 67 cvThreshold(image_Gray, image_Result_a_2, threshold, max_value, CV_THRESH_BINARY_INV); 68 cvShowImage("题目_a_2", image_Result_a_2); 69 70 71 IplImage * image_Result_a_3 = cvCloneImage(image_Gray); 72 cvZero(image_Result_a_3); 73 cvThreshold(image_Gray, image_Result_a_3, threshold, max_value, CV_THRESH_TRUNC); 74 cvShowImage("题目_a_3", image_Result_a_3); 75 76 IplImage * image_Result_a_4 = cvCloneImage(image_Gray); 77 cvZero(image_Result_a_4); 78 cvThreshold(image_Gray, image_Result_a_4, threshold, max_value, CV_THRESH_TOZERO_INV); 79 cvShowImage("题目_a_4", image_Result_a_4); 80 81 IplImage * image_Result_a_5 = cvCloneImage(image_Gray); 82 cvZero(image_Result_a_5); 83 cvThreshold(image_Gray, image_Result_a_5, threshold, max_value, CV_THRESH_TOZERO); 84 cvShowImage("题目_a_5", image_Result_a_5); 85 //---------------------------a：结束--------------------------------// 86 87 //---------------------------b：开始--------------------------------// 88 89 IplImage * image_Result_b_1 = cvCloneImage(image_Gray); 90 cvZero(image_Result_b_1); 91 cvAdaptiveThreshold(image_Gray, image_Result_b_1, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 3, 5); 92 93 cvShowImage("题目_b_1", image_Result_b_1); 94 95 96 IplImage * image_Result_b_2 = cvCloneImage(image_Gray); 97 cvZero(image_Result_b_2); 98 cvAdaptiveThreshold(image_Gray, image_Result_b_2, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY_INV,3,5); 99 100 cvShowImage("题目_b_2", image_Result_b_2); 101 102 //---------------------------b：结束--------------------------------// 103 104 //---------------------------c：开始--------------------------------// 105 106 IplImage * image_Result_c_1 = cvCloneImage(image_Gray); 107 cvZero(image_Result_c_1); 108 cvAdaptiveThreshold(image_Gray, image_Result_c_1, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 3, 0); 109 110 cvShowImage("题目_c_1", image_Result_c_1); 111 112 113 IplImage * image_Result_c_2 = cvCloneImage(image_Gray); 114 cvZero(image_Result_c_2); 115 cvAdaptiveThreshold(image_Gray, image_Result_c_2, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY_INV, 3, 0); 116 117 cvShowImage("题目_c_2", image_Result_c_2); 118 119 IplImage * image_Result_c_3 = cvCloneImage(image_Gray); 120 cvZero(image_Result_c_3); 121 cvAdaptiveThreshold(image_Gray, image_Result_c_3, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 3, -5); 122 123 cvShowImage("题目_c_3", image_Result_c_3); 124 125 126 IplImage * image_Result_c_4 = cvCloneImage(image_Gray); 127 cvZero(image_Result_c_4); 128 cvAdaptiveThreshold(image_Gray, image_Result_c_4, max_value, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY_INV, 3, -5); 129 130 cvShowImage("题目_c_4", image_Result_c_4); 131 132 //---------------------------c：结束--------------------------------// 133 134 cvWaitKey(0); 135 136 cvReleaseImage(&image_Resource); 137 cvReleaseImage(&image_Result_a_1); 138 cvReleaseImage(&image_Result_a_2); 139 cvReleaseImage(&image_Result_a_3); 140 cvReleaseImage(&image_Result_a_4); 141 cvReleaseImage(&image_Result_a_5); 142 cvReleaseImage(&image_Result_b_1); 143 cvReleaseImage(&image_Result_b_2); 144 cvReleaseImage(&image_Result_c_1); 145 cvReleaseImage(&image_Result_c_2); 146 cvReleaseImage(&image_Result_c_3); 147 cvReleaseImage(&image_Result_c_4); 148 149 cvDestroyWindow("原始图像"); 150 cvDestroyWindow("题目_a_1"); 151 cvDestroyWindow("题目_a_2"); 152 cvDestroyWindow("题目_a_3"); 153 cvDestroyWindow("题目_a_4"); 154 cvDestroyWindow("题目_a_5"); 155 cvDestroyWindow("题目_b_1"); 156 cvDestroyWindow("题目_b_2"); 157 cvDestroyWindow("题目_c_1"); 158 cvDestroyWindow("题目_c_2"); 159 cvDestroyWindow("题目_c_3"); 160 cvDestroyWindow("题目_c_4"); 161 162 return 0; 163 } 164
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①针对有很强照明或反射梯度的图像，需要根据梯度进行阈值化，自适应阈值技术非常有用，最有对比性的是《学习OpenCV》第161页使用cvThreshold和cvAdaptiveThreshold对图片处理的例子，当棋盘标定板有很强的照明梯度的时候，使用cvThreshold得不到完整的棋盘，但cvAdaptiveThreshold可以。
②cvAdaptiveThreshold的参数thresholdType ，官方文档的说明是 Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV .，并不是cvThreshold的5种类型都能用
③CV_THRESH_TRUNC,对应的操作时dsti=(srci>T)?T:srci，其中T代表阈值，这点《学习OpenCV》中笔误