| 题目要求 |
| 程序代码 |
| 结果图片 |
| 要言妙道 |
题目要求：
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
程序代码：
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
结果图片：
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要言妙道：
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
时间： 2024-11-08 16:13:20