非常简单的边缘检测

Question

我有一个简单的高对比度黑白视频图像，我需要在其上找到垂直边缘的 X 值和水平边缘的 Y 值。我还需要知道边缘的方向（白色->黑色或黑色->白色）。

会有：

1. 零或一个水平边缘
2. 零或一个垂直边缘。
3. 边缘完全笔直。
4. 边缘将由从黑色到白色或从白色到黑色的锐利（ish）过渡来定义。

我一直在阅读有关边缘检测算法的文章，但对它了解甚少，但我确实了解它们都比我的要求复杂得多。它们都旨在从复杂图像中生成简单边缘的图像。我的要求（我的图像）要简单得多，我只需要 X 和 Y 值为零到两条边。

如果我在图表上绘制一行像素（X=像素索引，Y=像素强度），我会得到一条线、一个上升斜率和另一条线。斜坡的中心点就是我需要的。但线条并不完美。它们很吵，可能会有小凸起或凹陷，或者如果看不到边缘，它可能（大部分）平坦。

那么有没有一种简单的算法来平滑该图并产生“斜坡”的方向和中心点？

Answer 1

我会直接从命令行使用 ImageMagick 来解决这个问题 - 它安装在大多数 Linux 发行版上，也可用于 OSX 和 Windows。

所以，首先，我会使用灰度并标准化对比度，这样您就不太容易受到曝光或照明变化的影响。然后我将阈值设置为 50%，以便所有像素都被强制为黑色或白色。

convert line.png -colorspace gray -normalize -threshold 50% result.png

然后我会运行一个又高又薄的中值滤波器（比如 100 像素高和 1 像素宽）以在垂直方向上平滑图像（这样我就可以找到水平边缘）。

convert line.png -colorspace gray -normalize -threshold 50% -virtual-pixel mirror -statistic median 1x100 result.png

然后我会将图像的大小调整为 1 像素宽和 100 像素高以减少输出量，因为我会说在图像高度的 1% 内找到边缘是你所希望的那样嘈杂的图像。

convert line.png -colorspace gray -normalize -threshold 50% -virtual-pixel mirror -statistic median 1x100 -resize 1x100! txt:

# ImageMagick pixel enumeration: 1,100,255,srgb
0,0: (32496,32496,32496)  #7E7E7E  srgb(126,126,126)
0,1: (32969,32969,32969)  #808080  srgb(128,128,128)
0,2: (33498,33498,33498)  #828282  srgb(130,130,130)
0,3: (32632,32632,32632)  #7F7F7F  srgb(127,127,127)
0,4: (32708,32708,32708)  #7F7F7F  srgb(127,127,127)
0,5: (32787,32787,32787)  #808080  srgb(128,128,128)
0,6: (32615,32615,32615)  #7F7F7F  srgb(127,127,127)
0,7: (33186,33186,33186)  #818181  srgb(129,129,129)
0,8: (33786,33786,33786)  #838383  srgb(131,131,131)
0,9: (33610,33610,33610)  #838383  srgb(131,131,131)
0,10: (32577,32577,32577)  #7F7F7F  srgb(127,127,127)
0,11: (33159,33159,33159)  #818181  srgb(129,129,129)
0,12: (33578,33578,33578)  #838383  srgb(131,131,131)
0,13: (33276,33276,33276)  #818181  srgb(129,129,129)
0,14: (33259,33259,33259)  #818181  srgb(129,129,129)
0,15: (32620,32620,32620)  #7F7F7F  srgb(127,127,127)
0,16: (32624,32624,32624)  #7F7F7F  srgb(127,127,127)
0,17: (33131,33131,33131)  #818181  srgb(129,129,129)
0,18: (33278,33278,33278)  #818181  srgb(129,129,129)
0,19: (32680,32680,32680)  #7F7F7F  srgb(127,127,127)
0,20: (33136,33136,33136)  #818181  srgb(129,129,129)
0,21: (32707,32707,32707)  #7F7F7F  srgb(127,127,127)
0,22: (33051,33051,33051)  #818181  srgb(129,129,129)
0,23: (33794,33794,33794)  #838383  srgb(131,131,131)
0,24: (33704,33704,33704)  #838383  srgb(131,131,131)
0,25: (33028,33028,33028)  #818181  srgb(129,129,129)
0,26: (33605,33605,33605)  #838383  srgb(131,131,131)
0,27: (33785,33785,33785)  #838383  srgb(131,131,131)
0,28: (33290,33290,33290)  #828282  srgb(130,130,130)
0,29: (33079,33079,33079)  #818181  srgb(129,129,129)
0,30: (33430,33430,33430)  #828282  srgb(130,130,130)
0,31: (33246,33246,33246)  #818181  srgb(129,129,129)
0,32: (33102,33102,33102)  #818181  srgb(129,129,129)
0,33: (33248,33248,33248)  #818181  srgb(129,129,129)
0,34: (33823,33823,33823)  #848484  srgb(132,132,132)
0,35: (33678,33678,33678)  #838383  srgb(131,131,131)
0,36: (33497,33497,33497)  #828282  srgb(130,130,130)
0,37: (33960,33960,33960)  #848484  srgb(132,132,132)
0,38: (33520,33520,33520)  #828282  srgb(130,130,130)
0,39: (33235,33235,33235)  #818181  srgb(129,129,129)
0,40: (32655,32655,32655)  #7F7F7F  srgb(127,127,127)
0,41: (32825,32825,32825)  #808080  srgb(128,128,128)
0,42: (32847,32847,32847)  #808080  srgb(128,128,128)
0,43: (33214,33214,33214)  #818181  srgb(129,129,129)
0,44: (32966,32966,32966)  #808080  srgb(128,128,128)
0,45: (33262,33262,33262)  #818181  srgb(129,129,129)
0,46: (33178,33178,33178)  #818181  srgb(129,129,129)
0,47: (32501,32501,32501)  #7E7E7E  srgb(126,126,126)
0,48: (32499,32499,32499)  #7E7E7E  srgb(126,126,126)
0,49: (31982,31982,31982)  #7C7C7C  srgb(124,124,124)
0,50: (32564,32564,32564)  #7F7F7F  srgb(127,127,127)
0,51: (32336,32336,32336)  #7E7E7E  srgb(126,126,126)
0,52: (30311,30311,30311)  #767676  srgb(118,118,118)
0,53: (27119,27119,27119)  #6A6A6A  srgb(106,106,106)
0,54: (13002,13002,13002)  #333333  srgb(51,51,51)
0,55: (1855,1855,1855)  #070707  srgb(7,7,7)
0,56: (187,187,187)  #010101  srgb(1,1,1)           <--- This is your edge at 56% of the image height down from the top
0,57: (72,72,72)  #000000  srgb(0,0,0)
0,58: (0,0,0)  #000000  black
0,59: (0,0,0)  #000000  black
0,60: (0,0,0)  #000000  black
0,61: (0,0,0)  #000000  black
0,62: (0,0,0)  #000000  black
0,63: (0,0,0)  #000000  black
0,64: (0,0,0)  #000000  black
0,65: (0,0,0)  #000000  black
0,66: (0,0,0)  #000000  black
0,67: (0,0,0)  #000000  black
0,68: (0,0,0)  #000000  black
0,69: (0,0,0)  #000000  black
0,70: (0,0,0)  #000000  black
0,71: (0,0,0)  #000000  black
0,72: (0,0,0)  #000000  black
0,73: (0,0,0)  #000000  black
0,74: (0,0,0)  #000000  black
0,75: (0,0,0)  #000000  black
0,76: (0,0,0)  #000000  black
0,77: (0,0,0)  #000000  black
0,78: (0,0,0)  #000000  black
0,79: (0,0,0)  #000000  black
0,80: (0,0,0)  #000000  black
0,81: (0,0,0)  #000000  black
0,82: (0,0,0)  #000000  black
0,83: (0,0,0)  #000000  black
0,84: (0,0,0)  #000000  black
0,85: (0,0,0)  #000000  black
0,86: (0,0,0)  #000000  black
0,87: (0,0,0)  #000000  black
0,88: (0,0,0)  #000000  black
0,89: (0,0,0)  #000000  black
0,90: (0,0,0)  #000000  black
0,91: (0,0,0)  #000000  black
0,92: (0,0,0)  #000000  black
0,93: (0,0,0)  #000000  black
0,94: (0,0,0)  #000000  black
0,95: (0,0,0)  #000000  black
0,96: (0,0,0)  #000000  black
0,97: (0,0,0)  #000000  black
0,98: (0,0,0)  #000000  black
0,99: (0,0,0)  #000000  black

垂直边缘也是如此...

convert line.png -colorspace gray -normalize -threshold 50% -virtual-pixel mirror -statistic median 100x1 -resize 100x1! txt:

# ImageMagick pixel enumeration: 100,1,255,srgb
0,0: (0,0,0)  #000000  black
1,0: (0,0,0)  #000000  black
2,0: (0,0,0)  #000000  black
3,0: (0,0,0)  #000000  black
4,0: (0,0,0)  #000000  black
5,0: (0,0,0)  #000000  black
6,0: (0,0,0)  #000000  black
7,0: (0,0,0)  #000000  black
8,0: (0,0,0)  #000000  black
9,0: (0,0,0)  #000000  black
10,0: (0,0,0)  #000000  black
11,0: (0,0,0)  #000000  black
12,0: (0,0,0)  #000000  black
13,0: (0,0,0)  #000000  black
14,0: (0,0,0)  #000000  black
15,0: (0,0,0)  #000000  black
16,0: (0,0,0)  #000000  black
17,0: (0,0,0)  #000000  black
18,0: (0,0,0)  #000000  black
19,0: (0,0,0)  #000000  black
20,0: (0,0,0)  #000000  black
21,0: (0,0,0)  #000000  black
22,0: (0,0,0)  #000000  black
23,0: (0,0,0)  #000000  black
24,0: (0,0,0)  #000000  black
25,0: (0,0,0)  #000000  black
26,0: (0,0,0)  #000000  black
27,0: (0,0,0)  #000000  black
28,0: (0,0,0)  #000000  black
29,0: (0,0,0)  #000000  black
30,0: (0,0,0)  #000000  black
31,0: (0,0,0)  #000000  black
32,0: (0,0,0)  #000000  black
33,0: (0,0,0)  #000000  black
34,0: (0,0,0)  #000000  black
35,0: (0,0,0)  #000000  black
36,0: (0,0,0)  #000000  black
37,0: (0,0,0)  #000000  black
38,0: (0,0,0)  #000000  black
39,0: (0,0,0)  #000000  black
40,0: (0,0,0)  #000000  black
41,0: (0,0,0)  #000000  black
42,0: (0,0,0)  #000000  black
43,0: (0,0,0)  #000000  black
44,0: (0,0,0)  #000000  black
45,0: (7,7,7)  #000000  srgb(0,0,0)
46,0: (87,87,87)  #000000  srgb(0,0,0)
47,0: (342,342,342)  #010101  srgb(1,1,1)
48,0: (3623,3623,3623)  #0E0E0E  srgb(14,14,14)
49,0: (16943,16943,16943)  #424242  srgb(66,66,66)      <-- This is your vertical edge, 49% of the image width across from the left side
50,0: (29779,29779,29779)  #747474  srgb(116,116,116)
51,0: (33852,33852,33852)  #848484  srgb(132,132,132)
52,0: (34745,34745,34745)  #878787  srgb(135,135,135)
53,0: (35643,35643,35643)  #8B8B8B  srgb(139,139,139)
54,0: (35893,35893,35893)  #8C8C8C  srgb(140,140,140)
55,0: (35932,35932,35932)  #8C8C8C  srgb(140,140,140)
56,0: (35934,35934,35934)  #8C8C8C  srgb(140,140,140)
57,0: (36041,36041,36041)  #8C8C8C  srgb(140,140,140)
58,0: (36095,36095,36095)  #8C8C8C  srgb(140,140,140)
59,0: (35975,35975,35975)  #8C8C8C  srgb(140,140,140)
60,0: (35937,35937,35937)  #8C8C8C  srgb(140,140,140)
61,0: (35937,35937,35937)  #8C8C8C  srgb(140,140,140)
62,0: (36042,36042,36042)  #8C8C8C  srgb(140,140,140)
63,0: (36094,36094,36094)  #8C8C8C  srgb(140,140,140)
64,0: (36148,36148,36148)  #8D8D8D  srgb(141,141,141)
65,0: (36409,36409,36409)  #8E8E8E  srgb(142,142,142)
66,0: (36409,36409,36409)  #8E8E8E  srgb(142,142,142)
67,0: (36375,36375,36375)  #8E8E8E  srgb(142,142,142)
68,0: (36252,36252,36252)  #8D8D8D  srgb(141,141,141)
69,0: (36214,36214,36214)  #8D8D8D  srgb(141,141,141)
70,0: (36176,36176,36176)  #8D8D8D  srgb(141,141,141)
71,0: (36274,36274,36274)  #8D8D8D  srgb(141,141,141)
72,0: (36406,36406,36406)  #8E8E8E  srgb(142,142,142)
73,0: (36366,36366,36366)  #8E8E8E  srgb(142,142,142)
74,0: (36128,36128,36128)  #8D8D8D  srgb(141,141,141)
75,0: (36004,36004,36004)  #8C8C8C  srgb(140,140,140)
76,0: (35900,35900,35900)  #8C8C8C  srgb(140,140,140)
77,0: (35872,35872,35872)  #8C8C8C  srgb(140,140,140)
78,0: (35855,35855,35855)  #8C8C8C  srgb(140,140,140)
79,0: (35779,35779,35779)  #8B8B8B  srgb(139,139,139)
80,0: (35777,35777,35777)  #8B8B8B  srgb(139,139,139)
81,0: (35664,35664,35664)  #8B8B8B  srgb(139,139,139)
82,0: (35774,35774,35774)  #8B8B8B  srgb(139,139,139)
83,0: (35785,35785,35785)  #8B8B8B  srgb(139,139,139)
84,0: (35753,35753,35753)  #8B8B8B  srgb(139,139,139)
85,0: (35671,35671,35671)  #8B8B8B  srgb(139,139,139)
86,0: (35675,35675,35675)  #8B8B8B  srgb(139,139,139)
87,0: (35694,35694,35694)  #8B8B8B  srgb(139,139,139)
88,0: (35621,35621,35621)  #8B8B8B  srgb(139,139,139)
89,0: (35819,35819,35819)  #8B8B8B  srgb(139,139,139)
90,0: (36080,36080,36080)  #8C8C8C  srgb(140,140,140)
91,0: (36300,36300,36300)  #8D8D8D  srgb(141,141,141)
92,0: (36550,36550,36550)  #8E8E8E  srgb(142,142,142)
93,0: (36561,36561,36561)  #8E8E8E  srgb(142,142,142)
94,0: (36425,36425,36425)  #8E8E8E  srgb(142,142,142)
95,0: (36268,36268,36268)  #8D8D8D  srgb(141,141,141)
96,0: (36317,36317,36317)  #8D8D8D  srgb(141,141,141)
97,0: (36409,36409,36409)  #8E8E8E  srgb(142,142,142)
98,0: (36521,36521,36521)  #8E8E8E  srgb(142,142,142)
99,0: (36568,36568,36568)  #8E8E8E  srgb(142,142,142)

这是垂直边缘的中值过滤图像：

而且，我可以像这样用红色绘制检测到的边缘：

convert line.png -stroke red -draw "line 0,268 640,268" -draw "line 313,0 313,480" result.png

因此，总而言之，您将在终端中运行的两个命令，无需编写或编译和链接任何代码，如下所示：

# Find horizontal edges
convert line.png -colorspace gray -normalize -threshold 50% -virtual-pixel mirror -statistic median 1x100 -resize 1x100! txt:

# Find vertical edges
convert line.png -colorspace gray -normalize -threshold 50% -virtual-pixel mirror -statistic median 100x1 -resize 100x1! txt:

Answer 2

如果您想平滑边缘，OpenCV 的2D-Convolution 过滤器可以帮助您。我不知道您是否已经在使用 OpenCV 库。这是图像处理中最有用的库之一。

如果您想填补行中的空白，可以再次使用 OpenCV 中的Opening Filter。

要使用边缘检测算法，您无需了解它们。优点是您可以简单地对其进行测试，看看它是否会给您想要的结果。我个人使用 Canny Edge Detection 来检测黑白 X 射线图像中的边缘。

包括“opencv2/imgproc/imgproc.hpp”

    //// find out largest gradient in image
    //edgeDetection( m_data->m_originalXRay , m_data->m_segmentedXRay, m_data->m_width, m_data->m_height);
    //double largestGradient = 0;
    //for (int y = 0; y<m_data->m_height; y++)
    //  for (int x = 0; x<m_data->m_width; x++) 
    //      if (m_data->m_segmentedXRay[y*m_data->m_width + x] > largestGradient)
    //          largestGradient = m_data->m_segmentedXRay[y*m_data->m_width + x];

    //upper = 0.2 * largestGradient;
    //lower = 0.08 * largestGradient;

在上面我写了一个算法来自动找到canny的上限和下限，当然你可以手动设置。

    //cv::morphologyEx(imageVector, imageVector, cv::MORPH_OPEN,     cv::Mat(5,5,CV_8UC1));

这就是我使用开放过滤器填充边缘的小间隙的地方。

int blurSize = 3;
    cv::blur(imageVector, imageVector, cv::Size(blurSize,blurSize));

我刚刚使用了平滑滤镜 Blur。

    bool L2gradient = false;
    cv::Canny(imageVector , imageVector, lower, upper, kernelSize, L2gradient);

Answer 3

我想出的东西非常适合这个特定的应用程序，所以我想我会分享它。

首先，我使用可调节阈值对图像进行二元过滤。

然后我运行“平滑”操作。我确定有一个我不知道的技术术语。我检查每个像素的邻居（邻居的数量是可调的）来决定该像素应该是黑色还是白色。

然后我抓取一个矩形区域（图像的每个顶部、底部、左侧和右侧部分）并注意每行（垂直）/列（水平）的第一个过渡（黑色到白色或白色到黑色） .

如果找到的转换数量明显少于该区域的行/列数，则没有行。

我生成这些转换的均值和总体标准差。

如果偏差大于某个可调值，则没有线。 如果偏差较小，则以均值作为直线的位置。