关于边缘检测的基础来自于一个事实，即在边缘部分，像素值出现”跳跃“或者较大的变化。如果在此边缘部分求取一阶导数，就会看到极值的出现。
而在一阶导数为极值的地方，二阶导数为0，基于这个原理，就可以进行边缘检测。
关于 Laplace 算法原理，可参考
0x01. Laplace 算法
下面的代码展示了分别对灰度化的图像和原始彩色图像中的边缘进行检测：
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import cv2.cv as cv im=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_COLOR) # Laplace on a gray scale picture gray = cv.CreateImage(cv.GetSize(im), 8, 1) cv.CvtColor(im, gray, cv.CV_BGR2GRAY) aperture=3 dst = cv.CreateImage(cv.GetSize(gray), cv.IPL_DEPTH_32F, 1) cv.Laplace(gray, dst,aperture) cv.Convert(dst,gray) thresholded = cv.CloneImage(im) cv.Threshold(im, thresholded, 50, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage('Laplaced grayscale',gray) #------------------------------------ # Laplace on color planes = [cv.CreateImage(cv.GetSize(im), 8, 1) for i in range(3)] laplace = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) colorlaplace = cv.CreateImage(cv.GetSize(im), 8, 3) cv.Split(im, planes[0], planes[1], planes[2], None) #Split channels to apply laplace on each for plane in planes: cv.Laplace(plane, laplace, 3) cv.ConvertScaleAbs(laplace, plane, 1, 0) cv.Merge(planes[0], planes[1], planes[2], None, colorlaplace) cv.ShowImage('Laplace Color', colorlaplace) #------------------------------------- cv.WaitKey(0) |
效果展示
原图
灰度化图片检测
原始彩色图片检测
0x02. Sobel 算法
Sobel 也是很常用的一种轮廓识别的算法。
关于 Sobel 导数原理的介绍，可参考
以下是使用 Sobel 算法进行轮廓检测的代码和效果
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import cv2.cv as cv im=cv.LoadImage('img/building.png', cv.CV_LOAD_IMAGE_GRAYSCALE) sobx = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) cv.Sobel(im, sobx, 1, 0, 3) #Sobel with x-order=1 soby = cv.CreateImage(cv.GetSize(im), cv.IPL_DEPTH_16S, 1) cv.Sobel(im, soby, 0, 1, 3) #Sobel withy-oder=1 cv.Abs(sobx, sobx) cv.Abs(soby, soby) result = cv.CloneImage(im) cv.Add(sobx, soby, result) #Add the two results together. cv.Threshold(result, result, 100, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage('Image', im) cv.ShowImage('Result', result) cv.WaitKey(0) |
处理之后效果图（感觉比Laplace效果要好些）
0x03. cv.MorphologyEx
cv.MorphologyEx 是另外一种边缘检测的算法
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import cv2.cv as cv image=cv.LoadImage('img/build.png', cv.CV_LOAD_IMAGE_GRAYSCALE) #Get edges morphed = cv.CloneImage(image) cv.MorphologyEx(image, morphed, None, None, cv.CV_MOP_GRADIENT) # Apply a dilate - Erode cv.Threshold(morphed, morphed, 30, 255, cv.CV_THRESH_BINARY_INV) cv.ShowImage("Image", image) cv.ShowImage("Morphed", morphed) cv.WaitKey(0) |
0x04. Canny 边缘检测
Canny 算法可以对直线边界做出很好的检测；
关于 Canny 算法原理的描述，可参考：
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import cv2.cv as cv import math im=cv.LoadImage('img/road.png', cv.CV_LOAD_IMAGE_GRAYSCALE) pi = math.pi #Pi value dst = cv.CreateImage(cv.GetSize(im), 8, 1) cv.Canny(im, dst, 200, 200) cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY) #---- Standard ---- color_dst_standard = cv.CreateImage(cv.GetSize(im), 8, 3) cv.CvtColor(im, color_dst_standard, cv.CV_GRAY2BGR)#Create output image in RGB to put red lines lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD, 1, pi / 180, 100, 0, 0) for (rho, theta) in lines[:100]: a = math.cos(theta) #Calculate orientation in order to print them b = math.sin(theta) x0 = a * rho y0 = b * rho pt1 = (cv.Round(x0 + 1000*(-b)), cv.Round(y0 + 1000*(a))) pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a))) cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2, 4) #Draw the line #---- Probabilistic ---- color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3) cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idem |
