Python + OpenCV边沿检测(Edge Detection)

Python + OpenCV边沿检测(Edge Detection)

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• [email protected]

OpenCV提供了3种边沿检测算法

• Laplacian
• sobel
• canny

本文分别采用这3种算法进行边沿检测，并给出比较结果

1 基于Laplacian的边沿检测

代码如下

    import numpy
    import argparse
    import cv2

    image = cv2.imread('1.jpg')
    cv2.imshow("Original", image)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv2.imshow("Gray", gray)

    #if don't use a floating point data type when computing
    #the gradient magnitude image, you will miss edges
    lap = cv2.Laplacian(gray, cv2.CV_64F)
    lap = numpy.uint8(numpy.absolute(lap))


    #display two images in a figure
    cv2.imshow("Edge detection by Laplacaian", numpy.hstack([lap, gray]))

    cv2.imwrite("1_edge_by_laplacian.jpg", numpy.hstack([gray, lap]))


    if(cv2.waitKey(0)==27):
     cv2.destroyAllWindows()

2 基于Sobel的边沿检测

代码如下

    import numpy
    import argparse
    import cv2

    image = cv2.imread('1.jpg')
    cv2.imshow("Original", image)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv2.imshow("Gray", gray)

    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)

    sobelx = numpy.uint8(numpy.absolute(sobelx))
    sobely = numpy.uint8(numpy.absolute(sobely))
    sobelcombine = cv2.bitwise_or(sobelx,sobely)
    #display two images in a figure
    cv2.imshow("Edge detection by Sobel", numpy.hstack([gray,sobelx,sobely, sobelcombine]))

    cv2.imwrite("1_edge_by_sobel.jpg", numpy.hstack([gray,sobelx,sobely, sobelcombine]))


    if(cv2.waitKey(0)==27):
      cv2.destroyAllWindows()

3 基于Canny的边沿检测

代码如下

    import numpy
    import argparse
    import cv2

    image = cv2.imread('1.jpg')
    cv2.imshow("Original", image)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    cv2.imshow("Gray", gray)

    #30 and 150 is the threshold, larger than 150 is considered as edge,
    #less than 30 is considered as not edge
    canny = cv2.Canny(gray, 30, 150)

    canny = numpy.uint8(numpy.absolute(canny))
    #display two images in a figure
    cv2.imshow("Edge detection by Canny", numpy.hstack([gray,canny]))

    cv2.imwrite("1_edge_by_canny.jpg", numpy.hstack([gray,canny]))


    if(cv2.waitKey(0)==27):
     cv2.destroyAllWindows()

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以下是3种边沿检测算法的处理结果。 从图中可以看出，Lapacian的检测效果最差，Canny的最明显，因为该算法将边沿检测结果进行了二值化处理。Sobel的检测结果也是较好的，从图中可以看到边沿检测结果是较为清晰的。

• Lapacian
  

• Sobel
  

• Canny