Python + OpenCV模糊处理(Bluring)

Python + OpenCV模糊处理(Bluring)

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• [email protected]

模糊处理在边沿检测和去噪声方面有较为广泛的应用。OpenCV中提供了4种模糊算法，列举如下：

• average
• median
• gaussian
• bilateral

本文将分别采用这4种算法对同一幅图像进行处理，并以图像的形式展现这几种算法之间的差别。

1 average

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)


    #[x,y] is the kernel for bluring
    #the large kernel becomes, the more blurred imag will appear
    #hstack is able to stack multiple images together
    #using simple mean to average
    blurred = numpy.hstack([
    cv2.blur(gray, (3,3)),
    cv2.blur(gray, (5,5)),
    cv2.blur(gray, (7,7))])

    #display two images in a figure
    cv2.imshow("Blurring by average", blurred)

    cv2.imwrite("1_blur_by_average.jpg", blurred)


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

2 Gaussian

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)


    #[x,y] is the kernel for bluring
    #the large kernel becomes, the more blurred imag will appear
    #hstack is able to stack multiple images together
    #using weighted mean
    #where neighborhood pixels that are closer to the central pixel contribute more "weight" to the average
    blurred = numpy.hstack([
    cv2.GaussianBlur(gray, (3,3), 0),
    cv2.GaussianBlur(gray, (5,5), 0),
    cv2.GaussianBlur(gray, (7,7), 0)])

    #display two images in a figure
    cv2.imshow("Blurring by Gaussian", blurred)

    cv2.imwrite("1_blur_by_Gaussian.jpg", blurred)


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

3 median

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)


    #[x,y] is the kernel for bluring
    #the large kernel becomes, the more blurred imag will appear
    #hstack is able to stack multiple images together
    #the central pixel is replaced with the median of the neighborhood
    #it is the most effective when removing salt-and-pepper noise
    blurred = numpy.hstack([
    cv2.medianBlur(gray, 3),
    cv2.medianBlur(gray, 5),
    cv2.medianBlur(gray, 7)])

    #display two images in a figure
    cv2.imshow("Blurring by Median", blurred)

    cv2.imwrite("1_blur_by_Median.jpg", blurred)


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

4 bilateral

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)


    #[x,y] is the kernel for bluring
    #the large kernel becomes, the more blurred imag will appear
    #hstack is able to stack multiple images together
    #in order to reduce noise while maintaining edges
    blurred = numpy.hstack([
    cv2.bilateralFilter(gray, 5,21,21),
    cv2.bilateralFilter(gray, 7,31,31),
    cv2.bilateralFilter(gray, 9,41,41)])

    #display two images in a figure
    cv2.imshow("Blurring by Bilateral", blurred)

    cv2.imwrite("1_blur_by_Bilateral.jpg", blurred)


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

处理结果

以下是4种模糊算法处理同一张图片的结果。

• Average
  

• Gaussian
  

• Median
  

• Bilateral