Python + opencv: image smoothing

Machine vision 001 2020-11-16 01:29:25
python opencv image smoothing


Python+OpenCV: Image smoothing

Two dimensional convolution ( Image filtering )

####################################################################################################
# Two dimensional convolution ( Image filtering )
def lmc_cv_2d_convolution():
"""
The functionality : Two dimensional convolution ( Image filtering ).
Like one-dimensional signals , Images can also be used with various low-pass filters (LPF)、 High pass filter (HPF) And so on .
low pass filter (LPF) Helps to eliminate noise , Blurred images, etc .
High pass filter (HPF) Filters help to find edges in the image .
"""
# Read images
image = lmc_cv.imread('D:/99-Research/Python/Image/Lena.jpg')
image = lmc_cv.cvtColor(image, lmc_cv.COLOR_RGB2BGR)
# Two dimensional convolution ( Image filtering )
kernel = np.ones((5, 5), np.float32) / 25
filter_image = lmc_cv.filter2D(image, -1, kernel)
# Display images
pyplot.figure('Image Display')
pyplot.subplot(121)
pyplot.imshow(image)
pyplot.title('Original')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(122)
pyplot.imshow(filter_image)
pyplot.title('Averaging')
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close()
return

Image blur ( Image smoothing )

####################################################################################################
# Blur the image ( Smooth the image )
def lmc_cv_blurring_image(index):
"""
The functionality : Blur the image ( Smooth the image ).
Image blur is realized by low-pass filtering kernel convolution image , It's very helpful to eliminate noise .
It actually removes high-frequency content from the image ( for example : noise 、 edge ), So in this operation, the edges will blur a little bit ( There are also blurring techniques that don't blur edges ).
OpenCV Provides four main fuzzy techniques :
The mean of fuzzy (Averaging Blurring)、 Gaussian blur (Gaussian Blurring)、 The median fuzzy (Median Blurring)、 Bilateral ambiguity (Bilateral Filtering).
"""
# Read images
image = lmc_cv.imread('D:/99-Research/Python/Image/Plaid.jpg')
image = lmc_cv.cvtColor(image, lmc_cv.COLOR_RGB2BGR)
# Display images
pyplot.figure('Image Display')
pyplot.subplot(121)
pyplot.imshow(image)
pyplot.title('Original Image')
pyplot.xticks([])
pyplot.yticks([])
pyplot.subplot(122)
pyplot.xticks([])
pyplot.yticks([])
# The mean of fuzzy (Averaging Blurring)
if 0 == index:
blur_image = lmc_cv.blur(image, (5, 5))
pyplot.imshow(blur_image)
pyplot.title('Averaging Blurring')
# Gaussian blur (Gaussian Blurring)
if 1 == index:
blur_image = lmc_cv.GaussianBlur(image, (5, 5), 0.5, 0.5, lmc_cv.BORDER_DEFAULT)
pyplot.imshow(blur_image)
pyplot.title('Gaussian Blurring')
# The median fuzzy (Median Blurring)
if 2 == index:
blur_image = lmc_cv.medianBlur(image, 5)
pyplot.imshow(blur_image)
pyplot.title('Median Blurring')
# Bilateral ambiguity (Bilateral Filtering)
if 3 == index:
blur_image = lmc_cv.bilateralFilter(image, 9, 75, 75, lmc_cv.BORDER_DEFAULT)
pyplot.imshow(blur_image)
pyplot.title('Bilateral Blurring')
# Display window
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close()
return

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