import matplotlib.pyplot as plt
import cv2
def showimage(myimage, figsize=[10,10]):if(myimage.ndim>2):#This only applies to RGB or RGBA images (e.g. not to Black and White images)
myimage = myimage[:,:,::-1]#OpenCV follows BGR order, while matplotlib likely follows RGB order
fig, ax = plt.subplots(figsize=figsize)
ax.imshow(myimage, cmap ='gray', interpolation ='bicubic')
plt.xticks([]), plt.yticks([])# to hide tick values on X and Y axis
plt.show()
img = cv2.imread('pexels-ovan-62689.jpg')
showimage(img)
img = cv2.imread('pexels-ovan-62689.jpg',0)
showimage(img)
2 Gaussian filter
Tested in Anaconda and Python 3.7
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
## generate the data and plot it for an ideal normal curve
## x-axis for the plot
x_data = np.arange(-5,5,0.001)## y-axis as the gaussian
y_data = stats.norm.pdf(x_data,0,1)## plot data
plt.plot(x_data, y_data)
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