To work with OpenCV from python, you need to install it first. We additionally install numpy and matplotlib as well

pip install opencv-python numpy matplotlib

Reading Images from file

After we import cv2 we can directly work with images like so:

import cv2 
img = cv2.imread("doc_brown.png")

For showing the image, it is recommended to use matplotlib

import matplotlib.pyplot as plt 
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
plt.imshow(img) 
plt.show()

OpenCV stores images internally in the BGR format – blue – green – red so we have to convert to RGB before displaying them.

Image Shape

We have now an image object which can already tell us more about the image itself:

print(img.shape) 
(205, 236, 3)

The tuple shows us the number of (rows, columns, channels)

Manipulate brightness

import numpy as np 
brightness = np.zeros(img.shape, dtype="uint8") + 30 
img = cv2.add(img, brightness)

Manipulating brightness works like this: you need a numpy array with the size of the image, add your brightness and add the brightness array to the original image.

Adding noise

Adding noise works the same way, but instead of adding a fixed value to every pixel you add normal distributed values.

noise = np.random.randint(0, 100, size=img.shape, dtype="uint8") 
img = cv2.add(img, noise)

Smoothing

Smoothing is reducing the noise of an image by adding a Gaussian blur:

img = cv2.GaussianBlur(img, ksize=(31, 31), sigmaX=5)

Gray-scale conversion

Last but not least we can convert an image to a gray scale. Beware that for showing / storing the image you need to add the flag cmap=”gray”

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
plt.imshow(img, cmap='gray')

Have fun fiddling around with OpenCV!

Github Repo

https://github.com/jboegeholz/introduction_to_opencv

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The Mother of all Distributions

The normal distribution was formulated by Carl Friedrich Gauß in 1809 and can be implemented in Python like the following :

def normal_distribution_pdf(x, mu=0, sigma=1): 
    sqrt_two_pi = math.sqrt(2*math.pi) 
    return (1 / (sqrt_two_pi * sigma)) * math.exp(-((x - mu) ** 2) / (2 * sigma ** 2))

Scipy

For professional use you should packages like scipy to generate the pdf of a normal distribution

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm

if __name__ == '__main__':
    fig, ax = plt.subplots(1, 1)
    x = np.linspace(norm.ppf(0.01), norm.ppf(0.99), 100)
    ax.plot(x, norm.pdf(x), 'r-', label='norm pdf')
    plt.savefig("pdf.png")

Further reading

https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html

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