Script from the raw image processing video

raw_image_processing.py

# This is the script for the video explaining it here:
#
# https://youtu.be/QrJ9LyA-Z-U

import numpy as np
import matplotlib.pyplot as plt


COLOR_STEPS = 5
FILENAME = "holo.jpg"


def rgb_to_gray(pix):
    WEIGHTS = [0.299, 0.587, 1.0 - (0.299 + 0.587)]
    return sum(w * pix[k] for k, w in enumerate(WEIGHTS))


def gradient(pix, other_pix):
    return other_pix - pix


def floor_step(pix):
    """See Plot[{x, 2^5 * Floor[x/2^5]}, {x, 0, 2^8}] on WolframAlpha."""
    MAX = 2**8 - 1
    coarseness = MAX / COLOR_STEPS
    return [coarseness * np.floor(val / coarseness) for val in pix]


if __name__=='__main__':
    # Load image.
    img = plt.imread(FILENAME)
    print('Data type:  {}\nDimensions: {}\n'.format(type(img), img.shape), img[30][7:11])

    # Write to copies of the image.
    # Note: The member function .copy() gives a writable instance of the previously loaded image.
    # Note Be aware of the data types here and the operations you can sacely do on them!
    img_2, img_3, img_4 = img.copy(), img.copy(), img.copy()
    for i, row in enumerate(img):
        for j, pix in enumerate(row):
            img_2[i, j] = rgb_to_gray(pix)
            img_3[i, j] = floor_step(pix)
            other_pix = img[i, (j + 1) % img.shape[1]] # the pixel on the right of i, j
            img_4[i, j] = gradient(pix, other_pix)

        if not i % 100:
            print("row number {}".format(i))

    # Plot all.
    images = [img, img_2, img_3, img_4]
    fig = plt.figure()
    for k, image in enumerate(images):
        fig.add_subplot(1, len(images), 1 + k)
        plt.imshow(image)
    plt.show()