completeAlgorithm.py

import cv2
import numpy as np


def applyClahe(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    h, s, v = cv2.split(hsv)

    clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(1, 1))
    v = clahe.apply(v)

    merged = cv2.merge([h, s, v])

    return cv2.cvtColor(merged, cv2.COLOR_HSV2BGR)


def simplest_cb(img, percent=1):
    out_channels = []
    cumstops = (
        img.shape[0] * img.shape[1] * percent / 200.0,
        img.shape[0] * img.shape[1] * (1 - percent / 200.0)
    )
    for channel in cv2.split(img):
        cumhist = np.cumsum(cv2.calcHist(
            [channel], [0], None, [256], (0, 256)))

        low_cut, high_cut = np.searchsorted(cumhist, cumstops)

        lut = np.concatenate((
            np.zeros(low_cut),
            np.around(np.linspace(0, 255, high_cut - low_cut + 1)),
            255 * np.ones(255 - high_cut)
        ))

        out_channels.append(cv2.LUT(channel, lut.astype('uint8')))
    return cv2.merge(out_channels)


if __name__ == '__main__':
    img = cv2.imread("samples/sample9.jpg")
    cv2.imwrite("out/results/before_sample9.jpg", img)

    out = simplest_cb(img, 1)

    out = applyClahe(out)

    out = cv2.fastNlMeansDenoisingColored(out, None, 10, 10, 3, 7)
    cv2.imwrite("out/results/after_sample9.jpg", out)