denoise in real time

opencv_denoise_rt_tutorial.py

import cv2
import numpy as np


def get_img(cap):
    ret, frame = cap.read()
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    return frame


def add_noise(img):
    # http://docs.opencv.org/3.1.0/d5/d69/tutorial_py_non_local_means.html
    img = [np.float64(i) for i in img]
    noise = np.random.randn(*img[1].shape) * 10
    noisy = [i + noise for i in img]
    return [np.uint8(np.clip(i, 0, 255)) for i in noisy]


def stitch_imgs(one, two):
    """
    Maybe there is a better way.. my thinking is
    Matrices are defined like
    [
        [ .. ]
        [ .. ]
    ]

    so an append would add to the bottom of the image, but I want side-by-side. The transpose will make my rows columns
    by definition so that I can do the append. The outer transpose is obviously to take my back to the beginning. The
    axis variable came from:
    https://docs.scipy.org/doc/numpy/reference/generated/numpy.append.html

    'cause I don't want to flatten
    :param one:
    :param two:
    :return:
    """
    one = add_noise(one)
    return np.transpose(np.append(np.transpose(one), np.transpose(two), axis=0))


def main():
    cap = cv2.VideoCapture(0)
    while cv2.waitKey(1) & 0xFF != ord('q'):
        img = get_img(cap)
        denoised = cv2.fastNlMeansDenoising(img, None, 10, 3, 17)
        cv2.imshow('frame', stitch_imgs(img, denoised))
    cap.release()
    cv2.destroyAllWindows()


if __name__ == "__main__":
    main()