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I have a live webcam windows which means there are six subdivided windows in a single window live. And show pictures in colored and black and white. Is there anyway I can make the code minimalistically minimized? I believe the code is considerably long.

import cv2
import cv
import numpy as np
import matplotlib.image as mpimg
from matplotlib import pyplot as plt

def threshold_slow(T, image):
    # grab the image dimensions
    h = image.shape[0]
    w = image.shape[1]
    d = image.shape[2]
    # loop over the image, pixel by pixel
    for y in range(0, h):
        for x in range(0, w):
            for z in range(0, d):
                # threshold the pixel

                if image[y, x,z] >= T:
                    image[y, x,z] = 255
                else:
                    image[y, x,z] = 0

    # return the thresholded image
    return image
def grab_frame(cam):





    #cv2.namedWindow("test")

    #img_counter = 0

    while True:
        ret, color1 = cam.read()
        #r = 100.0 / color1.shape[1]
        r = 640.0 / color1.shape[1]
        #r = 0.25
        dim = (100, int(color1.shape[0] * r))
        dim = (640,480)
        # perform the actual resizing of the image and show it
        color = cv2.resize(color1, dim, interpolation = cv2.INTER_AREA)


        #color = color1.copy()
        b = color.copy()
        # set green and red channels to 0
        b[:, :, 1] = 0
        b[:, :, 2] = 0


        g = color.copy()
        # set blue and red channels to 0
        g[:, :, 0] = 0
        g[:, :, 2] = 0

        r = color.copy()
        # set blue and green channels to 0
        r[:, :, 0] = 0
        r[:, :, 1] = 0

        #y= color.copy()
        #gray = cv2.cvtColor(l,cv2.COLOR_RGB2GRAY)
        #_,y = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)
        #y = cv2.cvtColor(y, cv2.COLOR_GRAY2RGB)

        y = cv2.add(r,g)

        d = color.copy()
        gray1 = cv2.cvtColor(d,cv2.COLOR_RGB2GRAY)
        _,p = cv2.threshold(gray1, 60, 255, cv2.THRESH_BINARY)
        p = cv2.cvtColor(p, cv2.COLOR_GRAY2RGB)
        #threshold_slow(220,p)
        return [color,b,g,r,y,p]


cam = cv2.VideoCapture(0)

    #cv2.waitKey(0)
while(1):
    ret, color = cam.read()
    [color,b,g,r,y,p] = grab_frame(cam)
    horiz = np.hstack((color,b,g))
    #verti = np.vstack((color,r))
    horiz1 = np.hstack((r,y,p))
    verti = np.vstack((horiz,horiz1))
    cv2.imshow('HORIZONTAL', verti)

    if not ret:
        break
    k = cv2.waitKey(1)

    if k%256 == 27:
        # ESC pressed
        print("Escape hit, closing...")
        break


cam.release()

cv2.destroyAllWindows()

enter image description here

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  • First, get rid of all the unneeded whitespace. Use consistent amount between functions (Python's official style-guide, PEP8, recommends two).

  • PEP8 also recommends using spaces in lists, after the commas, and lower_case for all variables and functions (your T in threshold_slow violates this).

  • Don't use magic numbers in your code. Give them readable names and if necessary make them global constants:

    WIDTH, HEIGHT = 640, 480
    
  • Next, since your images are already numpy arrays, use that fact. Your (unused) threshold_slow function can be replaced by a single line using numpy.where:

    def threshold_fast(T, image):
        return np.where(image >= T, 255, 0)
    

    Note that this does not modify the image inplace. It is a bad practice to do that and return a modified/new object. You should decide, either return a new object or modify in place and return None.

  • The import cv is not used (and I could not even find a way to install it anymore).

  • Tuple assignment works also without a list on the left side, just do color, b, g, r, y, p = grab_frame(cam). The same is true when returning a tuple (return color, b, g, r, y, p).

  • Arguably, I would split up your grab_frame code into subfunctions like red(image), green(image), blue(image), yellow(image), black_and_white(image).

    def red(image):
        """Copy only the red channel from image"""
        out = np.zeros_like(image)
        # for some reason red is in the last channel
        out[:, :, 2] = image[:, :, 2]
        return out
    ...
    

    While this move will not make your code shorter, it will make it more readable.

  • Note that the canonical order is red, green, blue (RGB). If at all possible I would stick to that. I'm not sure why openCV would deviate from that.

  • You should at least add a docstring to each of your functions as a rudimentary documentation. See above for a short example.

  • You can use while True instead of while(1). No parenthesis needed and True is unambiguous (even for those people who both know C-like languages, where 0 is False and shell scripting languages like bash, where non-zero is False).

  • I would also add a tile(images, rows) function that puts your images into rows and columns. You could just use the itertools recipe grouper for this:

    from itertools import zip_longest
    
    def grouper(iterable, n, fillvalue=None):
        "Collect data into fixed-length chunks or blocks"
        # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
        args = [iter(iterable)] * n
        return zip_longest(*args, fillvalue=fillvalue)
    
  • Since you seem to want to use different amounts of tiles, and different effects, it might make sense to keep a list of functions to apply to the base image, so that in the end you only need one call:

    def identity(x):
        return x
    
    def tile(images, cols, fillvalue=None):
        return np.vstack(np.hstack(group)
                         for group in grouper(images, cols, fillvalue))
    
    funcs = identity, red, black_and_white, canny
    images = (func(image) for func in funcs)
    # arrange them in a 2x2 grid
    cv2.imshow('HORIZONTAL', tile(images, cols=2, fillvalue=np.zeros_like(image)))
    

    If the number of images is not evenly divisible by the number of columns, the row is filled up with blank images.

  • You should put your main calling code under a if __name__ == "__main__" guard to allow importing from this script.

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