Image color-moment extractor [closed]

Question

I was just wondering if there is a way to speed up the performances of this for loops in Python.

I'm trying to process an image to get the color-moments without using libraries. It takes about 12sec to do the calculate_mean and calculate_standard_deviation functions for each part of the image.

import math

import cv2
import numpy as np

parts = 2
new_height = int(img.shape[0]/parts)
new_width = int(img.shape[1]/parts)

for i in range (0,img.shape[0],new_height):
    for j in range(0,img.shape[1],new_width):
        color_moments = [0,0,0,0,0,0,0,0,0]
        cropped_image = img[i:i+new_height,j:j+new_width]
        yuv_image = cv2.cvtColor(cropped_image,cv2.COLOR_BGR2YUV)
        Y,U,V = cv2.split(yuv_image)

        pixel_image_y = np.array(Y).flatten()
        pixel_image_u = np.array(U).flatten()
        pixel_image_v = np.array(V).flatten()

        calculate_mean(pixel_image_y,pixel_image_u,pixel_image_v,color_moments)
        calculate_standard_deviation(pixel_image_y, pixel_image_u, pixel_image_v, color_moments) 

And this are the two functions:

def calculate_mean(pixel_image_y,pixel_image_u,pixel_image_v,color_moments):
    for p in pixel_image_y:
        color_moments[0]+=(1/(new_height*new_width))*int(p)
    for p in pixel_image_u:
        color_moments[1]+=(1/(new_height*new_width))*int(p)
    for p in pixel_image_v:
        color_moments[2]+=(1/(new_height*new_width))*int(p)

def calculate_standard_deviation(pixel_image_y,pixel_image_u,pixel_image_v,color_moments):
    temp = [0,0,0]
    for p in pixel_image_y:
        temp[0]+=(p-color_moments[0])**2
    color_moments[3] = math.sqrt((1/(new_height*new_width))*temp[0])
    for p in pixel_image_u:
        temp[1]+=(p-color_moments[1])**2
    color_moments[4] = math.sqrt((1/(new_height*new_width))*temp[1])
    for p in pixel_image_v:
        temp[2]+=(p-color_moments[2])**2
    color_moments[5] = math.sqrt((1/(new_height*new_width))*temp[2])

Answer 1

1. Python 2 is end of life, many core libraries and tools are dropping Python 2 support.
2. Python is dumb. If you tell it to calculate (1/(new_height*new_width) each and every loop, then Python will.
3. Your style is not Pythonic, you should have more whitespace.

4. If we change one of the for loops in calculate_mean to math, we can see we can that ¶2 isn't needed inside the loop.

   for p in pixel_image_y:
       color_moments[0]+=(1/(new_height*new_width))*int(p)
   

   $$ \Sigma \frac{\lfloor p \rfloor}{hw} = \frac{\Sigma \lfloor p \rfloor}{hw} $$

5. We can utilize numpy to perform the loop, in calculate_standard_deviation, in C.

   color_moments[3] = (
       np.sum((pixel_image_y - color_moments[0]) ** 2)
       * math.sqrt((1/(new_height*new_width))
   )
   

6. You should change calculate_mean and calculate_standard_deviation to either work in one dimension or all dimensions.
7. You should remove color_moments from your code. You should return if you need to return.
8. You can determine new_height*new_width by the values you enter in your function. No need to pass them.

def mean(values):
    return sum(int(v) for v in values) / len(values)

def std_deviation(values):
    return np.sum((values - mean(values)) ** 2) / math.sqrt(len(values))