Converting multiple numpy images to gray scale

Question

I currently have a numpy array 'images' containing 2000 photos. I am looking for an improved way of converting all the photos in 'images' to gray scale. The shape of the images is (2000, 100, 100, 3). This is what I have so far:

# Function takes index value and convert images to gray scale 
def convert_gray(idx):
  gray_img = np.uint8(np.mean(images[idx], axis=-1))
  return gray_img

#create list
g = []
#loop though images 
for i in range(0, 2000):
  #call convert to gray function using index of image
  gray_img = convert_gray(i)
  
  #add grey image to list
  g.append(gray_img)

#transform list of grey images back to array
gray_arr = np.array(g)

I wondered if anyone could suggest a more efficient way of doing this? I need the output in an array format

Answer 1

With your mean over the last axis you do right now:

Gray = 1/3 * Red + 1/3 * Green + 1/3 * Blue

But actually another conversion formula is more common (See this answer ):

Gray = 299/1000 * Red + 587/1000 * Green + 114/1000 * Blue

The code provided by @unutbu also works for arrays of images:

import numpy as np

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

rgb = np.random.random((100, 512, 512, 3))
gray = rgb2gray(rgb)
# shape: (100, 512, 512)