Looping over pixels in an image

Question

I have the following code which iterates over all pixels of an image and does some manipulations on two images of the same size. I would like to speed it up and to avoid iterating over the positions in a for loop:

import numpy as np
import cv2
# Two images of same size 
image_in = cv2.imread('my_image.png')
image_in2 = cv2.imread('my_image2.png')
image_new = np.ones(image_in.shape[:2], dtype="uint8") * 255
counter = 0
counter2 = 0
for i in range(image_in.shape[0]):
 for j in range(image_in.shape[1]):
 if image_in[i, j] < 255:
 counter += 1
 if image_in2[i, j] == 0:
 image_new[i, j] = 0
 else:
 image_new[i, j] = 255
 counter2 += 1

How can I improve my code?

• snowflake: I simply threshold my image. If the pixel values are greater of smaller then a threshold, the pixel value of a new image should be set to 0 or 1.
• Peilonrayz: Do you really need counter and counter2?
• snowflake: Actually not, I could use np.count_nonzero on image_new at the end...Good point!

Answer 1

I think the trick is trying to vectorise this as much as possible:

By the look of it, the code is trying to threshold at 0 and count pixels under 255.

We can change the first part of the loop to:

counter = np.sum(image_in < 255) # Sums work on binary values
counter2 = np.sum(np.bitwise_and(image_in < 255, image_in2 != 0)) 

And the second to:

# This is 0 or 1 depending on whether it is == 0
image_new[:,:] = (image_in2 != 0) # image_new[i,j] = (image_in2[i,j] != 0)
# So scale the values up with a simple multiplcation
image_new = image_new*255 # image_new[i,j] = image_new[i,j]*255

• snowflake: But how do I get the value of the second counter, it should count only pixels with values of 255 where the same position of image2 has the value 0.
• snowflake: The rest of the image1 contain pixel with values of 255 but they should be excluded in the count.
• gbartonowen: Added counter2, this isn't the cleanest way of doing this but it works pretty efficiently
• snowflake: ~ 300x speed up, nice!