Polarizing an image--how to find the average of each RGB value

Question

For an exercise in image processing, I have to write a program that applies various effects to images. One of the effects was grayscale, which was done find the average of RGB values ((red+green+blue)/3). To polarize an image, however, I need to first find the averages of each individual component (i.e. all red values/number of red pixels). Would it be appropriate to then loop through the rows and columns (with a counter for pixels, red values, green values, and blue values) as a way to find the average? Is there a more efficient way?

Also, pixels are polarized based on the average pixel value. "If average R is 100, average G is 200, and average B is 300 and a pixel has R 150, G 150, and B, 100, the polarized pixel would be 255, 0, and 0." I don't understand the relationship? Is it if the current value is less than the average, then it would be polarized to 0/more than the average, polarized to 255?

You won't have to add to the total count as sum(red, green, blue) is the total number of pixels. — user1016274
Is sum(red,green,blue) the total number of pixels or the total number of values? (Or are they the same thing?) — abshi
"Is it if the current value is less than the average, then it would be polarized to 0/more than the average, polarized to 255?" That seems right given the sample data. Do you have any source? — KevinOrr

Shaikh Sonny Aman Shaikh Sonny Aman · Accepted Answer · 2016-10-04T20:25:46

Calculate average RGB value of an image:

    import numpy as np

    # load your image in a variable named `im`, then calculate average RGB value
    np.array(im).mean(axis=(0,1))

    # It gives a tuple like (71.710743801652896, 103.11570247933884, 64.165289256198349)

You don't need to iterate, numpy has it all :)

Polarizing an image--how to find the average of each RGB value

1 Answers