Optimization Basic Image Processing Python

Question

I'm trying to accomplish a basic image processing. Here is my algorithm :

Find n., n+1., n+2. pixel's RGB values in a row and create a new image from these values.

I'm taking first pixel's red value,second pixel's green value and third pixel's blue value and create pixel. This operation continue for every row in image.

Here is my example code in python :

import glob
import ntpath

import numpy
from PIL import Image

images = glob.glob('balls/*.png')
data_compressed = numpy.zeros((540, 2560, 3), dtype=numpy.uint8)
for image_file in images:

    print(f'Processing [{image_file}]')
    image = Image.open(image_file)
    data = numpy.loadasarray(image)

    for i in range(0, 2559):
        for j in range(0, 539):
            pix_x = j * 3 + 1
            red = data[pix_x - 1, i][0]
            green = data[pix_x, i][1]
            blue = data[pix_x + 1, i][2]
            data_compressed[j, i] = [red, green, blue]

    im = Image.fromarray(data_compressed)
    image_name = ntpath.basename(image_file)
    im.save(f'export/{image_name}')

My input and output images are in RGB format. My code is taking 5 second for every image. I'm open for any idea to optimization this task. I can use c++ or any other languages if necessary.

Are all pixels greyscale or can they be any color?

Simon Tulling
– Simon Tulling

2020-08-13 09:08:55 +00:00
Commented Aug 13, 2020 at 9:08 — Simon Tulling
– Simon Tulling, Commented Aug 13, 2020 at 9:08
can be any color

Barış Saçıkara
– Barış Saçıkara

2020-08-13 10:58:10 +00:00
Commented Aug 13, 2020 at 10:58 — Barış Saçıkara
– Barış Saçıkara, Commented Aug 13, 2020 at 10:58

mujjiga · Accepted Answer · 2020-08-13 11:51:02Z

1

data_compressed = np.concatenate((
    np.expand_dims(data[0:-2][:,:,0], axis=2),
    np.expand_dims(data[1:-1][:,:,1], axis=2),
    np.expand_dims(data[2:][:,:,2], axis=2)), axis=2)

Image1 : Original image
Image2: Original image shifted by one pixel
Image3: Original image shifted by two pixel
Take channel 0 of Image1, channel 1 of Image2 and channel 3 of Image3 concatenate.

Sample

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

image = Image.open("Lenna.png")
data = numpy.asarray(image)

data_compressed = np.concatenate((
    np.expand_dims(data[0:-2][:,:,0], axis=2),
    np.expand_dims(data[1:-1][:,:,1], axis=2),
    np.expand_dims(data[2:][:,:,2], axis=2)), axis=2)

new_image = Image.fromarray(data_compressed)

If you want a stride over 3 pixels for calculating the next pixel again then you can use numpy slicing

new_image = Image.fromarray(data_compressed[:, ::3])

Original Image:

Transformed Image with 3 stride:

edited Aug 13, 2020 at 11:51

answered Aug 13, 2020 at 9:43

mujjiga

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3 Comments

Barış Saçıkara Over a year ago

Thanks. It's much more faster than my code but it's working on all image. My input image is : 2560 x 1620 and final image is 2560 x 540. So it's need to be work on 1 axis not two. Is it possible to do in 1 axis.

Barış Saçıkara Over a year ago

thanks it's worked like a charm. my old code was 5 second per picture now 0.12 second per picture.

Barış Saçıkara Over a year ago

exported image is loosing 2 pixel in one dimension. Did you notice that ? Example image 220x220 => final image 74 x 218

JakobVinkas · Accepted Answer · 2020-08-13 09:14:29Z

0

Well if are only looking for a speed up you should take a look at the module Cython. It lets you specify the type of different variables and then compile the script to functioning c code. This can often lead to great improvements when it comes to time complexity.

answered Aug 13, 2020 at 9:14

JakobVinkas

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Comments

Simon Tulling · Accepted Answer · 2020-08-13 09:24:09Z

0

With plain python there's only so much you can do. Here is a small optimization which can help a bit since it will allocate less memory. Otherwise I would look at Cython/Numba as said previously or using other languages.

data_compressed[j, i, 0] = data[pix_x - 1, i][0]
data_compressed[j, i, 1] = data[pix_x, i][1]
data_compressed[j, i, 2] = data[pix_x + 1, i][2]

answered Aug 13, 2020 at 9:24

Simon Tulling

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