Fast image normalisation in python [closed]

Question

I am looking for a faster approach to normalise image in Python. I want to convert all pixels to values between 0 and 1.

INPUT: 150x150 RGB images in JPEG format.

OS/HARDWARE: LINUX/P40 GPU with 8GB RAM

USE-CASE: Image Preprocessing for a real-time classification task.

Current time per image is ~5-10 milliseconds. I am looking for a method that can reduce this time.

I tried two approaches, with numpy and opencv.

Using numpy (Approx time: 8ms):

norm = (img - np.min(img)) / (np.max(img) - np.min(img))

Using opencv (Approx time: 3ms):

norm = cv2.normalize(img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)

Both these methods are slow for my usecase. Can anyone guide me with a faster method for image normalisation?

Answer 1

Your timings seem very slow to me. Perhaps something is wrong with your install?

I tried this test program:

#!/usr/bin/python3

import sys
import numpy as np
import cv2
from PIL import Image
from profilehooks import profile

@profile
def try_numpy(img):
    ar = np.array(img).astype(np.float32)
    for i in range(1000):
        mn = np.min(ar)
        mx = np.max(ar)
        norm = (ar - mn) * (1.0 / (mx - mn))

@profile
def try_cv2(img):
    for i in range(1000):
        norm = cv2.normalize(img, None, alpha=0, beta=1,
                             norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)

img = Image.open(sys.argv[1])
try_numpy(img)

img = cv2.imread(sys.argv[1])
try_cv2(img)

And on this modest 2015 i5 laptop running Ubuntu 19.04 I see:

$ ./try291.py ~/pics/150x150.png 
*** PROFILER RESULTS ***
try_cv2 (./try291.py:17)
function called 1 times

         1002 function calls in 0.119 seconds

   Ordered by: cumulative time, internal time, call count

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.001    0.001    0.119    0.119 try291.py:17(try_cv2)
     1000    0.118    0.000    0.118    0.000 {normalize}

*** PROFILER RESULTS ***
try_numpy (./try291.py:9)
function called 1 times

         10067 function calls in 0.113 seconds

   Ordered by: cumulative time, internal time, call count
   List reduced from 52 to 40 due to restriction <40>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.064    0.064    0.113    0.113 try291.py:9(try_numpy)
     2000    0.004    0.000    0.045    0.000 fromnumeric.py:69(_wrapreduction)

So they both take about 0.1ms per call, ~50x faster than the numbers you see.

To speed it up further:

• Do you have any a priori knowledge about the range of pixel values? Perhaps you could skip the search for the max and min.
• Depending on your sampling density, it could be faster to normalize the whole input image, then cut out your 150x150 patches afterwards.