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Compute mean and standard deviation (std) per channel give a list of images
"""Give a list of image filenames, this script compute the mean and std over all the images.
Requires:
* OpenCV
* tqdm
In this example, I use `Stanford Dogs Datasets" (~20k images)
Example Outputs: (8 cores CPU i7 4970K)
(env) dat@desktop:****/StanfordDogs$ python compute_mean_std.py
100%|██████████████████████████████████████████████████████████| 20580/20580 [01:06<00:00, 308.06it/s]
[0.4761392 0.45182742 0.39101657] [0.23364353 0.2289059 0.22732813]
""""
import os
import cv2
import tqdm
import multiprocessing as mp
import scipy.io as sio # read matlab file
import numpy as np
def compute_func(filename):
img = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) / 255.
return np.mean(img, (0, 1)), np.std(img, (0, 1))
def main():
# Read filenames
matfile = sio.loadmat('file_list.mat')
fnames = [os.path.join('Images', str(i[0][0])) for i in matfile['file_list']]
with mp.Pool(mp.cpu_count()) as pool:
result = list(tqdm.tqdm(pool.imap(
func=compute_func,
iterable=[i for i in fnames]), total=len(fnames)))
mean, std = zip(*result)
print(np.mean(mean, 0), np.mean(std, 0))
if __name__ == '__main__':
main()
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