How to calculate the mean and standard deviation of an image dataset

Calculate_Mean_and_std_of_dataset.py

# calculate mean and std deviation

from pathlib import Path
import cv2

imageFilesDir = Path(r'C:\your\dataset\dir\here\trainData')
files = list(imageFilesDir.rglob('*.png'))

# Since the std can't be calculated by simply finding it for each image and averaging like  
# the mean can be, to get the std we first calculate the overall mean in a first run then  
# run it again to get the std.

mean = np.array([0.,0.,0.])
stdTemp = np.array([0.,0.,0.])
std = np.array([0.,0.,0.])

numSamples = len(files)

for i in range(numSamples):
    im = cv2.imread(str(files[i]))
    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    im = im.astype(float) / 255.
    
    for j in range(3):
        mean[j] += np.mean(im[:,:,j])

mean = (mean/numSamples)

for i in range(numSamples):
    im = cv2.imread(str(files[i]))
    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    im = im.astype(float) / 255.
    for j in range(3):
        stdTemp[j] += ((im[:,:,j] - mean[j])**2).sum()/(im.shape[0]*im.shape[1])

std = np.sqrt(stdTemp/numSamples)

print(mean)
print(std)

# out:
#[0.50707516 0.48654887 0.44091784]
#[0.26733429 0.25643846 0.27615047]