Huud

# 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  

Huud

#define _CRT_SECURE_NO_WARNINGS

#include <iostream>
#include <fstream>
#include <random>
#include <cmath>
#include <string>
#include <chrono>
#include <vector>
#include <windows.h>

Huud

# A fast - numpy based - CPU functions that take a height map and generate a normal map from it

import numpy as np
import matplotlib.image as mpimg

# a function that takes a vector - three numbers - and normalize it, i.e make it's length = 1
def normalizeRGB(vec):
    length = np.sqrt(vec[:,:,0]**2 + vec[:,:,1]**2 + vec[:,:,2]**2)
    vec[:,:,0] = vec[:,:,0] / length
    vec[:,:,1] = vec[:,:,1] / length

Huud

 from PIL import *
# from IPython.display import display # uncomment if you are using Jupyter

im = Image.open(r"C:\Put_your_B&W_img_here.jpg")
outIm =  Image.new('RGB', im.size, color = 'white')
draw = ImageDraw.Draw(outIm)
gridSpacing = 5
lineThickness = int(gridSpacing/2)

for i in range(0,im.size[0],gridSpacing):