create_DAGAN_database

create_DAGAN_database.py

#!/usr/bin/env python3
# coding: utf8
import numpy as np
from pathlib import Path
import matplotlib.image as mpimg

"""
Suppose a file tree i.e:
imgs/
    |class1/
        |images of type class1.png
    |class2/
        |...
    |...

the images are normalized into numpy arrays in the form: (n_classes, n_samples, h, w, c)
"""

def create_db(images_dir_path):
    if not Path(images_dir_path).is_dir():
        print('Error')
        exit()

    dataset = []
    for d in Path(images_dir_path).glob('*'):
        # skipping simple files (readme, licences, ...)
        if not d.is_dir():
            continue
        
        classData = []
        for f in Path(d).glob('*.png'):
            img = mpimg.imread(str(f)) 
            img = img.astype(np.float)
            img /= 255.0
            img = np.reshape(img, newshape=(img.shape[0], img.shape[1], 3))
            classData.append(img)
        
        dataset.append(classData)

    dataset.sort(key=len)

    return np.array(dataset)


train_dir = '/path/to/imgs'
data = create_db(train_dir)
print("dataset shape:", data.shape)
np.save('my_database.npy', data)

Hi,
Reading this (a year later, seeing that people still scratch their brains with some DAGAN experiments), I feel like to continue my previous comment :
...
Following the same logic, data.shape will give you (num_classes, num samples, h, w, c)
[I also updated the script to make the things easier for you :)]
This suppose that all your images are png of the same shape.
Use np.reshape(img, newshape=(img.shape[0], img.shape[1], 1)) if your images are all black and white.