Extra data sets for week 2

Course Q&A Deep Learning Specialization Neural Networks and Deep Learning
5 
import sys
import glob
import h5py
import numpy as np
from PIL import Image

IMG_WIDTH = 500
IMG_HEIGHT = 500

h5file = 'test1_catvnoncat.h5'

nfiles = len(glob.glob('images/TestData/*.jpg'))
print(f'count of image files nfiles={nfiles}')


with h5py.File(h5file,'w') as  h5f:

    dt = h5py.string_dtype(encoding='ascii',length=7)
    classes = h5f.create_dataset('list_classes',shape=(2), dtype=dt)
    classes[0] = "non-cat"
    classes[1] = "cat"

    binary_output = np.zeros(nfiles).reshape(nfiles,1)

    img_ds = h5f.create_dataset('data_set_x',shape=(nfiles, IMG_WIDTH, IMG_HEIGHT,3), dtype=int)
    for cnt, ifile in enumerate(glob.iglob('images/TestData/*.jpg')) :

        image_resized = np.array(Image.open(ifile).resize((IMG_WIDTH, IMG_HEIGHT)))

        print(ifile)
        img_ds[cnt:cnt+1:,:,:] = image_resized
        print(cnt)

        if ifile[16] == 'c':
            binary_output[cnt] = 1


    bin_ds = h5f.create_dataset('data_set_y',shape=(nfiles, 1), dtype=int)
    bin_ds[:] = binary_output