dcgan-1.py

import warnings
warnings.filterwarnings('ignore')

import glob
import numpy as np # linear algebra
import pandas as pd 

from PIL import Image
import matplotlib.pyplot as plt
import os

from tensorflow.keras import preprocessing
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D,Dropout,Dense,Flatten,Conv2DTranspose,BatchNormalization,LeakyReLU,Reshape

import tensorflow as tf
from tensorflow.keras.utils import plot_model

all_image_path = []

full_image_train_path = '../input/celeba-dataset/img_align_celeba/img_align_celeba'

# Now from this array 
for path in os.listdir(full_image_train_path):
  if '.jpg' in path:
    all_image_path.append(os.path.join(full_image_train_path, path))
    
image_path_50k = all_image_path[0:50000]

# Model Constants
PLOTS_DPI = 150

# Cropping 
cropping_box = (30, 55, 150, 175) 

# To load an image from a file, we use the open() function in the Image module, passing it the path to the image.
training_images = [np.array((Image.open(path).crop(cropping_box)).resize((64,64))) for path in image_path_50k]

# Normalizing images to range in 0-1
for i in range(len(training_images)):
    training_images[i] = ((training_images[i] - training_images[i].min())/(255 - training_images[i].min()))
    
training_images = np.array(training_images)