import zipfile
import os
import PIL

import torchvision.transforms as transforms

# once the images are loaded, how do we pre-process them before being passed into the network
# by default, we resize the images to 64 x 64 in size
# and normalize them to mean = 0 and standard-deviation = 1 based on statistics collected from
# the training set
data_transforms = {
    
    'train' : transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ColorJitter(hue=.00, saturation=.03),
    transforms.RandomVerticalFlip(),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10, resample=PIL.Image.BILINEAR),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]),
    
    'val':transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]),
    
    'test':transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
}
    


