discoGAN-processing/data_loader.py at master · twiiks/discoGAN-processing · GitHub

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import os
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm

import torch
from torchvision import transforms

PIX2PIX_DATASETS = [
    'facades', 'cityscapes', 'maps', 'edges2shoes', 'edges2handbags'
]


def makedirs(path):
    if not os.path.exists(path):
        os.makedirs(path)


def pix2pix_split_images(root):
    paths = glob(os.path.join(root, "train/*"))

    a_path = os.path.join(root, "A")
    b_path = os.path.join(root, "B")

    makedirs(a_path)
    makedirs(b_path)

    for path in tqdm(paths, desc="pix2pix processing"):
        filename = os.path.basename(path)

        a_image_path = os.path.join(a_path, filename)
        b_image_path = os.path.join(b_path, filename)

        if os.path.exists(a_image_path) and os.path.exists(b_image_path):
            continue

        image = Image.open(os.path.join(path)).convert('RGB')
        data = np.array(image)

        height, width, channel = data.shape

        a_image = Image.fromarray(data[:, :width / 2].astype(np.uint8))
        b_image = Image.fromarray(data[:, width / 2:].astype(np.uint8))

        a_image.save(a_image_path)
        b_image.save(b_image_path)


class Dataset(torch.utils.data.Dataset):

    def __init__(self,
                 root,
                 scale_size,
                 data_type,
                 skip_pix2pix_processing=False):
        self.root = root
        if not os.path.exists(self.root):
            raise Exception("[!] {} not exists.".format(root))

        self.name = os.path.basename(root)
        if self.name in PIX2PIX_DATASETS and not skip_pix2pix_processing:
            pix2pix_split_images(self.root)

        self.paths = glob(os.path.join(self.root, '{}/*'.format(data_type)))
        if len(self.paths) == 0:
            raise Exception("No images are found in {}".format(self.root))
        self.shape = list(Image.open(self.paths[0]).size) + [3]

        self.transform = transforms.Compose([
            transforms.Scale(scale_size),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ])

    def __getitem__(self, index):
        image = Image.open(self.paths[index]).convert('RGB')
        return self.transform(image)

    def __len__(self):
        return len(self.paths)


def get_loader(root,
               batch_size,
               scale_size,
               num_workers=2,
               skip_pix2pix_processing=False,
               shuffle=True):
    a_data_set, b_data_set = \
        Dataset(root, scale_size, "A", skip_pix2pix_processing), \
        Dataset(root, scale_size, "B", skip_pix2pix_processing)
    a_data_loader = torch.utils.data.DataLoader(
        dataset=a_data_set,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers)
    b_data_loader = torch.utils.data.DataLoader(
        dataset=b_data_set,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers)
    a_data_loader.shape = a_data_set.shape
    b_data_loader.shape = b_data_set.shape

    return a_data_loader, b_data_loader

def get_loader_a(root,
               batch_size,
               scale_size,
               num_workers=2,
               skip_pix2pix_processing=False,
               shuffle=True):
    a_data_set= \
        Dataset(root, scale_size, "A", skip_pix2pix_processing)
    a_data_loader = torch.utils.data.DataLoader(
        dataset=a_data_set,
        batch_size=batch_size,
        shuffle=True,
        num_workers=num_workers)

    a_data_loader.shape = a_data_set.shape
    return a_data_loader