File size: 4,035 Bytes
8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 b3f33c8 eaafd53 8a587d3 eaafd53 8a587d3 b3f33c8 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 8a587d3 eaafd53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import torch
import os
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from utils.utils import generate_mask
class TrainDataset(torch.utils.data.Dataset):
def __init__(self, data_path, transform = None, mults_amount = 1):
self.data = os.listdir(os.path.join(data_path, 'color'))
self.data_path = data_path
self.transform = transform
self.mults_amount = mults_amount
self.ToTensor = transforms.ToTensor()
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
image_name = self.data[idx]
color_img = plt.imread(os.path.join(self.data_path, 'color', image_name))
if self.mults_amount > 1:
mult_number = np.random.choice(range(self.mults_amount))
bw_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '.png'
dfm_name = image_name[:image_name.rfind('.')] + '_' + str(mult_number) + '_dfm.png'
else:
bw_name = self.data[idx]
dfm_name = os.path.splitext(self.data[idx])[0] + '0_dfm.png'
bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', bw_name)), 2)
dfm_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'bw', dfm_name)), 2)
bw_img = np.concatenate([bw_img, dfm_img], axis = 2)
if self.transform:
result = self.transform(image = color_img, mask = bw_img)
color_img = result['image']
bw_img = result['mask']
dfm_img = bw_img[:, :, 1]
bw_img = bw_img[:, :, 0]
color_img = self.ToTensor(color_img)
bw_img = self.ToTensor(bw_img)
dfm_img = self.ToTensor(dfm_img)
color_img = (color_img - 0.5) / 0.5
mask = generate_mask(bw_img.shape[1], bw_img.shape[2])
hint = torch.cat((color_img * mask, mask), 0)
return bw_img, color_img, hint, dfm_img
class FineTuningDataset(torch.utils.data.Dataset):
def __init__(self, data_path, transform = None, mult_amount = 1):
self.data = [x for x in os.listdir(os.path.join(data_path, 'real_manga')) if x.find('_dfm') == -1]
self.color_data = [x for x in os.listdir(os.path.join(data_path, 'color'))]
self.data_path = data_path
self.transform = transform
self.mults_amount = mult_amount
np.random.shuffle(self.color_data)
self.ToTensor = transforms.ToTensor()
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
color_img = plt.imread(os.path.join(self.data_path, 'color', self.color_data[idx]))
image_name = self.data[idx]
if self.mults_amount > 1:
mult_number = np.random.choice(range(self.mults_amount))
bw_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '.png'
dfm_name = image_name[:image_name.rfind('.')] + '_' + str(self.mults_amount) + '_dfm.png'
else:
bw_name = self.data[idx]
dfm_name = os.path.splitext(self.data[idx])[0] + '_dfm.png'
bw_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', image_name)), 2)
dfm_img = np.expand_dims(plt.imread(os.path.join(self.data_path, 'real_manga', dfm_name)), 2)
if self.transform:
result = self.transform(image = color_img)
color_img = result['image']
result = self.transform(image = bw_img, mask = dfm_img)
bw_img = result['image']
dfm_img = result['mask']
color_img = self.ToTensor(color_img)
bw_img = self.ToTensor(bw_img)
dfm_img = self.ToTensor(dfm_img)
color_img = (color_img - 0.5) / 0.5
return bw_img, dfm_img, color_img |