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# Copyright (c) 2020, Roy Or-El. All rights reserved.
#
# This work is licensed under the Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import torch.utils.data as data
import os
from PIL import Image
from src.utils.deeplab_util import preprocess_image
from src.utils.constant_util import CLASSES
class CelebASegmentation(data.Dataset):
# CLASSES = [
# 'background', # 0
# 'skin', # 1
# 'nose', # 2
# 'eye_g', # 3
# 'l_eye', # 4
# 'r_eye', # 5
# 'l_brow', # 6
# 'r_brow', # 7
# 'l_ear', # 8
# 'r_ear', # 9
# 'mouth', # 10
# 'u_lip', # 11
# 'l_lip', # 12
# 'hair', # 13
# 'hat', # 14
# 'ear_r', # 15
# 'neck_l', # 16
# 'neck', # 17
# 'cloth' # 18
# ]
CLASSES = CLASSES
def __init__(self, root, transform=None, crop_size=None):
self.root = root
self.transform = transform
self.crop_size = crop_size # ~!
self.images = []
self.images = [
os.path.join(self.root, f) for f in os.listdir(self.root)
]
# subdirs = next(os.walk(self.root))[1] #quick trick to get all subdirectories
# for subdir in subdirs:
# curr_images = [os.path.join(self.root,subdir,file) for file in os.listdir(os.path.join(self.root,subdir)) if file.endswith('.png')]
# self.images += curr_images
def __getitem__(self, index):
_img = Image.open(self.images[index]).convert('RGB')
_img=_img.resize((513,513),Image.BILINEAR)
_img = preprocess_image(_img,flip=False,scale=None,crop=(self.crop_size, self.crop_size))
# _img = preprocess_image(_img,flip=False,scale=None) # ~!
if self.transform is not None:
_img = self.transform(_img)
return _img
def __len__(self):
return len(self.images)
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