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from data.base_dataset import BaseDataset, get_params, get_transform |
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from PIL import Image |
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import util.util as util |
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import os |
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class Pix2pixDataset(BaseDataset): |
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@staticmethod |
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def modify_commandline_options(parser, is_train): |
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parser.add_argument('--no_pairing_check', action='store_true', |
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help='If specified, skip sanity check of correct label-image file pairing') |
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return parser |
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def initialize(self, opt): |
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self.opt = opt |
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label_paths, image_paths, instance_paths = self.get_paths(opt) |
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util.natural_sort(label_paths) |
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util.natural_sort(image_paths) |
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if not opt.no_instance: |
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util.natural_sort(instance_paths) |
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label_paths = label_paths[:opt.max_dataset_size] |
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image_paths = image_paths[:opt.max_dataset_size] |
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instance_paths = instance_paths[:opt.max_dataset_size] |
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if not opt.no_pairing_check: |
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for path1, path2 in zip(label_paths, image_paths): |
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assert self.paths_match(path1, path2), \ |
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"The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2) |
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self.label_paths = label_paths |
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self.image_paths = image_paths |
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self.instance_paths = instance_paths |
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size = len(self.label_paths) |
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self.dataset_size = size |
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def get_paths(self, opt): |
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label_paths = [] |
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image_paths = [] |
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instance_paths = [] |
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assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)" |
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return label_paths, image_paths, instance_paths |
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def paths_match(self, path1, path2): |
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filename1_without_ext = os.path.splitext(os.path.basename(path1))[0] |
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filename2_without_ext = os.path.splitext(os.path.basename(path2))[0] |
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return filename1_without_ext == filename2_without_ext |
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def __getitem__(self, index): |
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label_path = self.label_paths[index] |
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label = Image.open(label_path) |
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if self.opt.task != 'SIS': |
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label = label.convert('RGB') |
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params = get_params(self.opt, label.size) |
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if self.opt.task != 'SIS': |
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transform_label = get_transform(self.opt, params) |
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label_tensor = transform_label(label) |
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else: |
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transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) |
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label_tensor = transform_label(label) * 255.0 |
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label_tensor[label_tensor == 255] = self.opt.label_nc |
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image_path = self.image_paths[index] |
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print("Images_path",image_path) |
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assert self.paths_match(label_path, image_path), \ |
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"The label_path %s and image_path %s don't match." % \ |
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(label_path, image_path) |
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image = Image.open(image_path) |
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image = image.convert('RGB') |
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transform_image = get_transform(self.opt, params) |
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image_tensor = transform_image(image) |
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if self.opt.no_instance: |
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instance_tensor = 0 |
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else: |
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instance_path = self.instance_paths[index] |
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instance = Image.open(instance_path) |
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if instance.mode == 'L': |
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instance_tensor = transform_label(instance) * 255 |
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instance_tensor = instance_tensor.long() |
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else: |
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instance_tensor = transform_label(instance) |
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input_dict = {'label': label_tensor, |
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'instance': instance_tensor, |
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'image': image_tensor, |
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'path': image_path, |
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'cpath': label_path |
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} |
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len_dict=len(input_dict['label']) |
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print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",len_dict) |
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self.postprocess(input_dict) |
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return input_dict |
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def postprocess(self, input_dict): |
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return input_dict |
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def __len__(self): |
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return self.dataset_size |
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