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from PIL import Image | |
from torch.utils.data import Dataset | |
class Labelizer(): | |
def __init__(self): | |
super().__init__() | |
self.labels = {'background': 0, 'bien': 1} | |
self.inv_labels = {0: 'background', 1: 'bien'} | |
def transform(self, label): | |
return self.labels[label] | |
def inverse_transform(self, ys): | |
return self.inv_labels(ys) | |
def num_classes(self): | |
return len(self.labels) | |
class PoIDataset(Dataset): | |
def __init__(self, | |
data_path, | |
transforms=None): | |
self.data_path = data_path | |
self.transforms = transforms | |
def __len__(self): | |
return len(self.data_path) | |
def __getitem__(self, idx): | |
image = Image.open(self.data_path[idx]).convert('RGB') | |
target = {} | |
if self.transforms is not None: | |
image = self.transforms(image) | |
return image, target | |