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import torch.utils.data as data
import json
import random
from PIL import Image
import numpy as np
import torch
import os
class Dataset(data.Dataset):
def __init__(self, root, transform, target_transform, dataset_name, mode='test'):
self.root = root
self.transform = transform
self.target_transform = target_transform
self.data_all = []
meta_info = json.load(open(f'{self.root}/meta.json', 'r'))
name = self.root.split('/')[-1]
meta_info = meta_info[mode]
self.cls_names = list(meta_info.keys())
for cls_name in self.cls_names:
self.data_all.extend(meta_info[cls_name])
self.length = len(self.data_all)
self.obj_list = [folder for folder in os.listdir(root) if os.path.isdir(os.path.join(root, folder)) and not folder.startswith('.')]
self.class_name_map_class_id = {o: i for i, o in enumerate(self.obj_list)}
def __len__(self):
return self.length
def __getitem__(self, index):
data = self.data_all[index]
img_path, mask_path, cls_name, specie_name, anomaly = data['img_path'], data['mask_path'], data['cls_name'], \
data['specie_name'], data['anomaly']
img = Image.open(os.path.join(self.root, img_path))
if anomaly == 0:
img_mask = Image.fromarray(np.zeros((img.size[0], img.size[1])), mode='L')
else:
if os.path.isdir(os.path.join(self.root, mask_path)):
# just for classification not report error
img_mask = Image.fromarray(np.zeros((img.size[0], img.size[1])), mode='L')
else:
img_mask = np.array(Image.open(os.path.join(self.root, mask_path)).convert('L')) > 0
img_mask = Image.fromarray(img_mask.astype(np.uint8) * 255, mode='L')
# transforms
img = self.transform(img) if self.transform is not None else img
img_mask = self.target_transform(
img_mask) if self.target_transform is not None and img_mask is not None else img_mask
img_mask = [] if img_mask is None else img_mask
return {'img': img, 'img_mask': img_mask, 'cls_name': cls_name, 'anomaly': anomaly,
'img_path': os.path.join(self.root, img_path), "cls_id": self.class_name_map_class_id[cls_name]}
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