import os from re import L import cv2 import glob import torch import math import imageio import numpy as np import re from PIL import Image from core.aff_utils import * from tools.ai.augment_utils import * from tools.ai.torch_utils import one_hot_embedding from tools.general.xml_utils import read_xml from tools.general.json_utils import read_json from tools.dataset.voc_utils import get_color_map_dic class Iterator: def __init__(self, loader): self.loader = loader self.init() def init(self): self.iterator = iter(self.loader) def get(self): try: data = next(self.iterator) except StopIteration: self.init() data = next(self.iterator) return data class VOC_Dataset(torch.utils.data.Dataset): def __init__(self, root_dir, domain, with_id=False, with_tags=False, with_mask=False): self.root_dir = root_dir self.image_dir = self.root_dir + '1.training/' self.xml_dir = self.root_dir + 'Annotations/' self.mask_dir = self.root_dir + 'SegmentationClass/' self.image_id_list = [image_id.strip() for image_id in open('./data/%s.txt'%domain).readlines()] self.with_id = with_id self.with_tags = with_tags self.with_mask = with_mask def __len__(self): return len(self.image_id_list) def get_image(self, image_id): image = Image.open(self.image_dir + image_id + '.png').convert('RGB') return image def get_mask(self, image_id): mask_path = self.mask_dir + image_id + '.png' if os.path.isfile(mask_path): mask = Image.open(mask_path).convert('RGB') else: mask = None return mask def get_tags(self, image_id): _, tags = read_xml(self.xml_dir + image_id + '.xml') return tags def __getitem__(self, index): image_id = self.image_id_list[index] data_list = [self.get_image(image_id)] if self.with_id: data_list.append(image_id) if self.with_tags: data_list.append(self.get_tags(image_id)) if self.with_mask: data_list.append(self.get_mask(image_id)) return data_list class VOC_Dataset_For_Classification(VOC_Dataset): def __init__(self, root_dir, domain, transform=None): super().__init__(root_dir, domain, with_tags=True) self.transform = transform data = read_json('./data/VOC_2012.json') self.class_dic = data['class_dic'] self.classes = data['classes'] def __getitem__(self, index): image, tags = super().__getitem__(index) if self.transform is not None: image = self.transform(image) label = one_hot_embedding([self.class_dic[tag] for tag in tags], self.classes) return image, label class VOC_Dataset_For_Segmentation(VOC_Dataset): def __init__(self, root_dir, domain, transform=None): super().__init__(root_dir, domain, with_mask=True) self.transform = transform self.image_dir = self.root_dir + '2.validation/img_patch_256/' self.mask_dir = self.root_dir + '2.validation/mask_patch_256/' self.colors = np.array([[255, 255, 255], [0, 64, 128], [64, 128, 0], [243, 152, 0]], dtype=np.int32) def __getitem__(self, index): image, mask = super().__getitem__(index) mask = np.array(mask).astype(np.int32) mask = self.image2label(mask) if self.transform is not None: input_dic = {'image':image, 'mask':mask} output_dic = self.transform(input_dic) image = output_dic['image'] mask = output_dic['mask'] return image, mask def image2label(self, im): color2int = np.zeros(256 ** 3) # for idx, color in enumerate(self.colors): color2int[(color[0] * 256 + color[1]) * 256 + color[2]] = idx # data = np.array(im, dtype=np.int32) idx = (data[:, :, 0] * 256 + data[:, :, 1]) * 256 + data[:, :, 2] return np.array(color2int[idx], dtype=np.int32) # class VOC_Dataset_For_Evaluation(VOC_Dataset): def __init__(self, root_dir, domain, transform=None): super().__init__(root_dir, domain, with_id=True, with_mask=True) self.transform = transform cmap_dic, _, class_names = get_color_map_dic() self.colors = np.asarray([cmap_dic[class_name] for class_name in class_names]) def __getitem__(self, index): image, image_id, mask = super().__getitem__(index) if self.transform is not None: input_dic = {'image':image, 'mask':mask} output_dic = self.transform(input_dic) image = output_dic['image'] mask = output_dic['mask'] return image, image_id, mask class VOC_Dataset_For_WSSS(VOC_Dataset): def __init__(self, root_dir, domain, pred_dir, transform=None): super().__init__(root_dir, domain, with_id=True) self.pred_dir = pred_dir self.transform = transform self.colors = np.array([[255, 255, 255], [0, 64, 128], [64, 128, 0], [243, 152, 0]], dtype=np.int32) def __getitem__(self, index): image, image_id = super().__getitem__(index) mask = Image.open(self.pred_dir + image_id + '.png') if self.transform is not None: input_dic = {'image':image, 'mask':mask} output_dic = self.transform(input_dic) image = output_dic['image'] mask = output_dic['mask'] return image, mask class VOC_Dataset_For_Testing_CAM(VOC_Dataset): def __init__(self, root_dir, domain, transform=None): super().__init__(root_dir, domain, with_tags=True, with_mask=True) self.transform = transform cmap_dic, _, class_names = get_color_map_dic() self.colors = np.asarray([cmap_dic[class_name] for class_name in class_names]) data = read_json('./data/VOC_2012.json') self.class_dic = data['class_dic'] self.classes = data['classes'] def __getitem__(self, index): image, tags, mask = super().__getitem__(index) if self.transform is not None: input_dic = {'image':image, 'mask':mask} output_dic = self.transform(input_dic) image = output_dic['image'] mask = output_dic['mask'] label = one_hot_embedding([self.class_dic[tag] for tag in tags], self.classes) return image, label, mask class VOC_Dataset_For_Making_CAM(VOC_Dataset): def __init__(self, root_dir, domain): super().__init__(root_dir, domain, with_id=True, with_tags=False, with_mask=False) def __getitem__(self, index): image, image_id = super().__getitem__(index) label = self.get_label(image_id) return image, image_id, label def get_label(self, img_name): res = re.findall(r"\[(.*?)\]", img_name) label = torch.tensor(list(eval(res[0]))) return label class VOC_Dataset_For_Affinity(VOC_Dataset): def __init__(self, root_dir, domain, path_index, label_dir, transform=None): super().__init__(root_dir, domain, with_id=True) self.transform = transform self.label_dir = label_dir self.path_index = path_index self.extract_aff_lab_func = GetAffinityLabelFromIndices(self.path_index.src_indices, self.path_index.dst_indices) def __getitem__(self, idx): image, image_id = super().__getitem__(idx) label = imageio.imread(self.label_dir + image_id + '.png.png') label = Image.fromarray(label) output_dic = self.transform({'image':image, 'mask':label}) image, label = output_dic['image'], output_dic['mask'] return image, self.extract_aff_lab_func(label)