def getitem_additional(self, index): ID = self.additional_df.ID.values[index] ppths = '/common/danylokolinko/publichpa' X = load_RGBY_image(ppths, 'train', ID, channels=self.channels, b8=self.b8) y = list(map(int, self.additional_df.Label.values[index].split('|'))) y = one_hot_embedding(y, self.NUM_CL) ss_mask = None if self.segmentation: ppth = ppths + '_mask_semantic' ss_mask = self.load_mask_pred(ppth, ID, 'cell') ss_mask1 = self.load_mask_pred(ppth, ID, 'nuc') ss_mask1 = (cv2.resize(ss_mask1, ss_mask.shape[:-1])) ss_mask = np.concatenate((ss_mask, ss_mask1), axis=-1) if self.load_masks: cell_mask = self.load_mask(ppths + '_mask', ID, 'cell') if self.cell_input: X = np.concatenate((X, np.expand_dims(cell_mask, axis=0)), axis=0) out1 = self.transform_func(X, y, ID, cell_mask, ss_mask) return out1
def __getitem__(self, index): ss_mask = None if self.additional_data_path and index >= len(self.list_IDs): return self.getitem_additional(index - len(self.list_IDs)) ID = self.list_IDs[index] # return np.nan, np.nan, ID, (np.nan), np.nan X = load_RGBY_image(self.path, 'train', ID, channels=self.channels, b8=self.b8) y = list(map(int, self.labels[index].split('|'))) y = one_hot_embedding(y, self.NUM_CL) if self.segmentation: ss_mask = self.load_mask_pred(self.hpasegm_path, ID, 'cell') ss_mask1 = self.load_mask_pred(self.hpasegm_path, ID, 'nuc') ss_mask1 = (cv2.resize(ss_mask1, ss_mask.shape[:-1])) ss_mask = np.concatenate((ss_mask, ss_mask1), axis=-1) if self.load_masks: cell_mask = self.load_mask(self.load_masks_path, ID, 'cell') if self.cell_input: X = np.concatenate((X, np.expand_dims(cell_mask, axis=0)), axis=0) out1 = self.transform_func(X, y, ID, cell_mask, ss_mask) return out1
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
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
def __getitem__(self, index): image, image_id, tags, mask = super().__getitem__(index) label = one_hot_embedding([self.class_dic[tag] for tag in tags], self.classes) return image, image_id, label, mask