def _load_filenames(self): """Load preprocessed pair data (input and gt) in handler.""" for input_filename, gt_filename, roi_filename, metadata in self.filename_pairs: segpair = SegmentationPair(input_filename, gt_filename, metadata=metadata, slice_axis=self.slice_axis, soft_gt=self.soft_gt) input_data, gt_data = segpair.get_pair_data() metadata = segpair.get_pair_metadata() seg_pair = { 'input': input_data, 'gt': gt_data, 'input_metadata': metadata['input_metadata'], 'gt_metadata': metadata['gt_metadata'] } self.has_bounding_box = imed_obj_detect.verify_metadata( seg_pair, self.has_bounding_box) if self.has_bounding_box: self.prepro_transforms = imed_obj_detect.adjust_transforms( self.prepro_transforms, seg_pair, length=self.length, stride=self.stride) seg_pair, roi_pair = imed_transforms.apply_preprocessing_transforms( self.prepro_transforms, seg_pair=seg_pair) for metadata in seg_pair['input_metadata']: metadata['index_shape'] = seg_pair['input'][0].shape self.handlers.append((seg_pair, roi_pair))
def load_filenames(self): """Load preprocessed pair data (input and gt) in handler.""" for input_filenames, gt_filenames, roi_filename, metadata in self.filename_pairs: roi_pair = SegmentationPair( input_filenames, roi_filename, metadata=metadata, slice_axis=self.slice_axis, cache=self.cache, prepro_transforms=self.prepro_transforms) seg_pair = SegmentationPair( input_filenames, gt_filenames, metadata=metadata, slice_axis=self.slice_axis, cache=self.cache, prepro_transforms=self.prepro_transforms, soft_gt=self.soft_gt) input_data_shape, _ = seg_pair.get_pair_shapes() for idx_pair_slice in range(input_data_shape[-1]): slice_seg_pair = seg_pair.get_pair_slice(idx_pair_slice, gt_type=self.task) self.has_bounding_box = imed_obj_detect.verify_metadata( slice_seg_pair, self.has_bounding_box) if self.has_bounding_box: self.prepro_transforms = imed_obj_detect.adjust_transforms( self.prepro_transforms, slice_seg_pair) if self.slice_filter_fn and not self.slice_filter_fn( slice_seg_pair): continue # Note: we force here gt_type=segmentation since ROI slice is needed to Crop the image slice_roi_pair = roi_pair.get_pair_slice( idx_pair_slice, gt_type="segmentation") if self.slice_filter_roi and imed_loader_utils.filter_roi( slice_roi_pair['gt'], self.roi_thr): continue item = imed_transforms.apply_preprocessing_transforms( self.prepro_transforms, slice_seg_pair, slice_roi_pair) self.indexes.append(item)
def _slice_seg_pair(self, idx_pair_slice, seg_pair, roi_pair, useful_slices, input_volumes, gt_volume, roi_volume): """ Helper function to slice segmentation pair at load time """ slice_seg_pair = seg_pair.get_pair_slice(idx_pair_slice) self.has_bounding_box = imed_obj_detect.verify_metadata( slice_seg_pair, self.has_bounding_box) if self.has_bounding_box: imed_obj_detect.adjust_transforms(self.prepro_transforms, slice_seg_pair) # keeping idx of slices with gt if self.slice_filter_fn: filter_fn_ret_seg = self.slice_filter_fn(slice_seg_pair) if self.slice_filter_fn and filter_fn_ret_seg: useful_slices.append(idx_pair_slice) roi_pair_slice = roi_pair.get_pair_slice(idx_pair_slice) slice_seg_pair, roi_pair_slice = imed_transforms.apply_preprocessing_transforms( self.prepro_transforms, slice_seg_pair, roi_pair_slice) input_volumes.append(slice_seg_pair["input"][0]) # Handle unlabeled data if not len(slice_seg_pair["gt"]): gt_volume = [] else: gt_volume.append( (slice_seg_pair["gt"][0] * 255).astype(np.uint8) / 255.) # Handle data with no ROI provided if not len(roi_pair_slice["gt"]): roi_volume = [] else: roi_volume.append( (roi_pair_slice["gt"][0] * 255).astype(np.uint8) / 255.) return slice_seg_pair, roi_pair_slice
def load_filenames(self): """Load preprocessed pair data (input and gt) in handler.""" for input_filenames, gt_filenames, roi_filename, metadata in self.filename_pairs: roi_pair = SegmentationPair( input_filenames, roi_filename, metadata=metadata, slice_axis=self.slice_axis, cache=self.cache, prepro_transforms=self.prepro_transforms) seg_pair = SegmentationPair( input_filenames, gt_filenames, metadata=metadata, slice_axis=self.slice_axis, cache=self.cache, prepro_transforms=self.prepro_transforms, soft_gt=self.soft_gt) input_data_shape, _ = seg_pair.get_pair_shapes() path_temp = Path(create_temp_directory()) for idx_pair_slice in range(input_data_shape[-1]): slice_seg_pair = seg_pair.get_pair_slice(idx_pair_slice, gt_type=self.task) self.has_bounding_box = imed_obj_detect.verify_metadata( slice_seg_pair, self.has_bounding_box) if self.has_bounding_box: self.prepro_transforms = imed_obj_detect.adjust_transforms( self.prepro_transforms, slice_seg_pair) if self.slice_filter_fn and not self.slice_filter_fn( slice_seg_pair): continue # Note: we force here gt_type=segmentation since ROI slice is needed to Crop the image slice_roi_pair = roi_pair.get_pair_slice( idx_pair_slice, gt_type="segmentation") if self.slice_filter_roi and imed_loader_utils.filter_roi( slice_roi_pair['gt'], self.roi_thr): continue item: Tuple[ dict, dict] = imed_transforms.apply_preprocessing_transforms( self.prepro_transforms, slice_seg_pair, slice_roi_pair) # Run once code to keep track if disk cache is used if self.disk_cache is None: self.determine_cache_need(item, input_data_shape[-1]) # If is_2d_patch, create handlers list for indexing patch if self.is_2d_patch: for metadata in item[0][MetadataKW.INPUT_METADATA]: metadata[ MetadataKW.INDEX_SHAPE] = item[0]['input'][0].shape if self.disk_cache: path_item = path_temp / f"item_{get_timestamp()}.pkl" with path_item.open(mode="wb") as f: pickle.dump(item, f) self.handlers.append((path_item)) else: self.handlers.append((item)) # else, append the whole slice to self.indexes else: if self.disk_cache: path_item = path_temp / f"item_{get_timestamp()}.pkl" with path_item.open(mode="wb") as f: pickle.dump(item, f) self.indexes.append(path_item) else: self.indexes.append(item) # If is_2d_patch, prepare indices of patches if self.is_2d_patch: self.prepare_indices()
def _load_filenames(self): """Load preprocessed pair data (input and gt) in handler.""" for subject_id, input_filename, gt_filename, roi_filename, metadata in self.filename_pairs: # Creating/ getting the subject group if str(subject_id) in self.hdf5_file.keys(): grp = self.hdf5_file[str(subject_id)] else: grp = self.hdf5_file.create_group(str(subject_id)) roi_pair = imed_loader.SegmentationPair(input_filename, roi_filename, metadata=metadata, slice_axis=self.slice_axis, cache=False, soft_gt=self.soft_gt) seg_pair = imed_loader.SegmentationPair(input_filename, gt_filename, metadata=metadata, slice_axis=self.slice_axis, cache=False, soft_gt=self.soft_gt) print("gt filename", gt_filename) input_data_shape, _ = seg_pair.get_pair_shapes() useful_slices = [] input_volumes = [] gt_volume = [] roi_volume = [] for idx_pair_slice in range(input_data_shape[-1]): slice_seg_pair = seg_pair.get_pair_slice(idx_pair_slice) self.has_bounding_box = imed_obj_detect.verify_metadata( slice_seg_pair, self.has_bounding_box) if self.has_bounding_box: imed_obj_detect.adjust_transforms(self.prepro_transforms, slice_seg_pair) # keeping idx of slices with gt if self.slice_filter_fn: filter_fn_ret_seg = self.slice_filter_fn(slice_seg_pair) if self.slice_filter_fn and filter_fn_ret_seg: useful_slices.append(idx_pair_slice) roi_pair_slice = roi_pair.get_pair_slice(idx_pair_slice) slice_seg_pair, roi_pair_slice = imed_transforms.apply_preprocessing_transforms( self.prepro_transforms, slice_seg_pair, roi_pair_slice) input_volumes.append(slice_seg_pair["input"][0]) # Handle unlabeled data if not len(slice_seg_pair["gt"]): gt_volume = [] else: gt_volume.append( (slice_seg_pair["gt"][0] * 255).astype(np.uint8) / 255.) # Handle data with no ROI provided if not len(roi_pair_slice["gt"]): roi_volume = [] else: roi_volume.append( (roi_pair_slice["gt"][0] * 255).astype(np.uint8) / 255.) # Getting metadata using the one from the last slice input_metadata = slice_seg_pair['input_metadata'][0] gt_metadata = slice_seg_pair['gt_metadata'][0] roi_metadata = roi_pair_slice['input_metadata'][0] if grp.attrs.__contains__('slices'): grp.attrs['slices'] = list( set(np.concatenate((grp.attrs['slices'], useful_slices)))) else: grp.attrs['slices'] = useful_slices # Creating datasets and metadata contrast = input_metadata['contrast'] # Inputs print(len(input_volumes)) print("grp= ", str(subject_id)) key = "inputs/{}".format(contrast) print("key = ", key) if len(input_volumes) < 1: print("list empty") continue grp.create_dataset(key, data=input_volumes) # Sub-group metadata if grp['inputs'].attrs.__contains__('contrast'): attr = grp['inputs'].attrs['contrast'] new_attr = [c for c in attr] new_attr.append(contrast) grp['inputs'].attrs.create('contrast', new_attr, dtype=self.dt) else: grp['inputs'].attrs.create('contrast', [contrast], dtype=self.dt) # dataset metadata grp[key].attrs['input_filenames'] = input_metadata[ 'input_filenames'] grp[key].attrs['data_type'] = input_metadata['data_type'] if "zooms" in input_metadata.keys(): grp[key].attrs["zooms"] = input_metadata['zooms'] if "data_shape" in input_metadata.keys(): grp[key].attrs["data_shape"] = input_metadata['data_shape'] if "bounding_box" in input_metadata.keys(): grp[key].attrs["bounding_box"] = input_metadata['bounding_box'] # GT key = "gt/{}".format(contrast) grp.create_dataset(key, data=gt_volume) # Sub-group metadata if grp['gt'].attrs.__contains__('contrast'): attr = grp['gt'].attrs['contrast'] new_attr = [c for c in attr] new_attr.append(contrast) grp['gt'].attrs.create('contrast', new_attr, dtype=self.dt) else: grp['gt'].attrs.create('contrast', [contrast], dtype=self.dt) # dataset metadata grp[key].attrs['gt_filenames'] = input_metadata['gt_filenames'] grp[key].attrs['data_type'] = gt_metadata['data_type'] if "zooms" in gt_metadata.keys(): grp[key].attrs["zooms"] = gt_metadata['zooms'] if "data_shape" in gt_metadata.keys(): grp[key].attrs["data_shape"] = gt_metadata['data_shape'] if gt_metadata['bounding_box'] is not None: grp[key].attrs["bounding_box"] = gt_metadata['bounding_box'] # ROI key = "roi/{}".format(contrast) grp.create_dataset(key, data=roi_volume) # Sub-group metadata if grp['roi'].attrs.__contains__('contrast'): attr = grp['roi'].attrs['contrast'] new_attr = [c for c in attr] new_attr.append(contrast) grp['roi'].attrs.create('contrast', new_attr, dtype=self.dt) else: grp['roi'].attrs.create('contrast', [contrast], dtype=self.dt) # dataset metadata grp[key].attrs['roi_filename'] = roi_metadata['gt_filenames'] grp[key].attrs['data_type'] = roi_metadata['data_type'] if "zooms" in roi_metadata.keys(): grp[key].attrs["zooms"] = roi_metadata['zooms'] if "data_shape" in roi_metadata.keys(): grp[key].attrs["data_shape"] = roi_metadata['data_shape'] # Adding contrast to group metadata if grp.attrs.__contains__('contrast'): attr = grp.attrs['contrast'] new_attr = [c for c in attr] new_attr.append(contrast) grp.attrs.create('contrast', new_attr, dtype=self.dt) else: grp.attrs.create('contrast', [contrast], dtype=self.dt)