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nnUNet_plan_and_preprocess -t XXX --verify_dataset_integrity

def crop(task_string, override=False, num_threads=default_num_threads):cropped_out_dir = join(nnUNet_cropped_data, task_string)maybe_mkdir_p(cropped_out_dir)if override and isdir(cropped_out_dir):shutil.rmtree(cropped_out_dir)maybe_mkdir_p(cropped_out_dir)splitted_4d_output_dir_task = join(nnUNet_raw_data, task_string)lists, _ = create_lists_from_splitted_dataset(splitted_4d_output_dir_task)imgcrop = ImageCropper(num_threads, cropped_out_dir)imgcrop.run_cropping(lists, overwrite_existing=override)shutil.copy(join(nnUNet_raw_data, task_string, "dataset.json"), cropped_out_dir)

    imgcrop = ImageCropper(num_threads, cropped_out_dir)imgcrop.run_cropping(lists, overwrite_existing=override)

    def run_cropping(self, list_of_files, overwrite_existing=False, output_folder=None):"""also copied ground truth nifti segmentation into the preprocessed folder so that we can use them for evaluationon the cluster:param list_of_files: list of list of files [[PATIENTID_TIMESTEP_0000.nii.gz], [PATIENTID_TIMESTEP_0000.nii.gz]]:param overwrite_existing::param output_folder::return:"""if output_folder is not None:self.output_folder = output_folderoutput_folder_gt = os.path.join(self.output_folder, "gt_segmentations")maybe_mkdir_p(output_folder_gt)for j, case in enumerate(list_of_files):if case[-1] is not None:shutil.copy(case[-1], output_folder_gt)list_of_args = []for j, case in enumerate(list_of_files):case_identifier = get_case_identifier(case)list_of_args.append((case, case_identifier, overwrite_existing))p = Pool(self.num_threads)p.map(self._load_crop_save_star, list_of_args)p.close()p.join()

p.map(self._load_crop_save_star, list_of_args)

    def _load_crop_save_star(self, args):return self.load_crop_save(*args)def load_crop_save(self, case, case_identifier, overwrite_existing=False):try:print(case_identifier)if overwrite_existing or (not os.path.isfile(os.path.join(self.output_folder, "%s.npz" % case_identifier))or not os.path.isfile(os.path.join(self.output_folder, "%s.pkl" % case_identifier))):data, seg, properties = self.crop_from_list_of_files(case[:-1], case[-1])all_data = np.vstack((data, seg))np.savez_compressed(os.path.join(self.output_folder, "%s.npz" % case_identifier), data=all_data)with open(os.path.join(self.output_folder, "%s.pkl" % case_identifier), 'wb') as f:pickle.dump(properties, f)except Exception as e:print("Exception in", case_identifier, ":")print(e)raise e

    @staticmethoddef crop_from_list_of_files(data_files, seg_file=None):data, seg, properties = load_case_from_list_of_files(data_files, seg_file)return ImageCropper.crop(data, properties, seg)@staticmethoddef crop(data, properties, seg=None):shape_before = data.shapedata, seg, bbox = crop_to_nonzero(data, seg, nonzero_label=-1)shape_after = data.shapeprint("before crop:", shape_before, "after crop:", shape_after, "spacing:",np.array(properties["original_spacing"]), "n")# pkl文件中存储如下信息：bb，类个数（包含背景），crop后的sizeproperties["crop_bbox"] = bboxproperties['classes'] = np.unique(seg)seg[seg < -1] = 0properties["size_after_cropping"] = data[0].shapereturn data, seg, properties

   # 数据的shape是(C, X, Y, Z) 或者 (C, X, Y)nonzero_mask = np.zeros(data.shape[1:], dtype=bool)for c in range(data.shape[0]):this_mask = data[c] != 0 不等于0的都认为是背景nonzero_mask = nonzero_mask | this_masknonzero_mask = binary_fill_holes(nonzero_mask) # scipy.ndimage# 获取bounding box的函数。这个在日常会经常用到
def get_bbox_from_mask(mask, outside_value=0):mask_voxel_coords = np.where(mask != outside_value)minzidx = int(np.min(mask_voxel_coords[0]))maxzidx = int(np.max(mask_voxel_coords[0])) + 1 # +1如果超出图像大小怎么办？minxidx = int(np.min(mask_voxel_coords[1]))maxxidx = int(np.max(mask_voxel_coords[1])) + 1minyidx = int(np.min(mask_voxel_coords[2]))maxyidx = int(np.max(mask_voxel_coords[2])) + 1return [[minzidx, maxzidx], [minxidx, maxxidx], [minyidx, maxyidx]]# 根据bbox提取ROI，这个也经常会用到，为什么用slice做索引，而不直接bbox[0][0]:bbox[0][1]...
def crop_to_bbox(image, bbox):assert len(image.shape) == 3, "only supports 3d images"resizer = (slice(bbox[0][0], bbox[0][1]), slice(bbox[1][0], bbox[1][1]), slice(bbox[2][0], bbox[2][1]))return image[resizer]