| """ |
| Visual inspection of single transforms on the NIfTI files of one examination. |
| |
| python scripts/data_utils/inspect_transforms.py <path/to/one/examination_folder> |
| |
| Opens matplotlib windows; not a test, despite where this file used to live. |
| """ |
|
|
| import argparse |
| import os |
|
|
| import torch |
| import torchio as tio |
| import matplotlib.pyplot as plt |
| import torchvision |
| import pandas as pd |
|
|
| from auto_detect_breast_mri.config import resolve_path |
| from auto_detect_breast_mri.data.breast_mri_dataset import BreastMRISubjects |
| from auto_detect_breast_mri.data.nifti_io import read_nifti |
|
|
|
|
| def test_flips(): |
| flip_3d = tio.Compose([tio.RandomFlip(['LR', 'IS', 'AP'], flip_probability=0.5)]) |
| flip_2d = tio.Compose([tio.RandomFlip(['LR', 'IS'], flip_probability=0.5)]) |
|
|
| print("2D Flip as done until now.") |
| flipped3 = flip_3d(dyn0) |
| for i in range(0, nifti.shape[0]): |
| if flipped3[0, i, :, :].sum() == 0: |
| print("slice {} became zero.".format(i)) |
| if i % step == 0: |
| plt.imshow(flipped3[0][i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
| plt.imshow(seperation[0], cmap='gray', interpolation=None) |
| plt.show() |
| print("3D Flip.") |
| flipped2 = flip_2d(dyn0) |
| for i in range(0, nifti.shape[0]): |
| if flipped2[0, i, :, :].sum() == 0: |
| print("slice {} became zero.".format(i)) |
| if i % step == 0: |
| plt.imshow(flipped2[0][i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
|
|
| def test_rotation(): |
| rotation = tio.Compose([torchvision.transforms.RandomRotation(35)]) |
|
|
| print("Random Rotation --> correct axis?") |
| rotated = rotation(dyn0) |
| for i in range(0, nifti.shape[0], step): |
| plt.imshow(rotated[0][i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
|
|
| def test_rescale(): |
| rescale = tio.Compose([tio.RandomAffine(scales=(0.8, 1.2), degrees=0)]) |
|
|
| print("Check rescale") |
| rescaled = rescale(dyn0) |
| for i in range(0, nifti.shape[0], step): |
| plt.imshow(rescaled[0][i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
|
|
| def test_normalize(): |
| norm = tio.ZNormalization() |
| normed = norm(dyn0) |
| for i in range(0, nifti.shape[0], step): |
| plt.imshow(normed[0][i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
|
|
| def test_read_data(path_base: str, set_folder: str, feature_path: str, pre_image_shape: tuple, transform: torchvision.transforms.Compose, protocol: str): |
| fold = 0 |
| train_set_filename = "stratified_training_set-f{}.csv".format(fold) |
| eval_set_filename = "stratified_evaluation_set-f{}.csv".format(fold) |
| test_set_filename = "stratified_test_set.csv" |
| print("TRAINING DATASET") |
| traindata_set = BreastMRISubjects(path_base, set_folder + train_set_filename, |
| protocol=protocol, transform=transform) |
| print("EVALUATION DATASET") |
| evaldata_set = BreastMRISubjects(path_base, set_folder + eval_set_filename, |
| protocol=protocol, transform=transform) |
| print("TEST DATASET") |
| testdata_set = BreastMRISubjects(path_base, set_folder + test_set_filename, |
| protocol=protocol, transform=transform) |
|
|
|
|
| def test_inspect_summary_files(dataset_root: str, dicom_root: str, metadata_file: str = None): |
| """Report which examination IDs of the metadata export are covered by the split files.""" |
| metadata = pd.read_csv(resolve_path(metadata_file, "metadata_file", "metadata export")) |
| fold = 0 |
| train_set_filename = "stratified_training_set-f{}-0.csv".format(fold) |
| eval_set_filename = "stratified_evaluation_set-f{}-0.csv".format(fold) |
| test_set_filename = "stratified_test_set-f{}.csv".format(fold) |
| train = pd.read_csv(dataset_root + train_set_filename, dtype=str) |
| eval = pd.read_csv(dataset_root + eval_set_filename, dtype=str) |
| test = pd.read_csv(dataset_root + test_set_filename, dtype=str) |
|
|
| |
| all_pids = metadata['AnforderungsNrE'].astype('int64') |
| train_read = train['AnforderungsNrE'].drop_duplicates().astype('int64') |
| eval_read = eval['AnforderungsNrE'].drop_duplicates().astype('int64') |
| test_read = test['AnforderungsNrE'].drop_duplicates().astype('int64') |
|
|
| all_used = pd.concat([train_read.to_frame(), eval_read.to_frame(), test_read.to_frame()]) |
| missing = all_pids[~all_pids.isin(all_used['AnforderungsNrE'])] |
| if len(missing) + len(all_used) != len(all_pids): |
| print("{} PIDs in total. \n{} PIDS in use: {} Training, {} Evaluation, {} Testing \n{} PIDs from all_pids are not found in used PIDs.".format(len(all_pids), len(all_used), len(train), len(eval), len(test), len(missing))) |
| |
| origin_unclear = all_used[~all_used['AnforderungsNrE'].isin(all_pids)] |
| print("Found {} PIDs that are from unknown origin.".format(len(origin_unclear))) |
| pids_without_path = [] |
| |
| for pid in all_used['AnforderungsNrE']: |
| if not os.path.exists(dicom_root + str(pid)): |
| pids_without_path.append(pid) |
| print("{} PIDs to which no data folder exists.".format(len(pids_without_path))) |
|
|
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("examination_folder", |
| help="folder of a single examination whose NIfTI files are inspected") |
| parser.add_argument("--step", type=int, default=12, help="show every n-th slice. Default: 12") |
| args = parser.parse_args() |
|
|
| file_root = args.examination_folder.rstrip(os.sep) + os.sep |
| filenames = os.listdir(file_root) |
| for file in filenames: |
| nifti = read_nifti(file_root + file) |
| step = args.step |
| seperation = torch.zeros(nifti.shape) |
| dyn0 = torch.zeros((1,) + nifti.shape) |
| dyn0[0] = nifti |
|
|
| for i in range(0, nifti.shape[0], step): |
| plt.imshow(nifti[i], cmap='gray', interpolation=None) |
| plt.show() |
|
|
| plt.imshow(seperation[0], cmap='gray', interpolation=None) |
| plt.show() |
|
|
| |
| test_rotation() |
| |
| test_normalize() |
|
|
