def test_keep_largest_object(nii_seg):
# Set a voxel to 1 at the corner to make sure it is set to 0 by the function
coord = (1, 1, 1)
nii_seg.dataobj[coord] = 1
# Test function with array input
arr_seg_proc = imed_postpro.keep_largest_object(np.copy(np.asanyarray(nii_seg.dataobj)))
assert isinstance(arr_seg_proc, np.ndarray)
assert check_bin_vs_soft(nii_seg.dataobj, arr_seg_proc)
assert arr_seg_proc[coord] == 0
# Make sure it works with nibabel input
nii_seg_proc = imed_postpro.keep_largest_object(nii_seg)
assert isinstance(nii_seg_proc, nib.nifti1.Nifti1Image)
assert check_bin_vs_soft(nii_seg.dataobj, nii_seg_proc.dataobj)
assert nii_seg_proc.dataobj[coord] == 0
def generate_bounding_box_file(subject_list,
model_path,
path_output,
gpu_id=0,
slice_axis=0,
contrast_lst=None,
keep_largest_only=True,
safety_factor=None):
"""Creates json file containing the bounding box dimension for each images. The file has the following format:
{"path/to/img.nii.gz": [[x1_min, x1_max, y1_min, y1_max, z1_min, z1_max],
[x2_min, x2_max, y2_min, y2_max, z2_min, z2_max]]}
where each list represents the coordinates of an object on the image (2 instance of a given object in this example).
Args:
subject_list (list): List of all subjects in the BIDS directory.
model_path (string): Path to object detection model.
path_output (string): Output path.
gpu_id (int): If available, GPU number.
slice_axis (int): Slice axis (0: sagittal, 1: coronal, 2: axial).
contrast_lst (list): Contrasts.
keep_largest_only (bool): Boolean representing if only the largest object of the prediction is kept.
safety_factor (list or tuple): Factors to multiply each dimension of the bounding box.
Returns:
dict: Dictionary containing bounding boxes related to their image.
"""
bounding_box_dict = {}
if safety_factor is None:
safety_factor = [1.0, 1.0, 1.0]
for subject in subject_list:
if subject.record["modality"] in contrast_lst:
subject_path = str(subject.record["absolute_path"])
object_mask, _ = imed_inference.segment_volume(model_path,
[subject_path],
gpu_id=gpu_id)
object_mask = object_mask[0]
if keep_largest_only:
object_mask = imed_postpro.keep_largest_object(object_mask)
mask_path = os.path.join(path_output, "detection_mask")
if not os.path.exists(mask_path):
os.mkdir(mask_path)
nib.save(object_mask,
os.path.join(mask_path,
subject_path.split("/")[-1]))
ras_orientation = nib.as_closest_canonical(object_mask)
hwd_orientation = imed_loader_utils.orient_img_hwd(
ras_orientation.get_fdata(), slice_axis)
bounding_boxes = get_bounding_boxes(hwd_orientation)
bounding_box_dict[subject_path] = [
adjust_bb_size(bb, safety_factor) for bb in bounding_boxes
]
file_path = os.path.join(path_output, 'bounding_boxes.json')
with open(file_path, 'w') as fp:
json.dump(bounding_box_dict, fp, indent=4)
return bounding_box_dict