added intermediate crudecorrected aseg filepath in correct_chirality …

…function and additional argument iteration plus corresponding logic to handle each iteration approporiately and generate cleaner filenames

src/postbibsnet.py

@@ -170,15 +170,15 @@ def run_correct_chirality(crude_l_r_mask_nifti_fpath, l_r_mask_nifti_fpath, j_ar
     LOGGER.info(msg.format("Now"))
     nii_fpaths = correct_chirality(
         seg_BIBSnet_outfiles[0], segment_lookup_table_path,
-        crude_l_r_mask_nifti_fpath, chiral_out_dir
+        crude_l_r_mask_nifti_fpath, chiral_out_dir, 1
     )
 
     # Run chirality correction a second time using the refined LR mask generated from registration with template files applied to the segmentation corrected with the crude LR mask
-    msg = "{} running chirality correction on " + nii_fpaths["corrected"]
+    msg = "{} running chirality correction on " + nii_fpaths["crudecorrected"]
     LOGGER.info(msg.format("Now"))
     nii_fpaths = correct_chirality(
-        nii_fpaths["corrected"], segment_lookup_table_path,
-        l_r_mask_nifti_fpath, chiral_out_dir
+        nii_fpaths["crudecorrected"], segment_lookup_table_path,
+        l_r_mask_nifti_fpath, chiral_out_dir, 2
     )
 
     LOGGER.info(msg.format("Finished"))
@@ -340,7 +340,7 @@ def copy_to_derivatives_dir(file_to_copy, derivs_dir, sub_ses, space, new_fname_
 
 
 def correct_chirality(nifti_input_file_path, segment_lookup_table,
-                      nii_fpath_LR_mask, chiral_out_dir):
+                      nii_fpath_LR_mask, chiral_out_dir, iteration):
     """
     Creates an output file with chirality corrections fixed.
     :param nifti_input_file_path: String, path to a segmentation file with
@@ -351,42 +351,82 @@ def correct_chirality(nifti_input_file_path, segment_lookup_table,
     :param xfm_ref_img: String, path to (T1w, unless running in T2w-only mode) 
                         image to use as a reference when applying transform
     :param j_args: Dictionary containing all args
+    :param iteration: either 1 or 2 for iteration1 or iteration2 of chirality correction
     :return: Dict with paths to native and chirality-corrected images
     """
-    nifti_file_paths = dict()
-    for which_nii in ("native-T1", "native-T2", "corrected"):
-        nifti_file_paths[which_nii] = os.path.join(chiral_out_dir, "_".join((
-            which_nii, os.path.basename(nifti_input_file_path)
-        )))
+    if iteration==1:
+        nifti_file_paths = dict()
+        for which_nii in ("native-T1", "native-T2", "crudecorrected"):
+            nifti_file_paths[which_nii] = os.path.join(chiral_out_dir, "_".join((
+                which_nii, os.path.basename(nifti_input_file_path)
+            )))
+
+        free_surfer_label_to_region = get_id_to_region_mapping(segment_lookup_table)
+        segment_name_to_number = {v: k for k, v in free_surfer_label_to_region.items()}
+        img = nib.load(nifti_input_file_path)
+        data = img.get_data()
+        left_right_img = nib.load(nii_fpath_LR_mask)
+        left_right_data = left_right_img.get_data()
+
+        new_data = data.copy()
+        data_shape = img.header.get_data_shape()
+        left_right_data_shape = left_right_img.header.get_data_shape()
+        width = data_shape[0]
+        height = data_shape[1]
+        depth = data_shape[2]
+        assert \
+            width == left_right_data_shape[0] and height == left_right_data_shape[1] and depth == left_right_data_shape[2]
+        for i in range(width):
+            for j in range(height):
+                for k in range(depth):
+                    voxel = data[i][j][k]
+                    region = free_surfer_label_to_region[voxel]
+                    chirality_voxel = int(left_right_data[i][j][k])
+                    if not (region.startswith(LEFT) or region.startswith(RIGHT)):
+                        continue
+                    if chirality_voxel == CHIRALITY_CONST["LEFT"] or chirality_voxel == CHIRALITY_CONST["RIGHT"]:
+                        check_and_correct_region(
+                            chirality_voxel == CHIRALITY_CONST["LEFT"], region, segment_name_to_number, new_data, i, j, k)
+        fixed_img = nib.Nifti1Image(new_data, img.affine, img.header)
+        nib.save(fixed_img, nifti_file_paths["crudecorrected"])
+
+    elif iteration==2:
+        # Drop "crudecorrected_" from nifti_input_file_path to make filenames cleaner
+        nifti_input_file_path_mod=(os.path.basename(nifti_input_file_path)).split('_', 1)[1]
+        nifti_file_paths = dict()
+        for which_nii in ("native-T1", "native-T2", "corrected"):
+            nifti_file_paths[which_nii] = os.path.join(chiral_out_dir, "_".join((
+                which_nii, nifti_input_file_path_mod
+            )))
 
-    free_surfer_label_to_region = get_id_to_region_mapping(segment_lookup_table)
-    segment_name_to_number = {v: k for k, v in free_surfer_label_to_region.items()}
-    img = nib.load(nifti_input_file_path)
-    data = img.get_data()
-    left_right_img = nib.load(nii_fpath_LR_mask)
-    left_right_data = left_right_img.get_data()
-
-    new_data = data.copy()
-    data_shape = img.header.get_data_shape()
-    left_right_data_shape = left_right_img.header.get_data_shape()
-    width = data_shape[0]
-    height = data_shape[1]
-    depth = data_shape[2]
-    assert \
-        width == left_right_data_shape[0] and height == left_right_data_shape[1] and depth == left_right_data_shape[2]
-    for i in range(width):
-        for j in range(height):
-            for k in range(depth):
-                voxel = data[i][j][k]
-                region = free_surfer_label_to_region[voxel]
-                chirality_voxel = int(left_right_data[i][j][k])
-                if not (region.startswith(LEFT) or region.startswith(RIGHT)):
-                    continue
-                if chirality_voxel == CHIRALITY_CONST["LEFT"] or chirality_voxel == CHIRALITY_CONST["RIGHT"]:
-                    check_and_correct_region(
-                        chirality_voxel == CHIRALITY_CONST["LEFT"], region, segment_name_to_number, new_data, i, j, k)
-    fixed_img = nib.Nifti1Image(new_data, img.affine, img.header)
-    nib.save(fixed_img, nifti_file_paths["corrected"])
+        free_surfer_label_to_region = get_id_to_region_mapping(segment_lookup_table)
+        segment_name_to_number = {v: k for k, v in free_surfer_label_to_region.items()}
+        img = nib.load(nifti_input_file_path)
+        data = img.get_data()
+        left_right_img = nib.load(nii_fpath_LR_mask)
+        left_right_data = left_right_img.get_data()
+
+        new_data = data.copy()
+        data_shape = img.header.get_data_shape()
+        left_right_data_shape = left_right_img.header.get_data_shape()
+        width = data_shape[0]
+        height = data_shape[1]
+        depth = data_shape[2]
+        assert \
+            width == left_right_data_shape[0] and height == left_right_data_shape[1] and depth == left_right_data_shape[2]
+        for i in range(width):
+            for j in range(height):
+                for k in range(depth):
+                    voxel = data[i][j][k]
+                    region = free_surfer_label_to_region[voxel]
+                    chirality_voxel = int(left_right_data[i][j][k])
+                    if not (region.startswith(LEFT) or region.startswith(RIGHT)):
+                        continue
+                    if chirality_voxel == CHIRALITY_CONST["LEFT"] or chirality_voxel == CHIRALITY_CONST["RIGHT"]:
+                        check_and_correct_region(
+                            chirality_voxel == CHIRALITY_CONST["LEFT"], region, segment_name_to_number, new_data, i, j, k)
+        fixed_img = nib.Nifti1Image(new_data, img.affine, img.header)
+        nib.save(fixed_img, nifti_file_paths["corrected"])
     return nifti_file_paths