Delete 'data/template_creation_pipline/main.py'

data/template_creation_pipline/main.py

@@ -1,105 +0,0 @@
-from pre_processing import pre_process
-from co_registration import co_register
-from template_creation import create_template_from_images
-import helper_functions as hf
-import sys
-import os
-from shutil import copyfile
-
-
-def pre_process_brain_tiffs(tif_path, result_path, mouse_id):
-    """
-    Utility function pre-processing all tiffs in folder, outputting pre-processed nifties in result_path.
-    :param tif_path: The tiffs directory.
-    :param result_path: The output directory.
-    :param mouse_id: The mouse id.
-    :return: Returns nothing.
-    """
-    # Get all images and the enclosing tiff folders.
-    images, folders = hf.images_in_folders(tif_path)
-
-    # Ensure that the number of images matches the naming of the folders
-    if len(images) == int(folders[-1][-2:]):
-        print('Number of images matches the naming on the folders')
-    else:
-        sys.exit('Mismatch between number of images and naming of last TIF folder.')
-
-    # Make output directory.
-    hf.make_dirs([result_path])
-
-    # Loop through each image and pre-process.
-    for image_nr, image_path in enumerate(images, 1):
-        image_nr_beauty = str(image_nr).zfill(2)
-        output = result_path + mouse_id + '_S' + image_nr_beauty + '.nii'
-        if not os.path.exists(output):
-            pre_process(image_path, output)
-
-
-def main(preprocess=False, coreg=False, make_template=False):
-    """
-    Main function performing pre-processing of tiffs, 3-dimensional reconstruction to reference and population-based
-    template creation.
-    :param preprocess: Boolean [Default: False]. Will perform pre-processing of tiffs if set to true.
-    :param coreg: Boolean [Default: False]. Will perform brain reconstruction based on the sequential slices if set to
-    true.
-    :param make_template: Boolean [Default: False]. Will perform population-based template creation based on the
-    reconstructed brain volumes if set to true.
-    :return: Returns nothing.
-    """
-    # Working directory
-    sandbox = '/run/media/frederik/S_T1/frederik_filip_mqr375/auto-seg/' # Should be removed for final submission.
-
-    # Get list of mice ids.
-    sandbox_mice = sandbox + 'mice/'
-    mice = os.listdir(sandbox_mice)
-
-    if coreg or preprocess:
-        # Loop through mice ids.
-        for mouse in sorted(mice):
-            # Current mouse directory and subdirectories.
-            work_dir = sandbox_mice + mouse + '/'
-            raw_dir = work_dir + 'TIF/'
-            pre_processed_dir = work_dir + 'preprocessed/'
-            out_dir = work_dir + 'volumes/'
-
-            # Make volumes directory.
-            hf.make_dirs(out_dir)
-
-            # Define raw volume and initial transform path (if present).
-            initial_vol_trans = out_dir + 'initial_transform_' + mouse + '.txt'
-            raw_vol_path = out_dir + '00_hv_raw_' + mouse + '.nii'
-
-            # Pre process tiff images.
-            if preprocess:
-                pre_process_brain_tiffs(raw_dir, pre_processed_dir, mouse)
-
-            # Create raw volume if not already present.
-            if not os.path.exists(raw_vol_path):
-                hf.make_volume(hf.files_in_dir(pre_processed_dir, '.nii'), raw_vol_path)
-
-            # Check that an initial transform is present.
-            if not os.path.exists(initial_vol_trans):
-                sys.exit('An initial moving transform was not found for at least brain with id : ' + mouse +
-                         '\nMake sure it is named and placed exactly: ' + initial_vol_trans)
-
-            # -- This call should be manually modified depending on first or second iteration -- #
-            # Perform co-registration to reference (Allen in first iteration. DAPI template in second iteration).
-            # Manually created initial transform is only used in the first iteration.
-            # First iteration had 0 non-linear steps (parameter nl=0).
-            if coreg:
-                mouse_coreg = co_register(sandbox + 'second-iteration_average_allen_0_15.nii.gz',
-                                          pre_processed_dir, raw_vol_path, '', out_dir, mouse,
-                                          print_log=True, nl=2)
-                copyfile(mouse_coreg, sandbox + 'volumes/' + mouse + '.nii')
-
-    # Population based template creation using symmetric modelling.
-    if make_template:
-        create_template_from_images(
-            sandbox + 'volumes/',
-            sandbox + 'template/',
-            symmetric=True
-        )
-
-
-if __name__ == '__main__':
-    main(preprocess=True, coreg=True, make_template=True)