[ENH] Add resample nb points + dps

.github/workflows/test_package.yml

@@ -25,6 +25,7 @@ jobs:
 
     - name: Install dependencies
       run: |
+        export SETUPTOOLS_USE_DISTUTILS=stdlib
         pip install --upgrade pip
         pip install pytest
         pip install -e .

dwi_ml/data/hdf5/hdf5_creation.py

@@ -136,11 +136,17 @@ class HDF5Creator:
     def __init__(self, root_folder: Path, out_hdf_filename: Path,
                  training_subjs: List[str], validation_subjs: List[str],
                  testing_subjs: List[str], groups_config: dict,
-                 step_size: float = None, compress: float = None,
+                 dps_keys: List[str] = [],
+                 step_size: float = None,
+                 nb_points: int = None,
+                 compress: float = None,
+                 remove_invalid: bool = False,
                  enforce_files_presence: bool = True,
                  save_intermediate: bool = False,
                  intermediate_folder: Path = None):
         """
+        Params step_size, nb_points and compress are mutually exclusive.
+
         Params
         ------
         root_folder: Path
@@ -154,10 +160,16 @@ def __init__(self, root_folder: Path, out_hdf_filename: Path,
             List of subject names for each data set.
         groups_config: dict
             Information from json file loaded as a dict.
+        dps_keys: List[str]
+            List of keys to keep in data_per_streamline. Default: [].
         step_size: float
             Step size to resample streamlines. Default: None.
+        nb_points: int
+            Number of points per streamline. Default: None.
         compress: float
             Compress streamlines. Default: None.
+        remove_invalid: bool
+            Remove invalid streamline. Default: False
         enforce_files_presence: bool
             If true, will stop if some files are not available for a subject.
             Default: True.
@@ -174,8 +186,11 @@ def __init__(self, root_folder: Path, out_hdf_filename: Path,
         self.validation_subjs = validation_subjs
         self.testing_subjs = testing_subjs
         self.groups_config = groups_config
+        self.dps_keys = dps_keys
         self.step_size = step_size
+        self.nb_points = nb_points
         self.compress = compress
+        self.remove_invalid = remove_invalid
 
         # Optional
         self.save_intermediate = save_intermediate
@@ -188,7 +203,7 @@ def __init__(self, root_folder: Path, out_hdf_filename: Path,
             self._analyse_config_file()
 
         # -------- Performing checks
-
+        self._check_streamlines_operations()
         # Check that all subjects exist.
         logging.debug("Preparing hdf5 creator for \n"
                       "  training subjs {}, \n"
@@ -340,6 +355,19 @@ def flatten_list(a_list):
                                 self.enforce_files_presence,
                                 folder=subj_input_dir)
 
+    def _check_streamlines_operations(self):
+        valid = True
+        if self.step_size and self.nb_points:
+            valid = False
+        elif self.step_size and self.compress:
+            valid = False
+        elif self.nb_points and self.compress:
+            valid = False
+        if not valid:
+            raise ValueError(
+                "Only one option can be chosen: either resampling to "
+                "step_size, nb_points or compressing, not both.")
+
     def create_database(self):
         """
         Generates a hdf5 dataset from a group of subjects. Hdf5 dataset will
@@ -359,6 +387,8 @@ def create_database(self):
             hdf_handle.attrs['testing_subjs'] = self.testing_subjs
             hdf_handle.attrs['step_size'] = self.step_size if \
                 self.step_size is not None else 'Not defined by user'
+            hdf_handle.attrs['nb_points'] = self.nb_points if \
+                self.nb_points is not None else 'Not defined by user'
             hdf_handle.attrs['compress'] = self.compress if \
                 self.compress is not None else 'Not defined by user'
 
@@ -598,9 +628,16 @@ def _create_streamline_groups(self, ref, subj_input_dir, subj_id,
 
             if len(sft.data_per_point) > 0:
                 logging.debug('sft contained data_per_point. Data not kept.')
-            if len(sft.data_per_streamline) > 0:
-                logging.debug('sft contained data_per_streamlines. Data not '
-                              'kept.')
+
+            for dps_key in self.dps_keys:
+                if dps_key not in sft.data_per_streamline:
+                    raise ValueError(
+                        "The data_per_streamline key '{}' was not found in "
+                        "the sft. Check your tractogram file.".format(dps_key))
+
+                logging.debug("    Include dps \"{}\" in the HDF5.".format(dps_key))
+                streamlines_group.create_dataset('dps_' + dps_key,
+                                                 data=sft.data_per_streamline[dps_key])
 
             # Accessing private Dipy values, but necessary.
             # We need to deconstruct the streamlines into arrays with
@@ -632,6 +669,8 @@ def _process_one_streamline_group(
             Reference used to load and send the streamlines in voxel space and
             to create final merged SFT. If the file is a .trk, 'same' is used
             instead.
+        remove_invalid : bool
+            If True, invalid streamlines will be removed
 
         Returns
         -------
@@ -642,11 +681,6 @@ def _process_one_streamline_group(
         """
         tractograms = self.groups_config[group]['files']
 
-        if self.step_size and self.compress:
-            raise ValueError(
-                "Only one option can be chosen: either resampling to "
-                "step_size or compressing, not both.")
-
         # Silencing SFT's logger if our logging is in DEBUG mode, because it
         # typically produces a lot of outputs!
         set_sft_logger_level('WARNING')
@@ -679,19 +713,12 @@ def _process_one_streamline_group(
         if self.save_intermediate:
             output_fname = self.intermediate_folder.joinpath(
                 subj_id + '_' + group + '.trk')
-            logging.debug('      *Saving intermediate streamline group {} '
-                          'into {}.'.format(group, output_fname))
+            logging.debug("      *Saving intermediate streamline group {} "
+                          "into {}.".format(group, output_fname))
             # Note. Do not remove the str below. Does not work well
             # with Path.
             save_tractogram(final_sft, str(output_fname))
 
-        # Removing invalid streamlines
-        logging.debug('      *Total: {:,.0f} streamlines. Now removing '
-                      'invalid streamlines.'.format(len(final_sft)))
-        final_sft.remove_invalid_streamlines()
-        logging.info("         Final number of streamlines: {:,.0f}."
-                     .format(len(final_sft)))
-
         conn_matrix = None
         conn_info = None
         if 'connectivity_matrix' in self.groups_config[group]:
@@ -735,9 +762,10 @@ def _load_and_process_sft(self, tractogram_file, header):
                 "We do not support file's type: {}. We only support .trk "
                 "and .tck files.".format(tractogram_file))
         if file_extension == '.trk':
-            if not is_header_compatible(str(tractogram_file), header):
-                raise ValueError("Streamlines group is not compatible with "
-                                 "volume groups\n ({})"
+            if header and not is_header_compatible(str(tractogram_file),
+                                                   header):
+                raise ValueError("Streamlines group is not compatible "
+                                 "with volume groups\n ({})"
                                  .format(tractogram_file))
             # overriding given header.
             header = 'same'
@@ -748,6 +776,9 @@ def _load_and_process_sft(self, tractogram_file, header):
         sft = load_tractogram(str(tractogram_file), header)
 
         # Resample or compress streamlines
-        sft = resample_or_compress(sft, self.step_size, self.compress)
+        sft = resample_or_compress(sft, self.step_size,
+                                   self.nb_points,
+                                   self.compress,
+                                   self.remove_invalid)
 
         return sft

dwi_ml/data/hdf5/utils.py

@@ -59,11 +59,11 @@ def add_hdf5_creation_args(p: ArgumentParser):
                    help="A txt file containing the list of subjects ids to "
                         "use for training. \n(Can be an empty file.)")
     p.add_argument('validation_subjs',
-                   help="A txt file containing the list of subjects ids to use "
-                        "for validation. \n(Can be an empty file.)")
+                   help="A txt file containing the list of subjects ids"
+                        " to use for validation. \n(Can be an empty file.)")
     p.add_argument('testing_subjs',
-                   help="A txt file containing the list of subjects ids to use "
-                        "for testing. \n(Can be an empty file.)")
+                   help="A txt file containing the list of subjects ids"
+                        " to use for testing. \n(Can be an empty file.)")
 
     # Optional arguments
     p.add_argument('--enforce_files_presence', type=bool, default=True,
@@ -76,9 +76,13 @@ def add_hdf5_creation_args(p: ArgumentParser):
                         "each subject inside the \nhdf5 folder, in sub-"
                         "folders named subjid_intermediate.\n"
                         "(Final concatenated standardized volumes and \n"
-                        "final concatenated resampled/compressed streamlines.)")
+                        "final concatenated resampled/compressed "
+                        "streamlines.)")
+    p.add_argument('--dps_keys', type=str, nargs='+', default=[],
+                   help="List of keys to keep in data_per_streamline. "
+                        "Default: Empty.")
 
 
 def add_streamline_processing_args(p: ArgumentParser):
-    g = p.add_argument_group('Streamlines processing options:')
+    g = p.add_argument_group('Streamlines processing options')
     add_resample_or_compress_arg(g)

dwi_ml/data/processing/streamlines/data_augmentation.py

@@ -9,18 +9,33 @@
 import numpy as np
 
 from scilpy.tractograms.streamline_operations import \
-    resample_streamlines_step_size, compress_sft
+    resample_streamlines_num_points, resample_streamlines_step_size, \
+    compress_sft
 
 
 def resample_or_compress(sft, step_size_mm: float = None,
-                         compress: float = None):
+                         nb_points: int = None,
+                         compress: float = None,
+                         remove_invalid: bool = False):
     if step_size_mm is not None:
         # Note. No matter the chosen space, resampling is done in mm.
-        logging.debug("            Resampling: {}".format(step_size_mm))
+        logging.debug("    Resampling (step size): {}mm".format(step_size_mm))
         sft = resample_streamlines_step_size(sft, step_size=step_size_mm)
-    if compress is not None:
-        logging.debug("            Compressing: {}".format(compress))
+    elif nb_points is not None:
+        logging.debug("    Resampling: " +
+                      "{} points per streamline".format(nb_points))
+        sft = resample_streamlines_num_points(sft, nb_points)
+    elif compress is not None:
+        logging.debug("    Compressing: {}".format(compress))
         sft = compress_sft(sft, compress)
+
+    if remove_invalid:
+        logging.debug("    Total: {:,.0f} streamlines. Now removing "
+                      "invalid streamlines.".format(len(sft)))
+        sft.remove_invalid_streamlines()
+        logging.info("    Final number of streamlines: {:,.0f}."
+                     .format(len(sft)))
+
     return sft
 
 

dwi_ml/io_utils.py

@@ -2,20 +2,23 @@
 import os
 from argparse import ArgumentParser
 
-from scilpy.io.utils import add_processes_arg
-
 
 def add_resample_or_compress_arg(p: ArgumentParser):
+    p.add_argument("--remove_invalid", action='store_true',
+                   help="If set, remove invalid streamlines.")
     g = p.add_mutually_exclusive_group()
     g.add_argument(
         '--step_size', type=float, metavar='s',
         help="Step size to resample the data (in mm). Default: None")
+    g.add_argument('--nb_points', type=int, metavar='n',
+                   help='Number of points per streamline in the output.'
+                        'Default: None')
     g.add_argument(
         '--compress', type=float, metavar='r', const=0.01, nargs='?',
         dest='compress_th',
         help="Compression ratio. Default: None. Default if set: 0.01.\n"
-             "If neither step_size nor compress are chosen, streamlines "
-             "will be kept \nas they are.")
+             "If neither step_size, nb_points nor compress "
+             "are chosen, \nstreamlines will be kept as they are.")
 
 
 def add_arg_existing_experiment_path(p: ArgumentParser):

dwi_ml/models/main_models.py

@@ -17,8 +17,7 @@
     prepare_neighborhood_vectors
 from dwi_ml.experiment_utils.prints import format_dict_to_str
 from dwi_ml.io_utils import add_resample_or_compress_arg
-from dwi_ml.models.direction_getter_models import keys_to_direction_getters, \
-    AbstractDirectionGetterModel
+from dwi_ml.models.direction_getter_models import keys_to_direction_getters
 from dwi_ml.models.embeddings import (keys_to_embeddings, NNEmbedding,
                                       NoEmbedding)
 from dwi_ml.models.utils.direction_getters import add_direction_getter_args
@@ -37,6 +36,7 @@ class MainModelAbstract(torch.nn.Module):
     def __init__(self, experiment_name: str,
                  # Target preprocessing params for the batch loader + tracker
                  step_size: float = None,
+                 nb_points: int = None,
                  compress_lines: float = False,
                  # Other
                  log_level=logging.root.level):
@@ -74,17 +74,20 @@ def __init__(self, experiment_name: str,
 
         # To tell our batch loader how to resample streamlines during training
         # (should also be the step size during tractography).
-        if step_size and compress_lines:
-            raise ValueError("You may choose either resampling or compressing,"
-                             "but not both.")
+        if (step_size and compress_lines) or (step_size and nb_points) or (nb_points and compress_lines):
+            raise ValueError("You may choose either resampling (step_size or nb_points)"
+                             " or compressing, but not two of them or more.")
         elif step_size and step_size <= 0:
             raise ValueError("Step size can't be 0 or less!")
+        elif nb_points and nb_points <= 0:
+            raise ValueError("Number of points can't be 0 or less!")
             # Note. When using
             # scilpy.tracking.tools.resample_streamlines_step_size, a warning
             # is shown if step_size < 0.1 or > np.max(sft.voxel_sizes), saying
             # that the value is suspicious. Not raising the same warnings here
             # as you may be wanting to test weird things to understand better
             # your model.
+        self.nb_points = nb_points
         self.step_size = step_size
         self.compress_lines = compress_lines
 

dwi_ml/testing/testers.py

@@ -109,6 +109,7 @@ def run_model_on_sft(self, sft, compute_loss=False):
             The mean eos error per line.
         """
         sft = resample_or_compress(sft, self.model.step_size,
+                                   self.model.nb_points,
                                    self.model.compress_lines)
         sft.to_vox()
         sft.to_corner()

dwi_ml/training/batch_loaders.py

@@ -199,6 +199,7 @@ def _data_augmentation_sft(self, sft):
                          "the hdf5 dataset. Not compressing again.")
         else:
             sft = resample_or_compress(sft, self.model.step_size,
+                                       self.model.nb_points,
                                        self.model.compress_lines)
 
         # Splitting streamlines

scripts_python/dwiml_create_hdf5_dataset.py

@@ -87,7 +87,12 @@ def prepare_hdf5_creator(args):
     # Instantiate a creator and perform checks
     creator = HDF5Creator(Path(args.dwi_ml_ready_folder), args.out_hdf5_file,
                           training_subjs, validation_subjs, testing_subjs,
-                          groups_config, args.step_size, args.compress_th,
+                          groups_config,
+                          args.dps_keys,
+                          args.step_size,
+                          args.nb_points,
+                          args.compress_th,
+                          args.remove_invalid,
                           args.enforce_files_presence,
                           args.save_intermediate, intermediate_subdir)
 

scripts_python/dwiml_visualize_noise_on_streamlines.py

@@ -68,7 +68,9 @@ def main():
     subj_sft_data = subj_data.sft_data_list[streamline_group_idx]
     sft = subj_sft_data.as_sft()
 
-    sft = resample_or_compress(sft, args.step_size, args.compress_th)
+    sft = resample_or_compress(sft, args.step_size,
+                               args.nb_points,
+                               args.compress_th)
     sft.to_vox()
     sft.to_corner()