changes to hit.py

hit.py

@@ -11,22 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
 
-
-import csv
 import json
 import os
-from glob import glob
 import datasets
 import pickle
-import requests
 import gzip
 
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
+# citation
 _CITATION = """\
 @inproceedings{Keller:CVPR:2024,
   title = {{HIT}: Estimating Internal Human Implicit Tissues from the Body Surface},
@@ -38,18 +30,17 @@ _CITATION = """\
   month_numeric = {6}}
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
+# description
 _DESCRIPTION = """\
 The HIT dataset is a structured dataset of paired observations of body's inner tissues and the body surface. More concretely, it is a dataset of paired full-body volumetric segmented (bones, lean, and adipose tissue) MRI scans and SMPL meshes capturing the body surface shape for male (N=157) and female (N=241) subjects respectively. This is relevant for medicine, sports science, biomechanics, and computer graphics as it can ease the creation of personalized anatomic digital twins that model our bones, lean, and adipose tissue."""
 
-# TODO: Add a link to an official homepage for the dataset here
+# link to official homepage for the dataset
 _HOMEPAGE = "https://hit.is.tue.mpg.de/"
 
-# TODO: Add the licence for the dataset here if you can find it
+# licence for the dataset
 _LICENSE = "see https://huggingface.co/datasets/varora/HIT/blob/main/README.md"
 
-# TODO: Add link to the official dataset URLs here
+# official dataset URLs
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _BASE_URL = "https://huggingface.co/datasets/varora/HIT/tree/main"
@@ -58,10 +49,7 @@ _PATHS = {
     "female": "/female",
 }
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
+class HIT(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
 
     # This is an example of a dataset with multiple configurations.
@@ -123,7 +111,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # downloading/extracting the data and defining the splits depending on the configuration
         # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
@@ -131,7 +119,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         splits = ["train", "val", "test"]
 
         gender = self.config.name
-        #data_urls = _BASE_URL + rel_path
         print(f"Config: {gender}")
 
         file_structure_url = "hit_dataset.json"
@@ -174,23 +161,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # handling input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         # List all files in the path .gz
         for subject_path in filepath:
             with gzip.open(subject_path, 'rb') as f:
                 data = pickle.load(f)
-                print(data.keys())
-                print(f"pc shape: {data['body_cont_pc'].shape}")
-                print(f"mri_seg shape: {data['mri_seg'].shape}")
-                print(f"mri_seg_dict shape: {data['body_mask'].shape}")
             key = data['subject_ID']
-            #del data['body_mask'] # nan
-            #del data['smpl_dict']
-            #del data['mri_seg'] # reshape
-            #del data['mri_labels']
-            #del data['resolution']
-            #del data['center']
-            #del data['body_cont_pc']  # nan
-
             yield key, data