Delete ColonCancerCTDatasetScript.py

ColonCancerCTDatasetScript.py

@@ -1,158 +0,0 @@
-import pydicom
-from PIL import Image
-import numpy as np
-import io
-import datasets
-import gdown
-import re
-import s3fs
-import random
-
-example_manifest_url = "https://drive.google.com/uc?id=1JBkQTXeieyN9_6BGdTF_DDlFFyZrGyU6"
-example_manifest_file = gdown.download(example_manifest_url, 'manifest_file.s5cmd', quiet = False)
-full_manifest_url = "https://drive.google.com/uc?id=1KP6qxcQoPF4MJdEPNwW7J6BlL_sUJ17j"
-full_manifest_file = gdown.download(full_manifest_url, 'full_manifest_file.s5cmd', quiet = False)
-fs = s3fs.S3FileSystem(anon=True)
-
-_DESCRIPTION = "This is the description"
-_HOMEPAGE = "https://imaging.datacommons.cancer.gov/"
-_LICENSE = "https://fairsharing.org/FAIRsharing.0b5a1d"
-_CITATION = "National Cancer Institute Imaging Data Commons (IDC) Collections was accessed on DATE from https://registry.opendata.aws/nci-imaging-data-commons"
-
-
-class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
-  """TODO: Short description of my dataset."""
-  VERSION = datasets.Version("1.1.0")
-
-  BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="example", version=VERSION, description="This is a subset of the full dataset for demonstration purposes"),
-        datasets.BuilderConfig(name="full_data", version=VERSION, description="This is the complete dataset"),
-    ]
-  DEFAULT_CONFIG_NAME = "example"
-
-  def _info(self):
-    return datasets.DatasetInfo(
-        description=_DESCRIPTION,
-        features=datasets.Features(
-            {
-                "image": datasets.Image(),
-                "ImageType": datasets.Sequence(datasets.Value('string')), 
-                "StudyDate": datasets.Value('string'), 
-                "SeriesDate": datasets.Value('string'),
-                "Manufacturer": datasets.Value('string'),
-                "StudyDescription": datasets.Value('string'),
-                "SeriesDescription": datasets.Value('string'),
-                "PatientSex": datasets.Value('string'),
-                "PatientAge": datasets.Value('string'),
-                "PregnancyStatus": datasets.Value('string'),
-                "BodyPartExamined": datasets.Value('string'),
-            }),
-        homepage = _HOMEPAGE,
-        license = _LICENSE,
-        citation = _CITATION
-        
-    )
-
-  def _split_generators(self, dl_manager):
-    """Returns SplitGenerators."""
-    # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the
-    s3_series_paths = []
-    s3_individual_paths = []
-    if self.config.name == 'example':
-        manifest_file = example_manifest_file
-    else:
-        manifest_file = full_manifest_file
-    
-    with open(manifest_file, 'r') as file:
-        for line in file:
-            match = re.search(r'cp (s3://[\S]+) .', line)
-            if match:
-                s3_series_paths.append(match.group(1)[:-2]) # Deleting the '/*' in directories
-    for series in s3_series_paths:
-        for content in fs.ls(series):
-            s3_individual_paths.append(fs.info(content)['Key'])
-
-    random.shuffle(s3_individual_paths)
-
-    # Define the split sizes
-    train_size = int(0.7 * len(s3_individual_paths))
-    val_size = int(0.15 * len(s3_individual_paths))
-    # Split the paths into train, validation, and test sets
-    train_paths = s3_individual_paths[:train_size]
-    val_paths = s3_individual_paths[train_size:train_size + val_size]
-    test_paths = s3_individual_paths[train_size + val_size:]
-
-    return [
-        datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "paths": train_paths,
-                "split": "train"
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.VALIDATION,
-            gen_kwargs={
-                "paths": val_paths,
-                "split": "dev"
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.TEST,
-            gen_kwargs={
-                "paths": test_paths,
-                "split": "test"
-            },
-        ),
-    ]
-
-  def _generate_examples(self, paths, split):
-    """Yields examples."""
-    # TODO: This method will yield examples, i.e. rows in the dataset.
-    for path in paths:
-      key = path
-      with fs.open(path, 'rb') as f:
-        dicom_data = pydicom.dcmread(f)
-        pixel_array = dicom_data.pixel_array
-        # Adjust for MONOCHROME1 to invert the grayscale values
-        if dicom_data.PhotometricInterpretation == "MONOCHROME1":
-          pixel_array = np.max(pixel_array) - pixel_array
-        # Normalize or scale 16-bit or other depth images to 8-bit
-        if pixel_array.dtype != np.uint8:
-            pixel_array = (np.divide(pixel_array, np.max(pixel_array)) * 255).astype(np.uint8)
-        # Convert to RGB if it is not already (e.g., for color images)
-        if len(pixel_array.shape) == 2:
-            im = Image.fromarray(pixel_array, mode="L")  # L mode is for grayscale
-        elif len(pixel_array.shape) == 3 and pixel_array.shape[2] in [3, 4]:
-            im = Image.fromarray(pixel_array, mode="RGB")
-        else:
-            raise ValueError("Unsupported DICOM image format")
-        with io.BytesIO() as output:
-            im.save(output, format="PNG")
-            png_image = output.getvalue()
-        # Extracting metadata
-        ImageType = dicom_data.get("ImageType", "")
-        StudyDate = dicom_data.get("StudyDate", "")
-        SeriesDate = dicom_data.get("SeriesDate", "")
-        Manufacturer = dicom_data.get("Manufacturer", "")
-        StudyDescription = dicom_data.get("StudyDescription", "")
-        SeriesDescription = dicom_data.get("SeriesDescription", "")
-        PatientSex = dicom_data.get("PatientSex", "")
-        PatientAge = dicom_data.get("PatientAge", "")
-        PregnancyStatus = dicom_data.get("PregnancyStatus", "")
-        if PregnancyStatus == None:
-           PregnancyStatus = "None"
-        else:
-           PregnancyStatus = "Yes"
-        BodyPartExamined = dicom_data.get("BodyPartExamined", "")
-        yield key, {"image": png_image, 
-                    "ImageType": ImageType, 
-                    "StudyDate": StudyDate, 
-                    "SeriesDate": SeriesDate, 
-                    "Manufacturer": Manufacturer, 
-                    "StudyDescription": StudyDescription, 
-                    "SeriesDescription": SeriesDescription,
-                    "PatientSex": PatientSex, 
-                    "PatientAge": PatientAge, 
-                    "PregnancyStatus": PregnancyStatus, 
-                    "BodyPartExamined": BodyPartExamined}