refactor: remove useless

facial_keypoint_detection.py

@@ -1,8 +1,5 @@
 import datasets
-import numpy as np
 import pandas as pd
-import PIL.Image
-import PIL.ImageOps
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
@@ -27,66 +24,6 @@ _LICENSE = "cc-by-nc-nd-4.0"
 _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
 
 
-def exif_transpose(img):
-    if not img:
-        return img
-
-    exif_orientation_tag = 274
-
-    # Check for EXIF data (only present on some files)
-    if hasattr(img, "_getexif") and isinstance(
-            img._getexif(), dict) and exif_orientation_tag in img._getexif():
-        exif_data = img._getexif()
-        orientation = exif_data[exif_orientation_tag]
-
-        # Handle EXIF Orientation
-        if orientation == 1:
-            # Normal image - nothing to do!
-            pass
-        elif orientation == 2:
-            # Mirrored left to right
-            img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
-        elif orientation == 3:
-            # Rotated 180 degrees
-            img = img.rotate(180)
-        elif orientation == 4:
-            # Mirrored top to bottom
-            img = img.rotate(180).transpose(PIL.Image.FLIP_LEFT_RIGHT)
-        elif orientation == 5:
-            # Mirrored along top-left diagonal
-            img = img.rotate(-90,
-                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
-        elif orientation == 6:
-            # Rotated 90 degrees
-            img = img.rotate(-90, expand=True)
-        elif orientation == 7:
-            # Mirrored along top-right diagonal
-            img = img.rotate(90,
-                             expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
-        elif orientation == 8:
-            # Rotated 270 degrees
-            img = img.rotate(90, expand=True)
-
-    return img
-
-
-def load_image_file(file, mode='RGB'):
-    # Load the image with PIL
-    img = PIL.Image.open(file)
-
-    if hasattr(PIL.ImageOps, 'exif_transpose'):
-        # Very recent versions of PIL can do exit transpose internally
-        img = PIL.ImageOps.exif_transpose(img)
-    else:
-        # Otherwise, do the exif transpose ourselves
-        img = exif_transpose(img)
-
-    img = img.convert(mode)
-    img.thumbnail((1000, 1000), PIL.Image.Resampling.LANCZOS)
-
-    return img
-
-
 class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
 
     def _info(self):
@@ -103,13 +40,9 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
                                     license=_LICENSE)
 
     def _split_generators(self, dl_manager):
-        # images = dl_manager.download_and_extract(f"{_DATA}images.zip")
-        # masks = dl_manager.download_and_extract(f"{_DATA}masks.zip")
         images = dl_manager.download(f"{_DATA}images.tar.gz")
         masks = dl_manager.download(f"{_DATA}masks.tar.gz")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        # images = dl_manager.iter_files(images)
-        # masks = dl_manager.iter_files(masks)
         images = dl_manager.iter_archive(images)
         masks = dl_manager.iter_archive(masks)
 
@@ -138,30 +71,3 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
                 },
                 'key_points': annotations_df['key_points'].iloc[idx]
             }
-        # images_data = pd.DataFrame(
-        #     columns=['image_name', 'image_path', 'mask_path'])
-        # for idx, ((image_path, image),
-        #           (mask_path, mask)) in enumerate(zip(images, masks)):
-        #     images_data.loc[idx] = {
-        #         'image_name': image_path.split('/')[-1],
-        #         'image_path': image_path,
-        #         'mask_path': mask_path
-        #     }
-
-        # annotations_df = pd.merge(annotations_df,
-        #                           images_data,
-        #                           how='left',
-        #                           on=['image_name'])
-
-        # annotations_df[['image_path', 'mask_path'
-        #                ]] = annotations_df[['image_path',
-        #                                     'mask_path']].astype('string')
-
-        # for row in annotations_df.sort_values(['image_name'
-        #                                       ]).itertuples(index=False):
-        #     yield idx, {
-        #         'image_id': row[0],
-        #         'image': row[3],
-        #         'mask': row[4],
-        #         'key_points': row[2]
-        #     }