refactor: data and script

data/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16db8eda5522a7451e803be4f636b44ab8efc5d5c7a2bcec3c479f65a1ec4712
+size 100867838

face_masks.py

@@ -1,21 +1,23 @@
 import datasets
+import numpy as np
 import pandas as pd
+import PIL.Image
+import PIL.ImageOps
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {selfies_and_id},
+title = {face_masks},
 author = {TrainingDataPro},
 year = {2023}
 }
 """
 
 _DESCRIPTION = """\
-4083 sets, which includes 2 photos of a person from his documents and
-13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians.
-Photo documents contains only a photo of a person.
-All personal information from the document is hidden.
+Dataset includes 250 000 images, 4 types of mask worn on 28 000 unique faces.
+All images were collected using the Toloka.ai crowdsourcing service and
+validated by TrainingData.pro
 """
-_NAME = 'selfies_and_id'
+_NAME = 'face_masks'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -24,46 +26,89 @@ _LICENSE = "cc-by-nc-nd-4.0"
 _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
 
 
-class SelfiesAndId(datasets.GeneratorBasedBuilder):
-    """Small sample of image-text pairs"""
+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)
+
+    return np.array(img)
+
+
+class FaceMasks(datasets.GeneratorBasedBuilder):
 
     def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                'id_1': datasets.Image(),
-                'id_2': datasets.Image(),
-                'selfie_1': datasets.Image(),
-                'selfie_2': datasets.Image(),
-                'selfie_3': datasets.Image(),
-                'selfie_4': datasets.Image(),
-                'selfie_5': datasets.Image(),
-                'selfie_6': datasets.Image(),
-                'selfie_7': datasets.Image(),
-                'selfie_8': datasets.Image(),
-                'selfie_9': datasets.Image(),
-                'selfie_10': datasets.Image(),
-                'selfie_11': datasets.Image(),
-                'selfie_12': datasets.Image(),
-                'selfie_13': datasets.Image(),
-                'user_id': datasets.Value('string'),
-                'set_id': datasets.Value('string'),
-                'user_race': datasets.Value('string'),
-                'name': datasets.Value('string'),
-                'age': datasets.Value('int8'),
-                'country': datasets.Value('string'),
-                'gender': datasets.Value('string')
-            }),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE
-        )
+        return datasets.DatasetInfo(description=_DESCRIPTION,
+                                    features=datasets.Features({
+                                        'photo_1': datasets.Image(),
+                                        'photo_2': datasets.Image(),
+                                        'photo_3': datasets.Image(),
+                                        'photo_4': datasets.Image(),
+                                        'selfie_5': datasets.Image(),
+                                        'worker_id': datasets.Value('string'),
+                                        'age': datasets.Value('int8'),
+                                        'country': datasets.Value('string'),
+                                        'sex': datasets.Value('string')
+                                    }),
+                                    supervised_keys=None,
+                                    homepage=_HOMEPAGE,
+                                    citation=_CITATION,
+                                    license=_LICENSE)
 
     def _split_generators(self, dl_manager):
-        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        images = dl_manager.download_and_extract(f"{_DATA}images.zip")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        images = dl_manager.iter_archive(images)
+        images = dl_manager.iter_files(images)
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
@@ -73,24 +118,22 @@ class SelfiesAndId(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, images, annotations):
-        annotations_df = pd.read_csv(annotations, sep=';')
-        images_data = pd.DataFrame(columns=['URL', 'Bytes'])
-        for idx, (image_path, image) in enumerate(images):
-            images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()}
+        annotations_df = pd.read_csv(annotations, sep=',')
+        images_data = pd.DataFrame(columns=['Link', 'Image'])
+        for idx, image_path in enumerate(images):
+            image = load_image_file(image_path)
+            images_data.loc[idx] = {'Link': image_path, 'Image': image}
 
         annotations_df = pd.merge(annotations_df,
                                   images_data,
                                   how='left',
-                                  on=['URL'])
-        for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])):
-            annotation = annotations_df.loc[annotations_df['UserId'] ==
-                                            worker_id]
-            annotation = annotation.sort_values(['FName'])
+                                  on=['Link'])
+        for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
+            annotation: pd.DataFrame = annotations_df.loc[
+                annotations_df['WorkerId'] == worker_id]
+            annotation = annotation.sort_values(['Link'])
             data = {
-                row[5].lower(): {
-                    'path': row[6],
-                    'bytes': row[10]
-                } for row in annotation.itertuples()
+                f'photo_{row[0]}': row[6] for row in annotation.itertuples()
             }
 
             age = annotation.loc[annotation['FName'] ==