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import datasets
import numpy as np
import pandas as pd
import PIL.Image
import PIL.ImageOps
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {face_masks},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
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 = 'face_masks'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_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)
return np.array(img)
class FaceMasks(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(description=_DESCRIPTION,
features=datasets.Features({
'photo_1': datasets.Image(),
'photo_2': datasets.Image(),
'photo_3': datasets.Image(),
'photo_4': 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_and_extract(f"{_DATA}images.zip")
annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
images = dl_manager.iter_files(images)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN,
gen_kwargs={
"images": images,
'annotations': annotations
}),
]
def _generate_examples(self, images, annotations):
annotations_df = pd.read_csv(annotations, sep=',')
images_data = pd.DataFrame(columns=['Link', 'Path'])
for idx, image_path in enumerate(images):
images_data.loc[idx] = {
'Link': '/'.join(image_path.split('/')[-2:]),
'Path': image_path
}
annotations_df = pd.merge(annotations_df,
images_data,
how='left',
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 = {
f'photo_{row[5]}': load_image_file(row[7])
for row in annotation.itertuples()
}
age = annotation.loc[annotation['Type'] == 1]['Age'].values[0]
country = annotation.loc[annotation['Type'] ==
1]['Country'].values[0]
sex = annotation.loc[annotation['Type'] == 1]['Sex'].values[0]
data['worker_id'] = worker_id
data['age'] = age
data['country'] = country
data['sex'] = sex
yield idx, data
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