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import datasets |
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import numpy as np |
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import pandas as pd |
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import PIL.Image |
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import PIL.ImageOps |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {face_masks}, |
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author = {TrainingDataPro}, |
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year = {2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Dataset includes 250 000 images, 4 types of mask worn on 28 000 unique faces. |
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All images were collected using the Toloka.ai crowdsourcing service and |
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validated by TrainingData.pro |
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""" |
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_NAME = 'face_masks' |
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_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
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_LICENSE = "cc-by-nc-nd-4.0" |
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
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def exif_transpose(img): |
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if not img: |
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return img |
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exif_orientation_tag = 274 |
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if hasattr(img, "_getexif") and isinstance( |
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img._getexif(), dict) and exif_orientation_tag in img._getexif(): |
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exif_data = img._getexif() |
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orientation = exif_data[exif_orientation_tag] |
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if orientation == 1: |
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pass |
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elif orientation == 2: |
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img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT) |
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elif orientation == 3: |
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img = img.rotate(180) |
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elif orientation == 4: |
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img = img.rotate(180).transpose(PIL.Image.FLIP_LEFT_RIGHT) |
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elif orientation == 5: |
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img = img.rotate(-90, |
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expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT) |
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elif orientation == 6: |
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img = img.rotate(-90, expand=True) |
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elif orientation == 7: |
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img = img.rotate(90, |
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expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT) |
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elif orientation == 8: |
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img = img.rotate(90, expand=True) |
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return img |
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def load_image_file(file, mode='RGB'): |
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img = PIL.Image.open(file) |
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if hasattr(PIL.ImageOps, 'exif_transpose'): |
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img = PIL.ImageOps.exif_transpose(img) |
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else: |
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img = exif_transpose(img) |
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img = img.convert(mode) |
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return np.array(img) |
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class FaceMasks(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo(description=_DESCRIPTION, |
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features=datasets.Features({ |
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'photo_1': datasets.Image(), |
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'photo_2': datasets.Image(), |
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'photo_3': datasets.Image(), |
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'photo_4': datasets.Image(), |
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'worker_id': datasets.Value('string'), |
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'age': datasets.Value('int8'), |
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'country': datasets.Value('string'), |
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'sex': datasets.Value('string') |
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}), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE) |
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def _split_generators(self, dl_manager): |
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images = dl_manager.download_and_extract(f"{_DATA}images.zip") |
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annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
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images = dl_manager.iter_files(images) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": images, |
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'annotations': annotations |
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}), |
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] |
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def _generate_examples(self, images, annotations): |
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annotations_df = pd.read_csv(annotations, sep=',') |
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images_data = pd.DataFrame(columns=['Link', 'Path']) |
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for idx, image_path in enumerate(images): |
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images_data.loc[idx] = { |
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'Link': '/'.join(image_path.split('/')[-2:]), |
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'Path': image_path |
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} |
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annotations_df = pd.merge(annotations_df, |
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images_data, |
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how='left', |
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on=['Link']) |
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for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])): |
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annotation: pd.DataFrame = annotations_df.loc[ |
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annotations_df['WorkerId'] == worker_id] |
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annotation = annotation.sort_values(['Link']) |
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data = { |
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f'photo_{row[5]}': load_image_file(row[7]) |
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for row in annotation.itertuples() |
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} |
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age = annotation.loc[annotation['Type'] == 1]['Age'].values[0] |
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country = annotation.loc[annotation['Type'] == |
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1]['Country'].values[0] |
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sex = annotation.loc[annotation['Type'] == 1]['Sex'].values[0] |
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data['worker_id'] = worker_id |
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data['age'] = age |
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data['country'] = country |
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data['sex'] = sex |
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yield idx, data |
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