Vadzim Kashko
commited on
Commit
•
250cd79
1
Parent(s):
38952e9
refactor: remove useless
Browse files- facial_keypoint_detection.py +0 -94
facial_keypoint_detection.py
CHANGED
@@ -1,8 +1,5 @@
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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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@@ -27,66 +24,6 @@ _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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# Check for EXIF data (only present on some files)
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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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# Handle EXIF Orientation
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if orientation == 1:
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# Normal image - nothing to do!
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pass
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elif orientation == 2:
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# Mirrored left to right
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img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
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elif orientation == 3:
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# Rotated 180 degrees
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img = img.rotate(180)
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elif orientation == 4:
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# Mirrored top to bottom
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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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# Mirrored along top-left diagonal
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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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# Rotated 90 degrees
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img = img.rotate(-90, expand=True)
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elif orientation == 7:
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# Mirrored along top-right diagonal
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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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# Rotated 270 degrees
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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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# Load the image with PIL
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img = PIL.Image.open(file)
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if hasattr(PIL.ImageOps, 'exif_transpose'):
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# Very recent versions of PIL can do exit transpose internally
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img = PIL.ImageOps.exif_transpose(img)
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else:
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# Otherwise, do the exif transpose ourselves
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img = exif_transpose(img)
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img = img.convert(mode)
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img.thumbnail((1000, 1000), PIL.Image.Resampling.LANCZOS)
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return img
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class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
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def _info(self):
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@@ -103,13 +40,9 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
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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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# masks = dl_manager.download_and_extract(f"{_DATA}masks.zip")
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images = dl_manager.download(f"{_DATA}images.tar.gz")
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masks = dl_manager.download(f"{_DATA}masks.tar.gz")
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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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# masks = dl_manager.iter_files(masks)
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images = dl_manager.iter_archive(images)
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masks = dl_manager.iter_archive(masks)
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@@ -138,30 +71,3 @@ class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
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},
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'key_points': annotations_df['key_points'].iloc[idx]
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}
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# images_data = pd.DataFrame(
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# columns=['image_name', 'image_path', 'mask_path'])
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# for idx, ((image_path, image),
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# (mask_path, mask)) in enumerate(zip(images, masks)):
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# images_data.loc[idx] = {
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# 'image_name': image_path.split('/')[-1],
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# 'image_path': image_path,
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# 'mask_path': mask_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=['image_name'])
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# annotations_df[['image_path', 'mask_path'
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# ]] = annotations_df[['image_path',
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# 'mask_path']].astype('string')
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# for row in annotations_df.sort_values(['image_name'
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# ]).itertuples(index=False):
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# yield idx, {
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# 'image_id': row[0],
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# 'image': row[3],
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# 'mask': row[4],
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# 'key_points': row[2]
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# }
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import datasets
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import pandas as pd
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
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class FacialKeypointDetection(datasets.GeneratorBasedBuilder):
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def _info(self):
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license=_LICENSE)
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def _split_generators(self, dl_manager):
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images = dl_manager.download(f"{_DATA}images.tar.gz")
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masks = dl_manager.download(f"{_DATA}masks.tar.gz")
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annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
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images = dl_manager.iter_archive(images)
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masks = dl_manager.iter_archive(masks)
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},
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'key_points': annotations_df['key_points'].iloc[idx]
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}
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