take EXIF orientation tags into account when fixing corrupt images (#5270)
* take EXIF orientation tags into account when fixing corrupt images * fit 120 char * sort imports * Update local exif_transpose comment We have a local inplace version that is faster than the official as the image is not copied. AutoShape() uses this for Hub models, but here it is not important as the datasets.py usage is infrequent (AutoShape() it is applied every image). * Update datasets.py Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -22,7 +22,7 @@ import numpy as np
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import torch
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import torch
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import torch.nn.functional as F
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import torch.nn.functional as F
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import yaml
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import yaml
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from PIL import Image, ExifTags
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from PIL import Image, ImageOps, ExifTags
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from torch.utils.data import Dataset
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from tqdm import tqdm
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@ -69,7 +69,7 @@ def exif_size(img):
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def exif_transpose(image):
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def exif_transpose(image):
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"""
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"""
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Transpose a PIL image accordingly if it has an EXIF Orientation tag.
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Transpose a PIL image accordingly if it has an EXIF Orientation tag.
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From https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
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:param image: The image to transpose.
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:param image: The image to transpose.
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:return: An image.
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:return: An image.
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@ -896,7 +896,7 @@ def verify_image_label(args):
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with open(im_file, 'rb') as f:
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with open(im_file, 'rb') as f:
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f.seek(-2, 2)
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f.seek(-2, 2)
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if f.read() != b'\xff\xd9': # corrupt JPEG
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if f.read() != b'\xff\xd9': # corrupt JPEG
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Image.open(im_file).save(im_file, format='JPEG', subsampling=0, quality=100) # re-save image
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ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
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msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
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msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
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# verify labels
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# verify labels
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