pierreguillou
commited on
Commit
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f49e93c
1
Parent(s):
824285e
Update files/functions.py
Browse files- files/functions.py +22 -2
files/functions.py
CHANGED
@@ -50,6 +50,26 @@ print(os.popen(f'cat /etc/issue').read())
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print(os.popen(f'apt search tesseract').read())
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import pytesseract
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## Key parameters
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# categories colors
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@@ -231,8 +251,8 @@ def set_image_dpi_resize(image):
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length_x, width_y = image.size
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factor = min(1, float(1024.0 / length_x))
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size = int(factor * length_x), int(factor * width_y)
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image_resize = image.resize(size, Image.Resampling.LANCZOS)
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='1.png')
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temp_filename = temp_file.name
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image_resize.save(temp_filename, dpi=(300, 300))
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print(os.popen(f'apt search tesseract').read())
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import pytesseract
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## model / feature extractor / tokenizer
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from transformers import LayoutLMv2ForTokenClassification # LayoutXLMTokenizerFast,
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# model
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# tokenizer = LayoutXLMTokenizerFast.from_pretrained(model_id)
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model = LayoutLMv2ForTokenClassification.from_pretrained(model_id);
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model.to(device);
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# feature extractor
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from transformers import LayoutLMv2FeatureExtractor
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feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)
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# tokenizer
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
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## Key parameters
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# categories colors
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length_x, width_y = image.size
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factor = min(1, float(1024.0 / length_x))
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size = int(factor * length_x), int(factor * width_y)
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# image_resize = image.resize(size, Image.Resampling.LANCZOS)
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image_resize = image.resize(size, Image.LANCZOS)
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='1.png')
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temp_filename = temp_file.name
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image_resize.save(temp_filename, dpi=(300, 300))
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