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import os |
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import torch |
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print(torch.__version__) |
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torch_ver, cuda_ver = torch.__version__.split('+') |
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os.system(f'pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/{cuda_ver}/torch{torch_ver}/index.html --no-cache-dir') |
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os.system('cd src/ndl_layout/mmdetection && python setup.py bdist_wheel && pip install dist/*.whl') |
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os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/text_recognition/mojilist_NDL.txt -P ./src/text_recognition/models') |
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os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/text_recognition/ndlenfixed64-mj0-synth1.pth -P ./src/text_recognition/models') |
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os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/ndl_layout_config.py -P ./src/ndl_layout/models') |
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os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/epoch_140_all_eql_bt.pth -P ./src/ndl_layout/models') |
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os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/separate_pages_ssd/weights.hdf5 -P ./src/separate_pages_ssd/ssd_tools') |
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os.system("wget https://i.imgur.com/fSL1CGG.jpg") |
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os.environ["PYTHONPATH"]=os.environ["PYTHONPATH"]+":"+f"{os.getcwd()}/src/text_recognition/deep-text-recognition-benchmark" |
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import gradio as gr |
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from uuid import uuid4 |
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from pathlib import Path |
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def inference(im): |
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dir_name = uuid4() |
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Path(f'{dir_name}/img').mkdir(parents=True) |
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im.save(f'{dir_name}/img/image.jpg') |
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os.system(f'python main.py infer {dir_name}/img/image.jpg {dir_name}_output -s f -i') |
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with open(f'{dir_name}_output/image/txt/image_main.txt') as f: |
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return f'{dir_name}_output/image/pred_img/image_L.jpg', f.read() |
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title = "NDLOCR" |
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description = "Gradio demo for NDLOCR. NDLOCR is a text recognition (OCR) Program." |
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article = "<p style='text-align: center'><a href='https://github.com/ndl-lab' target='_blank'>NDL Lab</a> | <a href='https://github.com/ndl-lab/ndlocr_cli' target='_blank'>NDLOCR Repo</a></p>" |
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gr.Interface( |
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inference, |
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gr.inputs.Image(label='image', type='pil'), |
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['image', 'text'], |
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title=title, |
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description=description, |
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article=article, |
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examples=['fSL1CGG.jpg'] |
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).launch(enable_queue=True, cache_examples=True) |