| import os | |
| import torch | |
| print(torch.__version__) | |
| torch_ver, cuda_ver = torch.__version__.split('+') | |
| 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') | |
| os.system('cd src/ndl_layout/mmdetection && python setup.py bdist_wheel && pip install dist/*.whl') | |
| os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/text_recognition/mojilist_NDL.txt -P ./src/text_recognition/models') | |
| os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/text_recognition/ndlenfixed64-mj0-synth1.pth -P ./src/text_recognition/models') | |
| os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/ndl_layout_config.py -P ./src/ndl_layout/models') | |
| os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/epoch_140_all_eql_bt.pth -P ./src/ndl_layout/models') | |
| os.system('wget https://lab.ndl.go.jp/dataset/ndlocr/separate_pages_ssd/weights.hdf5 -P ./src/separate_pages_ssd/ssd_tools') | |
| os.system("wget https://i.imgur.com/fSL1CGG.jpg") | |
| os.environ["PYTHONPATH"]=os.environ["PYTHONPATH"]+":"+f"{os.getcwd()}/src/text_recognition/deep-text-recognition-benchmark" | |
| import gradio as gr | |
| from PIL import Image | |
| from uuid import uuid4 | |
| from pathlib import Path | |
| def inference(im): | |
| dir_name = uuid4() | |
| Path(f'{dir_name}/img').mkdir(parents=True) | |
| im.save(f'{dir_name}/img/image.jpg') | |
| os.system(f'python main.py infer {dir_name}/img/image.jpg {dir_name}_output -s f -i') | |
| image_path = f'{dir_name}_output/image/pred_img/image_L.jpg' | |
| if Path(f'{dir_name}_output/image/pred_img/image_R.jpg').exists(): | |
| image_L = Image.open(f'{dir_name}_output/image/pred_img/image_L.jpg') | |
| image_R = Image.open(f'{dir_name}_output/image/pred_img/image_R.jpg') | |
| dst = Image.new('RGB', (image_L.width + image_R.width, image_L.height)) | |
| dst.paste(image_L, (0, 0)) | |
| dst.paste(image_R, (image_L.width, 0)) | |
| dst.save(f'{dir_name}_output/image/pred_img/image_LR.jpg') | |
| image_path = f'{dir_name}_output/image/pred_img/image_LR.jpg' | |
| with open(f'{dir_name}_output/image/txt/image_main.txt') as f: | |
| return image_path, f.read() | |
| title = "NDLOCR" | |
| description = "Gradio demo for NDLOCR. NDLOCR is a text recognition (OCR) Program." | |
| 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>" | |
| gr.Interface( | |
| inference, | |
| gr.inputs.Image(label='image', type='pil'), | |
| ['image', 'text'], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=['fSL1CGG.jpg', 'b3b3963f-d577-4a30-acb9-0b395d4d87f7.jpeg', '8016ed5e-c0c7-4979-b6c6-d6b6c3945d7f.jpeg'] | |
| ).launch(enable_queue=True, cache_examples=True) |