# coding: utf-8 import os from pix2text import Pix2Text, set_logger set_logger() def test_recognize_pdf(): pdf_fn = '1804.07821' img_fp = f'./docs/examples/{pdf_fn}.pdf' text_formula_config = dict( languages=('en', 'ch_sim'), mfd=dict( # 声明 MFD 的初始化参数 model_path=os.path.expanduser( '~/.pix2text/1.1/mfd-onnx/mfd-v20240618.onnx' ), # 注:修改成你的模型文件所存储的路径 ), formula=dict( model_name='mfr-pro', model_backend='onnx', model_dir=os.path.expanduser( '~/.pix2text/1.1/mfr-pro-onnx' ), # 注:修改成你的模型文件所存储的路径 ), text=dict( rec_model_name='doc-densenet_lite_666-gru_large', rec_model_backend='onnx', rec_model_fp=os.path.expanduser( '~/.cnocr/2.3/doc-densenet_lite_666-gru_large/cnocr-v2.3-doc-densenet_lite_666-gru_large-epoch=005-ft-model.onnx' # noqa ), # 注:修改成你的模型文件所存储的路径 ), ) total_config = { 'layout': {}, 'text_formula': text_formula_config, } p2t = Pix2Text.from_config(total_configs=total_config, enable_formula=True) out_md = p2t.recognize_pdf( img_fp, page_numbers=[0, 7, 8], table_as_image=True, save_debug_res=f'./outputs-{pdf_fn}', ) out_md.to_markdown('page-output') # print(out_page) # out_page.to_markdown('page-output') def test_recognize_page(): # img_fp = './docs/examples/formula.jpg' img_fp = './docs/examples/page2.png' # img_fp = './docs/examples/mixed.jpg' total_config = { 'layout': {}, 'text_formula': { 'formula': { 'model_name': 'mfr-1.5', 'model_backend': 'onnx', 'more_model_configs': {'provider': 'CPUExecutionProvider'}, } }, } p2t = Pix2Text.from_config(total_configs=total_config) out_page = p2t.recognize_page( img_fp, page_id='test_page_1', title_contain_formula=False, text_contain_formula=True, save_debug_res='./outputs', ) # print(out_page) out_page.to_markdown('page-output') def test_spell_checker(): from spellchecker import SpellChecker spell = SpellChecker() # 找到拼写错误 misspelled = spell.unknown(["speci-fied"]) for word in misspelled: # Get the one `most likely` answer print('word:', word, ' ->', spell.correction(word)) # Get a list of `likely` options print('suggestions:', spell.candidates(word)) def test_blog_example(): img_fp = './docs/examples/mixed.jpg' text_formula_config = dict( mfd=dict( # 声明 MFD 的初始化参数 model_path=os.path.expanduser( '~/.pix2text/1.1/mfd-onnx/mfd-v20240618.onnx' ), # 注:修改成你的模型文件所存储的路径 ), formula=dict( model_name='mfr-pro', model_backend='onnx', model_dir=os.path.expanduser( '~/.pix2text/1.1/mfr-pro-onnx' ), # 注:修改成你的模型文件所存储的路径 ), ) total_config = { 'layout': {'scores_thresh': 0.2}, 'text_formula': text_formula_config, } p2t = Pix2Text.from_config(total_configs=total_config) outs = p2t.recognize_page( img_fp, resized_shape=608, page_id='test_page_2', save_layout_res='./layout_res-mixed.jpg', ) # 也可以使用 `p2t(img_fp)` 获得相同的结果 print(outs) def test_blog_pro_example(): img_fp = './docs/examples/mixed.jpg' text_formula_config = dict( languages=('en', 'ch_sim'), mfd=dict( # 声明 MFD 的初始化参数 model_path=os.path.expanduser( '~/.pix2text/1.1/mfd-onnx/mfd-v20240618.onnx' ), # 注:修改成你的模型文件所存储的路径 ), formula=dict( model_name='mfr-pro', model_backend='onnx', model_dir=os.path.expanduser( '~/.pix2text/1.1/mfr-pro-onnx' ), # 注:修改成你的模型文件所存储的路径 ), text=dict( rec_model_name='doc-densenet_lite_666-gru_large', rec_model_backend='onnx', rec_model_fp=os.path.expanduser( '~/.cnocr/2.3/doc-densenet_lite_666-gru_large/cnocr-v2.3-doc-densenet_lite_666-gru_large-epoch=005-ft-model.onnx' # noqa ), # 注:修改成你的模型文件所存储的路径 ), ) p2t = Pix2Text.from_config(total_configs={'text_formula': text_formula_config}) outs = p2t.recognize_page( img_fp, resized_shape=608, page_id='test_page_3' ) # 也可以使用 `p2t(img_fp)` 获得相同的结果 print(outs) def test_example_mixed(): img_fp = './docs/examples/en1.jpg' p2t = Pix2Text.from_config() outs = p2t.recognize_page( img_fp, resized_shape=608, page_id='test_page_4' ) # 也可以使用 `p2t(img_fp)` 获得相同的结果 print(outs) def test_example_formula(): img_fp = './docs/examples/math-formula-42.png' p2t = Pix2Text.from_config() outs = p2t.recognize_formula(img_fp) print(outs) def test_example_text(): img_fp = './docs/examples/general.jpg' p2t = Pix2Text(enable_formula=False) outs = p2t.recognize_text(img_fp) print(outs) def test_vlm_recognize_page(): import dotenv dotenv.load_dotenv() model_name=os.getenv("GEMINI_MODEL") api_key=os.getenv("GEMINI_API_KEY") # img_fp = './docs/examples/formula.jpg' # img_fp = './docs/examples/page2.png' img_fp = './docs/examples/mixed.jpg' total_config = { 'layout': None, 'text_formula': { "model_type": "VlmTextFormulaOCR", "model_name": model_name, "api_key": api_key, }, "table": { "model_type": "VlmTableOCR", "model_name": model_name, "api_key": api_key, }, } p2t = Pix2Text.from_config(total_configs=total_config) tf_out = p2t.recognize_text_formula(img=img_fp, resized_shape=768, return_text=False) print(tf_out) # out_page = p2t.recognize_page( # img_fp, # page_id='test_page_1', # title_contain_formula=False, # text_contain_formula=True, # save_debug_res='./outputs', # ) # print(out_page) # out_page.to_markdown('page-output') def test_multilingual_ocr(): img_fp = 'docs/examples/vietnamese.jpg' img_fp = 'docs/feedbacks/ru.png' total_config = { "layout": {}, "text_formula": {"languages": ("ru",)}, } p2t = Pix2Text.from_config(total_configs=total_config) outs = p2t.recognize( img_fp, file_type="text_formula", return_text=True, auto_line_break=False ) print(outs)