Pix2Text / tests /test_pix2text.py
fasdfsa's picture
init
2c67080
# 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)