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import asyncio
import functools
import uuid
from paddleocr import PaddleOCR, draw_ocr
from PIL import Image
import gradio as gr
LANG_CONFIG = {
"ch": {"num_workers": 4},
"en": {"num_workers": 4},
"fr": {"num_workers": 1},
"german": {"num_workers": 1},
"korean": {"num_workers": 1},
"japan": {"num_workers": 1},
}
CONCURRENCY_LIMIT = 8
class PaddleOCRModelWrapper(object):
def __init__(self, model, name=None):
super().__init__()
self._model = model
self._name = name or self._get_random_name()
self._state = "IDLE"
@property
def name(self):
return self._name
@property
def state(self):
return self._state
@state.setter
def state(self, state):
self._state = state
def infer(self, **kwargs):
img_path = kwargs["img"]
result = self._model.ocr(**kwargs)[0]
image = Image.open(img_path).convert("RGB")
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores,
font_path="./simfang.ttf")
return im_show
def _get_random_name(self):
return str(uuid.uuid4())
class PaddleOCRModelManager(object):
def __init__(self,
num_models,
model_factory,
*,
polling_interval=0.1):
super().__init__()
self._num_models = num_models
self._model_factory = model_factory
self._polling_interval = polling_interval
self._models = {}
self.new_models()
def new_models(self):
self._models.clear()
for _ in range(self._num_models):
model = self._new_model()
self._models[model.name] = model
async def infer(self, **kwargs):
while True:
model = self._get_available_model()
if not model:
await asyncio.sleep(self._polling_interval)
continue
model.state = "RUNNING"
# NOTE: I take an optimistic approach here, assuming that the model
# is not broken even if inference fails.
try:
result = await self._new_inference_task(model, **kwargs)
finally:
model.state = "IDLE"
return result
def _new_model(self):
real_model = self._model_factory()
model = PaddleOCRModelWrapper(real_model)
return model
def _get_available_model(self):
if not self._models:
raise RuntimeError("No living models")
for model in self._models.values():
if model.state == "IDLE":
return model
return None
def _new_inference_task(self, model,
**kwargs):
return asyncio.get_running_loop().run_in_executor(
None, functools.partial(model.infer, **kwargs))
def create_model(lang):
return PaddleOCR(lang=lang, use_angle_cls=True, use_gpu=False)
model_managers = {}
for lang, config in LANG_CONFIG.items():
model_manager = PaddleOCRModelManager(config["num_workers"], functools.partial(create_model, lang=lang))
model_managers[lang] = model_manager
async def inference(img, lang):
ocr = model_managers[lang]
result = await ocr.infer(img=img, cls=True)
return result
title = 'PaddleOCR'
description = '''
- Gradio demo for PaddleOCR. PaddleOCR demo supports Chinese, English, French, German, Korean and Japanese.
- To use it, simply upload your image and choose a language from the dropdown menu, or click one of the examples to load them. Read more at the links below.
- [Docs](https://paddlepaddle.github.io/PaddleOCR/), [Github Repository](https://github.com/PaddlePaddle/PaddleOCR).
'''
examples = [
['en_example.jpg','en'],
['cn_example.jpg','ch'],
['jp_example.jpg','japan'],
]
css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
gr.Interface(
inference,
[
gr.Image(type='filepath', label='Input'),
gr.Dropdown(choices=list(LANG_CONFIG.keys()), value='en', label='language')
],
gr.Image(type='pil', label='Output'),
title=title,
description=description,
examples=examples,
cache_examples=False,
css=css,
concurrency_limit=CONCURRENCY_LIMIT,
).launch(debug=False)
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