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d23d897
1
Parent(s):
de54dd3
update ckpt and update process
Browse files- app.py +3 -3
- ezocr/easyocrlite/reader.py +2 -2
app.py
CHANGED
@@ -24,8 +24,8 @@ import gradio as gr
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# Register refcoco task
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tasks.register_task('ocr', OcrTask)
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os.system('wget https://shuangqing-multimodal.oss-cn-zhangjiakou.aliyuncs.com/
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'mkdir -p checkpoints; mv
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# turn on cuda if GPU is available
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use_cuda = torch.cuda.is_available()
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@@ -189,7 +189,7 @@ description = "Gradio Demo for OFA-OCR. Upload your own image or click any one o
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"\"Submit\" and then wait for the generated OCR result. "
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article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
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"Repo</a></p> "
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examples = [['lihe.png']
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io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='pil'),
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outputs=[gr.outputs.Image(type='pil'), gr.outputs.Textbox(label="OCR result")],
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title=title, description=description, article=article, examples=examples,
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# Register refcoco task
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tasks.register_task('ocr', OcrTask)
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os.system('wget https://shuangqing-multimodal.oss-cn-zhangjiakou.aliyuncs.com/ocr_general_clean.pt; '
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'mkdir -p checkpoints; mv ocr_general_clean.pt checkpoints/ocr.pt')
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# turn on cuda if GPU is available
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use_cuda = torch.cuda.is_available()
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"\"Submit\" and then wait for the generated OCR result. "
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article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
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"Repo</a></p> "
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examples = [['lihe.png']] #, ['chinese.jpg'], ['paibian.jpeg'], ['shupai.png'], ['zuowen.jpg']]
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io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='pil'),
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outputs=[gr.outputs.Image(type='pil'), gr.outputs.Textbox(label="OCR result")],
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title=title, description=description, article=article, examples=examples,
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ezocr/easyocrlite/reader.py
CHANGED
@@ -104,7 +104,7 @@ class ReaderLite(object):
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def process(
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self,
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max_size: int = 960,
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expand_ratio: float = 1.0,
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sharp: float = 1.0,
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@@ -121,7 +121,7 @@ class ReaderLite(object):
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min_size: float = 0.01,
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) -> Tuple[BoxTuple, list[np.ndarray]]:
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image =
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tensor, inverse_ratio = self.preprocess(
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image, max_size, expand_ratio, sharp, contrast
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def process(
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self,
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img,
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max_size: int = 960,
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expand_ratio: float = 1.0,
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sharp: float = 1.0,
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min_size: float = 0.01,
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) -> Tuple[BoxTuple, list[np.ndarray]]:
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image = img.convert('RGB')
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tensor, inverse_ratio = self.preprocess(
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image, max_size, expand_ratio, sharp, contrast
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