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""" |
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# File : myself_train_model.py |
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# Time ：2023/8/21 9:25 |
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# Author ：小吕同学 |
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""" |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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import gradio as gr |
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import os |
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class xiaolv_ocr_model(): |
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def __init__(self): |
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model_small = r"./output_small" |
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model_big = r"./output_big" |
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self.ocr_recognition_small = pipeline(Tasks.ocr_recognition, model=model_small) |
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self.ocr_recognition1_big = pipeline(Tasks.ocr_recognition, model=model_big) |
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def run(self,pict_path,moshi = "small", context=[]): |
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pict_path = pict_path.name |
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context = [pict_path] |
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if moshi == "small": |
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result = self.ocr_recognition_small(pict_path) |
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else: |
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result = self.ocr_recognition1_big(pict_path) |
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context += [str(result['text'][0])] |
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responses = [(u, b) for u, b in zip(context[::2], context[1::2])] |
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print(f"识别的结果为：{result}") |
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os.remove(pict_path) |
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return responses,context |
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if __name__ == "__main__": |
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pict_path = r"C:\Users\admin\Desktop\图片识别测试\企业微信截图_16895911221007.png" |
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ocr_model = xiaolv_ocr_model() |
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with gr.Blocks() as demo: |
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gr.HTML("""<h1 align="center">常见验证码识别——小吕</h1>""") |
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with gr.Tab("验证码模型"): |
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gr.Markdown(""" |
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#### 专门识别常见验证码的模型，训练模型有2个： |
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* 1.**small**:训练数据大小为700MB，约8.4万张验证码图片，训练轮次27轮，最终的精度将近100%； |
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* 2.**big**:训练数据大小为11G，约135万个验证码图片，训练轮次1轮，最终的精度将近93.95%； |
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#### 训练验证码图片包括： |
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* 类型：1. 纯数字型；2. 数字+字母型；3.纯字母型（大小写） |
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* 长度：4位、5位、6位 |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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select_types = gr.Radio(label="模型类型选择", choices=["small", "big"], value="small") |
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img_input = gr.File(label='输入图像,目前支持图片格式(png\jpg等)，建议使用png格式。',file_types=[".jpg",".jpeg",".png"]) |
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with gr.Column(): |
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chatbot = gr.Chatbot([]) |
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with gr.Row(): |
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btn_submit = gr.Button(value="一键识别") |
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state = gr.State([]) |
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btn_submit.click(fn=ocr_model.run, inputs=[img_input, select_types], outputs=[chatbot, state]) |
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gr.Markdown(""" |
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#### 后续计划： |
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1. 推出专门识别手机号码和邮箱的模型 |
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""") |
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demo.launch(show_error=True,server_name='192.168.8.173') |
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