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36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import cv2
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import numpy as np
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import gradio as gr
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from infer import Face_inference
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# 当设置置信度阈值较大时，人脸检测返回值为空，此时直接返回原图
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def inference(image,thresh,model_type):
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global name
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if image is None:
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yield image
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if model_type == "人脸识别":
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if m_model.face_detect(image,thresh,is_show=True) is None:
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yield image
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else:
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name,prob,res_frame = m_model.face_detect(image,thresh,is_show=True)
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yield res_frame
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else:
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if m_model.live_detect(image,name,thresh,is_show=True) is None:
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yield image
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else:
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label,prob,res_frame = m_model.live_detect(image,name,thresh,is_show=True)
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yield res_frame
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model_name = gr.Radio(["人脸识别","活体检测"],value="人脸识别",label="Model",info="选择需要的模型")
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threshold = gr.Slider(0.001,1,value=0.1,label="threshold",info="设置置信度阈值")
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inputs_webcam = gr.Image(sources=["webcam"],streaming=True)
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outputs_image = gr.Image(type="numpy",label="结果图片")
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if __name__ == "__main__":
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m_model = Face_inference()
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name = ""
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demo = gr.Interface(inference,inputs=[inputs_webcam,threshold,model_name],outputs=outputs_image,live=True,title="检测")
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demo.queue().launch(max_threads=8)
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