import cv2
import numpy as np
import gradio as gr
from infer import Face_inference


# 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
def inference(image,thresh,model_type):
    global name
    if image is None:
        yield image
    if model_type == "人脸识别":
        if m_model.face_detect(image,thresh,is_show=True) is None:
            yield image
        else:
            name,prob,res_frame = m_model.face_detect(image,thresh,is_show=True)
            yield res_frame
    else:
        if m_model.live_detect(image,name,thresh,is_show=True) is None:
            yield image
        else:
            label,prob,res_frame = m_model.live_detect(image,name,thresh,is_show=True)
            yield res_frame


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")
threshold = gr.Slider(0.001,1,value=0.1,label="threshold",info="设置置信度阈值")
inputs_webcam = gr.Image(sources=["webcam"],streaming=True)
outputs_image = gr.Image(type="numpy",label="结果图片")

if __name__ == "__main__":
    m_model = Face_inference()
    name = ""
    demo = gr.Interface(inference,inputs=[inputs_webcam,threshold,model_name],outputs=outputs_image,live=True,title="检测")
    demo.queue().launch(max_threads=8)