优化这段代码，当模型预测的置信度小于0.7时，不显示预测结果def show_camera(video_label,cap,model): width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) stat_time = time.time() ret, frame = cap.read() if ret: current_time = datetime.datetime.now().strftime(('%Y-%m-%d %H:%M:%S')) cv2.putText(frame,current_time,(width-500,30),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1) # 将图像帧转换成Numpy数组，方便处理和分析 frame = np.asarray(frame) #将frame输入到模型model中预测 results = model(frame) # 标志出电瓶车 for result in results.xyxy[0]: x1, y1, x2, y2 = map(int, result[:4]) label = f'{model.names[int(result[5])]} {result[4]:.2f}' # 字符串label对应的是yolov5中的类别索引，和预测结果中的置信度（保留2位小数） print(label)#打印标签和置信度 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0,255), 2) cv2.putText(frame, "dianpingche", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image = Image.fromarray(frame) # 将 PIL.Image 转换为 Tkinter.PhotoImage 格式 photo = ImageTk.PhotoImage(image) # 更新 Label 的图像 video_label.config(image=photo) video_label.image = photo print(time.time()-stat_time) video_label.after(10, lambda:show_camera(video_label,cap,model) )

可以修改代码如下：

def show_camera(video_label, cap, model, confidence_threshold=0.7):
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    stat_time = time.time()
    ret, frame = cap.read()
    if ret:
        current_time = datetime.datetime.now().strftime(('%Y-%m-%d %H:%M:%S'))
        cv2.putText(frame, current_time, (width-500, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)
        frame = np.asarray(frame)
        results = model(frame)
        for result in results.xyxy[0]:
            x1, y1, x2, y2 = map(int, result[:4])
            label = f'{model.names[int(result[5])]} {result[4]:.2f}'
            if result[4] >= confidence_threshold:
                print(label)
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
                cv2.putText(frame, "dianpingche", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame)
        photo = ImageTk.PhotoImage(image)
        video_label.config(image=photo)
        video_label.image = photo
    print(time.time()-stat_time)
    video_label.after(10, lambda:show_camera(video_label,cap,model, confidence_threshold))

主要修改了两点：

1. 添加了一个新的参数 `confidence_threshold`，用于控制置信度的阈值。
2. 在标志电瓶车前加了一个判断，只有当置信度大于等于阈值时才进行标志。

这样，当模型预测的置信度小于阈值时，预测结果就不会被显示出来了。