Update app.py

app.py

@@ -1,46 +1,30 @@
-import streamlit as st
 import cv2
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.applications.mobilenet import preprocess_input
-from tensorflow.keras.applications.mobilenet import decode_predictions
 
-st.title("物体识别应用")
-st.write("通过摄像头识别物体，从左到右显示主要物体的名称")
+# 打开默认摄像头，通常为0
+cap = cv2.VideoCapture(0)
 
-# 设置摄像头
-video_capture = cv2.VideoCapture(0)
-stframe = st.empty()
+# 检查摄像头是否成功打开
+if not cap.isOpened():
+    print("无法打开摄像头")
+    exit()
 
-# 加载 MobileNet SSD 预训练模型
-model = tf.keras.applications.MobileNet(weights="imagenet")
-
-def detect_objects(frame):
-    # 预处理图像
-    image_resized = cv2.resize(frame, (224, 224))
-    image_array = np.expand_dims(image_resized, axis=0)
-    processed_image = preprocess_input(image_array)
-    
-    # 使用模型进行预测
-    preds = model.predict(processed_image)
-    decoded_preds = decode_predictions(preds, top=3)[0]  # 取前3个结果
-    objects = [f"{label}: {round(score * 100, 2)}%" for (_, label, score) in decoded_preds]
-    return objects
-
-# 读取摄像头流并显示
+# 读取并显示摄像头画面
 while True:
-    ret, frame = video_capture.read()
+    # 从摄像头读取一帧
+    ret, frame = cap.read()
+    
+    # 检查是否成功读取帧
     if not ret:
-        st.write("无法读取摄像头数据。")
+        print("无法接收帧，结束程序")
         break
-
-    # 检测物体
-    objects = detect_objects(frame)
-    detected_text = " | ".join(objects)
-
-    # 显示检测结果
-    cv2.putText(frame, detected_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2, cv2.LINE_AA)
     
-    # 将 OpenCV 图像格式转换为 Streamlit 显示格式
-    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    stframe.image(frame_rgb, caption="检测到的物体", channels="RGB")
+    # 显示帧
+    cv2.imshow('摄像头', frame)
+    
+    # 按下 'q' 键退出
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# 释放摄像头并关闭所有窗口
+cap.release()
+cv2.destroyAllWindows()