Update app.py

app.py

@@ -1,33 +1,46 @@
 import streamlit as st
-from streamlit_webrtc import VideoTransformerBase, webrtc_streamer
-import tensorflow as tf
+import cv2
 import numpy as np
-from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
+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")
 
-# 加载 MobileNet 预训练模型
-model = tf.keras.applications.MobileNet(weights="imagenet")
+# 设置摄像头
+video_capture = cv2.VideoCapture(0)
+stframe = st.empty()
 
-class ObjectDetectionTransformer(VideoTransformerBase):
-    def transform(self, frame):
-        img = frame.to_ndarray(format="bgr24")
+# 加载 MobileNet SSD 预训练模型
+model = tf.keras.applications.MobileNet(weights="imagenet")
 
-        # 将图像调整大小并进行预处理
-        image_resized = cv2.resize(img, (224, 224))
-        image_array = np.expand_dims(image_resized, axis=0)
-        processed_image = preprocess_input(image_array)
+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
 
-        # 进行物体识别
-        preds = model.predict(processed_image)
-        decoded_preds = decode_predictions(preds, top=3)[0]
-        objects = [f"{label}: {round(score * 100, 2)}%" for (_, label, score) in decoded_preds]
-        detected_text = " | ".join(objects)
+# 读取摄像头流并显示
+while True:
+    ret, frame = video_capture.read()
+    if not ret:
+        st.write("无法读取摄像头数据。")
+        break
 
-        # 将检测结果写在图像上
-        cv2.putText(img, detected_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2, cv2.LINE_AA)
-        return img
+    # 检测物体
+    objects = detect_objects(frame)
+    detected_text = " | ".join(objects)
 
-# 使用 streamlit-webrtc 启动摄像头流
-webrtc_streamer(key="object-detection", video_transformer_factory=ObjectDetectionTransformer)
+    # 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")