Create app.py

app.py

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+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")
+
+# 设置摄像头
+video_capture = cv2.VideoCapture(0)
+stframe = st.empty()
+
+# 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
+while True:
+    ret, frame = video_capture.read()
+    if not ret:
+        st.write("无法读取摄像头数据。")
+        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")