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import streamlit as st
import cv2
import tempfile
import torch
import numpy as np
from ultralytics import YOLO
from PIL import Image
from io import BytesIO
import requests
@st.cache_resource
def load_yolo_model():
return YOLO("yolov8n.pt")
def analyze_with_deepseek(text):
prompt = f"请分析学生的以下行为并提供教学建议：{text}"
return f"分析：学生可能在积极参与小组讨论。建议教师鼓励团队合作，提升学习主动性。"
def process_video(uploaded_file, model):
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_file.read())
cap = cv2.VideoCapture(tfile.name)
frames = []
heatmap = np.zeros((480, 640))
behavior_summary = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame = cv2.resize(frame, (640, 480))
results = model(frame)
boxes = results[0].boxes.xyxy.cpu().numpy() if results else []
for box in boxes:
x1, y1, x2, y2 = map(int, box[:4])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cx, cy = int((x1+x2)/2), int((y1+y2)/2)
heatmap[cy, cx] += 1
frames.append(frame)
behavior_summary.append("检测到学生行为")
cap.release()
return frames, heatmap, behavior_summary
def display_heatmap(heatmap):
import matplotlib.pyplot as plt
import seaborn as sns
fig, ax = plt.subplots()
sns.heatmap(heatmap, cmap="YlOrRd", ax=ax)
st.pyplot(fig)
st.title("🎓 学生课堂行为自动识别与智能反馈系统")
st.markdown("---")
model = load_yolo_model()
uploaded_file = st.file_uploader("请上传课堂视频（mp4格式）", type=["mp4"])
if uploaded_file:
st.video(uploaded_file)
with st.spinner("正在分析视频..."):
frames, heatmap, behavior_summary = process_video(uploaded_file, model)
st.success("分析完成！")
st.subheader("📌 注意力热力图")
display_heatmap(heatmap)
st.subheader("📊 行为语义分析")
for idx, summary in enumerate(behavior_summary[:3]):
text_analysis = analyze_with_deepseek(summary)
st.info(f"帧 {idx+1}: {text_analysis}")
st.subheader("📄 教学优化建议（示例）")
st.markdown("- 增加互动提问频次")
st.markdown("- 鼓励小组讨论与合作")
st.markdown("- 适当调整教学节奏，吸引注意力")
st.download_button("📥 导出教学分析报告", data="报告内容示例...", file_name="teaching_report.txt")
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