双十一视频智能拆条推荐

import cv2
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

# 视频拆条函数
def split_video(video_path, segment_duration=30):
    cap = cv2.VideoCapture(video_path)
    segments = []
    start_time = 0
    while True:
        cap.set(cv2.CAP_PROP_POS_MSEC, start_time * 1000)
        frames = []
        for _ in range(segment_duration * 30):  # 30 FPS
            ret, frame = cap.read()
            if not ret:
                break
            frames.append(frame)
        if not frames:
            break
        segments.append(frames)
        start_time += segment_duration
    cap.release()
    return segments

# 推荐系统函数
def recommend_segments(user_profile, segments):
    vectorizer = TfidfVectorizer()
    segment_descriptions = [describe_segment(seg) for seg in segments]
    tfidf_matrix = vectorizer.fit_transform(segment_descriptions)
    user_vector = vectorizer.transform([user_profile])
    cosine_similarities = linear_kernel(user_vector, tfidf_matrix).flatten()
    recommended_indices = cosine_similarities.argsort()[-5:][::-1]
    return [segments[i] for i in recommended_indices]

# 辅助函数：描述视频片段
def describe_segment(segment):
    # 这里可以使用图像识别技术提取关键信息
    return "This segment contains a person walking in a park."

# 示例使用
video_path = "example.mp4"
user_profile = "outdoor activities"
segments = split_video(video_path)
recommended_segments = recommend_segments(user_profile, segments)
print("Recommended segments:", recommended_segments)