敏感视频审核新年促销

import cv2
import tensorflow as tf

# 加载预训练的模型
model = tf.keras.models.load_model('sensitive_content_detection_model.h5')

def detect_sensitive_content(video_path):
    cap = cv2.VideoCapture(video_path)
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        # 预处理帧
        processed_frame = preprocess_frame(frame)
        # 使用模型进行预测
        prediction = model.predict(processed_frame)
        if prediction[0][0] > 0.5:  # 假设阈值为0.5
            print("敏感内容检测到！")
            break
    cap.release()

def preprocess_frame(frame):
    # 这里可以添加具体的预处理步骤，如缩放、归一化等
    resized_frame = cv2.resize(frame, (224, 224))
    normalized_frame = resized_frame / 255.0
    return normalized_frame.reshape((1, 224, 224, 3))

# 示例调用
detect_sensitive_content('example_video.mp4')