12.12图片智能识别推荐

import cv2
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# 图片特征提取函数（示例）
def extract_features(image_path):
    img = cv2.imread(image_path)
    # 这里可以添加具体的特征提取逻辑，如颜色直方图、HOG特征等
    return img.flatten()  # 简化为像素值的向量表示

# 推荐函数
def recommend_similar_images(target_image_path, image_database, top_k=5):
    target_features = extract_features(target_image_path)
    similarities = []
    
    for img_path in image_database:
        features = extract_features(img_path)
        sim = cosine_similarity([target_features], [features])[0][0]
        similarities.append((img_path, sim))
    
    similarities.sort(key=lambda x: x[1], reverse=True)
    return [img[0] for img in similarities[:top_k]]

# 示例使用
image_db = ['image1.jpg', 'image2.jpg', 'image3.jpg', ...]  # 图片数据库路径列表
recommended_images = recommend_similar_images('target_image.jpg', image_db)
print("Recommended Images:", recommended_images)