双11图像理解推荐

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

# 加载商品图像
def load_images(image_paths):
    images = []
    for path in image_paths:
        img = cv2.imread(path)
        img = cv2.resize(img, (128, 128))  # 统一图像大小
        img = img.flatten()  # 展平图像为向量
        images.append(img)
    return np.array(images)

# 计算图像特征向量
image_paths = ['path_to_image1.jpg', 'path_to_image2.jpg', 'path_to_image3.jpg']
images = load_images(image_paths)

# 计算图像之间的相似度
similarity_matrix = cosine_similarity(images)

# 推荐最相似的商品
def recommend_similar_product(target_image_path, similarity_matrix, image_paths):
    target_image = load_images([target_image_path])[0]
    similarities = cosine_similarity([target_image], images)[0]
    most_similar_index = np.argmax(similarities)
    return image_paths[most_similar_index]

# 示例使用
recommended_product = recommend_similar_product('path_to_target_image.jpg', similarity_matrix, image_paths)
print(f"推荐的商品路径: {recommended_product}")