12.12图像识别推荐

import cv2
import tensorflow as tf
from sklearn.neighbors import NearestNeighbors

# 加载预训练的图像识别模型
model = tf.keras.applications.MobileNetV2(weights='imagenet')

# 加载商品图像数据库
product_images = [...]  # 商品图像列表
product_features = []  # 存储商品图像的特征向量

# 提取每个商品图像的特征
for img_path in product_images:
    img = cv2.imread(img_path)
    img = cv2.resize(img, (224, 224))  # 调整图像大小以适应模型输入
    img = tf.keras.applications.mobilenet_v2.preprocess_input(img)
    feature = model.predict(tf.expand_dims(img, axis=0))
    product_features.append(feature.flatten())

# 使用KNN算法进行相似度匹配
knn = NearestNeighbors(n_neighbors=5)
knn.fit(product_features)

# 用户上传的图像
user_img_path = 'user_image.jpg'
user_img = cv2.imread(user_img_path)
user_img = cv2.resize(user_img, (224, 224))
user_img = tf.keras.applications.mobilenet_v2.preprocess_input(user_img)
user_feature = model.predict(tf.expand_dims(user_img, axis=0)).flatten()

# 获取推荐结果
distances, indices = knn.kneighbors([user_feature])
recommended_products = [product_images[i] for i in indices[0]]

print("推荐的商品:", recommended_products)