import numpy as np

# 假设这是从图像中提取的2个特征图
feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])

# 1) 全代码：将特征图展平为向量
vector1 = feature_map1.flatten()  # 展平
vector2 = feature_map2.flatten()  # 展平

print("vector1:", vector1)
print("vector2:", vector2)
print("-" * 40)

# 1. 计算 vector1 和 vector2 的欧几里得距离
euclidean_dist = np.linalg.norm(vector1 - vector2)
print(f"欧几里得距离: {euclidean_dist:.4f}")

# 2. 计算 vector1 和 vector2 的余弦相似度
dot_product = np.dot(vector1, vector2)
norm_a = np.linalg.norm(vector1)
norm_b = np.linalg.norm(vector2)
cos_similarity = dot_product / (norm_a * norm_b)
print(f"余弦相似度: {cos_similarity:.4f}")