import numpy as np

image = np.array([
    [100, 150, 200],
    [80,  120, 180],
    [60,  90,  140]
], dtype=np.uint8)

print("原图：")
print(image)
image_dark=np.clip(image-20,0,255).astype(np.uint8)
print("\n变暗20后的图像：")
print(image_dark)
image_crop=image[0:2,0:2]
print("\n裁剪后的图像：")
print(image_crop)
image_flip=np.fliplr(image)
print("\n水平翻转后的图像：")
print(image_flip)


import numpy as np
img = np.array([
    [255, 255, 0,   0  ],
    [255, 255, 0,   0  ],
    [0,   0,   255, 255],
    [0,   0,   255, 255]
], dtype=np.uint8)
while_count=np.sum(img==255)
black_count=np.sum(img==0)
print(f"白色像素数量：{while_count}")
print(f"黑色像素数量：{black_count}")
img_flip=np.fliplr(img)
print("\n水平翻转后的图像：")
print(img_flip)
img_rotate=np.fliplr(img.T)
print("\n逆时针旋转90度后的图像：")
print(img_rotate)

import numpy as np

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]])

vector1 = feature_map1.flatten()  
vector2 = feature_map2.flatten()  

print("vector1:", vector1)
print("vector2:", vector2)
euclidean_dist=np.linalg.norm(vector1-vector2)
print("\n欧几里得距离：",euclidean_dist)
cos_sim=np.dot(vector1,vector2)/(np.linalg.norm(vector1)*np.linalg.norm(vector2))
print("余弦相似度：",cos_sim)