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 = image - 20
print("\n1. 变暗20后的图像：")
print(image_dark)
image_crop = image[0:2, 0:2]
print("\n2. 裁剪左上角2x2区域：")
print(image_crop)

image_flip = np.fliplr(image)
print("\n3. 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)

print("原图：")
print(img)

white_count = np.sum(img == 255)
black_count = np.sum(img == 0)
print(f"\n1. 白色像素(255)数量：{white_count}")
print(f"   黑色像素(0)数量：{black_count}")

img_flip = np.fliplr(img)
print("\n2. 水平翻转后的图像：")
print(img_flip)

img_rotate = np.transpose(np.fliplr(img))
print("\n3. 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("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)