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cqy.py

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+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)