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260416 09.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=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)