完成作业3-1-3

练习1.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)
+print()
+
+darkened = np.clip(image.astype(np.int16) - 20, 0, 255).astype(np.uint8)
+print("变暗20后的图像:")
+print(darkened)
+print()
+
+cropped = image[0:2, 0:2]
+print("裁剪左上角(2×2区域):")
+print(cropped)
+print()
+
+flipped = np.fliplr(image)
+print("水平翻转后的图像：")
+print(flipped)

练习2.py

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+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)
+print()
+
+white_pixels = np.sum(img == 255)
+black_pixels = np.sum(img == 0)
+
+print(f"白色像素(255)的数量：{white_pixels}")
+print(f"黑色像素(0)的数量：{black_pixels}")
+print()
+
+
+flipped_horizontal = np.fliplr(img)
+print("水平翻转后的图像：")
+print(flipped_horizontal)
+print()
+
+rotated_ccw = np.flipud(img.T)
+
+print("逆时针旋转90度后的图像:")
+print(rotated_ccw)

练习3.py

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+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)
+print()
+
+euclidean_distance = np.linalg.norm(vector1 - vector2)
+print(f"欧几里得距离: {euclidean_distance:.4f}")
+
+dot_product = np.dot(vector1, vector2)
+norm1 = np.linalg.norm(vector1)
+norm2 = np.linalg.norm(vector2)
+cosine_similarity = dot_product / (norm1 * norm2)
+print(f"余弦相似度: {cosine_similarity:.4f}")
+
+cosine_distance = 1 - cosine_similarity
+print(f"余弦距离: {cosine_distance:.4f}")