74 lines
1.7 KiB
Python
74 lines
1.7 KiB
Python
# 练习1：图像矩阵操作
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import numpy as np
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image = np.array([
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[100, 150, 200],
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[80, 120, 180],
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[60, 90, 140]
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], dtype=np.uint8)
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print("原图：")
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print(image)
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image_dark = image - 20
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print("\n变暗20后的图像:")
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print(image_dark)
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image_crop = image[0:2,0:2]
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print("\n裁剪左上角后的图像:")
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print(image_crop)
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image_flip = np.fliplr(image)
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print("\n水平翻转后的图像:")
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print(image_flip)
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# 练习2：看图写代码
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import numpy as np
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img = np.array([
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[255, 255, 0, 0 ],
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[255, 255, 0, 0 ],
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[0, 0, 255, 255],
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[0, 0, 255, 255]
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], dtype=np.uint8)
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print("原始图像:")
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print(img)
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white_pixel_count = np.sum(img == 255)
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black_pixel_count = np.sum(img == 0)
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print(f"\n白色像素(255)的数量:{white_pixel_count}")
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print(f"黑色像素(0)的数量:{black_pixel_count}")
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flipped_img_horizontal = img[:,::-1]
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print("\n水平翻转后的图像:")
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print(flipped_img_horizontal)
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rotated_img_ccw = img.T[:,::-1]
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print("\n逆时针旋转90度后的图像:")
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print(rotated_img_ccw)
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# 练习3：特征向量计算
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import numpy as np
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# 假设这是从图像中提取的2个特征图
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feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
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feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
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# 补全代码：将特征图展平为向量
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vector1 = feature_map1.flatten() # 展平
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vector2 = feature_map2.flatten() # 展平
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print("vector1:", vector1)
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print("vector2:", vector2)
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euclidean_distance = np.linalg.norm(vector1 - vector2)
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print("\n欧几里得距离:",euclidean_distance)
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cosine_similarity = np.dot(vector1,vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
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print("余弦相似度:",cosine_similarity) |