59 lines
1.4 KiB
Python
59 lines
1.4 KiB
Python
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("\n1. 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("\n2. 裁剪左上角2x2区域：")
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print(image_crop)
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image_flip = np.fliplr(image)
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print("\n3. 水平翻转后的图像：")
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print(image_flip)
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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("原图：")
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print(img)
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white_count = np.sum(img == 255)
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black_count = np.sum(img == 0)
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print(f"\n1. 白色像素(255)数量：{white_count}")
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print(f" 黑色像素(0)数量：{black_count}")
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img_flip = np.fliplr(img)
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print("\n2. 水平翻转后的图像：")
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print(img_flip)
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img_rotate = np.transpose(np.fliplr(img))
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print("\n3. 逆时针旋转90度后的图像：")
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print(img_rotate)
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import numpy as np
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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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vector1 = feature_map1.flatten()
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vector2 = feature_map2.flatten()
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print("vector1:", vector1)
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print("vector2:", vector2)
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euclidean_dist = np.linalg.norm(vector1 - vector2)
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print("欧几里得距离:", euclidean_dist)
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cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
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print("余弦相似度:", cos_sim) |