53 lines
1.4 KiB
Python
53 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=np.clip(image-20,0,255).astype(np.uint8)
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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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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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while_count=np.sum(img==255)
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black_count=np.sum(img==0)
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print(f"白色像素数量：{while_count}")
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print(f"黑色像素数量：{black_count}")
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img_flip=np.fliplr(img)
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print("\n水平翻转后的图像：")
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print(img_flip)
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img_rotate=np.fliplr(img.T)
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print("\n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("\n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) |