48 lines
1.2 KiB
Python
48 lines
1.2 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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brighter = image-20
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print("变暗20：",brighter)
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top_left = image[0:2, 0:2]
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print("裁剪左上角：",top_left)
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flipped1 = np.fliplr(image)
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print("水平翻转：",flipped1)
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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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flipped2 = np.fliplr(img)
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print("水平翻转：",flipped2)
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rotated = np.transpose(img)
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flipped3 = np.fliplr(img)
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print("旋转90度：",flipped3)
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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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vocab = ["Python", "学习", "数据", "人工智能", "编程"]
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doc1 = "Python学习编程"
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doc2 = "Python人工智能数据"
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def text_to_vector(text, vocab):
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words = text.split()
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vector = np.zeros(len(vocab))
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for i, word in enumerate(vocab):
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vector[i] = words.count(word)
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return vector
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v1 = text_to_vector(doc1, vocab)
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v2 = text_to_vector(doc2, vocab)
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print("doc1向量:", v1)
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print("doc2向量:", v2) |