matplotlib plt 显示随机生成的灰度值(0-255)矩阵
【摘要】 环境信息ModelArtsCodeLabConda-python3matplotlib 2.1.0 代码示例import numpy as npimport matplotlib.pyplot as plt%matplotlib inline# [low,high)# 黑色是0# 白色是255# 为了Mnist数据集,而创建的28*28像素矩阵image = np.random.rand...
环境信息
- ModelArts
- CodeLab
- Conda-python3
- matplotlib 2.1.0
- Conda-python3
- CodeLab
代码示例
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# [low,high)
# 黑色是0
# 白色是255
# 为了Mnist数据集,而创建的28*28像素矩阵
image = np.random.randint(low=0,high=256,size=(28,28))
image
array([[ 2, 97, 23, 168, 87, 99, 70, 47, 49, 2, 254, 37, 73,
43, 90, 43, 21, 147, 7, 66, 100, 22, 162, 123, 234, 54,
99, 178],
[190, 33, 151, 8, 227, 10, 157, 65, 74, 16, 49, 75, 73,
160, 158, 249, 215, 40, 1, 208, 147, 209, 85, 50, 253, 246,
232, 35],
[ 44, 107, 47, 192, 231, 232, 52, 207, 70, 236, 86, 46, 4,
87, 80, 124, 105, 91, 168, 253, 48, 128, 109, 96, 226, 31,
217, 170],
[187, 158, 81, 172, 43, 236, 117, 151, 223, 40, 198, 215, 145,
136, 206, 186, 178, 244, 49, 73, 251, 52, 68, 68, 33, 38,
231, 125],
[231, 182, 87, 61, 94, 241, 195, 164, 230, 222, 27, 92, 67,
144, 210, 111, 23, 23, 135, 171, 32, 74, 158, 74, 186, 84,
20, 187],
[ 16, 44, 74, 142, 49, 226, 239, 107, 212, 130, 30, 190, 247,
203, 102, 183, 242, 26, 120, 165, 27, 133, 90, 177, 192, 33,
66, 135],
[226, 155, 146, 201, 187, 39, 169, 198, 160, 132, 50, 231, 20,
28, 76, 84, 24, 137, 249, 246, 93, 24, 135, 112, 254, 8,
140, 27],
[ 94, 64, 147, 190, 140, 38, 227, 5, 209, 249, 85, 155, 26,
244, 53, 162, 134, 222, 252, 194, 56, 16, 39, 226, 199, 133,
53, 71],
[229, 153, 7, 203, 14, 227, 6, 65, 251, 234, 215, 137, 149,
182, 71, 165, 139, 218, 155, 89, 64, 72, 21, 130, 40, 210,
53, 41],
[237, 59, 37, 236, 78, 162, 181, 249, 50, 172, 14, 144, 249,
229, 26, 124, 42, 50, 207, 98, 12, 247, 151, 28, 190, 14,
41, 70],
[248, 51, 60, 115, 138, 32, 134, 233, 18, 229, 51, 199, 4,
170, 8, 230, 193, 169, 102, 181, 130, 113, 46, 149, 131, 167,
60, 198],
[ 61, 199, 143, 21, 252, 221, 77, 154, 170, 163, 139, 142, 28,
126, 70, 64, 247, 113, 164, 214, 42, 22, 95, 152, 105, 125,
16, 67],
[172, 207, 10, 57, 77, 19, 35, 213, 240, 72, 211, 246, 121,
141, 108, 235, 218, 121, 37, 177, 83, 153, 149, 190, 51, 30,
138, 91],
[118, 174, 13, 226, 241, 78, 34, 24, 5, 98, 184, 128, 136,
122, 52, 87, 163, 228, 68, 183, 179, 77, 184, 184, 42, 7,
220, 241],
[ 24, 170, 3, 223, 139, 85, 131, 208, 208, 83, 115, 154, 244,
95, 90, 28, 138, 120, 49, 241, 50, 241, 187, 45, 151, 245,
36, 145],
[200, 69, 116, 46, 75, 200, 80, 79, 25, 17, 128, 150, 195,
103, 36, 205, 126, 141, 48, 194, 18, 8, 157, 8, 153, 109,
140, 158],
[154, 44, 139, 38, 187, 29, 5, 218, 36, 132, 214, 171, 58,
148, 206, 213, 201, 204, 170, 129, 61, 120, 65, 88, 0, 50,
144, 102],
[ 62, 186, 197, 22, 144, 121, 250, 217, 219, 135, 179, 197, 141,
252, 86, 135, 187, 122, 208, 164, 146, 2, 203, 164, 122, 74,
162, 137],
[149, 103, 79, 98, 166, 182, 74, 56, 117, 129, 29, 65, 83,
14, 221, 149, 35, 33, 8, 183, 243, 128, 34, 114, 62, 195,
38, 126],
[ 16, 114, 254, 91, 89, 146, 207, 57, 62, 189, 209, 230, 165,
115, 60, 223, 177, 91, 188, 243, 124, 216, 45, 231, 116, 54,
123, 204],
[206, 198, 105, 190, 144, 254, 249, 15, 104, 135, 165, 78, 123,
99, 160, 6, 75, 246, 51, 229, 189, 59, 198, 37, 76, 95,
226, 78],
[247, 196, 230, 114, 228, 229, 252, 86, 81, 107, 117, 56, 120,
208, 87, 208, 151, 208, 2, 133, 222, 78, 209, 181, 56, 97,
150, 20],
[ 95, 51, 227, 122, 184, 162, 56, 238, 55, 243, 243, 226, 184,
233, 98, 8, 244, 27, 8, 215, 53, 250, 253, 168, 245, 240,
122, 247],
[ 13, 63, 20, 135, 198, 115, 58, 35, 247, 191, 134, 112, 11,
242, 162, 84, 122, 8, 101, 207, 95, 151, 239, 138, 228, 254,
27, 190],
[ 82, 82, 166, 210, 160, 158, 218, 79, 237, 5, 158, 130, 206,
252, 181, 115, 92, 143, 58, 110, 120, 94, 57, 32, 181, 52,
235, 22],
[191, 104, 223, 71, 144, 91, 200, 215, 164, 28, 195, 51, 97,
52, 70, 35, 136, 238, 151, 247, 114, 196, 61, 4, 202, 7,
130, 191],
[151, 48, 0, 77, 241, 209, 148, 235, 142, 36, 45, 194, 237,
154, 74, 148, 33, 163, 160, 8, 143, 253, 236, 200, 31, 5,
199, 121],
[152, 129, 144, 126, 216, 192, 38, 167, 105, 195, 109, 176, 178,
184, 178, 145, 230, 47, 239, 87, 39, 244, 138, 163, 56, 147,
139, 41]])
plt.imshow(image)
# cmap="Greys" 灰度
plt.imshow(image,cmap="Greys")
源码学习
help(np.random.randint)
elp on built-in function randint:
randint(...) method of numpy.random.mtrand.RandomState instance
randint(low, high=None, size=None, dtype=int)
Return random integers from `low` (inclusive) to `high` (exclusive).
Return random integers from the "discrete uniform" distribution of
the specified dtype in the "half-open" interval [`low`, `high`). If
`high` is None (the default), then results are from [0, `low`).
.. note::
New code should use the ``integers`` method of a ``default_rng()``
instance instead; please see the :ref:`random-quick-start`.
......
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