<p>当然,假设你有一个<code>numpy.ndarray</code>:</p>
<pre><code>>>> arr = np.random.randint(0, 500, (10,10))
>>> arr
array([[411, 403, 291, 357, 319, 318, 302, 419, 145, 338],
[388, 93, 487, 442, 0, 125, 174, 329, 178, 326],
[305, 281, 476, 65, 102, 101, 115, 370, 367, 139],
[410, 492, 426, 460, 384, 419, 241, 423, 326, 465],
[263, 127, 166, 364, 11, 100, 85, 303, 328, 287],
[321, 53, 406, 150, 291, 322, 33, 24, 0, 294],
[259, 279, 455, 60, 479, 157, 460, 46, 109, 486],
[203, 309, 53, 336, 116, 0, 326, 282, 305, 324],
[399, 131, 494, 429, 294, 175, 392, 185, 48, 408],
[473, 143, 414, 189, 159, 483, 168, 321, 285, 364]])
</code></pre>
<p>我相信你想要的是:</p>
<pre><code>>>> (arr + 50).clip(max=255)
array([[255, 255, 255, 255, 255, 255, 255, 255, 195, 255],
[255, 143, 255, 255, 50, 175, 224, 255, 228, 255],
[255, 255, 255, 115, 152, 151, 165, 255, 255, 189],
[255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
[255, 177, 216, 255, 61, 150, 135, 255, 255, 255],
[255, 103, 255, 200, 255, 255, 83, 74, 50, 255],
[255, 255, 255, 110, 255, 207, 255, 96, 159, 255],
[253, 255, 103, 255, 166, 50, 255, 255, 255, 255],
[255, 181, 255, 255, 255, 225, 255, 235, 98, 255],
[255, 193, 255, 239, 209, 255, 218, 255, 255, 255]])
>>>
</code></pre>