Question

我有一个3维的numpy数组。直观地说，它是二维的，其中每个行列位置代表一种RGB颜色，该颜色存储为三个数字的向量。（如果将颜色存储为三元组，会容易得多！）我有一个函数（基于答案here），可以将RGB三元组转换为颜色名称。是否有一种简单的方法（除嵌套循环外）将函数应用于数组的row-col元素。（因为numpy尝试将函数应用于RGB向量的每个元素，所以将其直接应用于数组本身是行不通的。）

谢谢。

Answer 1

IIUC，您可以只使用np.dstack和reshape或np.dstack和concatenate

np.dstack(arr).reshape(-1,3)
# equivalent:
np.concatenate(np.dstack(arr))

例如：

arr = np.random.randint(0,256,(3,5,5))
>>> arr
array([[[150,  38,  34,  41,  24],
        [ 76, 135,  93, 149, 142],
        [150, 123, 198,  11,  34],
        [ 24, 179, 132, 175, 218],
        [ 46, 233, 138, 215,  97]],

       [[194, 153,  29, 200, 133],
        [247, 101,  18,  70, 112],
        [164, 225, 141, 196, 131],
        [ 15,  86,  22, 234, 166],
        [163,  97,  94, 205,  56]],

       [[117,  56,  28,   1, 104],
        [138, 138, 148, 241,  44],
        [ 73,  57, 179, 142, 140],
        [ 55, 160, 240, 189,  13],
        [244,  36,  56, 241,  33]]])

>>> np.dstack(arr).reshape(-1,3)
array([[150, 194, 117],
       [ 38, 153,  56],
       [ 34,  29,  28],
       [ 41, 200,   1],
       [ 24, 133, 104],
       [ 76, 247, 138],
       [135, 101, 138],
       [ 93,  18, 148],
       [149,  70, 241],
       [142, 112,  44],
       [150, 164,  73],
       [123, 225,  57],
       [198, 141, 179],
       [ 11, 196, 142],
       [ 34, 131, 140],
       [ 24,  15,  55],
       [179,  86, 160],
       [132,  22, 240],
       [175, 234, 189],
       [218, 166,  13],
       [ 46, 163, 244],
       [233,  97,  36],
       [138,  94,  56],
       [215, 205, 241],
       [ 97,  56,  33]])

使用the answer you linked中提供的功能，您可以获得该图像的最接近颜色：

>>> [get_colour_name(i)[1] for i in np.dstack(arr).reshape(-1,3)]
['darkseagreen', 'forestgreen', 'black', 'limegreen', 'seagreen', 'mediumaquamarine', 'grey', 'indigo', 'blueviolet', 'sienna', 'yellowgreen', 'yellowgreen', 'rosybrown', 'lightseagreen', 'darkcyan', 'midnightblue', 'palevioletred', 'blueviolet', 'powderblue', 'goldenrod', 'dodgerblue', 'chocolate', 'sienna', 'gainsboro', 'saddlebrown']

Answer 2

如果您的函数未设计为接受向量参数，那么除了使用循环并仅将其隐藏或可能隐藏一些jit恶作剧的那种方式之外，没有其他魔术。

请注意秘密执行循环的魔法，即np.vectorize。要使其传递一维子空间到您的函数，可以使用signature关键字

pseudo_vect_func = np.vectorize(your_func, ('O',), signature='(m)->()')

我还添加了一个otypes参数，因为如果没有它，vectorize似乎会盲目地使用U1，即在第一个字母后截断

如果您要真正进行矢量化操作，请使用从零开始的方法。

如果您的列表或字典的值是（颜色名称，（r，g，b）），并且可以通过最小距离匹配确定，那么您可以利用KDTree进行有效查找：

import numpy as np
from scipy.spatial import cKDTree as KDTree

# set up lookup

# borrow a list of named colors from matplotlib
from matplotlib import colors
named_colors = {k: tuple(int(v[i:i+2], 16) for i in range(1, 7, 2))
                for k, v in colors.cnames.items()}

no_match = named_colors['purple']

# make arrays containing the RGB values ...
color_tuples = list(named_colors.values())
color_tuples.append(no_match)
color_tuples = np.array(color_tuples)
# ... and another array with the names in same order
color_names = list(named_colors)
color_names.append('no match')
color_names = np.array(color_names)
# build tree
tree = KDTree(color_tuples[:-1])

def img2colornames(img, tolerance):
    # find closest color in tree for each pixel in picture
    dist, idx = tree.query(img, distance_upper_bound=tolerance)
    # look up their names
    return color_names[idx]

# an example
result = img2colornames(face(), 40)
# show a small patch
import Image
Image.fromarray(face()[410:510, 325:425]).show()
# same as names, downsampled
print(result[415:510:10, 330:425:10])

输出：

[['darkgrey' 'silver' 'dimgray' 'darkgrey' 'black' 'darkslategrey'
  'silver' 'silver' 'dimgray' 'darkgrey']
 ['darkslategrey' 'gray' 'darkgrey' 'gray' 'darkslategrey' 'gray'
  'darkgrey' 'lightsteelblue' 'darkslategrey' 'darkslategrey']
 ['darkolivegreen' 'no match' 'dimgray' 'dimgray' 'darkslategrey' 'gray'
  'slategray' 'lightslategrey' 'dimgray' 'darkslategrey']
 ['dimgray' 'dimgray' 'gray' 'dimgray' 'dimgray' 'darkslategrey'
  'dimgray' 'dimgray' 'black' 'darkseagreen']
 ['no match' 'no match' 'darkolivegreen' 'dimgray' 'dimgray' 'no match'
  'darkkhaki' 'darkkhaki' 'no match' 'dimgray']
 ['darkkhaki' 'darkkhaki' 'darkkhaki' 'tan' 'tan' 'no match'
  'darkslategrey' 'no match' 'darkslategrey' 'dimgray']
 ['no match' 'no match' 'no match' 'no match' 'no match' 'no match'
  'no match' 'no match' 'no match' 'dimgray']
 ['no match' 'black' 'no match' 'no match' 'no match' 'no match'
  'no match' 'no match' 'no match' 'darkslategrey']
 ['darkkhaki' 'no match' 'olivedrab' 'darkolivegreen' 'darkolivegreen'
  'darkolivegreen' 'darkolivegreen' 'darkolivegreen' 'darkolivegreen'
  'darkolivegreen']
 ['darkseagreen' 'no match' 'no match' 'no match' 'no match' 'no match'
  'no match' 'no match' 'no match' 'no match']]

Answer 3

您可以使用map并尝试例如：

list(map(your_RGB2Name_function, 2D_np_array))

假设您有一个函数，可以处理数字列表

def dummy_fct(numlist):
    return '-'.join(map(str, numlist))

dummy_fct([1,2,3])
Out: '1-2-3'

当应用于许多号码清单中的清单时，显然工作不正常

dummy_fct([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
Out: '[1, 2, 3]-[4, 5, 6]-[7, 8, 9]'

然后您可以使用map，它遍历一个可迭代对象（这里是外部列表，或者是您的numpy数组的第二维），然后将该函数应用于每个子列表：

list(map(dummy_fct, [[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
Out: ['1-2-3', '4-5-6', '7-8-9']

将函数应用于numpy数组的行/列维度的最佳方法是什么

3 个答案: