用整数替换numpy数组中的RGB值非常慢 [英] Replacing RGB values in numpy array by integer is extremely slow

问题描述

我想将numpy数组的rgb值替换为单个整数表示。我的代码有效，但速度太慢，我正在迭代每个元素。我可以加快速度吗？我是numpy的新手。

I want to replace the rgb values of a numpy array to single integer representations. My code works but it's too slow, I am iterating over every element right now. Can I speed this up? I am new to numpy.

from skimage import io

# dictionary of color codes for my rgb values
_color_codes = {
    (255, 200, 100): 1,
    (223, 219, 212): 2,
    ...
}

# get the corresponding color code for the rgb vector supplied
def replace_rgb_val(rgb_v):
    rgb_triple = (rgb_v[0], rgb_v[1], rgb_v[2])
    if rgb_triple in _color_codes:
        return _color_codes[rgb_triple]
    else:
        return -1

# function to replace, this is where I iterate
def img_array_to_single_val(arr):
    return np.array([[replace_rgb_val(arr[i][j]) for j in range(arr.shape[1])] for i in range(arr.shape[0])])


# my images are square so the shape of the array is (n,n,3)
# I want to change the arrays to (n,n,1)
img_arr = io.imread(filename)
# this takes from ~5-10 seconds, too slow!
result = img_array_to_single_val(img_arr)

推荐答案

替换相反的颜色值。查找每个RGB三元组，并在新数组中设置相应的索引：

Replace the color values the other way round. Look for each RGB-triple, and set the corresponding index in a new array:

def img_array_to_single_val(arr, color_codes):
    result = numpy.ndarray(shape=arr.shape[:2], dtype=int)
    result[:,:] = -1
    for rgb, idx in color_codes.items():
        result[(arr==rgb).all(2)] = idx
    return result

让我们将颜色索引分配分开：首先 arr == rgb 将每个pixel-rgb-values与列表 rgb进行比较，导致anxnx 3 - 布尔数组。只有当所有三个颜色部分都相同时，我们才发现匹配，所以 .all（2）减少最后一个轴，使用<$对于匹配 rgb 的每个像素，c $ c> True 。最后一步是，使用这个掩码设置相应像素的索引。

Let's take the color-index assignment apart: First arr==rgb compares each pixel-rgb-values with the list rgb, leading to a n x n x 3 - boolean array. Only if all three color-parts are the same, we found a match, so .all(2) reduces the last axis, leeding to a n x n - boolean array, with True for every pixel matching rgb. Last step is, to use this mask to set the index of the corresponding pixels.

更快，可能是，首先将RGB数组转换为int32，并且然后做索引翻译：

Even faster, it might be, to first convert the RGB-array to int32, and then do the index translation:

def img_array_to_single_val(image, color_codes):
    image = image.dot(numpy.array([65536, 256, 1], dtype='int32'))
    result = numpy.ndarray(shape=image.shape, dtype=int)
    result[:,:] = -1
    for rgb, idx in color_codes.items():
        rgb = rgb[0] * 65536 + rgb[1] * 256 + rgb[2]
        result[arr==rgb] = idx
    return result

对于真正大或多的图像，首先应创建直接颜色映射：

For really large or many images you should first create a direct color mapping:

color_map = numpy.ndarray(shape=(256*256*256), dtype='int32')
color_map[:] = -1
for rgb, idx in color_codes.items():
    rgb = rgb[0] * 65536 + rgb[1] * 256 + rgb[2]
    color_map[rgb] = idx

def img_array_to_single_val(image, color_map):
    image = image.dot(numpy.array([65536, 256, 1], dtype='int32'))
    return color_map[image]

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