当一个像素在所有3个颜色通道(RGB)中的值相同时，该像素将始终处于灰度格式。

将RGB图像转换为灰度的一个简单而直观的方法是通过取每个像素中所有颜色通道的平均值并将值分配回该像素。

import numpy as np
from PIL import Image

img=np.array(Image.open('sample.jpg')) #Input - Color image
gray_img=img.copy()

for clr in range(img.shape[2]):
    gray_img[:,:,clr]=img.mean(axis=2) #Take mean of all 3 color channels of each pixel and assign it back to that pixel(in copied image)

#plt.imshow(gray_img) #Result - Grayscale image

输入图像:

输出图像:

你也可以使用scikit-image，它提供了一些函数来转换ndarray中的图像，比如rgb2gray。

from skimage import color
from skimage import io

img = color.rgb2gray(io.imread('image.png'))

注:此转换中使用的权重是针对当代CRT荧光粉校准的:Y = 0.2125 R + 0.7154 G + 0.0721 B

或者，您可以通过以下方法读取灰度图像:

from skimage import io
img = io.imread('image.png', as_gray=True)

The tutorial is cheating because it is starting with a greyscale image encoded in RGB, so they are just slicing a single color channel and treating it as greyscale. The basic steps you need to do are to transform from the RGB colorspace to a colorspace that encodes with something approximating the luma/chroma model, such as YUV/YIQ or HSL/HSV, then slice off the luma-like channel and use that as your greyscale image. matplotlib does not appear to provide a mechanism to convert to YUV/YIQ, but it does let you convert to HSV.

尝试使用matplotlib.colors.rgb_to_hsv(img)，然后从数组中切片最后一个值(V)来获取灰度。它与亮度值不完全相同，但它意味着您可以在matplotlib中完成所有操作。

背景:

http://matplotlib.sourceforge.net/api/colors_api.html http://en.wikipedia.org/wiki/HSL_and_HSV

或者，您可以使用PIL或内置的colorsys.rgb_to_yiq()来转换为具有真正亮度值的色彩空间。你也可以完全投入到你自己的光转换器中，尽管这可能有点过头了。

在Ubuntu 16.04 LTS (Xeon E5 2670 with SSD)上运行Python 3.5，测试了1000个RGBA PNG图像(224 x 256像素)的速度。

平均运行时间

pil: 1.037秒

1040秒

Sk: 2秒120

PIL和SciPy给出了相同的numpy数组(范围从0到255)。SkImage给出了从0到1的数组。此外，颜色转换略有不同，请参阅来自CUB-200数据集的示例。

SkImage:

PIL:

SciPy:

原:

差异:

Code

Performance run_times = dict(sk=list(), pil=list(), scipy=list()) for t in range(100): start_time = time.time() for i in range(1000): z = random.choice(filenames_png) img = skimage.color.rgb2gray(skimage.io.imread(z)) run_times['sk'].append(time.time() - start_time) start_time = time.time() for i in range(1000): z = random.choice(filenames_png) img = np.array(Image.open(z).convert('L')) run_times['pil'].append(time.time() - start_time) start_time = time.time() for i in range(1000): z = random.choice(filenames_png) img = scipy.ndimage.imread(z, mode='L') run_times['scipy'].append(time.time() - start_time) for k, v in run_times.items(): print('{:5}: {:0.3f} seconds'.format(k, sum(v) / len(v))) Output z = 'Cardinal_0007_3025810472.jpg' img1 = skimage.color.rgb2gray(skimage.io.imread(z)) * 255 IPython.display.display(PIL.Image.fromarray(img1).convert('RGB')) img2 = np.array(Image.open(z).convert('L')) IPython.display.display(PIL.Image.fromarray(img2)) img3 = scipy.ndimage.imread(z, mode='L') IPython.display.display(PIL.Image.fromarray(img3)) Comparison img_diff = np.ndarray(shape=img1.shape, dtype='float32') img_diff.fill(128) img_diff += (img1 - img3) img_diff -= img_diff.min() img_diff *= (255/img_diff.max()) IPython.display.display(PIL.Image.fromarray(img_diff).convert('RGB')) Imports import skimage.color import skimage.io import random import time from PIL import Image import numpy as np import scipy.ndimage import IPython.display Versions skimage.version 0.13.0 scipy.version 0.19.1 np.version 1.13.1

最快和当前的方式是使用枕头，安装通过pip安装枕头。

代码是:

from PIL import Image
img = Image.open('input_file.jpg').convert('L')
img.save('output_file.jpg')

使用img.Convert()，支持“L”，“RGB”和“CMYK”。”模式

import numpy as np
from PIL import Image

img = Image.open("IMG/center_2018_02_03_00_34_32_784.jpg")
img.convert('L')

print np.array(img)

输出:

[[135 123 134 ...,  30   3  14]
 [137 130 137 ...,   9  20  13]
 [170 177 183 ...,  14  10 250]
 ..., 
 [112  99  91 ...,  90  88  80]
 [ 95 103 111 ..., 102  85 103]
 [112  96  86 ..., 182 148 114]]