image=myCamera.getImage().crop(xx,xx,xx,xx).scale(xx,xx).greyscale()

您可以直接使用灰度()进行转换。

用Pillow怎么做呢:

from PIL import Image
img = Image.open('image.png').convert('L')
img.save('greyscale.png')

如果在输入图像中存在alpha(透明)通道，并且应该保留，则使用模式LA:

img = Image.open('image.png').convert('LA')

使用matplotlib和公式

Y' = 0.2989 R + 0.5870 G + 0.1140 B 

你可以这样做:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

img = mpimg.imread('image.png')     
gray = rgb2gray(img)    
plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1)
plt.show()

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

我通过谷歌找到了这个问题，寻找一种将已经加载的图像转换为灰度的方法。

下面是一种使用SciPy的方法:

import scipy.misc
import scipy.ndimage

# Load an example image
# Use scipy.ndimage.imread(file_name, mode='L') if you have your own
img = scipy.misc.face()

# Convert the image
R = img[:, :, 0]
G = img[:, :, 1]
B = img[:, :, 2]
img_gray = R * 299. / 1000 + G * 587. / 1000 + B * 114. / 1000

# Show the image
scipy.misc.imshow(img_gray)

image=myCamera.getImage().crop(xx,xx,xx,xx).scale(xx,xx).greyscale()

您可以直接使用灰度()进行转换。