你可以这样做:

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

def rgb_to_gray(img):
        grayImage = np.zeros(img.shape)
        R = np.array(img[:, :, 0])
        G = np.array(img[:, :, 1])
        B = np.array(img[:, :, 2])

        R = (R *.299)
        G = (G *.587)
        B = (B *.114)

        Avg = (R+G+B)
        grayImage = img.copy()

        for i in range(3):
           grayImage[:,:,i] = Avg
           
        return grayImage       

image = mpimg.imread("your_image.png")   
grayImage = rgb_to_gray(image)  
plt.imshow(grayImage)
plt.show()

你可以使用OpenCV的imread从一开始就读取灰度图像文件:

img = cv2.imread('messi5.jpg', 0)

此外，如果你想读取图像为RGB，做一些处理，然后转换为灰度，你可以使用cvtcolor从OpenCV:

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

使用这个公式

Y' = 0.299 R + 0.587 G + 0.114 B 

我们可以

import imageio
import numpy as np
import matplotlib.pyplot as plt

pic = imageio.imread('(image)')
gray = lambda rgb : np.dot(rgb[... , :3] , [0.299 , 0.587, 0.114]) 
gray = gray(pic)  
plt.imshow(gray, cmap = plt.get_cmap(name = 'gray'))

然而，将彩色图像转换为灰度图像的GIMP软件有三种算法来完成这项任务。

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)

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()