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()

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()

OpenCV很简单:

import cv2

im = cv2.imread("flower.jpg")

# To Grayscale
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
cv2.imwrite("grayscale.jpg", im)

# To Black & White
im = cv2.threshold(im, 127, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite("black-white.jpg", im)

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

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]]

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。