How can I convert an RGB image into grayscale in Python?

How about doing it with Pillow:

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

If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA:

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

Using matplotlib and the formula

Y' = 0.2989 R + 0.5870 G + 0.1140 B 

you could do:

import numpy as npimport matplotlib.pyplot as pltimport matplotlib.image as mpimgdef 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()

You can also use scikit-image, which provides some functions to convert an image in ndarray, like rgb2gray.

from skimage import colorfrom skimage import ioimg = color.rgb2gray(io.imread('image.png'))

Notes: The weights used in this conversion are calibrated for contemporary CRT phosphors: Y = 0.2125 R + 0.7154 G + 0.0721 B

Alternatively, you can read image in grayscale by:

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

Three of the suggested methods were tested for speed with 1000 RGBA PNG images (224 x 256 pixels) running with Python 3.5 on Ubuntu 16.04 LTS (Xeon E5 2670 with SSD).

Average run times

pil : 1.037 seconds

scipy: 1.040 seconds

sk : 2.120 seconds

PIL and SciPy gave identical numpy arrays (ranging from 0 to 255). SkImage gives arrays from 0 to 1. In addition the colors are converted slightly different, see the example from the CUB-200 dataset.

SkImage:

PIL :

SciPy :

Original:

Diff :

Code

1. 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)))

2. Output

   z = 'Cardinal_0007_3025810472.jpg'img1 = skimage.color.rgb2gray(skimage.io.imread(z)) * 255IPython.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))

3. 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'))

4. Imports

   import skimage.colorimport skimage.ioimport randomimport timefrom PIL import Imageimport numpy as npimport scipy.ndimageimport IPython.display

5. Versions

   skimage.version0.13.0scipy.version0.19.1np.version1.13.1