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

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

How about doing it with Pillow:

from PIL import Image
img = 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 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()

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

from skimage import color
from skimage import io

img = 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 io
img = io.imread(image.png, as_gray=True)

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

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: SkImage

PIL : PIL

SciPy : SciPy

Original: Original

Diff : enter

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)) * 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))
    
  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.color
    import skimage.io
    import random
    import time
    from PIL import Image
    import numpy as np
    import scipy.ndimage
    import IPython.display
    
  5. Versions
    skimage.version
    0.13.0
    scipy.version
    0.19.1
    np.version
    1.13.1
    

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