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])

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

``````

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
``````

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:`

`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)
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)
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
IPython.display.display(PIL.Image.fromarray(img1).convert(RGB))
img2 = np.array(Image.open(z).convert(L))
IPython.display.display(PIL.Image.fromarray(img2))
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
``````