How can I convert an RGB image into grayscale in P

问题:

I\'m trying to use matplotlib to read in an RGB image and convert it to grayscale.

In matlab I use this:

img = rgb2gray(imread(\'image.png\'));

In the matplotlib tutorial they don\'t cover it. They just read in the image

import matplotlib.image as mpimg
img = mpimg.imread(\'image.png\')

and then they slice the array, but that\'s not the same thing as converting RGB to grayscale from what I understand.

lum_img = img[:,:,0]

I find it hard to believe that numpy or matplotlib doesn\'t have a built-in function to convert from rgb to gray. Isn\'t this a common operation in image processing?

I wrote a very simple function that works with the image imported using imread in 5 minutes. It\'s horribly inefficient, but that\'s why I was hoping for a professional implementation built-in.

Sebastian has improved my function, but I\'m still hoping to find the built-in one.

matlab\'s (NTSC/PAL) implementation:

import numpy as np

def rgb2gray(rgb):

    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b

    return gray

回答1:

How about doing it with Pillow:

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

---

Using matplotlib and the formula

Y\' = 0.299 R + 0.587 G + 0.114 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], [0.299, 0.587, 0.114])

img = mpimg.imread(\'image.png\')     
gray = rgb2gray(img)    
plt.imshow(gray, cmap = plt.get_cmap(\'gray\'))
plt.show()

回答2:

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_grey=True)

回答3:

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)) * 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
   

回答4:

You can always read the image file as grayscale right from the beginning using imread from OpenCV:

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

Furthermore, in case you want to read the image as RGB, do some processing and then convert to Gray Scale you could use cvtcolor from OpenCV:

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

回答5:

The fastest and current way is to use Pillow, installed via pip install Pillow.

The code is then:

from PIL import Image
img = Image.open(\'input_file.jpg\').convert(\'L\')
img.save(\'output_file.jpg\')

回答6:

The tutorial is cheating because it is starting with a greyscale image encoded in RGB, so they are just slicing a single color channel and treating it as greyscale. The basic steps you need to do are to transform from the RGB colorspace to a colorspace that encodes with something approximating the luma/chroma model, such as YUV/YIQ or HSL/HSV, then slice off the luma-like channel and use that as your greyscale image. matplotlib does not appear to provide a mechanism to convert to YUV/YIQ, but it does let you convert to HSV.

Try using matplotlib.colors.rgb_to_hsv(img) then slicing the last value (V) from the array for your grayscale. It\'s not quite the same as a luma value, but it means you can do it all in matplotlib.

Background:

• http://matplotlib.sourceforge.net/api/colors_api.html
• http://en.wikipedia.org/wiki/HSL_and_HSV

Alternatively, you could use PIL or the builtin colorsys.rgb_to_yiq() to convert to a colorspace with a true luma value. You could also go all in and roll your own luma-only converter, though that\'s probably overkill.

回答7:

If you\'re using NumPy/SciPy already you may as well use:

scipy.ndimage.imread(file_name, mode=\'L\')

Hth, dtk

回答8:

I came to this question via Google, searching for a way to convert an already loaded image to grayscale.

Here is a way to do it with SciPy:

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)

回答9:

you could do:

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

        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()

回答10:

Use img.Convert(), supports “L”, “RGB” and “CMYK.” mode

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)

Output:

[[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]]

回答11:

Using this formula

Y\' = 0.299 R + 0.587 G + 0.114 B 

We can do

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\'))

However, the GIMP converting color to grayscale image software has three algorithms to do the task.

回答12:

image=myCamera.getImage().crop(xx,xx,xx,xx).scale(xx,xx).greyscale()

You can use greyscale() directly for the transformation.