Convert RGB to black OR white

问题:

How would I take an RGB image in Python and convert it to black OR white? Not grayscale, I want each pixel to be either fully black (0, 0, 0) or fully white (255, 255, 255).

Is there any built-in functionality for this in the popular Python image processing libraries? If not, would the best way be just to loop through each pixel, if it's closer to white set it to white, if it's closer to black set it to black?

回答1:

Scaling to Black and White

Convert to grayscale and then scale to white or black (whichever is closest).

Original:

Result:

Pure Pillow implementation

Install pillow if you haven't already:

$ pip install pillow

Pillow (or PIL) can help you work with images effectively.

from PIL import Image

col = Image.open("cat-tied-icon.png")
gray = col.convert('L')
bw = gray.point(lambda x: 0 if x<128 else 255, '1')
bw.save("result_bw.png")

Alternatively, you can use Pillow with numpy.

Pillow + Numpy Bitmasks Approach

You'll need to install numpy:

$ pip install numpy

Numpy needs a copy of the array to operate on, but the result is the same.

from PIL import Image
import numpy as np

col = Image.open("cat-tied-icon.png")
gray = col.convert('L')

# Let numpy do the heavy lifting for converting pixels to pure black or white
bw = np.asarray(gray).copy()

# Pixel range is 0...255, 256/2 = 128
bw[bw < 128] = 0    # Black
bw[bw >= 128] = 255 # White

# Now we put it back in Pillow/PIL land
imfile = Image.fromarray(bw)
imfile.save("result_bw.png")

Black and White using Pillow, with dithering

Using pillow you can convert it directly to black and white. It will look like it has shades of grey but your brain is tricking you! (Black and white near each other look like grey)

from PIL import Image 
image_file = Image.open("cat-tied-icon.png") # open colour image
image_file = image_file.convert('1') # convert image to black and white
image_file.save('/tmp/result.png')

Original:

Converted:

Black and White using Pillow, without dithering

from PIL import Image 
image_file = Image.open("cat-tied-icon.png") # open color image
image_file = image_file.convert('1', dither=Image.NONE) # convert image to black and white
image_file.save('/tmp/result.png')

回答2:

I would suggest converting to grayscale, then simply applying a threshold (halfway, or mean or meadian, if you so choose) to it.

from PIL import Image

col = Image.open('myimage.jpg')
gry = col.convert('L')
grarray = np.asarray(gry)
bw = (grarray > grarray.mean())*255
imshow(bw)

回答3:

Pillow, with dithering

Using pillow you can convert it directly to black and white. It will look like it has shades of grey but your brain is tricking you! (Black and white near each other look like grey)

from PIL import Image 
image_file = Image.open("cat-tied-icon.png") # open colour image
image_file = image_file.convert('1') # convert image to black and white
image_file.save('/tmp/result.png')

Original:

Converted:

回答4:

And you can use colorsys (in the standard library) to convert rgb to hls and use the lightness value to determine black/white:

import colorsys
# convert rgb values from 0-255 to %
r = 120/255.0
g = 29/255.0
b = 200/255.0
h, l, s = colorsys.rgb_to_hls(r, g, b)
if l >= .5:
    # color is lighter
    result_rgb = (255, 255, 255)
elif l < .5:
    # color is darker
    result_rgb = (0,0,0)

回答5:

Using opencv You can easily convert rgb to binary image

import cv2
%matplotlib inline 
import matplotlib.pyplot as plt
from skimage import io
from PIL import Image
import numpy as np

img = io.imread('http://www.bogotobogo.com/Matlab/images/MATLAB_DEMO_IMAGES/football.jpg')
img = cv2.cvtColor(img, cv2.IMREAD_COLOR)
imR=img[:,:,0] #only taking gray channel
print(img.shape)
plt.imshow(imR, cmap=plt.get_cmap('gray'))

#Gray Image
plt.imshow(imR)
plt.title('my picture')
plt.show()

#Histogram Analyze

imgg=imR
hist = cv2.calcHist([imgg],[0],None,[256],[0,256])
plt.hist(imgg.ravel(),256,[0,256])

# show the plotting graph of an image

plt.show()

#Black And White
height,width=imgg.shape
for i in range(0,height):
  for j in range(0,width):
     if(imgg[i][j]>60):
        imgg[i][j]=255
     else:
        imgg[i][j]=0

plt.imshow(imgg)

回答6:

img_rgb = cv2.imread('image.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
(threshi, img_bw) = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)