Dithering python opencv source code (Floyd–Steinberg dithering)

This is dithering example, it make image like a stippling effect.

I referenced to blew website.
wiki page:
https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering 

youtube tutorial (thank you, good job!) :
https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

In the source code, there are two functions those are :

dithering_gray, dithering_color

One is for Gary image (1 channel), other is for color (3 channel).

It's very easy to use, just call function~ ^^

import cv2

import numpy as np

def minmax(v):

if v > 255:

v = 255

if v < 0:

v = 0

return v

def dithering_gray(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_p = inMat[y, x]

new_p = np.round(samplingF * old_p/255.0) * (255/samplingF)

inMat[y, x] = new_p

quant_error_p = old_p - new_p

# inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

# inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

# inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

# inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

def dithering_color(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_b = inMat[y, x, 0]

old_g = inMat[y, x, 1]

old_r = inMat[y, x, 2]

new_b = np.round(samplingF * old_b/255.0) * (255/samplingF)

new_g = np.round(samplingF * old_g/255.0) * (255/samplingF)

new_r = np.round(samplingF * old_r/255.0) * (255/samplingF)

inMat[y, x, 0] = new_b

inMat[y, x, 1] = new_g

inMat[y, x, 2] = new_r

quant_error_b = old_b - new_b

quant_error_g = old_g - new_g

quant_error_r = old_r - new_r

inMat[y, x+1, 0] = minmax(inMat[y, x+1, 0] + quant_error_b * 7 / 16.0)

inMat[y, x+1, 1] = minmax(inMat[y, x+1, 1] + quant_error_g * 7 / 16.0)

inMat[y, x+1, 2] = minmax(inMat[y, x+1, 2] + quant_error_r * 7 / 16.0)

inMat[y+1, x-1, 0] = minmax(inMat[y+1, x-1, 0] + quant_error_b * 3 / 16.0)

inMat[y+1, x-1, 1] = minmax(inMat[y+1, x-1, 1] + quant_error_g * 3 / 16.0)

inMat[y+1, x-1, 2] = minmax(inMat[y+1, x-1, 2] + quant_error_r * 3 / 16.0)

inMat[y+1, x, 0] = minmax(inMat[y+1, x, 0] + quant_error_b * 5 / 16.0)

inMat[y+1, x, 1] = minmax(inMat[y+1, x, 1] + quant_error_g * 5 / 16.0)

inMat[y+1, x, 2] = minmax(inMat[y+1, x, 2] + quant_error_r * 5 / 16.0)

inMat[y+1, x+1, 0] = minmax(inMat[y+1, x+1, 0] + quant_error_b * 1 / 16.0)

inMat[y+1, x+1, 1] = minmax(inMat[y+1, x+1, 1] + quant_error_g * 1 / 16.0)

inMat[y+1, x+1, 2] = minmax(inMat[y+1, x+1, 2] + quant_error_r * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg') #lena.png')

#color ditering

outMat_color = dithering_color(inMat.copy(), 1)

cv2.imwrite('out_color.jpg', outMat_color)

#gray ditering

grayMat = cv2.cvtColor(inMat, cv2.COLOR_BGR2GRAY)

outMat_gray = dithering_gray(grayMat.copy(), 1)

cv2.imwrite('out_gray.jpg', outMat_gray)