Elastic image effect, python opencv example source code.

Elastic effect source code.
It can be useful when you want to augment image dataset.
and it is also good to make image effect.

Please refer code:

import numpy as np

import cv2

from scipy.ndimage.interpolation import map_coordinates

from scipy.ndimage.filters import gaussian_filter

def elastic(image, alpha, sigma, random_state=None):

"""Elastic deformation of images as described in [Simard2003]_.

.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for

Convolutional Neural Networks applied to Visual Document Analysis", in

Proc. of the International Conference on Document Analysis and

Recognition, 2003.

"""

if random_state is None:

random_state = np.random.RandomState(None)

#print(random_state)

shape = image.shape

dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha

dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha

dz = np.zeros_like(dx)

x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))

indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))

distored_image = map_coordinates(image, indices, order=1, mode='nearest') #wrap,reflect, nearest

return distored_image.reshape(image.shape)

#main

#file read

o_img = cv2.imread('izone_oy.png')

elMat = elastic(o_img, alpha=5000, sigma=8, random_state=None)

cv2.namedWindow('origin',0)

cv2.imshow('origin', o_img)

cv2.namedWindow('elastic',0)

cv2.imshow('elastic', elMat)

cv2.waitKey(0)

GitHub url :

https://github.com/MareArts/Elastic_Effect.git

Test it here:
http://www.marearts.com/webapp/elastic/

TypeError: Incorrect type of self (must be 'Feature2D' or its derivative), FastFeatureDetector

This is because of opencv version difference.
Some function is changed as XX_create().
Refer to below code. This is case of FastFeatureDetector.
Thank you.

fast = cv2.FastFeatureDetector_create() # <- FastFeatureDetector()

kp = fast.detect(blurGrayMat)

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)

python opencv, example code for image pixel access

The code is example to access every pixel in opencv python.
More detail, just see below code.
Thank you.

import cv2

def OpenCV_Access_RGBValue(inMat):

#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):

for x in range(0, w):

# threshold the pixel

b = inMat[y, x, 0]

g = inMat[y, x, 1]

r = inMat[y, x, 2]

b = 255 - b

g = 255 - g

r = 255 - r

inMat[y, x, 0] = b

inMat[y, x, 1] = g

inMat[y, x, 2] = r

#image[y, x] = 255 if image[y, x] >= 128T else 0

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg')

#test value access

OpenCV_Access_RGBValue(inMat)

#display

cv2.namedWindow('test',0)

cv2.imwrite('output.jpg', inMat)

cv2.imshow('test',inMat)

cv2.waitKey(0)