recep_field.py

####################################################### README #########################################################

# This file consists of function that convolves an image with a receptive field so that input to the network is 
# close to the form perceived by our eyes. 

#########################################################################################################################


import numpy as np
import cv2
from parameters import param as par

def rf(inp):
	sca1 =  0.625
	sca2 =  0.125
	sca3 = -0.125
	sca4 = -.5

	#Receptive field kernel
	w = [[	sca4 ,sca3 , sca2 ,sca3 ,sca4],
	 	[	sca3 ,sca2 , sca1 ,sca2 ,sca3],
	 	[ 	sca2 ,sca1 , 	1 ,sca1 ,sca2],
	 	[	sca3 ,sca2 , sca1 ,sca2 ,sca3],
	 	[	sca4 ,sca3 , sca2 ,sca3 ,sca4]]

	pot = np.zeros([par.pixel_x,par.pixel_x])
	ran = [-2,-1,0,1,2]
	ox = 2
	oy = 2

	#Convolution
	for i in range(par.pixel_x):
		for j in range(par.pixel_x):
			summ = 0
			for m in ran:
				for n in ran:
					if (i+m)>=0 and (i+m)<=par.pixel_x-1 and (j+n)>=0 and (j+n)<=par.pixel_x-1:
						summ = summ + w[ox+m][oy+n]*inp[i+m][j+n]/255
			pot[i][j] = summ
	return pot		

if __name__ == '__main__':

	img = cv2.imread("mnist1/" + str(1) + ".png", 0)
	pot = rf(img)
	max_a = []
	min_a = []
	for i in pot:
		max_a.append(max(i))
		min_a.append(min(i))
	print "max", max(max_a)
	print "min", min(min_a)