reconstruct.py

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

# This file is used to leverage the generative property of a Spiking Neural Network. reconst_weights function is used
# for that purpose. Looking at the reconstructed images helps to analyse training process.

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


import numpy as np
from numpy import interp
import cv2
from recep_field import rf
from parameters import param as par


def reconst_weights(weights, num):
    weights = np.array(weights)
    weights = np.reshape(weights, (par.pixel_x, par.pixel_x))
    img = np.zeros((par.pixel_x, par.pixel_x))
    for i in range(par.pixel_x):
        for j in range(par.pixel_x):
            img[i][j] = int(interp(weights[i][j], [par.w_min, par.w_max], [0, 255]))

    cv2.imwrite('neuron' + str(num) + '.png', img)
    print "Returning image reconstructed from weight"
    return img


def reconst_rf(weights, num):
    weights = np.array(weights)
    weights = np.reshape(weights, (par.pixel_x, par.pixel_x))
    img = np.zeros((par.pixel_x, par.pixel_x))
    for i in range(par.pixel_x):
        for j in range(par.pixel_x):
            img[i][j] = int(interp(weights[i][j], [-2, 3.625], [0, 255]))

    cv2.imwrite('neuron' + str(num) + '.png', img)
    return img


if __name__ == '__main__':
    img = cv2.imread("images2/" + "69" + ".png", 0)
    pot = rf(img)
    reconst_rf(pot, 12)