one_scale_one_phase.py

import scipy as sp
from scipy import arange, sqrt, array, amax, ceil, io, fliplr, flipud
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import ipdb
import time
from tqdm import tqdm

rot = np.array([90,-67.5,-45,-22.5,0,22.5,45,67.5])
RF_siz = [33]
Div = sp.arange(4,3.15,-.05)

def create_gabor(rot,RF_siz,Div,plot,num=10):
    count = 0
    numFilterSizes   = len(RF_siz)
    numSimpleFilters = len(rot)
    lamb = (RF_siz[0] * 2)/4.0
    sigma  = [lamb * 0.8]
    G      = 0.3
    phases = [0]

    # Initialize Filterbank
    alt_fb = np.zeros((33,33,1,8),dtype=np.float32)


    # for k in tqdm(range(0,numFilterSizes-1)):
    for k in tqdm(range(1,numFilterSizes+1)):
        for r in tqdm(range(1,numSimpleFilters+1)):

            f = np.zeros([RF_siz[numFilterSizes-1],RF_siz[numFilterSizes-1]])
            fx = np.zeros([RF_siz[numFilterSizes-1],RF_siz[numFilterSizes-1]])

            ## Parameters
            theta     = rot[r-1]*(np.pi/180)
            filtSize  = RF_siz[k-1]

            img_center = ceil(filtSize/2.0) ## New center for padding with zeros

            center    = ceil(filtSize/2.0) ## Old and possibly more accurate center


            filtSizeL = center-1
            filtSizeR = filtSize-filtSizeL-1
            sigmaq    = (sigma[k-1]) * (sigma[k-1])


            # Compute filter values

            for i in range(int(-1 * filtSizeL),int(filtSizeR+1)):
                for j in range(int(-1 * filtSizeL),int(filtSizeR+1)):

                    if (sqrt((i**2)+(j**2))>(filtSize/2 )) :
                        E = 0
                    else :
                        x = i*np.cos(theta) - j*np.sin(theta)
                        y = i*np.sin(theta) + j*np.cos(theta)

                        E = np.exp((-1*((x**2)+ (G**2) * (y**2)))/(2*sigmaq))*np.cos(2*np.pi*x/lamb + phases)

                    f[int(j+img_center-1),int(i+img_center-1)] = E


            ## Append to fb (filterbank)
            f = f - np.mean(np.mean(f))
            f = f / sqrt(np.sum(np.sum(f**2)))

            # Reshaped image
            alt_fb[:,:,0,count] = f
            count += 1

            if (plot):
                if count % num == 0:
                    plt.imshow(f,cmap='Greys')
                    plt.show()

    return (np.array(alt_fb))



## Create Dictionary ---------------

gabor_array = create_gabor(rot,RF_siz,Div,False)

bias = []

gabor_dictionary = {}
gabor_dictionary['gabors'] = gabor_array,bias

f,x = gabor_dictionary['gabors']

## Save Dictionary ----------------------
np.save('/Users/charlieholtz/Desktop/gabors/gabor_dictionary.npy', gabor_dictionary)