Gabor.py

#Features extracted from this are "Local Energy" and "Mean Amplitude" at different angles and wavelengths (frequencies)
#Number of features extracted = number of angles chosen * number of wavelengths chosen

import cv2
import os
import glob
import numpy as np 
import matplotlib.pyplot as plt
import time
import csv

tic=time.time()

#Importing the images
img_dir = ".../...../..." # Enter Directory where all the images are stored
data_path = os.path.join(img_dir,'*g')
files = glob.glob(data_path)

eo=len(files)

img = []
for f1 in files:
    data = cv2.imread(f1)
    img.append(data)

gamma=0.5
sigma=0.56
theta_list=[0, np.pi, np.pi/2, np.pi/4, 3*np.pi/4] #Angles
phi=0
lamda_list=[2*np.pi/1, 2*np.pi/2, 2*np.pi/3, 2*np.pi/4, 2*np.pi/5] #wavelengths
num=1

#Creating headings for the csv file
gabor_label=[]
for i in range(50):
    gabor_label.append('Gabor'+str(i+1))

with open('Gabor.csv','a+',newline='') as file:
    writer=csv.writer(file)
    #writer.writerow(gabor_label)
    
    for i in range(eo):
        img[i] = cv2.cvtColor(img[i] , cv2.COLOR_BGR2GRAY)
        print("For image number"+str(i+1)+'\n')
        local_energy_list=[]
        mean_ampl_list=[]
        
        for theta in theta_list:
            print("For theta = "+str(theta/np.pi)+"pi\n")
            
            for lamda in lamda_list:
                kernel=cv2.getGaborKernel((3,3),sigma,theta,lamda,gamma,phi,ktype=cv2.CV_32F)
                fimage = cv2.filter2D(img[i], cv2.CV_8UC3, kernel)
                
                mean_ampl=np.sum(abs(fimage))
                mean_ampl_list.append(mean_ampl)
                
                local_energy=np.sum(fimage**2)
                local_energy_list.append(local_energy)
                
                num+=1
        print('\n\n')
        writer.writerow(local_energy_list+mean_ampl_list)

toc=time.time()
print("Computation time is {} seconds".format(str(toc-tic)))