#Ref: <NAME>



import numpy as np
import cv2
import pandas as pd
 
#img = cv2.imread('BSE_Image.jpg')
img = cv2.imread('images/synthetic.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
#Here, if you have multichannel image then extract the right channel instead of converting the image to grey. 
#For example, if DAPI contains nuclei information, extract the DAPI channel image first. 

#Multiple images can be used for training. For that, you need to concatenate the data

#Save original image pixels into a data frame. This is our Feature #1.
img2 = img.reshape(-1)
df = pd.DataFrame()
df['Original Image'] = img2

#Generate Gabor features
num = 1  #To count numbers up in order to give Gabor features a lable in the data frame
kernels = []  #Create empty list to hold all kernels that we will generate in a loop
for theta in range(2):   #Define number of thetas. Here only 2 theta values 0 and 1/4 . pi 
    theta = theta / 4. * np.pi
    for sigma in (1, 3):  #Sigma with values of 1 and 3
        for lamda in np.arange(0, np.pi, np.pi / 4):   #Range of wavelengths
            for gamma in (0.05, 0.5):   #Gamma values of 0.05 and 0.5
                           
                gabor_label = 'Gabor' + str(num)  #Label Gabor columns as Gabor1, Gabor2, etc.
#                print(gabor_label)
                ksize=9
                kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, lamda, gamma, 0, ktype=cv2.CV_32F)    
                kernels.append(kernel)
                #Now filter the image and add values to a new column 
                fimg = cv2.filter2D(img2, cv2.CV_8UC3, kernel)
                filtered_img = fimg.reshape(-1)
                df[gabor_label] = filtered_img  #Labels columns as Gabor1, Gabor2, etc.
                print(gabor_label, ': theta=', theta, ': sigma=', sigma, ': lamda=', lamda, ': gamma=', gamma)
                num += 1  #Increment for gabor column label
                
print(df.head())

df.to_csv("Gabor.csv")
#Ref: <NAME>



import numpy as np
import cv2
import pandas as pd
 
#img = cv2.imread('BSE_Image.jpg')
img = cv2.imread('images/synthetic.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
#Here, if you have multichannel image then extract the right channel instead of converting the image to grey. 
#For example, if DAPI contains nuclei information, extract the DAPI channel image first. 

#Multiple images can be used for training. For that, you need to concatenate the data

#Save original image pixels into a data frame. This is our Feature #1.
img2 = img.reshape(-1)
df = pd.DataFrame()
df['Original Image'] = img2

#Generate Gabor features
num = 1  #To count numbers up in order to give Gabor features a lable in the data frame
kernels = []  #Create empty list to hold all kernels that we will generate in a loop
for theta in range(2):   #Define number of thetas. Here only 2 theta values 0 and 1/4 . pi 
    theta = theta / 4. * np.pi
    for sigma in (1, 3):  #Sigma with values of 1 and 3
        for lamda in np.arange(0, np.pi, np.pi / 4):   #Range of wavelengths
            for gamma in (0.05, 0.5):   #Gamma values of 0.05 and 0.5
                           
                gabor_label = 'Gabor' + str(num)  #Label Gabor columns as Gabor1, Gabor2, etc.
#                print(gabor_label)
                ksize=9
                kernel = cv2.getGaborKernel((ksize, ksize), sigma, theta, lamda, gamma, 0, ktype=cv2.CV_32F)    
                kernels.append(kernel)
                #Now filter the image and add values to a new column 
                fimg = cv2.filter2D(img2, cv2.CV_8UC3, kernel)
                filtered_img = fimg.reshape(-1)
                df[gabor_label] = filtered_img  #Labels columns as Gabor1, Gabor2, etc.
                print(gabor_label, ': theta=', theta, ': sigma=', sigma, ': lamda=', lamda, ': gamma=', gamma)
                num += 1  #Increment for gabor column label
                
print(df.head())

df.to_csv("Gabor.csv")
#Ref: <NAME> #img = cv2.imread('BSE_Image.jpg') #Here, if you have multichannel image then extract the right channel instead of converting the image to grey. #For example, if DAPI contains nuclei information, extract the DAPI channel image first. #Multiple images can be used for training. For that, you need to concatenate the data #Save original image pixels into a data frame. This is our Feature #1. #Generate Gabor features #To count numbers up in order to give Gabor features a lable in the data frame #Create empty list to hold all kernels that we will generate in a loop #Define number of thetas. Here only 2 theta values 0 and 1/4 . pi #Sigma with values of 1 and 3 #Range of wavelengths #Gamma values of 0.05 and 0.5 #Label Gabor columns as Gabor1, Gabor2, etc. #                print(gabor_label) #Now filter the image and add values to a new column #Labels columns as Gabor1, Gabor2, etc. #Increment for gabor column label