dataPreprocessing.py

import cv2,os

data_path='Training'
categories=os.listdir(data_path)

labels=[i for i in range(len(categories))]

label_dict=dict(zip(categories,labels)) #empty dictionary

print(label_dict)
print(categories)
print(labels)

img_size = 100
data = []
target = []

for category in categories:
    folder_path = os.path.join(data_path, category)
    img_names = os.listdir(folder_path)
  

    for img_name in img_names:
        img_path = os.path.join(folder_path, img_name)
        img = cv2.imread(img_path)
      

        try:
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

            resized = cv2.resize(gray, (img_size, img_size))

            data.append(resized)
            target.append(label_dict[category])
            

        except Exception as e:
            print('Exception:', e)

import numpy as np

data=np.array(data)/255.0
data=np.reshape(data,(data.shape[0],img_size,img_size,1))
target=np.array(target)

from keras.utils import np_utils

new_target=np_utils.to_categorical(target)

np.save('data',data)
np.save('target',new_target)