data_processing_1.py

import cv2
import os
import numpy as np
from keras.utils import np_utils

data_path = 'dataset'
categories = os.listdir(data_path)
labels = [i for i in range(len(categories))]

label_dict = dict(zip(categories, labels))

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

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)
            # Converting the image into gray scale
            resized = cv2.resize(gray, (img_size, img_size))
            # resizing the gray scale into 50x50, since we need a fixed common size for all the images in the dataset
            data.append(resized)
            target.append(label_dict[category])
            # appending the image and the label(categorized) into the list (dataset)

        except Exception as e:
            print('Exception:', e)
            # if any exception raised, the exception will be printed here. And pass to the next image



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

new_target = np_utils.to_categorical(target)

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