This is an augmented version of the original QR-code dataset (https://www.kaggle.com/datasets/coledie/qr-codes). The purpose of this set is to simulate real-life data by adding noise, random cropping, shear, and rotation. This would help create more robust Object-detection and Generative models. In addition, the below code would also help with creating your very own Augmented dataset.

import cv2  import os datadir = 'qr_dataset'  #you'll have to change datadir accordingly array = [] array_small =[] from tqdm import tqdm def create_training_data():         for img in tqdm(list(os.listdir(datadir))):  # iterate over each image per dogs and cats             try:                 img_array = cv2.imread(datadir+'/'+img ,cv2.IMREAD_COLOR)  # convert to array                 new_array = cv2.resize(img_array, (128, 128))  # resize to normalize data size                 array.append([new_array])                  array_small.append([cv2.resize(img_array, (32,32),                                interpolation=cv2.INTER_AREA)]) # add this to our training_data             except Exception as e:  # in the interest in keeping the output clean...                 pass create_training_data() 

#augmenting the data from keras.preprocessing.image import ImageDataGenerator #this generator will save files in a physical format from skimage import io datagen = ImageDataGenerator(                 rotation_range = 40,         shear_range = 0.2,         zoom_range = 0.2,         horizontal_flip = True,         brightness_range = (0.5, 1.5))  for a in X:  i = 0  a = a.reshape((1, ) + a.shape)  for batch in datagen.flow(a, batch_size=1,  save_to_dir= 'Augmented-images', save_prefix='dr', save_format='jpeg'):       try: #iterate over every image and augment it     i += 1        if i>= 10:       break    except Exception: #in case the image doesn't exist     print("error")     pass