Computer Vision News - January 2023

6 Computer Vision Tools import numpy as np import tensorflow as tf from tensorflow.keras.preprocessing import image_dataset_from_directory # Reproducability def set_seed(seed=31415): np.random.seed(seed) tf.random.set_seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) os.environ['TF_DETERMINISTIC_OPS'] = '1' set_seed() # Set Matplotlib defaults plt.rc ('figure', autolayout=True) plt.rc ('axes', labelweight='bold', labelsize='large', titleweight='bold', titlesize=18, titlepad=10) plt.rc ('image', cmap='magma') warnings.filterwarnings("ignore") # to clean up output cells # Load training and validation sets ds_train_ = image_dataset_from_directory( '../input/car-or-truck/train', labels='inferred', label_mode='binary', image_size=[128, 128], interpolation='nearest', batch_size=64, shuffle=True, ) ds_valid_ = image_dataset_from_directory( '../input/car-or-truck/valid', labels='inferred', label_mode='binary', image_size=[128, 128], interpolation='nearest', batch_size=64, shuffle=False, ) # Data Pipeline def convert_to_float(image, label): image = tf.image.convert_image_dtype(image, dtype=tf.float32) return image, label AUTOTUNE = tf.data.experimental.AUTOTUNE ds_train = ( ds_train_ .map(convert_to_float) .cache() .prefetch(buffer_size=AUTOTUNE) ) ds_valid = ( ds_valid_ .map(convert_to_float) .cache()