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preprocessing.py
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preprocessing.py
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import tensorflow as tf
import matplotlib.pyplot as plt
# load image and parse
def load_and_parse(img_path):
"""This function takes an image path and parse it to two image"""
img = tf.io.read_file(img_path)
img = tf.io.decode_jpeg(img)
img = tf.image.resize(img, [256, 512])
width = tf.shape(img)[1]
w = width // 2
original_image = img[:, :w, :]
transformed_image = img[:, w:, :]
# resize img
original_image = tf.image.resize(original_image, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
transformed_image = tf.image.resize(transformed_image, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
# normalize
original_image = (tf.cast(original_image, tf.float32) / 127.5) - 1
transformed_image = (tf.cast(transformed_image, tf.float32) / 127.5) - 1
return original_image, transformed_image
# random crop
def random_crop(original, transformed):
"""This method crops the images randomly"""
stacked = tf.stack([original, transformed], axis=0)
cropped = tf.image.random_crop(stacked, size=[2, 256, 256, 3])
return cropped[0], cropped[1]
# random jitter
@tf.function
def random_jitter(original, transformed):
"""This method for jittering the images"""
original, transformed = random_crop(original, transformed)
if tf.random.uniform(()) > 0.4:
original = tf.image.flip_left_right(original)
transformed = tf.image.flip_left_right(transformed)
return original, transformed
# concated function
def load_dataset(img_file):
"""This method will be concated in the function"""
original, transformed = load_and_parse(img_file)
original, transformed = random_jitter(original, transformed)
return original, transformed
# prepare the dataset
training_dataset = tf.data.Dataset.list_files("../DATASETS/gd/*.jpg")
training_dataset = training_dataset.map(load_dataset, num_parallel_calls=tf.data.experimental.AUTOTUNE)
training_dataset = training_dataset.shuffle(buffer_size=288)
training_dataset = training_dataset.batch(1)