Keep Same Dataset Augmentation For Input And Output In Tensorflow

I have a batch dataset which contains image as input and output. Code is like this: os.chdir(r'E:/trainTest') def process_img(file_path): img = tf.io.read_file(file_path)

Solution 1:

tf.image has a bunch of random transformations you can use. For instance:

• random_brightness
• random_hue
• random_saturation

Here's an example on a completely random image of a cat.

from skimage import data
import matplotlib.pyplot as plt
import tensorflow as tf

cat = data.chelsea()

plt.imshow(cat)
plt.show()

Image with transformation:

from skimage import data
import matplotlib.pyplot as plt
import tensorflow as tf

cat = data.chelsea()

plt.imshow(tf.image.random_hue(cat, .2, .5))
plt.show()

You can implement this in you tf.data.Dataset like this:

def process_img(file_path):
    img = tf.io.read_file(file_path)
    img = tf.image.decode_png(img, channels=3)
    img = tf.image.convert_image_dtype(img, tf.float32)
    img = tf.image.resize(img, size=(img_height, img_width))
    img = tf.image.random_hue(img, 0., .5) # Here
    return img

I found a way to keep the same transformation in graph mode. It's basically to pass two images in the same call to the transformers.

import os
import tensorflow as tf
os.chdir(r'c:/users/user/Pictures')
from glob2 import glob
import matplotlib.pyplot as plt

x_files = glob('inputs/*.jpg')
y_files = glob('targets/*.jpg')

files_ds = tf.data.Dataset.from_tensor_slices((x_files, y_files))

def load(file_path):
    img = tf.io.read_file(file_path)
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.convert_image_dtype(img, tf.float32)
    img = tf.image.resize(img, size=(28, 28))
    return img

def process_img(file_path1, file_path2):
    img = tf.stack([load(file_path1), load(file_path2)])
    img = tf.image.random_hue(img, max_delta=.5)
    return img[0], img[1]

files_ds = files_ds.map(lambda x, y: process_img(x, y)).batch(1)

a, b = next(iter(files_ds))

plt.imshow(a[0, ...])
plt.imshow(b[0, ...])

Solution 2:

You can use tf.keras.preprocessing.image.ImageDataGenerator for preprocessing. This is the documentation page for complete options. I have shared a small example on how you could use that with flow_from_directory. You dont have read the images beforehand and use up your RAM. The images are loaded from directory, preprocessed and fed into the model as and when required.

# we create two instances with the same arguments
data_gen_args = dict(rescale=1./255,
                     shear_range=0.2,
                     horizontal_flip=True,
                     rotation_range=90,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     zoom_range=0.2)

image_datagen = ImageDataGenerator(**data_gen_args)
mask_datagen = ImageDataGenerator(**data_gen_args)

# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1

image_generator = image_datagen.flow_from_directory(
    'data/images',
    class_mode=None,
    seed=seed)

mask_generator = mask_datagen.flow_from_directory(
    'data/masks',
    class_mode=None,
    seed=seed)

# combine generators into one which yields image and masks
train_generator = zip(image_generator, mask_generator)

model.fit(
    train_generator,
    steps_per_epoch=2000,
    epochs=50)