How to build a Custom Data Generator for Keras/tf.Keras where X images are being augmented and corresponding Y labels are also images
Question:
I am working on Image Binarization using UNet and have a dataset of 150 images and their binarized versions too. My idea is to augment the images randomly to make them look like they are differentso I have made a function which inserts any of the 4-5 types of Noises, skewness, shearing and so on to an image. I could have easily used
ImageDataGenerator(preprocess_function=my_aug_function) to augment the images but the problem is that my y target is also an image. Also, I could have used something like:
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
)
But it has 2 problems:
- With larger dataset, it’ll blow up the memory as data needs to be already in the memory
- This is the crucial part that I need to augment the images on the go to make it look like I have a huge dataset.
Another Solution could be saving augmented images to a directory and making them 30-40K and then loading them. It would be silly thing to do.
Now the idea part is that I can use Sequence as the parent class but How can I keep on augmenting and generating new images on the fly with respective Y binarized images?
I have an idea as the below code. Can somebody help me with the augmentation and generation of y images. I have my X_DIR, Y_DIR where image names for binarised and original are same but stored in different directories.
class DataGenerator(tensorflow.keras.utils.Sequence):
def __init__(self, files_path, labels_path, batch_size=32, shuffle=True, random_state=42):
'Initialization'
self.files = files_path
self.labels = labels_path
self.batch_size = batch_size
self.shuffle = shuffle
self.random_state = random_state
self.on_epoch_end()
def on_epoch_end(self):
'Updates indexes after each epoch'
# Shuffle the data here
def __len__(self):
return int(np.floor(len(self.files) / self.batch_size))
def __getitem__(self, index):
# What do I do here?
def __data_generation(self, files):
# I think this is responsible for Augmentation but no idea how should I implement it and how does it works.
Answers:
You can use libraries like albumentations and imgaug, both are good but I have heard there are issues with random seed with albumentations.
Here’s an example of imgaug taken from the documentation here:
seq = iaa.Sequential([
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.0, 1.0)), # sharpen the image
iaa.Affine(rotate=(-45, 45)), # rotate by -45 to 45 degrees (affects segmaps)
iaa.ElasticTransformation(alpha=50, sigma=5) # apply water effect (affects segmaps)
], random_order=True)
# Augment images and segmaps.
images_aug = []
segmaps_aug = []
for _ in range(len(input_data)):
images_aug_i, segmaps_aug_i = seq(image=image, segmentation_maps=segmap)
images_aug.append(images_aug_i)
segmaps_aug.append(segmaps_aug_i)
You are going in the right way with the custom generator. In __getitem__, make a batch using batch_x = self.files[index:index+batch_size] and same with batch_y, then augment them using X,y = __data_generation(batch_x, batch_y) which will load images(using any library you like, I prefer opencv), and return the augmented pairs (and any other manipulation).
Your __getitem__ will then return the tuple (X,y)
You can use ImageDataGenerator even if your label is an image.
Here is a simple example of how you can do that:
Code:
# Specifying your data augmentation here for both image and label
image_datagen = tf.keras.preprocessing.image.ImageDataGenerator()
mask_datagen = tf.keras.preprocessing.image.ImageDataGenerator()
# Provide the same seed and keyword arguments to the flow methods
seed = 1
image_generator = image_datagen.flow_from_directory(
data_dir,
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
data_dir,
class_mode=None,
seed=seed)
# Combine the image and label generator.
train_generator = zip(image_generator, mask_generator)
Now, if you iterate over it you will get:
for image, label in train_generator:
print(image.shape,label.shape)
break
Output:
(32, 256, 256, 3) (32, 256, 256, 3)
You can use this train_generator with fit() command.
Code:
model.fit_generator(
train_generator,
steps_per_epoch=2000,
epochs=50)
With flow_from_directory your memory won’t be cluttered and Imagedatagenerator will take care of the augmentation part.
Custom Image Data Generator
load Directory data into dataframe for CustomDataGenerator
def data_to_df(data_dir, subset=None, validation_split=None):
df = pd.DataFrame()
filenames = []
labels = []
for dataset in os.listdir(data_dir):
img_list = os.listdir(os.path.join(data_dir, dataset))
label = name_to_idx[dataset]
for image in img_list:
filenames.append(os.path.join(data_dir, dataset, image))
labels.append(label)
df["filenames"] = filenames
df["labels"] = labels
if subset == "train":
split_indexes = int(len(df) * validation_split)
train_df = df[split_indexes:]
val_df = df[:split_indexes]
return train_df, val_df
return df
train_df, val_df = data_to_df(train_dir, subset="train", validation_split=0.2)
Custom Data Generator
import tensorflow as tf
from PIL import Image
import numpy as np
class CustomDataGenerator(tf.keras.utils.Sequence):
''' Custom DataGenerator to load img
Arguments:
data_frame = pandas data frame in filenames and labels format
batch_size = divide data in batches
shuffle = shuffle data before loading
img_shape = image shape in (h, w, d) format
augmentation = data augmentation to make model rebust to overfitting
Output:
Img: numpy array of image
label : output label for image
'''
def __init__(self, data_frame, batch_size=10, img_shape=None, augmentation=True, num_classes=None):
self.data_frame = data_frame
self.train_len = len(data_frame)
self.batch_size = batch_size
self.img_shape = img_shape
self.num_classes = num_classes
print(f"Found {self.data_frame.shape[0]} images belonging to {self.num_classes} classes")
def __len__(self):
''' return total number of batches '''
self.data_frame = shuffle(self.data_frame)
return math.ceil(self.train_len/self.batch_size)
def on_epoch_end(self):
''' shuffle data after every epoch '''
# fix on epoch end it's not working, adding shuffle in len for alternative
pass
def __data_augmentation(self, img):
''' function for apply some data augmentation '''
img = tf.keras.preprocessing.image.random_shift(img, 0.2, 0.3)
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
return img
def __get_image(self, file_id):
""" open image with file_id path and apply data augmentation """
img = np.asarray(Image.open(file_id))
img = np.resize(img, self.img_shape)
img = self.__data_augmentation(img)
img = preprocess_input(img)
return img
def __get_label(self, label_id):
""" uncomment the below line to convert label into categorical format """
#label_id = tf.keras.utils.to_categorical(label_id, num_classes)
return label_id
def __getitem__(self, idx):
batch_x = self.data_frame["filenames"][idx * self.batch_size:(idx + 1) * self.batch_size]
batch_y = self.data_frame["labels"][idx * self.batch_size:(idx + 1) * self.batch_size]
# read your data here using the batch lists, batch_x and batch_y
x = [self.__get_image(file_id) for file_id in batch_x]
y = [self.__get_label(label_id) for label_id in batch_y]
return tf.convert_to_tensor(x), tf.convert_to_tensor(y)
ImageDataGenerator is useful. but for files, their types are not supported by Keras, or for multiple-input multiple-output (mimo) models, you can use MIMODataGenerator.
https://pypi.org/project/mimo-keras/
I am working on Image Binarization using UNet and have a dataset of 150 images and their binarized versions too. My idea is to augment the images randomly to make them look like they are differentso I have made a function which inserts any of the 4-5 types of Noises, skewness, shearing and so on to an image. I could have easily used
ImageDataGenerator(preprocess_function=my_aug_function) to augment the images but the problem is that my y target is also an image. Also, I could have used something like:
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
)
But it has 2 problems:
- With larger dataset, it’ll blow up the memory as data needs to be already in the memory
- This is the crucial part that I need to augment the images on the go to make it look like I have a huge dataset.
Another Solution could be saving augmented images to a directory and making them 30-40K and then loading them. It would be silly thing to do.
Now the idea part is that I can use Sequence as the parent class but How can I keep on augmenting and generating new images on the fly with respective Y binarized images?
I have an idea as the below code. Can somebody help me with the augmentation and generation of y images. I have my X_DIR, Y_DIR where image names for binarised and original are same but stored in different directories.
class DataGenerator(tensorflow.keras.utils.Sequence):
def __init__(self, files_path, labels_path, batch_size=32, shuffle=True, random_state=42):
'Initialization'
self.files = files_path
self.labels = labels_path
self.batch_size = batch_size
self.shuffle = shuffle
self.random_state = random_state
self.on_epoch_end()
def on_epoch_end(self):
'Updates indexes after each epoch'
# Shuffle the data here
def __len__(self):
return int(np.floor(len(self.files) / self.batch_size))
def __getitem__(self, index):
# What do I do here?
def __data_generation(self, files):
# I think this is responsible for Augmentation but no idea how should I implement it and how does it works.
You can use libraries like albumentations and imgaug, both are good but I have heard there are issues with random seed with albumentations.
Here’s an example of imgaug taken from the documentation here:
seq = iaa.Sequential([
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.0, 1.0)), # sharpen the image
iaa.Affine(rotate=(-45, 45)), # rotate by -45 to 45 degrees (affects segmaps)
iaa.ElasticTransformation(alpha=50, sigma=5) # apply water effect (affects segmaps)
], random_order=True)
# Augment images and segmaps.
images_aug = []
segmaps_aug = []
for _ in range(len(input_data)):
images_aug_i, segmaps_aug_i = seq(image=image, segmentation_maps=segmap)
images_aug.append(images_aug_i)
segmaps_aug.append(segmaps_aug_i)
You are going in the right way with the custom generator. In __getitem__, make a batch using batch_x = self.files[index:index+batch_size] and same with batch_y, then augment them using X,y = __data_generation(batch_x, batch_y) which will load images(using any library you like, I prefer opencv), and return the augmented pairs (and any other manipulation).
Your __getitem__ will then return the tuple (X,y)
You can use ImageDataGenerator even if your label is an image.
Here is a simple example of how you can do that:
Code:
# Specifying your data augmentation here for both image and label
image_datagen = tf.keras.preprocessing.image.ImageDataGenerator()
mask_datagen = tf.keras.preprocessing.image.ImageDataGenerator()
# Provide the same seed and keyword arguments to the flow methods
seed = 1
image_generator = image_datagen.flow_from_directory(
data_dir,
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
data_dir,
class_mode=None,
seed=seed)
# Combine the image and label generator.
train_generator = zip(image_generator, mask_generator)
Now, if you iterate over it you will get:
for image, label in train_generator:
print(image.shape,label.shape)
break
Output:
(32, 256, 256, 3) (32, 256, 256, 3)
You can use this train_generator with fit() command.
Code:
model.fit_generator(
train_generator,
steps_per_epoch=2000,
epochs=50)
With flow_from_directory your memory won’t be cluttered and Imagedatagenerator will take care of the augmentation part.
Custom Image Data Generator
load Directory data into dataframe for CustomDataGenerator
def data_to_df(data_dir, subset=None, validation_split=None):
df = pd.DataFrame()
filenames = []
labels = []
for dataset in os.listdir(data_dir):
img_list = os.listdir(os.path.join(data_dir, dataset))
label = name_to_idx[dataset]
for image in img_list:
filenames.append(os.path.join(data_dir, dataset, image))
labels.append(label)
df["filenames"] = filenames
df["labels"] = labels
if subset == "train":
split_indexes = int(len(df) * validation_split)
train_df = df[split_indexes:]
val_df = df[:split_indexes]
return train_df, val_df
return df
train_df, val_df = data_to_df(train_dir, subset="train", validation_split=0.2)
Custom Data Generator
import tensorflow as tf
from PIL import Image
import numpy as np
class CustomDataGenerator(tf.keras.utils.Sequence):
''' Custom DataGenerator to load img
Arguments:
data_frame = pandas data frame in filenames and labels format
batch_size = divide data in batches
shuffle = shuffle data before loading
img_shape = image shape in (h, w, d) format
augmentation = data augmentation to make model rebust to overfitting
Output:
Img: numpy array of image
label : output label for image
'''
def __init__(self, data_frame, batch_size=10, img_shape=None, augmentation=True, num_classes=None):
self.data_frame = data_frame
self.train_len = len(data_frame)
self.batch_size = batch_size
self.img_shape = img_shape
self.num_classes = num_classes
print(f"Found {self.data_frame.shape[0]} images belonging to {self.num_classes} classes")
def __len__(self):
''' return total number of batches '''
self.data_frame = shuffle(self.data_frame)
return math.ceil(self.train_len/self.batch_size)
def on_epoch_end(self):
''' shuffle data after every epoch '''
# fix on epoch end it's not working, adding shuffle in len for alternative
pass
def __data_augmentation(self, img):
''' function for apply some data augmentation '''
img = tf.keras.preprocessing.image.random_shift(img, 0.2, 0.3)
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
return img
def __get_image(self, file_id):
""" open image with file_id path and apply data augmentation """
img = np.asarray(Image.open(file_id))
img = np.resize(img, self.img_shape)
img = self.__data_augmentation(img)
img = preprocess_input(img)
return img
def __get_label(self, label_id):
""" uncomment the below line to convert label into categorical format """
#label_id = tf.keras.utils.to_categorical(label_id, num_classes)
return label_id
def __getitem__(self, idx):
batch_x = self.data_frame["filenames"][idx * self.batch_size:(idx + 1) * self.batch_size]
batch_y = self.data_frame["labels"][idx * self.batch_size:(idx + 1) * self.batch_size]
# read your data here using the batch lists, batch_x and batch_y
x = [self.__get_image(file_id) for file_id in batch_x]
y = [self.__get_label(label_id) for label_id in batch_y]
return tf.convert_to_tensor(x), tf.convert_to_tensor(y)
ImageDataGenerator is useful. but for files, their types are not supported by Keras, or for multiple-input multiple-output (mimo) models, you can use MIMODataGenerator.
https://pypi.org/project/mimo-keras/