Using ImageDataGenerator with images in .npy format

now the method has moved here for upper answer

https://github.com/keras-team/keras-preprocessing/blob/3e380065d4afc7347aaee8d89325a16b22158438/keras_preprocessing/image/iterator.py

change the load img here

tf.keras.preprocessing.image.ImageDataGenerator(
    featurewise_center=False, samplewise_center=False,
    featurewise_std_normalization=False, samplewise_std_normalization=False,
    zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0,
    height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0,
    channel_shift_range=0.0, fill_mode='nearest', cval=0.0,
    horizontal_flip=False, vertical_flip=False, rescale=None,
    preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None
)

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = utils.to_categorical(y_train, num_classes)
y_test = utils.to_categorical(y_test, num_classes)
datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2)
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(x_train)
# fits the model on batches with real-time data augmentation:
model.fit(datagen.flow(x_train, y_train, batch_size=32,
         subset='training'),
         validation_data=datagen.flow(x_train, y_train,
         batch_size=8, subset='validation'),
         steps_per_epoch=len(x_train) / 32, epochs=epochs)
# here's a more "manual" example
for e in range(epochs):
    print('Epoch', e)
    batches = 0
    for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
        model.fit(x_batch, y_batch)
        batches += 1
        if batches >= len(x_train) / 32:
            # we need to break the loop by hand because
            # the generator loops indefinitely
            break

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        'data/train',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
        'data/validation',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')
model.fit(
        train_generator,
        steps_per_epoch=2000,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=800)

# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=True,
                     featurewise_std_normalization=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_datagen.fit(images, augment=True, seed=seed)
mask_datagen.fit(masks, augment=True, seed=seed)
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)

apply_transform(
    x, transform_parameters
)

# Importing the required libraries
from numpy import expand_dims
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot

# Loading desired images
img = load_img('Car.jpg')

# For processing, we are converting the image(s) to an array
data = img_to_array(img)

# Expanding dimension to one sample
samples = expand_dims(data, 0)

# Calling ImageDataGenerator for creating data augmentation generator.
datagen = ImageDataGenerator(rotation_range=90)

# Creating an iterator for data augmentation
it = datagen.flow(samples, batch_size=1)

# Preparing the Samples and Plot for displaying output
for i in range(9):
	# preparing the subplot
	pyplot.subplot(330 + 1 + i)
	# generating images in batches
	batch = it.next()
	# Remember to convert these images to unsigned integers for viewing 
	image = batch[0].astype('uint8')
	# Plotting the data
	pyplot.imshow(image)
# Displaying the figure
pyplot.show()

# For processing, we are converting the image(s) to an array
data = img_to_array(img)

# Expanding dimension to one sample
samples = expand_dims(data, 0)

# Calling ImageDataGenerator for creating data augmentation generator.
datagen = ImageDataGenerator(width_shift_range=[-200,200])

# Creating an iterator for data augmentation
it = datagen.flow(samples, batch_size=1)

# Preparing the Samples and Plot for displaying output
for i in range(9):
	# preparing the subplot
	pyplot.subplot(330 + 1 + i)
	# generating images in batches
	batch = it.next()
	# Remember to convert these images to unsigned integers for viewing 
	image = batch[0].astype('uint8')
	# Plotting the data
	pyplot.imshow(image)
# Displaying the figure
pyplot.show()

# For processing, we are converting the image(s) to an array
data = img_to_array(img)

# Expanding dimension to one sample
samples = expand_dims(data, 0)

# Calling ImageDataGenerator for creating data augmentation generator.
datagen = ImageDataGenerator(height_shift_range=0.5)

# Creating an iterator for data augmentation
it = datagen.flow(samples, batch_size=1)

# Preparing the Samples and Plot for displaying output
for i in range(9):
	# preparing the subplot
	pyplot.subplot(330 + 1 + i)
	# generating images in batches
	batch = it.next()
	# Remember to convert these images to unsigned integers for viewing 
	image = batch[0].astype('uint8')
	# Plotting the data
	pyplot.imshow(image)
# Displaying the figure
pyplot.show()

# For processing, we are converting the image(s) to an array
data = img_to_array(img)

# Expanding dimension to one sample
samples = expand_dims(data, 0)

# Calling ImageDataGenerator for creating data augmentation generator.
datagen = ImageDataGenerator(horizontal_flip=True)

# Creating an iterator for data augmentation
it = datagen.flow(samples, batch_size=1)

# Preparing the Samples and Plot for displaying output
for i in range(9):
	# preparing the subplot
	pyplot.subplot(330 + 1 + i)
	# generating images in batches
	batch = it.next()
	# Remember to convert these images to unsigned integers for viewing 
	image = batch[0].astype('uint8')
	# Plotting the data
	pyplot.imshow(image)
# Displaying the figure
pyplot.show()

# For processing, we are converting the image(s) to an array
data = img_to_array(img)

# Expanding dimension to one sample
samples = expand_dims(data, 0)

# Calling ImageDataGenerator for creating data augmentation generator.
datagen = ImageDataGenerator(brightness_range=[0.2,1.0])

# Creating an iterator for data augmentation
it = datagen.flow(samples, batch_size=1)

# Preparing the Samples and Plot for displaying output
for i in range(9):
	# preparing the subplot
	pyplot.subplot(330 + 1 + i)
	# generating images in batches
	batch = it.next()
	# Remember to convert these images to unsigned integers for viewing 
	image = batch[0].astype('uint8')
	# Plotting the data
	pyplot.imshow(image)
# Displaying the figure
pyplot.show()