AI01, Using image data augmentation in keras

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(width_shift_range=0.9)
#datagen = ImageDataGenerator(width_shift_range=[-200,200])

# example of horizental shift image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(width_shift_range=0.9)
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,12,figsize=(20,3))
for i in range(3):
    for j in range(12):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(height_shift_range=0.9)
#datagen = ImageDataGenerator(height_shift_range=[-200,200])

# example of vertical shift image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(height_shift_range=0.9)
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,12,figsize=(20,3))
for i in range(3):
    for j in range(12):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(horizontal_flip=True)

# example of horizontal flip image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(horizontal_flip=True)
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,3,figsize=(10,10))
for i in range(3):
    for j in range(3):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(vertical_flip=True)

# example of vertical flip image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(vertical_flip=True)
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,3,figsize=(10,10))
for i in range(3):
    for j in range(3):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(rotation_range=90)

# example of random rotation image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(rotation_range=90)
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,3,figsize=(9,9))
for i in range(3):
    for j in range(3):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(brightness_range=[0.2,1.0])

# example of brighting image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(brightness_range=[0.2,1.0])
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,3,figsize=(9,9))
for i in range(3):
    for j in range(3):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(zoom_range=[0.5,1.0])

# example of zoom image augmentation
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

# load the image
img = load_img('bird.jpg')
data = img_to_array(img)
samples = expand_dims(data, 0)

# create image data augmentation generator
datagen = ImageDataGenerator(zoom_range=[0.5,1.0])
iterator = datagen.flow(samples, batch_size=1)

# generate samples and plot
fig , axes = pyplot.subplots(3,3,figsize=(9,9))
for i in range(3):
    for j in range(3):
        batch = iterator.next()
        image = batch[0].astype('uint8')
        axes[i,j].imshow(image)
pyplot.show()