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122 lines (98 loc) · 4.23 KB
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#!/usr/bin/env python
__author__ = "Sreenivas Bhattiprolu"
__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
# https://youtu.be/ccdssX4rIh8
"""
@author: Sreenivas Bhattiprolu
Image shifts via the width_shift_range and height_shift_range arguments.
Image flips via the horizontal_flip and vertical_flip arguments.
Image rotations via the rotation_range argument
Image brightness via the brightness_range argument.
Image zoom via the zoom_range argument.
"""
from keras.preprocessing.image import ImageDataGenerator
from skimage import io
# Construct an instance of the ImageDataGenerator class
# Pass the augmentation parameters through the constructor.
datagen = ImageDataGenerator(
rotation_range=45, #Random rotation between 0 and 45
width_shift_range=0.2, #% shift
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='constant', cval=125) #Also try nearest, constant, reflect, wrap
######################################################################
#Loading a single image for demonstration purposes.
#Using flow method to augment the image
# Loading a sample image
#Can use any library to read images but they need to be in an array form
#If using keras load_img convert it to an array first
x = io.imread('monalisa_einstein/monalisa/monalisa.jpg') #Array with shape (256, 256, 3)
# Reshape the input image because ...
#x: Input data to datagen.flow must be Numpy array of rank 4 or a tuple.
#First element represents the number of images
x = x.reshape((1, ) + x.shape) #Array with shape (1, 256, 256, 3)
i = 0
for batch in datagen.flow(x, batch_size=16,
save_to_dir='augmented',
save_prefix='aug',
save_format='png'):
i += 1
if i > 20:
break # otherwise the generator would loop indefinitely
####################################################################
#Multiple images.
#Manually read each image and create an array to be supplied to datagen via flow method
dataset = []
import numpy as np
from skimage import io
import os
from PIL import Image
image_directory = 'test_folder/'
SIZE = 128
dataset = []
my_images = os.listdir(image_directory)
for i, image_name in enumerate(my_images):
if (image_name.split('.')[1] == 'jpg'):
image = io.imread(image_directory + image_name)
image = Image.fromarray(image, 'RGB')
image = image.resize((SIZE,SIZE))
dataset.append(np.array(image))
x = np.array(dataset)
#Let us save images to get a feel for the augmented images.
#Create an iterator either by using image dataset in memory (using flow() function)
#or by using image dataset from a directory (using flow_from_directory)
#from directory can beuseful if subdirectories are organized by class
# Generating and saving 10 augmented samples
# using the above defined parameters.
#Again, flow generates batches of randomly augmented images
"""
i = 0
for batch in datagen.flow(x, batch_size=16,
save_to_dir='augmented',
save_prefix='aug',
save_format='png'):
i += 1
if i > 20:
break # otherwise the generator would loop indefinitely
"""
#####################################################################
#Multiclass. Read dirctly from the folder structure using flow_from_directory
i = 0
for batch in datagen.flow_from_directory(directory='monalisa_einstein/',
batch_size=16,
target_size=(256, 256),
color_mode="rgb",
save_to_dir='augmented',
save_prefix='aug',
save_format='png'):
i += 1
if i > 31:
break
#Creates 32 images for each class.
#Once data is augmented, you can use it to fit a model via: fit.generator
#instead of fit()
#model =
#fit model on augmented data
#model.fit_generator(datagen.flow(x))