For example, if you specify the zoom_range value as 0.3, then the zoom range will be. The zoom range for the specified float value will be. This zoom augmentation technique zooms in the original image and either add new pixels around the image or interpolates the pixel values respectively.įor the zoom augmentation process, the ImageDataGenerator class takes a float value in the zoom_range argument. In this argument, we can specify the minimum and maximum range for selecting a brightness amount.Įxample: from numpy import expand_dims from import load_img from import img_to_array from import ImageDataGenerator import matplotlib.pyplot as plt img = load_img('bird.jpeg') data = img_to_array(img) samples = expand_dims(data, 0) datagen = ImageDataGenerator( brightness_range=) aug_iter = datagen.flow(samples, batch_size=1) for i in range(9): pyplot.subplot(330 + 1 + i) batch = aug_iter.next() image = batch.astype('uint8') pyplot.imshow(image) pyplot.show() ![]() The ImageDataGenerator class allows us to control the brightness using the brightness_range argument. It augments new images by either brightening original images or darkening the images. The brightness augmentation technique augments new images by randomly changing the brightness of the image. This technique is very useful as most of the time the object will not be under perfect lighting conditions. The purpose of brightness augmentation is to allow a model to generalize across the images, trained on different lighting levels. ![]() This image flipping technique should be applied according to the object in the image.įor example, in our bird image, horizontal flipping may make sense, but vertical flipping would not.Įxample: from numpy import expand_dims from import load_img from import img_to_array from import ImageDataGenerator import matplotlib.pyplot as plt img = load_img('bird.jpeg') data = img_to_array(img) # expand dimension to one sample samples = expand_dims(data, 0) # create image data augmentation generator datagen = ImageDataGenerator(horizontal_flip=True) # prepare iterator aug_iter = datagen.flow(samples, batch_size=1) # generate samples and plot for i in range(9): pyplot.subplot(330 + 1 + i) batch = aug_iter.next() image = batch.astype('uint8') pyplot.imshow(image) pyplot.show() In Keras, ImageDataGenerator class has parameters such as vertical_flip and horizontal_flip that allow us to flip the image along the vertical or the horizontal axis. Image flipping is an image augmentation technique that allows us to generate new images by reversing the rows and columns of image pixels in the case of a vertical and horizontal flip respectively. Techniques of Image Augmentation Random Flip Augmentation This is to make sure that the model does not overfit by seeing the original images multiple times. It is the responsibility of ImageDataGeneator class to ensure that the model receives new transformed images after each epoch and directly returns those transformed images to the model, instead of adding them to the original dataset. ![]() The main advantage of using this ImageDataGenerator class is that it provides real-time data augmentation, which means it generates augmented images while the model is in the training stage and it requires lower memory usage as well. In the Keras library, the ImageDataGenerator class provides a simple and easy way to augment our image data. The deep learning library, Keras, provides us the ability to augment the images automatically when training our model. This image augmentation technique not only expands the size of our dataset but also provides a new perspective of the object in the image, which allows our deep learning model to generalize better on unseen and new data. Therefore, each copy is different from the original image in certain aspects depending on the augmentation technique that we have applied to the images. In the image augmentation process, we apply various augmentation techniques to the images such as rotation, cropping, padding, scaling, and flipping, etc. Image augmentation is a technique in which we apply different transformations to the existing original image data in order to generate multiple transformed copies of the existing images.
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