The downside is that the callback we specify only takes one input: an image, and there is no easy way to specify the augmentation parameters. You would call it like this: ImageDataGenerator(preprocessing_function=my_custom_function) and simpy implement my_custom_function(image_input). Option 1: Specifying preprocessing_function parameter on ImageDataGenerator There are two options of getting our own preprocessing function executed in the data augmentation pipeline, simply through a callback, or by extending ImageDataGenerator. Plt.subplot(n_rows, n_cols, image_index+1)įig.tight_layout(pad=0.0) Adding a Callback vs. def plot_augmentation(datagen, data, n_rows=1, n_cols=5):Īspect_ratio = data.shape / data.shapeįig_size = (n_cols*base_size/aspect_ratio, n_rows*base_size) The computation and use of the image aspect ratio is to have a clean grid of images without padding in-between. To simplify visualization, we create a helper function that generates the augmented data and plots it in a grid. Plt.imshow(data) Our original sample image, directly from Cinque Terre, Italy! R = requests.get(url, allow_redirects=True)
Keras data generator example free#
Feel free to change the image to make the tutorial more personalized! url = ''
Keras data generator example download#
Next we download a sample image from the Github repository, load it into memory and display it as a reference. import numpy as npįrom import load_img, img_to_arrayįrom import ImageDataGenerator This allows you to play around with the parameters and explore what we discuss here in more detail.Ĭlick the link on the top of the post to either open the notebook in Google Colab or download it to run on your machine.Īs in the previous tutorial, we start by defining all the imports we need. This is a good point to look at the Colab notebook and follow along.
In that callback we can then further modify the image with our own routine. This callback will be executed for every image after all other standard Keras data augmentation has been performed. If we look at the Keras documentation for the ImageDataGenerator class, we see that we can plug in our own preprocessing function by simply specifying a callback with the parameter preprocessing_function. In this tutorial, we will implement a method to slightly scaling each color channel, thereby varying the color of the output image. One such case is handling color: Keras provides only a way of randomly changing the brightness, but no way of varying the color in our augmented dataset. However, it is often necessary to implement our own preprocessing function (our own ImageDataGenerator) if we want to add specific types of data augmentation. Out of the box, Keras provides a lot of good data augmentation techniques, as you might have seen in the previous tutorial.
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