![]() Of course, translations are not the only way in which an image can change, but still visually be the same image. Consider rotating the image by even a single degree, or 5 degrees. Traning a CNN without including translated and rotated versions of the image may cause the CNN to overfit and assume that all images of Androids have to be perfectly upright and centered. Providing deep learning frameworks with images that are translated, rotated, scaling, intensified and flipped is what we mean when we talk about data augmentation. In this post we’ll look at how to apply these transformations to an image, even in 3D and see how it affects the performance of a deep learning framework. ![]() We will use an image from flickr user andy_emcee as an example of a 2D nautral image. As this is an RGB (color) image it has shape, one layer for each colour channel. We could take one layer to make this grayscale and truly 2D, but most images we deal with will be color so let’s leave it. RGB Image shape= AugmentationsĪs usual, we are going to write our augmentation functions in python. We’ll just be using simple functions from numpy and scipy. In our functions, image is a 2 or 3D array - if it’s a 3D array, we need to be careful about specifying our translation directions in the argument called offset. We don’t really want to move images in the z direction for a couple of reasons: firstly, if it’s a 2D image, the third dimension will be the colour channel, if we move the image through this dimension the image will either become all red, all blue or all black if we move it -2, 2 or greater than these respectively second, in a full 3D image, the third dimension is often the smallest e.g. In our translation function below, the offset is given as a length 2 array defining the shift in the y and x directions respectively (dont forget index 0 is which horizontal row we’re at in python). ![]() ![]() We hard-code z-direction to 0 but you’re welcome to change this if your use-case demands it. Return (image, (int(offset), int(offset), 0), order=order, mode='nearest') def translateit(image, offset, isseg=False): To ensure we get integer-pixel shifts, we enforce type int too. Here we have also provided the option for what kind of interpolation we want to perform: order = 0 means to just use the nearest-neighbour pixel intensity and order = 5 means to perform bspline interpolation with order 5 (taking into account many pixels around the target). This is triggered with a Boolean argument to the scaleit function called isseg so named because when dealing with image-segmentations, we want to keep their integer class numbers and not get a result which is a float with a value between two classes. ![]()
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