random_saturation ( x, 0.6, 1.6 ) x = tf. random_flip_up_down ( x ) return x def color ( x : tf. zeros (( 32 * n_images, 32 * samples_per_image, 3 )) row = 0 for images in dataset. Import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def plot_images ( dataset, n_images, samples_per_image ): output = np. To make sure that these ranges are not exceeded a clipping function such as tf.clip_by_value is recommended. Note: Some of these operations can result in images that have values outside the normal range of. It is advised to set this parameter to the number of CPUs available. When this parameter is higher than one functions will be executed in parallel. With the tf.Data API this is done using the num_parallel_calls parameter of the map function.
To drastically increase the speed of these operations we can execute them in parallel, practically all Tensorflow operations support this. Chaining map functions makes it very easy to iteratively add new data mapping operations, like augmentations. When this new dataset is evaluated, the data operations defined in the function will be applied to all elements in the set. The map function takes a function and returns a new and augmented dataset. Applying these functions to a Tensorflow Dataset is very easy using the map function. With all functions defined we can combine them in to a single pipeline. cond ( choice < 0.5, lambda : x, lambda : random_crop ( x )) float32 ) # Only apply cropping 50% of the time Return crops, minval = 0, maxval = len ( scales ), dtype = tf. zeros ( len ( scales )), crop_size = ( 32, 32 )) # Return a random crop crop_and_resize (, boxes = boxes, box_ind = np. zeros (( len ( scales ), 4 )) for i, scale in enumerate ( scales ): x1 = y1 = 0.5 - ( 0.5 * scale ) x2 = y2 = 0.5 + ( 0.5 * scale ) boxes = def random_crop ( img ): # Create different crops for an imageĬrops = tf. """ # Generate 20 crop settings, ranging from a 1% to 20% crop. These are only evaluated once in the TF data pipeline and will result in the same augmentation applied to all images.ĭef zoom ( x : tf. Note: Do not use np.random functions for generating random numbers in these augmenter functions. true_fn is set to the cropping function and false_fn to a identity function returning the original image.
#Tf image resize generator#
In our case we use a random number generator to return true in 50% of the calls. The predicate should be an operation that evaluates to true or false, after which true_fn or false_fn is called respectively. tf.cond expects three parameters: a predicate (or condition), a true function true_fn and a false function false_fn. To make sure that some part of our data retains it original dimension, a tf.cond call can be used.
tf.random_uniform will give new random numbers during training so is safe to use here. By using tf.random_uniform we can randomly select one of these crops. This function returns a new image for each crop box, resulting in 20 potential cropped images for each input image. These boxes are created once and then passed on to the crop_and_resize function. In the augmentation function below we first create 20 crop boxes using numpy. The function requires a list of ‘crop boxes’ that contain normalized coordinates (between 0 and 1) for cropping. The Tensorflow function crop_and_resize function comes close as it can crop an image and then resize it to an arbitrary size. This augmentation is a bit harder to implement as there is no single function that performs this operation completely. Zooming is a powerful augmentation that can make a network robust to (small) changes in object size. random_contrast ( x, 0.7, 1.3 ) return x Random functions from Tensorflow are evaluated for every input, functions from numpy or basic python only once which would result in a static augmentation.ĭef color ( x : tf. To get a new random rotation for each image we need to use a random function from Tensorflow itself. For this we can use the rot90 function of Tensorflow. One of the most simplest augmentations is rotating the image 90 degrees. Of course, there are many more augmentations that could be useful, but most of them follow the same approach. Nevertheless, I show them here as an example as they can be useful for tasks that are more orientation invariant. learning to detect flipped trucks is maybe not that beneficial for the task at hand. Not all of these augmentations are necessarily applicable to CIFAR10 e.g.
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