AI, practical course. Preprocessing and supplementing data with images

Published on June 25, 2018

AI, practical course. Preprocessing and supplementing data with images

Original author: Intel AI Academy
  • Transfer
Pre-processing is a general term for all manipulations performed on data before transferring their model, including centering, normalizing, shifting, rotating, trimming, etc. As a rule, preprocessing is required in two cases.

  • Data cleansing . Suppose there are some artifacts in the images. To facilitate model learning, artifacts must be removed during the preprocessing stage.
  • Supplement data . Sometimes small datasets are not enough for high-quality deep learning of the model. Approach with the addition of data is very useful in solving this problem. It is the process of transforming each sample of data in various ways and adding such modified samples to the data set. In this way, you can increase the effective size of the data set.

Let us consider some possible transformation methods for preprocessing and their implementation through Keras.


Data


In this and the following articles, a data set will be used to analyze the emotional coloring of images. It contains 1500 examples of images divided into two classes - positive and negative. Consider some examples.


Negative examples


Positive examples

Cleaning transformations


Now consider the set of possible transformations that are commonly used to clean data, their implementation and the impact on images.

All code snippets can be found in the Preprocessing.ipynb book .

Rescaling


Images are usually stored in RGB (Red Green Blue) format. In this format, the image is represented by a three-dimensional (or three-channel) array.


RGB decomposition of the image. The diagram is taken from Wikiwand.

One dimension is used for channels (red, green and blue), the other two represent a location. Thus, each pixel is encoded by three numbers. Each number is usually stored as an 8-bit unsigned integer type (from 0 to 255).

Rescaling is an operation that changes the numeric range of data by simply dividing it by a predetermined constant. In deep neural networks, it may be necessary to limit the input data to a range from 0 to 1 due to possible overflow, optimization issues, stability, etc.

For example, we will rescale our data from the range [0; 255] in the range [0; one]. Hereinafter, we will use the Keras ImageDataGenerator class , which allows performing all the transformations on the fly.

Create two instances of this class: one for transformed data, the other for source:


(or for default data). You only need to specify a scaling constant. Moreover, the ImageDataGenerator class allows you to stream data directly from a folder on your hard disk using the flow_from_directory method .

All parameters can be found in the documentation.but the main parameters are: the path to the stream and the target image size (if the image does not match the target size, the generator simply cuts it or increases it). Finally, we will get a sample from the generator and consider the results.

Visually, both images are identical, but the reason for this is that the Python tools * automatically rescale the images



in the default range so that they can be displayed on the screen. Consider the raw data (arrays). As you can see, raw arrays differ exactly 255 times.

Grayscale Translation


Another type of transformation that can be useful is a conversion to shades of gray , which converts a color RGB image into an image in which all colors are represented by shades of gray. Conventional image processing can translate to grayscale in combination with a subsequent threshold setting. This pair of transformations can discard noisy pixels and determine the shapes in the image. Today, all these operations are performed by convolutional neural networks (CNN), but converting to shades of gray as a preprocessing stage can still be useful. Run this step in Keras with the same generator class.



Here we create only one instance of the class and take two different generators from it. The second generator sets the color_mode parameter in “grayscale” (default is “RGB”).

Sample centering


We have already seen that the raw data values ​​are in the range from 0 to 255. Thus, one sample is a three-dimensional array of numbers from 0 to 255. In the light of the principles of stability optimization (getting rid of the problem of disappearing or saturating values), it may be necessary to normalize the data set so that the average of each sample data is 0 .



To do this, calculate the average value for the entire sample and subtract it from each number in the sample.
In Keras, this is done using the samplewise_center parameter .

Normalization of SD of samples


This preprocessing stage is based on the same idea as centering the samples, but instead of setting the average to 0 устанавливает, it sets the standard deviation to 1. The



normalization of the mean deviation is controlled by the parameter samplewise_std_normalization . It should be noted that these two methods of normalizing samples are often used together.

This transformation can be applied in deep learning models to increase the stability of optimization by reducing the effect of exploding gradient problems.

Feature centering


In the two previous sections, the normalization technique was used, examining each individual data sample. There is an alternative approach to the normalization procedure. Consider each number in the image array as a sign. Then each image is a feature vector . There are many such vectors in the data set; therefore, we can consider them as some unknown distribution. This distribution is multiparameter, and its dimension will be equal to the number of features, that is, width × height × 3. Although the true distribution of the data is unknown, you can try to normalize it by subtracting the average value of the distribution. It should be noted that the mean value is a vector of the same dimension, that is, is also an image. In other words, we average over the entire data set, and not over one sample.

There is a special Keras parameter called featurewise_centering, but, unfortunately, as of August 2017 there was an error in its implementation; therefore, we implement it independently. First, we count the entire data set into memory (we can afford it, since we are dealing with a small data set). We did this by setting the packet size to be equal to the size of the data set. Then we calculate the average image over the entire data set and, finally, subtract it from the test image.



Normalization of RMS signs


The idea of ​​normalizing the standard deviation is exactly the same as the idea of ​​centering. The only difference is that instead of subtracting the average value, we divide by the value of the standard deviation. Visually, the result is not much different. The same thing happened


when rescaling, since normalization of the deviation of the standard deviation is nothing more than rescaling with a certainly calculated constant, and for simple rescaling, the constant is specified manually. Note that a similar idea of ​​normalizing data packets underlies the modern technology of deep learning, called BatchNormalization .



Transformations with addition


In this section, we will look at several transformations that are dependent on data that explicitly use the graphical nature of the data. These types of transformations are often used in data completion procedures.

Rotation


This kind of transformation rotates the image in a certain direction (clockwise or counterclockwise).

The parameter that allows rotation is called rotation_range . It indicates the range in degrees, from which a rotation angle is selected randomly with a uniform distribution. It should be noted that during rotation the image size does not change. Thus, some parts of the image can be cropped, and some filled.



The fill mode is set using the fill_mode parameter . It supports various ways of filling, but here we use the constant method as an example .



Horizontal shift


This kind of transformation shifts the image in a certain direction along the horizontal axis (left or right).



The shift size can be determined using the width_shift_range parameter and is measured as part of the full width of the image.

Vertical shift




Shifts the image along the vertical axis (up or down). The parameter that controls the shift range is called the height_shift generator and is also measured as part of the full height of the image.

Pruning


Converting a clipping or cropping shifts each point in the vertical direction by an amount proportional to the distance from that point to the edge of the image. Note that in the general case the direction does not have to be vertical and is arbitrary.



The parameter controlling the displacement is called shear_range and corresponds to the deflection angle (in radians) between the horizontal line in the original image and the image (in the mathematical sense) of this line in the transformed image.

Approximation / removal



This type of transformation approximates or removes the original image. The zoom_range parameter controls the zoom factor.



For example, if zoom_range is 0.5, then the approximation factor will be selected from the range [0.5, 1.5].



Horizontal coup




Flips the image about the vertical axis. It can be turned on or off using the horizontal_flip parameter .

Vertical coup




Flips the image about the horizontal axis. The vertical_flip parameter (of type Boolean) controls the presence or absence of this transformation.

Combination


Let's apply all described types of transformations of an add-on at the same time and see what happens. Recall that the parameters for all transformations are randomly selected from a specific range; thus, we must obtain a collection of samples with a considerable degree of diversity.

We initiate the ImageDataGenerator with all the available parameters and check on the image of the red hydrant.



Note that the constant filling mode was used only for better visualization. Now we will use a more advanced fill mode, called the nearest ; this mode assigns the empty pixel the color of the nearest existing pixel.


Conclusion


This article provides an overview of the basic techniques of image preprocessing, such as: scaling, normalizing, rotating, shifting and cropping. They also demonstrated the implementation of these transformation techniques with the help of Keras and their introduction into the process of deep learning, both technically (class ImageDataGenerator ) and ideologically (data supplement).