Evaluating Artificial Images Through Score-based Classifications

This repository holds all the necessary code to run the very-same experiments described in the paper "Evaluating Artificial Images Through Score-based Classifications".

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References

If you use our work to fulfill any of your needs, please cite us:

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Structure

• data/
  • 1: Folder containing a batch of (960, 3) metrics from sampled images;
  • 2: Folder containing a batch of (960, 3) metrics from sampled images;
• utils/
  • data.py: Methods to aid in extracting desired features from data;
  • loader.py: Loads .txt data and saves it in .npy files;
  • math.py: Provides mathematical helpers.

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Package Guidelines

Installation

Install all the pre-needed requirements using:

pip install -r requirements.txt

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Usage

Creating the Data

Our first step is to create the data from the available metrics. With that in mind, just run the following script with the input arguments:

python create_data.py path files -n_samples -normalize -outlier

Or, if necessary, invoke the script with its helper:

python create_data.py -h

Note that it will output a helper file in order to assist in choosing the correct arguments for the script.

Digitizing the Data

After creating the features.npy file, we want to divide each one of its features into equivalent intervals and discretize their values. In other words, we want to assign a label for each variable concerning each sample. Just choose the following script with the input arguments:

python create_targets.py input -n_bins

SVM Classification

Finally, after creating the features.npy and targets.npy, it is now possible to train a classifier and further predict new data. For now, we are using a standard Support Vector Machine classification. Run the following script in order to fulfill that purpose:

python svm.py features targets

(Optional) Statistical Measures

As an optional procedure, one can also calculate and plot some statistical measures regarding the data. Please use the following scripts in order to accomplish such an approach:

python make_boxplot.py input

or

python make_violinplot.py input

or

python make_histogram.py input

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Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.

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