algorithms.quality.AttentionQuality.java Source code

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Here is the source code for algorithms.quality.AttentionQuality.java

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/*
 * Copyright 2014 Fraunhofer IGD
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package algorithms.quality;

import java.awt.Color;
import java.awt.geom.Point2D;

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;

import algorithms.sampling.SamplingStrategy;
import colormaps.Colormap2D;
import colorspaces.CIELABLch;

/**
 * Computes the variance in attention steering
 * @author Martin Steiger
 */
public class AttentionQuality implements ColormapQuality {
    private final SamplingStrategy sampling;

    /**
     * @param sampling the sampling to use
     */
    public AttentionQuality(SamplingStrategy sampling) {
        this.sampling = sampling;
    }

    @Override
    public double getQuality(Colormap2D colormap) {
        // max L + max c (which is the same as a or b)
        double normFac = Math.sqrt(100 * 100 + 150 * 150);

        DescriptiveStatistics stats = new DescriptiveStatistics();

        for (Point2D pt : sampling.getPoints()) {
            Color color = colormap.getColor(pt.getX(), pt.getY());
            double[] lch = new CIELABLch().fromColor(color);
            double attention = Math.sqrt(lch[0] * lch[0] + lch[1] * lch[1]) / normFac;

            stats.addValue(attention);
        }

        return stats.getVariance();
    }

    @Override
    public String getName() {
        return "Attention";
    }

    @Override
    public String getDescription() {
        return "Compute the attention values based on a sampling strategy and returns the variance across the colormap";
    }
}