Package jjil.algorithm.j2se

Source Code of jjil.algorithm.j2se.Gray8Statistics

/*
* Gray8Statistics.java
*
* Created on November 11, 2006, 2:17 PM
*
* To change this template, choose Tools | Template Manager
* and open the template in the editor.
*
* Copyright 2007 by Jon A. Webb
*     This program is free software: you can redistribute it and/or modify
*    it under the terms of the GNU Lesser General Public License as published by
*    the Free Software Foundation, either version 3 of the License, or
*    (at your option) any later version.
*
*    This program is distributed in the hope that it will be useful,
*    but WITHOUT ANY WARRANTY; without even the implied warranty of
*    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
*    GNU Lesser General Public License for more details.
*
*    You should have received a copy of the Lesser GNU General Public License
*    along with this program.  If not, see <http://www.gnu.org/licenses/>.
*
*/

package jjil.algorithm.j2se;
import jjil.algorithm.ErrorCodes;
import jjil.core.Error;
import jjil.core.Gray8Image;
import jjil.core.Image;

/**
* Gray8Statistics is used to measure the mean and variance of a gray
* image.
*
*
* @author webb
*/
public class Gray8Statistics {
    private float fMean; // mean image value
    private float fVariance; // image variance
   
    /**
     * Creates a new instance of Gray8Statistics
     */
    public Gray8Statistics() {
    }
   
    /** Estimate the mean and variance of an input gray image.
     *
     * @param image the input image.
     * @throws jjil.core.Error if the input image is not gray.
     */
    public void push(Image image) throws jjil.core.Error
    {
        if (!(image instanceof Gray8Image)) {
            throw new Error(
                      Error.PACKAGE.ALGORITHM,
                      ErrorCodes.IMAGE_NOT_GRAY8IMAGE,
                      image.toString(),
                      null,
                      null);
        }
        Gray8Image gray = (Gray8Image) image;
        int nSum = 0, nSumSq = 0;
        byte[] data = gray.getData();
        for (int i=0; i<gray.getHeight(); i++) {
            for (int j=0; j<gray.getWidth(); j++) {
                int pixel = (data[i*image.getWidth()+j]) - Byte.MIN_VALUE;
                nSum += pixel;
                nSumSq += pixel*pixel;
            }
        }
        /** Compute mean and variance. Both are scaled by 256 for accuracy.
         */
        int nCount = image.getHeight() * image.getWidth();
        this.fMean = ((float)nSum) / ((float)nCount);
        // expanded form of variance computation
        // note order of multiplications and divisions. we're trying to
        // avoid overflow here.
        this.fVariance = 
                (nSumSq / (nCount - 1) -
                    nSum / nCount * nSum  / (nCount - 1));
    }
   
    /** Return computed mean, times 256.
     *
     * @return the mean value, times 256.
     */
    public float getMean() {
        return this.fMean;
    }
   
    /**
     * Return standard deviation, times 256 using Newton's iteration.
     * @return the standard deviation, times 256.
     * @throws jjil.core.Error if the variance computed in push() is less than zero.
     */
    public float getStdDev() throws jjil.core.Error {
        float f = getVariance(); // getVariance()
        if (f < 0) throw new Error(
                  Error.PACKAGE.ALGORITHM,
                  ErrorCodes.STATISTICS_VARIANCE_LESS_THAN_ZERO,
                  new Float(f).toString(),
                  null,
                  null);
        // return sqrt(variance)
        return (float) Math.sqrt(f);
    }
   
    /** Return computed variance.
     *
     * @return the computed variance value.
     */
    public float getVariance() {
        return this.fVariance;
    }
}
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