com.github.ojil.algorithm.Gray8GaussSmoothVert Maven / Gradle / Ivy
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/*
* GaussHoriz.java
*
* Created on August 27, 2006, 4:32, PM
*
* To change this templatef, 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
* Hungarian prefix is 'gshz'.
* @author webb
*/
public class Gray8GaussSmoothVert extends PipelineStage {
/** cSigma is the sigma value we'll be using. It has been multiplied
* by 10.0 and converted to integer because CLDC 1.0 doesn't allow
* floating point. The minimum legal value for cSigma is 1; the maximum
* is given by the length of nCoeff below.
*/
private int cSigma;
/** nCoeff row i is the precomputed Gauss coefficients for sigma = i/10.0.
* They have been scaled by 256 and converted to integer because CLDC 1.0
* doesn't allow floating point. The coefficients have been scaled and
* normalized so the sum is 0 and the sum of the absolute values is
* 256 (counting both sides of the Canny operator -- the operator is
* symmetric so we just give one side below.)
*/
/** The array below was generated using two Excel macros:
* In cell b2 and filled out to column AM and down:
* =EXP(-(COLUMN(B2)-1)*(COLUMN(B2)-1)/((ROW(B2)-1)/10*(ROW(B2)-1)/10))
* To compute the value shown below:
* =ROUND(B2/SUM($B2:$AM2)*256,0)
*/
private Integer[][] nCoeff = {
{0}, // unused, inserted to make lookup simpler
{256},
{256},
{256},
{256},
{256},
{256},
{255,1},
{254,2},
{250,6},
{244,12},
{236,20},
{227,28,1},
{217,37,2},
{207,45,4},
{198,52,6},
{189,58,8,1},
{180,64,11,1},
{172,68,15,2},
{164,71,18,3},
{157,74,21,4},
{150,76,24,5,1},
{144,77,28,6,1},
{138,78,30,8,1},
{132,79,33,10,2},
{127,79,35,12,3},
{122,79,37,13,4,1},
{118,78,39,15,4,1},
{114,78,41,17,5,1},
{110,77,42,18,6,2},
{106,76,44,20,7,2,1},
{103,75,45,22,8,3,1},
{99,74,46,23,10,3,1},
{96,73,46,24,11,4,1},
{93,72,47,26,12,5,1},
{91,71,47,27,13,5,2,1},
{88,70,48,28,14,6,2,1},
{86,69,48,29,15,7,3,1},
{83,68,48,29,16,7,3,1},
{81,67,48,30,17,8,3,1},
{79,65,48,31,18,9,4,2,1},
{77,64,48,32,18,10,4,2,1},
{75,63,48,32,19,10,5,2,1},
{73,62,48,33,20,11,5,2,1},
{72,61,47,33,21,12,6,3,1},
{70,60,47,33,21,12,7,3,1,1},
{68,59,47,34,22,13,7,3,2,1},
{67,58,46,34,23,14,8,4,2,1},
{65,57,46,34,23,14,8,4,2,1},
{64,56,46,34,24,15,9,5,2,1},
{63,55,45,34,24,15,9,5,3,1,1},
{61,55,45,34,24,16,10,5,3,1,1},
{60,54,45,34,25,16,10,6,3,2,1},
{59,53,44,35,25,17,11,6,3,2,1},
{58,52,44,35,25,17,11,7,4,2,1},
{57,51,43,34,26,18,12,7,4,2,1,1},
{56,50,43,34,26,18,12,7,4,2,1,1},
{55,50,43,34,26,19,12,8,5,3,1,1},
{54,49,42,34,26,19,13,8,5,3,2,1},
{53,48,42,34,26,19,13,9,5,3,2,1},
{52,48,41,34,27,20,14,9,6,3,2,1},
{51,47,41,34,27,20,14,9,6,4,2,1,1},
{50,46,41,34,27,20,14,10,6,4,2,1,1},
{49,46,40,34,27,20,15,10,7,4,2,1,1},
{48,45,40,33,27,21,15,10,7,4,3,1,1},
{48,44,39,33,27,21,15,11,7,5,3,2,1},
{47,44,39,33,27,21,16,11,7,5,3,2,1,1},
{46,43,39,33,27,21,16,11,8,5,3,2,1,1},
{45,42,38,33,27,21,16,12,8,5,3,2,1,1},
{45,42,38,33,27,21,16,12,8,6,4,2,1,1},
{44,41,37,32,27,22,17,12,9,6,4,2,1,1},
{43,41,37,32,27,22,17,12,9,6,4,3,2,1,1},
{43,40,37,32,27,22,17,13,9,6,4,3,2,1,1},
{42,40,36,32,27,22,17,13,9,7,4,3,2,1,1},
{41,39,36,32,27,22,17,13,10,7,5,3,2,1,1},
{41,39,35,31,27,22,17,13,10,7,5,3,2,1,1},
{40,38,35,31,27,22,18,14,10,7,5,3,2,1,1},
{40,38,35,31,27,22,18,14,10,7,5,4,2,1,1,1},
{39,37,34,31,26,22,18,14,11,8,5,4,2,2,1,1},
{39,37,34,30,26,22,18,14,11,8,6,4,3,2,1,1},
{38,36,34,30,26,22,18,14,11,8,6,4,3,2,1,1},
{38,36,33,30,26,22,18,14,11,8,6,4,3,2,1,1},
{37,36,33,30,26,22,18,15,11,9,6,4,3,2,1,1,1},
{37,35,33,30,26,22,18,15,12,9,6,5,3,2,1,1,1},
{36,35,32,29,26,22,18,15,12,9,7,5,3,2,2,1,1},
{36,34,32,29,26,22,18,15,12,9,7,5,4,2,2,1,1},
{35,34,32,29,26,22,19,15,12,9,7,5,4,3,2,1,1},
{35,34,32,29,26,22,19,15,12,9,7,5,4,3,2,1,1},
{35,33,31,29,25,22,19,15,12,10,7,5,4,3,2,1,1,1},
{34,33,31,28,25,22,19,15,12,10,8,6,4,3,2,1,1,1},
{34,33,31,28,25,22,19,16,13,10,8,6,4,3,2,1,1,1},
{33,32,30,28,25,22,19,16,13,10,8,6,4,3,2,2,1,1},
{33,32,30,28,25,22,19,16,13,10,8,6,5,3,2,2,1,1},
{33,32,30,27,25,22,19,16,13,10,8,6,5,3,2,2,1,1,1},
{32,31,30,27,25,22,19,16,13,11,8,6,5,4,3,2,1,1,1},
{32,31,29,27,25,22,19,16,13,11,8,7,5,4,3,2,1,1,1},
{32,31,29,27,24,22,19,16,13,11,9,7,5,4,3,2,1,1,1},
{31,30,29,27,24,22,19,16,13,11,9,7,5,4,3,2,1,1,1},
{31,30,28,26,24,21,19,16,13,11,9,7,5,4,3,2,2,1,1},
{31,30,28,26,24,21,19,16,14,11,9,7,6,4,3,2,2,1,1,1},
{30,29,28,26,24,21,19,16,14,11,9,7,6,4,3,2,2,1,1,1}
};
/** Creates a new instance of GaussHoriz
*
* @param cSigma the sigma value for the operator, which is the sigma
* in the Gaussian distribution multipied by 10.0 and converted to integer.
* @throws com.github.ojil.core.Error is sigma is out of range.
*/
public Gray8GaussSmoothVert(int cSigma) throws com.github.ojil.core.Error {
this.setSigma(cSigma);
}
/** Apply the Gaussian horizontally to the input input image.
* The sigma value for the operator is set in the class constructor.
* We handle the borders of the image a little carefully to avoid creating
* spurious edges at them. The image value at the border is reflected so
* that image(-1,0), for example, is made equal to image(1,0).
*
* @param image the input Gray8Image
* @throws com.github.ojil.core.Error if image is not a Gray8Image
*/
public void push(Image image) throws com.github.ojil.core.Error {
if (!(image instanceof Gray8Image)) {
throw new Error(
Error.PACKAGE.ALGORITHM,
ErrorCodes.IMAGE_NOT_GRAY8IMAGE,
image.toString(),
null,
null);
}
Gray8Image input = (Gray8Image) image;
Gray8Image result = new Gray8Image(image.getWidth(), image.getHeight());
Byte[] bIn = input.getData();
Byte[] bResult = result.getData();
Integer[] wCoeff = this.nCoeff[this.cSigma];
int cHeight = input.getHeight();
for (int j=0; j= cHeight so
// 2*cWidth - (i + k + 1) < cHeight
int cPos = 2*cHeight - (i + k + 1);
wSum += wCoeff[k] * bIn[cPos*cHeight + j];
}
}
/* Gaussian coefficients are scaled so sum is 256.
*/
wSum = wSum >> 8;
bResult[i*cHeight + j] = (byte) wSum;
}
}
super.setOutput(result);
}
/** Returns the current value of sigma.
*
* @return the sigma value
*/
public int getSigma() {
return this.cSigma;
}
/** sets a new value for sigma. Sigma controls the frequency of edges
* that the operator responds to. A small sigma value gives less blur.
*
* @param cSigma the new sigma value
* @throws com.github.ojil.core.Error if cSigma is out of range --
* less than or equal to 1 or greater than the number of coefficients
* we're precomputed.
*/
public void setSigma(int cSigma) throws com.github.ojil.core.Error {
if (cSigma <= 1 || cSigma >= this.nCoeff.length) {
throw new Error(
Error.PACKAGE.ALGORITHM,
ErrorCodes.PARAMETER_OUT_OF_RANGE,
new Integer(cSigma).toString(),
new Integer(1).toString(),
new Integer(this.nCoeff.length).toString());
}
this.cSigma = cSigma;
}
/** returns a string describing this Gaussian blur.
*
* @return a string describing the Gaussian blur.
*/
public String toString() {
return super.toString() + " (" + this.cSigma + ")"; //$NON-NLS-1$ //$NON-NLS-2$
}
}