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/* ============================================================
*
* This file is a part of kipi-plugins project
* http://www.kipi-plugins.org
*
* Date : 2004-10-01
* Description : a kipi plugin to batch process images
*
* Copyright (C) 2004-2007 by Gilles Caulier <caulier dot gilles at gmail dot com>
*
* This program is free software; you can redistribute it
* and/or modify it under the terms of the GNU General
* Public License as published by the Free Software Foundation;
* either version 2, 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 General Public License for more details.
*
* ============================================================ */
// Include files for TQt
#include <tqvbox.h>
#include <tqlayout.h>
#include <tqwidget.h>
#include <tqwhatsthis.h>
#include <tqlabel.h>
#include <tqpushbutton.h>
#include <tqgroupbox.h>
#include <tqcombobox.h>
#include <tqcolor.h>
// Include files for KDE
#include <tdelocale.h>
#include <knuminput.h>
#include <kcolorbutton.h>
// Local includes
#include "filteroptionsdialog.h"
#include "filteroptionsdialog.moc"
namespace KIPIBatchProcessImagesPlugin
{
FilterOptionsDialog::FilterOptionsDialog(TQWidget *parent, int FilterType)
: KDialogBase( parent, "FilterOptionsDialog", true,
i18n("Filter Options"), Ok|Cancel, Ok, false)
{
TQWidget* box = new TQWidget( this );
setMainWidget(box);
TQVBoxLayout *dvlay = new TQVBoxLayout( box, 10, spacingHint() );
TQString whatsThis;
if (FilterType == 0) // Add noise
{
TQLabel *m_label_noiseType = new TQLabel (i18n("Noise algorithm:"), box);
dvlay->addWidget( m_label_noiseType );
m_noiseType = new TQComboBox( false, box );
m_noiseType->insertItem(i18n("Uniform"));
m_noiseType->insertItem(i18n("Gaussian"));
m_noiseType->insertItem(i18n("Multiplicative"));
m_noiseType->insertItem(i18n("Impulse"));
m_noiseType->insertItem(i18n("Laplacian"));
m_noiseType->insertItem(i18n("Poisson"));
TQWhatsThis::add( m_noiseType, i18n("<p>Select here the algorithm method which will used "
"to add random noise to the images.") );
m_label_noiseType->setBuddy( m_noiseType );
dvlay->addWidget( m_noiseType );
}
if (FilterType == 2) // Blur
{
TQLabel *m_label_blurRadius = new TQLabel (i18n("Radius:"), box);
dvlay->addWidget( m_label_blurRadius );
m_blurRadius = new KIntNumInput(3, box);
m_blurRadius->setRange(0, 20, 1, true );
TQWhatsThis::add( m_blurRadius, i18n("<p>Select here the blur radius of the Gaussian, in pixels, "
"not counting the center pixel. For reasonable results, the "
"radius should be larger than deviation. If you use a radius of 0 "
"the blur operations selects a suitable radius.") );
m_label_blurRadius->setBuddy( m_blurRadius );
dvlay->addWidget( m_blurRadius );
TQLabel *m_label_blurDeviation = new TQLabel (i18n("Deviation:"), box);
dvlay->addWidget( m_label_blurDeviation );
m_blurDeviation = new KIntNumInput(1, box);
m_blurDeviation->setRange(0, 20, 1, true );
TQWhatsThis::add( m_blurDeviation, i18n("<p>Select here the standard deviation of the blur Gaussian, "
"in pixels.") );
m_label_blurDeviation->setBuddy( m_blurDeviation );
dvlay->addWidget( m_blurDeviation );
}
if (FilterType == 5) // Median
{
TQLabel *m_label_medianRadius = new TQLabel (i18n("Radius:"), box);
dvlay->addWidget( m_label_medianRadius );
m_medianRadius = new KIntNumInput(3, box);
m_medianRadius->setRange(0, 20, 1, true );
TQWhatsThis::add( m_medianRadius, i18n("<p>Select here the median radius of the pixel neighborhood. "
"The algorithm applies a digital filter that improves the quality "
"of noisy images. Each pixel is replaced by the median in a "
"set of neighboring pixels as defined by the radius.") );
m_label_medianRadius->setBuddy( m_medianRadius );
dvlay->addWidget( m_medianRadius );
}
if (FilterType == 6) // Noise reduction
{
TQLabel *m_label_noiseRadius = new TQLabel (i18n("Radius:"), box);
dvlay->addWidget( m_label_noiseRadius );
m_noiseRadius = new KIntNumInput(3, box);
m_noiseRadius->setRange(0, 20, 1, true );
TQWhatsThis::add( m_noiseRadius, i18n("<p>Select here the noise reduction radius value, in pixels. "
"The algorithm smooths the contours of an image while still "
"preserving edge information. The algorithm works by replacing "
"each pixel with its neighbor closest in value. A neighbor is "
"defined by the radius. If you use a radius of 0 the algorithm "
"selects a suitable radius.") );
m_label_noiseRadius->setBuddy( m_noiseRadius );
dvlay->addWidget( m_noiseRadius );
}
if (FilterType == 7) // Sharpen
{
TQLabel *m_label_sharpenRadius = new TQLabel (i18n("Radius:"), box);
dvlay->addWidget( m_label_sharpenRadius );
m_sharpenRadius = new KIntNumInput(3, box);
m_sharpenRadius->setRange(0, 20, 1, true );
TQWhatsThis::add( m_sharpenRadius, i18n("<p>Select here the radius of the sharpen Gaussian, in "
"pixels, not counting the center pixel. For reasonable "
"results, the radius should be larger than deviation. "
"if you use a radius of 0 the sharpen operation selects a "
"suitable radius.") );
m_label_sharpenRadius->setBuddy( m_sharpenRadius );
dvlay->addWidget( m_sharpenRadius );
TQLabel *m_label_sharpenDeviation = new TQLabel (i18n("Deviation:"), box);
dvlay->addWidget( m_label_sharpenDeviation );
m_sharpenDeviation = new KIntNumInput(1, box);
m_sharpenDeviation->setRange(0, 20, 1, true );
TQWhatsThis::add( m_sharpenDeviation, i18n("<p>Select here the sharpen deviation value of the "
"Laplacian in pixels.") );
m_label_sharpenDeviation->setBuddy( m_sharpenDeviation );
dvlay->addWidget( m_sharpenDeviation );
}
if (FilterType == 8) // Unsharp
{
TQLabel *m_label_unsharpenRadius = new TQLabel (i18n("Radius:"), box);
dvlay->addWidget( m_label_unsharpenRadius );
m_unsharpenRadius = new KIntNumInput(3, box);
m_unsharpenRadius->setRange(0, 20, 1, true );
TQWhatsThis::add( m_unsharpenRadius, i18n("<p>Select here the radius of the unsharpen Gaussian, "
"in pixels, not counting the center pixel. The algorithm "
"convolve the image with a Gaussian operator of the given "
"radius and standard deviation. For reasonable results, "
"radius should be larger than sigma. If you use a radius of 0 "
"the algorithm selects a suitable radius.") );
m_label_unsharpenRadius->setBuddy( m_unsharpenRadius );
dvlay->addWidget( m_unsharpenRadius );
TQLabel *m_label_unsharpenDeviation = new TQLabel (i18n("Deviation:"), box);
dvlay->addWidget( m_label_unsharpenDeviation );
m_unsharpenDeviation = new KIntNumInput(1, box);
m_unsharpenDeviation->setRange(0, 20, 1, true );
TQWhatsThis::add( m_unsharpenDeviation, i18n("<p>Select here the unsharpen deviation value of the "
"Gaussian, in pixels.") );
m_label_unsharpenDeviation->setBuddy( m_unsharpenDeviation );
dvlay->addWidget( m_unsharpenDeviation );
TQLabel *m_label_unsharpenPercent = new TQLabel (i18n("Percent:"), box);
dvlay->addWidget( m_label_unsharpenPercent );
m_unsharpenPercent = new KIntNumInput(3, box);
m_unsharpenPercent->setRange(1, 100, 1, true );
TQWhatsThis::add( m_unsharpenPercent, i18n("<p>Select here the percentage difference between original "
"and blurred image which should be added to original.") );
m_label_unsharpenPercent->setBuddy( m_unsharpenPercent );
dvlay->addWidget( m_unsharpenPercent );
TQLabel *m_label_unsharpenThreshold = new TQLabel (i18n("Threshold:"), box);
dvlay->addWidget( m_label_unsharpenThreshold );
m_unsharpenThreshold = new KIntNumInput(1, box);
m_unsharpenThreshold->setRange(0, 20, 1, true );
TQWhatsThis::add( m_unsharpenThreshold, i18n("<p>Select here the unsharpen threshold value, in "
"pixels, needed to apply the diffence amount."));
m_label_unsharpenThreshold->setBuddy( m_unsharpenThreshold );
dvlay->addWidget( m_unsharpenThreshold );
}
}
FilterOptionsDialog::~FilterOptionsDialog()
{
}
} // NameSpace KIPIBatchProcessImagesPlugin
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