File indexing completed on 2025-01-05 04:00:15

 /* ============================================================
  *
  * This file is a part of digiKam project
  * https://www.digikam.org
  *
  * Date        : 2013-08-19
  * Description : Image Quality setup page
  *
  * SPDX-FileCopyrightText: 2013-2024 by Gilles Caulier <caulier dot gilles at gmail dot com>
  * SPDX-FileCopyrightText: 2013-2014 by Gowtham Ashok <gwty93 at gmail dot com>
  *
  * SPDX-License-Identifier: GPL-2.0-or-later
  *
  * ============================================================ */
 
 #include "setupimagequalitysorter.h"
 
 // Qt includes
 
 #include <QLabel>
 
 // KDE includes
 
 #include <klocalizedstring.h>
 
 // Local includes
 
 #include "imagequalitysettings.h"
 #include "dlayoutbox.h"
 
 namespace Digikam
 {
 
 class Q_DECL_HIDDEN SetupImageQualitySorter::Private
 {
 public:
 
     explicit Private()
       : settingsWidget(nullptr)
     {
     }
 
     ImageQualitySettings* settingsWidget;
 };
 
 // --------------------------------------------------------
 
 SetupImageQualitySorter::SetupImageQualitySorter(QWidget* const parent)
     : QScrollArea(parent),
       d          (new Private)
 {
     DVBox* const vbox = new DVBox(viewport());
 
     QLabel* const explanation = new QLabel(i18nc("@label", "These settings determines the quality of an image and convert it into a score, "
                                                  "stored in database as Pick Label property. This information can be evaluated by two ways: "
                                                  "using four basic factors sabotaging the images (blur, noise, exposure, and compression), "
                                                  "or using a deep learning neural network engine. The first one helps to determine whether "
                                                  "images are distorted by the basic factors, however it demands some drawbacks as fine-tuning "
                                                  "from the user’s side and it cannot work along the aesthetic image processing. "
                                                  "The second one uses an IA approach based on %1 model to predict the score. "
                                                  "As deep learning is an end-to-end solution, it doesn’t require hyper-parameter settings, "
                                                  "and make this feature easier to use.",
                                            QString::fromUtf8("<a href='https://expertphotography.com/aesthetic-photography/'>%1</a>")
                                                  .arg(i18nc("@label", "aesthetic image quality"))), vbox);
     explanation->setOpenExternalLinks(true);
     explanation->setWordWrap(true);
     explanation->setTextFormat(Qt::RichText);
 
     d->settingsWidget = new ImageQualitySettings(vbox);
 
     setWidget(vbox);
     setWidgetResizable(true);
 
     d->settingsWidget->readSettings();
 }
 
 SetupImageQualitySorter::~SetupImageQualitySorter()
 {
     delete d;
 }
 
 void SetupImageQualitySorter::applySettings()
 {
     d->settingsWidget->applySettings();
 }
 
 void SetupImageQualitySorter::readSettings()
 {
     d->settingsWidget->readSettings();
 }
 
 ImageQualityContainer SetupImageQualitySorter::getImageQualityContainer() const
 {
     return d->settingsWidget->getImageQualityContainer();
 }
 
 } // namespace Digikam
 
 #include "moc_setupimagequalitysorter.cpp"