File indexing completed on 2025-03-09 03:54:58

 /* ============================================================
  *
  * This file is a part of digiKam
  *
  * Date        : 2019-08-08
  * Description : Derived class to perform SSD neural network inference
  *               for face detection
  *
  * SPDX-FileCopyrightText: 2019 by Thanh Trung Dinh <dinhthanhtrung1996 at gmail dot com>
  * SPDX-FileCopyrightText: 2020-2024 by Gilles Caulier <caulier dot gilles at gmail dot com>
  *
  * SPDX-License-Identifier: GPL-2.0-or-later
  *
  * ============================================================ */
 
 #include "dnnfacedetectorssd.h"
 
 // Qt includes
 
 #include <QList>
 #include <QRect>
 #include <QString>
 #include <QFileInfo>
 #include <QMutexLocker>
 #include <QStandardPaths>
 
 // Local includes
 
 #include "digikam_debug.h"
 #include "digikam_config.h"
 
 namespace Digikam
 {
 
 DNNFaceDetectorSSD::DNNFaceDetectorSSD()
     : DNNFaceDetectorBase(1.0, cv::Scalar(104.0, 177.0, 123.0), cv::Size(300, 300))
 {
     loadModels();
 }
 
 DNNFaceDetectorSSD::~DNNFaceDetectorSSD()
 {
 }
 
 bool DNNFaceDetectorSSD::loadModels()
 {
     QString appPath = QStandardPaths::locate(QStandardPaths::GenericDataLocation,
                                              QLatin1String("digikam/facesengine"),
                                              QStandardPaths::LocateDirectory);
 
     QString model   = QLatin1String("deploy.prototxt");
     QString data    = QLatin1String("res10_300x300_ssd_iter_140000_fp16.caffemodel");
 
     QString nnmodel = appPath + QLatin1Char('/') + model;
     QString nndata  = appPath + QLatin1Char('/') + data;
 
     if (QFileInfo::exists(nnmodel) && QFileInfo::exists(nndata))
     {
         try
         {
             qCDebug(DIGIKAM_FACEDB_LOG) << "SSD model:" << model << ", SSD data:" << data;
 
 #ifdef Q_OS_WIN
 
             net = cv::dnn::readNetFromCaffe(nnmodel.toLocal8Bit().constData(),
                                             nndata.toLocal8Bit().constData());
 
 #else
 
             net = cv::dnn::readNetFromCaffe(nnmodel.toStdString(),
                                             nndata.toStdString());
 
 #endif
 
 #if (OPENCV_VERSION == QT_VERSION_CHECK(4, 7, 0))
 
             net.enableWinograd(false);
 
 #endif
 
         }
         catch (cv::Exception& e)
         {
             qCWarning(DIGIKAM_FACEDB_LOG) << "cv::Exception:" << e.what();
 
             return false;
         }
         catch (...)
         {
            qCWarning(DIGIKAM_FACEDB_LOG) << "Default exception from OpenCV";
 
            return false;
         }
     }
     else
     {
         qCCritical(DIGIKAM_FACEDB_LOG) << "Cannot found faces engine DNN model" << model << "or" << data;
         qCCritical(DIGIKAM_FACEDB_LOG) << "Faces detection feature cannot be used!";
 
         return false;
     }
 
     return true;
 }
 
 void DNNFaceDetectorSSD::detectFaces(const cv::Mat& inputImage,
                                      const cv::Size& paddedSize,
                                      std::vector<cv::Rect>& detectedBboxes)
 {
     if (inputImage.empty())
     {
         qCDebug(DIGIKAM_FACESENGINE_LOG) << "Invalid image given, not detecting faces.";
         return;
     }
 
     cv::Mat detection;
     cv::Mat inputBlob = cv::dnn::blobFromImage(inputImage, scaleFactor, inputImageSize, meanValToSubtract, true, false);
 
     if (!net.empty())
     {
         QMutexLocker lock(&mutex);
         net.setInput(inputBlob);
         detection = net.forward();
     }
 
     postprocess(detection, paddedSize, detectedBboxes);
 }
 
 void DNNFaceDetectorSSD::postprocess(cv::Mat detection,
                                      const cv::Size& paddedSize,
                                      std::vector<cv::Rect>& detectedBboxes) const
 {
     std::vector<float> goodConfidences, doubtConfidences, confidences;
     std::vector<cv::Rect> goodBoxes, doubtBoxes, boxes;
 
     cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
 
     // TODO: model problem, confidence of ssd output too low ===> false detection
 
     for (int i = 0 ; i < detectionMat.rows ; ++i)
     {
         float confidence = detectionMat.at<float>(i, 2);
 
         if (confidence > confidenceThreshold)
         {
             float leftRatio   = detectionMat.at<float>(i, 3);
             float topRatio    = detectionMat.at<float>(i, 4);
             float rightRatio  = detectionMat.at<float>(i, 5);
             float bottomRatio = detectionMat.at<float>(i, 6);
 
             int left          = (int)(leftRatio   * inputImageSize.width);
             int right         = (int)(rightRatio  * inputImageSize.width);
             int top           = (int)(topRatio    * inputImageSize.height);
             int bottom        = (int)(bottomRatio * inputImageSize.height);
 
             selectBbox(paddedSize,
                        confidence,
                        left,
                        right,
                        top,
                        bottom,
                        goodConfidences,
                        goodBoxes,
                        doubtConfidences,
                        doubtBoxes);
         }
     }
 /*
     qCDebug(DIGIKAM_FACESENGINE_LOG) << "nb of doubtbox = " << doubtBoxes.size();
     qCDebug(DIGIKAM_FACESENGINE_LOG) << "nb of goodbox = " << goodBoxes.size();
 */
     if (goodBoxes.empty())
     {
         boxes       = doubtBoxes;
         confidences = doubtConfidences;
     }
     else
     {
         boxes       = goodBoxes;
         confidences = goodConfidences;
     }
 
     // Perform non maximum suppression to eliminate redundant overlapping boxes with lower confidences
 
     std::vector<int> indices;
     cv::dnn::NMSBoxes(boxes, confidences, confidenceThreshold, nmsThreshold, indices);
 
     // Get detected bounding boxes
 
     for (size_t i = 0 ; i < indices.size() ; ++i)
     {
         cv::Rect bbox = boxes[indices[i]];
         correctBbox(bbox, paddedSize);
         detectedBboxes.push_back(cv::Rect(bbox.x, bbox.y, bbox.width, bbox.height));
     }
 }
 
 } // namespace Digikam