Custom handling of FaceRecognizer results

predict method rewritten to use with PredictCollector interface (abstract class)
now face recognizer allow to perform different scenarios of result handling

modules/face/include/opencv2/face.hpp

@@ -48,6 +48,7 @@ the use of this software, even if advised of the possibility of such damage.
 */
 
 #include "opencv2/core.hpp"
+#include "face/predict_collector.hpp"
 #include <map>
 
 namespace cv { namespace face {
@@ -255,7 +256,8 @@ public:
     CV_WRAP virtual void update(InputArrayOfArrays src, InputArray labels);
 
     /** @overload */
-    virtual int predict(InputArray src) const = 0;
+    CV_WRAP int predict(InputArray src) const;
+
 
     /** @brief Predicts a label and associated confidence (e.g. distance) for a given input image.
 
@@ -292,7 +294,18 @@ public:
     model->predict(img, predicted_label, predicted_confidence);
     @endcode
      */
-    CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0;
+    CV_WRAP void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const;
+
+
+    /** @brief - if implemented - send all result of prediction to collector that can be used for somehow custom result handling
+    @param src Sample image to get a prediction from.
+    @param collector User-defined collector object that accepts all results
+    @param state - optional user-defined state token that should be passed back from FaceRecognizer implementation
+
+    To implement this method u just have to do same internal cycle as in predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) but
+    not try to get "best@ result, just resend it to caller side with given collector
+    */
+    CV_WRAP virtual void predict(InputArray src, Ptr<PredictCollector> collector, const int state = 0) const = 0;
 
     /** @brief Saves a FaceRecognizer and its model state.
 
@@ -345,7 +358,8 @@ public:
     info.
      */
     CV_WRAP virtual std::vector<int> getLabelsByString(const String& str) const;
-
+    /** @brief threshhold parameter accessor - required for default BestMinDist collector */
+    virtual double getThreshold() const = 0;
 protected:
     // Stored pairs "label id - string info"
     std::map<int, String> _labelsInfo;

modules/face/include/opencv2/face/predict_collector.hpp

+/*
+By downloading, copying, installing or using the software you agree to this license.
+If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015, Itseez Inc., all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall copyright holders or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_PREDICT_COLLECTOR_HPP__
+#define __OPENCV_PREDICT_COLLECTOR_HPP__
+#include <cfloat>
+#include "opencv2/core/cvdef.h"
+#include "opencv2/core/cvstd.hpp"
+namespace cv {
+namespace face {
+//! @addtogroup face
+//! @{
+/** @brief Abstract base class for all strategies of prediction result handling
+*/
+class CV_EXPORTS_W PredictCollector {
+protected:
+    double _threshhold;
+    int _size;
+    int _state;
+public:
+    /** @brief creates new predict collector with given threshhold */
+    PredictCollector(double threshhold = DBL_MAX) :_threshhold(threshhold) {};
+    CV_WRAP virtual ~PredictCollector() {}
+    /** @brief called once at start of recognition
+    @param size total size of prediction evaluation that recognizer could perform
+    @param state user defined send-to-back optional value to allow multi-thread, multi-session or aggregation scenarios
+    */
+    CV_WRAP virtual void init(const int size, const int state = 0);
+    /** @brief called with every recognition result
+    @param label current prediction label
+    @param dist current prediction distance (confidence)
+    @param state user defined send-to-back optional value to allow multi-thread, multi-session or aggregation scenarios
+    @return true if recognizer should proceed prediction , false - if recognizer should terminate prediction
+    */
+    CV_WRAP virtual bool emit(const int label, const double dist, const int state = 0); //not abstract while Python generation require non-abstract class
+};
+
+/** @brief default predict collector that trace minimal distance with treshhold checking (that is default behavior for most predict logic)
+*/
+class CV_EXPORTS_W MinDistancePredictCollector : public PredictCollector {
+private:
+    int _label;
+    double _dist;
+public:
+    /** @brief creates new MinDistancePredictCollector with given threshhold */
+    CV_WRAP MinDistancePredictCollector(double threshhold = DBL_MAX) : PredictCollector(threshhold) {
+        _label = 0;
+        _dist = DBL_MAX;
+    };
+    CV_WRAP bool emit(const int label, const double dist, const int state = 0);
+    /** @brief result label, 0 if not found */
+    CV_WRAP int getLabel() const;
+    /** @brief result distance (confidence) DBL_MAX if not found */
+    CV_WRAP double getDist() const;
+    /** @brief factory method to create cv-pointers to MinDistancePredictCollector */
+    CV_WRAP static Ptr<MinDistancePredictCollector> create(double threshold = DBL_MAX);
+};
+//! @}
+}
+}
+#endif
\ No newline at end of file

modules/face/src/eigen_faces.cpp

@@ -41,11 +41,8 @@ public:
     // in labels.
     void train(InputArrayOfArrays src, InputArray labels);
 
-    // Predicts the label of a query image in src.
-    int predict(InputArray src) const;
-
-    // Predicts the label and confidence for a given sample.
-    void predict(InputArray _src, int &label, double &dist) const;
+    // Send all predict results to caller side for custom result handling
+    void predict(InputArray src, Ptr<PredictCollector> collector, const int state) const;
 };
 
 //------------------------------------------------------------------------------
@@ -102,7 +99,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
     }
 }
 
-void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const {
+void Eigenfaces::predict(InputArray _src, Ptr<PredictCollector> collector, const int state) const {
     // get data
     Mat src = _src.getMat();
     // make sure the user is passing correct data
@@ -116,25 +113,15 @@ void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const
         CV_Error(Error::StsBadArg, error_message);
     }
     // project into PCA subspace
-    Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
-    minDist = DBL_MAX;
-    minClass = -1;
-    for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
+    Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1, 1));
+    collector->init((int)_projections.size(), state);
+    for (size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
         double dist = norm(_projections[sampleIdx], q, NORM_L2);
-        if((dist < minDist) && (dist < _threshold)) {
-            minDist = dist;
-            minClass = _labels.at<int>((int)sampleIdx);
-        }
+        int label = _labels.at<int>((int)sampleIdx);
+        if (!collector->emit(label, dist, state))return;
     }
 }
 
-int Eigenfaces::predict(InputArray _src) const {
-    int label;
-    double dummy;
-    predict(_src, label, dummy);
-    return label;
-}
-
 Ptr<BasicFaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
 {
     return makePtr<Eigenfaces>(num_components, threshold);

modules/face/src/facerec.cpp

@@ -72,6 +72,20 @@ void FaceRecognizer::save(const String &filename) const
     fs.release();
 }
 
+int FaceRecognizer::predict(InputArray src) const {
+    int _label;
+    double _dist;
+    predict(src, _label, _dist);
+    return _label;
+}
+
+void FaceRecognizer::predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const {
+    Ptr<MinDistancePredictCollector> collector = MinDistancePredictCollector::create(getThreshold());
+    predict(src, collector, 0);
+    label = collector->getLabel();
+    confidence = collector->getDist();
+}
+
 }
 }
 

modules/face/src/fisher_faces.cpp

@@ -36,11 +36,8 @@ public:
     // in labels.
     void train(InputArrayOfArrays src, InputArray labels);
 
-    // Predicts the label of a query image in src.
-    int predict(InputArray src) const;
-
-    // Predicts the label and confidence for a given sample.
-    void predict(InputArray _src, int &label, double &dist) const;
+    // Send all predict results to caller side for custom result handling
+    void predict(InputArray src, Ptr<PredictCollector> collector, const int state) const;
 };
 
 // Removes duplicate elements in a given vector.
@@ -123,7 +120,7 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
     }
 }
 
-void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const {
+void Fisherfaces::predict(InputArray _src, Ptr<PredictCollector> collector, const int state) const {
     Mat src = _src.getMat();
     // check data alignment just for clearer exception messages
     if(_projections.empty()) {
@@ -137,24 +134,14 @@ void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const
     // project into LDA subspace
     Mat q = LDA::subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
     // find 1-nearest neighbor
-    minDist = DBL_MAX;
-    minClass = -1;
-    for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
+    collector->init((int)_projections.size(), state);
+    for (size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
         double dist = norm(_projections[sampleIdx], q, NORM_L2);
-        if((dist < minDist) && (dist < _threshold)) {
-            minDist = dist;
-            minClass = _labels.at<int>((int)sampleIdx);
-        }
+        int label = _labels.at<int>((int)sampleIdx);
+        if (!collector->emit(label, dist, state))return;
     }
 }
 
-int Fisherfaces::predict(InputArray _src) const {
-    int label;
-    double dummy;
-    predict(_src, label, dummy);
-    return label;
-}
-
 Ptr<BasicFaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
 {
     return makePtr<Fisherfaces>(num_components, threshold);

modules/face/src/lbph_faces.cpp

@@ -91,11 +91,8 @@ public:
     // corresponding labels in labels.
     void update(InputArrayOfArrays src, InputArray labels);
 
-    // Predicts the label of a query image in src.
-    int predict(InputArray src) const;
-
-    // Predicts the label and confidence for a given sample.
-    void predict(InputArray _src, int &label, double &dist) const;
+    // Send all predict results to caller side for custom result handling
+    void predict(InputArray src, Ptr<PredictCollector> collector, const int state = 0) const;
 
     // See FaceRecognizer::load.
     void load(const FileStorage& fs);
@@ -386,7 +383,7 @@ void LBPH::train(InputArrayOfArrays _in_src, InputArray _in_labels, bool preserv
     }
 }
 
-void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
+void LBPH::predict(InputArray _src, Ptr<PredictCollector> collector, const int state) const {
     if(_histograms.empty()) {
         // throw error if no data (or simply return -1?)
         String error_message = "This LBPH model is not computed yet. Did you call the train method?";
@@ -402,24 +399,14 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
             _grid_y, /* grid size y */
             true /* normed histograms */);
     // find 1-nearest neighbor
-    minDist = DBL_MAX;
-    minClass = -1;
-    for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
+    collector->init((int)_histograms.size(), state);
+    for (size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
         double dist = compareHist(_histograms[sampleIdx], query, HISTCMP_CHISQR_ALT);
-        if((dist < minDist) && (dist < _threshold)) {
-            minDist = dist;
-            minClass = _labels.at<int>((int) sampleIdx);
-        }
+        int label = _labels.at<int>((int)sampleIdx);
+        if (!collector->emit(label, dist, state))return;
     }
 }
 
-int LBPH::predict(InputArray _src) const {
-    int label;
-    double dummy;
-    predict(_src, label, dummy);
-    return label;
-}
-
 Ptr<LBPHFaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
                                              int grid_x, int grid_y, double threshold)
 {

modules/face/src/predict_collector.cpp

+/*
+By downloading, copying, installing or using the software you agree to this license.
+If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015, Itseez Inc., all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are disclaimed.
+In no event shall copyright holders or contributors be liable for any direct,
+indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+#include "opencv2/face/predict_collector.hpp"
+#include "opencv2/core/cvstd.hpp"
+namespace cv {
+namespace face {
+
+void PredictCollector::init(const int size, const int state) {
+    //reserve for some-how usage in descendants
+    _size = size;
+    _state = state;
+}
+
+bool PredictCollector::emit(const int, const double, const int state) {
+    if (_state == state) {
+        return false; // if it's own session - terminate it while default PredictCollector does nothing
+    }
+    return true;
+}
+
+bool MinDistancePredictCollector::emit(const int label, const double dist, const int state) {
+    if (_state != state) {
+        return true; // it works only in one (same) session doesn't accept values for other states
+    }
+    if (dist < _threshhold && dist < _dist) {
+        _label = label;
+        _dist = dist;
+    }
+    return true;
+}
+
+int MinDistancePredictCollector::getLabel() const {
+    return _label;
+}
+
+double MinDistancePredictCollector::getDist() const {
+    return _dist;
+}
+
+Ptr<MinDistancePredictCollector> MinDistancePredictCollector::create(double threshold) {
+    return Ptr<MinDistancePredictCollector>(new MinDistancePredictCollector(threshold));
+}
+
+}
+}
\ No newline at end of file