Derive ACFFeatureEvaluator from Algorithm

modules/xobjdetect/include/opencv2/xobjdetect.hpp

@@ -60,37 +60,31 @@ namespace xobjdetect
 */
 void computeChannels(InputArray image, OutputArrayOfArrays channels);
 
-class CV_EXPORTS ACFFeatureEvaluator
+class CV_EXPORTS ACFFeatureEvaluator : public Algorithm
 {
 public:
-    /* Construct evaluator, set features to evaluate */
-    ACFFeatureEvaluator(const std::vector<Point3i>& features);
-
     /* Set channels for feature evaluation */
-    void setChannels(InputArrayOfArrays channels);
+    virtual void setChannels(InputArrayOfArrays channels) = 0;
 
     /* Set window position */
-    void setPosition(Size position);
+    virtual void setPosition(Size position) = 0;
 
     /* Evaluate feature with given index for current channels
         and window position */
-    int evaluate(size_t feature_ind) const;
+    virtual int evaluate(size_t feature_ind) const = 0;
 
     /* Evaluate all features for current channels and window position
 
     Returns matrix-column of features
     */
-    void evaluateAll(OutputArray feature_values) const;
+    virtual void evaluateAll(OutputArray feature_values) const = 0;
 
-private:
-    /* Features to evaluate */
-    std::vector<Point3i> features_;
-    /* Channels for feature evaluation */
-    std::vector<Mat> channels_;
-    /* Channels window position */
-    Size position_;
 };
 
+/* Construct evaluator, set features to evaluate */
+CV_EXPORTS Ptr<ACFFeatureEvaluator>
+createACFFeatureEvaluator(const std::vector<Point3i>& features);
+
 /* Generate acf features
 
     window_size — size of window in which features should be evaluated
@@ -158,7 +152,7 @@ private:
     float pos_value_, neg_value_;
 };
 
-class CV_EXPORTS WaldBoost
+class CV_EXPORTS WaldBoost : public Algorithm
 {
 public:
     /* Initialize WaldBoost cascade with default of specified parameters */

modules/xobjdetect/src/acffeature.cpp

@@ -45,33 +45,44 @@ using std::vector;
 
 using std::min;
 
+#include <iostream>
+using std::cout;
+using std::endl;
+
 namespace cv
 {
 namespace xobjdetect
 {
 
-ACFFeatureEvaluator::ACFFeatureEvaluator(const vector<Point3i>& features):
-    features_(features), channels_(), position_()
-{
-}
-int ACFFeatureEvaluator::evaluate(size_t feature_ind) const
+class ACFFeatureEvaluatorImpl : public ACFFeatureEvaluator
 {
-    /* Assume there are 10 channels */
-    CV_Assert(channels_.size() == 10);
-    CV_Assert(feature_ind < features_.size());
-
-    Point3i feature = features_.at(feature_ind);
-    int x = feature.x;
-    int y = feature.y;
-    int n = feature.z;
-    return channels_[n].at<int>(y, x);
-}
+public:
+    ACFFeatureEvaluatorImpl(const vector<Point3i>& features):
+        features_(features), channels_(), position_()
+    {
+        CV_Assert(features.size() > 0);
+    }
 
-void ACFFeatureEvaluator::setChannels(cv::InputArrayOfArrays channels)
+    virtual void setChannels(InputArrayOfArrays channels);
+    virtual void setPosition(Size position);
+    virtual int evaluate(size_t feature_ind) const;
+    virtual void evaluateAll(OutputArray feature_values) const;
+
+private:
+    /* Features to evaluate */
+    std::vector<Point3i> features_;
+    /* Channels for feature evaluation */
+    std::vector<Mat> channels_;
+    /* Channels window position */
+    Size position_;
+};
+
+void ACFFeatureEvaluatorImpl::setChannels(cv::InputArrayOfArrays channels)
 {
     channels_.clear();
     vector<Mat> ch;
     channels.getMatVector(ch);
+    CV_Assert(ch.size() == 10);
     for( size_t i = 0; i < ch.size(); ++i )
     {
         const Mat &channel = ch[i];
@@ -92,12 +103,24 @@ void ACFFeatureEvaluator::setChannels(cv::InputArrayOfArrays channels)
     }
 }
 
-void ACFFeatureEvaluator::setPosition(Size position)
+void ACFFeatureEvaluatorImpl::setPosition(Size position)
 {
     position_ = position;
 }
 
-void ACFFeatureEvaluator::evaluateAll(OutputArray feature_values) const
+int ACFFeatureEvaluatorImpl::evaluate(size_t feature_ind) const
+{
+    CV_Assert(channels_.size() == 10);
+    CV_Assert(feature_ind < features_.size());
+
+    Point3i feature = features_.at(feature_ind);
+    int x = feature.x;
+    int y = feature.y;
+    int n = feature.z;
+    return channels_[n].at<int>(y, x);
+}
+
+void ACFFeatureEvaluatorImpl::evaluateAll(OutputArray feature_values) const
 {
     Mat_<int> feature_vals(1, (int)features_.size());
     for( int i = 0; i < (int)features_.size(); ++i )
@@ -107,6 +130,12 @@ void ACFFeatureEvaluator::evaluateAll(OutputArray feature_values) const
     feature_values.setTo(feature_vals);
 }
 
+Ptr<ACFFeatureEvaluator>
+createACFFeatureEvaluator(const vector<Point3i>& features)
+{
+    return Ptr<ACFFeatureEvaluator>(new ACFFeatureEvaluatorImpl(features));
+}
+
 vector<Point3i> generateFeatures(Size window_size, int count)
 {
     CV_Assert(count > 0);

modules/xobjdetect/src/icfdetector.cpp

@@ -118,7 +118,7 @@ void ICFDetector::train(const vector<string>& image_filenames,
         labels(0, i) = -1;
 
     vector<Point3i> features = generateFeatures(model_size);
-    ACFFeatureEvaluator feature_evaluator(features);
+    Ptr<ACFFeatureEvaluator> feature_evaluator = createACFFeatureEvaluator(features);
 
     Mat_<int> data((int)features.size(), (int)samples.size());
     Mat_<int> feature_col;
@@ -127,8 +127,8 @@ void ICFDetector::train(const vector<string>& image_filenames,
     for( int i = 0; i < (int)samples.size(); ++i )
     {
         computeChannels(samples[i], channels);
-        feature_evaluator.setChannels(channels);
-        feature_evaluator.evaluateAll(feature_col);
+        feature_evaluator->setChannels(channels);
+        feature_evaluator->evaluateAll(feature_col);
         for( int j = 0; j < feature_col.rows; ++j )
             data(i, j) = feature_col(0, j);
     }