Smarter use of pointers

Jordan Cheney
1 parent a231cbed
Showing 9 changed files with 93 additions and 32 deletions
openbr/core/boost.cpp
openbr/core/cascade.cpp
openbr/openbr_plugin.h
openbr/plugins/classification/boostedforest.cpp
openbr/plugins/classification/cascade.cpp
openbr/plugins/imgproc/slidingwindow.cpp
openbr/plugins/representation/haar.cpp
openbr/plugins/representation/mblbp.cpp
openbr/plugins/representation/npd.cpp
@@ -144,7 +144,7 @@ void FeatureEvaluator::init(Representation *_representation, int _maxSampleCount
     representation = _representation;
     int dx, dy;
-    Size windowSize = representation->windowSize(dx, dy);
+    Size windowSize = representation->windowSize(&dx, &dy);
     data.create((int)_maxSampleCount, Size(windowSize.width + dx, windowSize.height + dy).area(), CV_32SC1);
     cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
 }
@@ -154,7 +154,7 @@ void FeatureEvaluator::setImage(const Mat &amp;img, uchar clsLabel, int idx)
     cls.ptr<float>(idx)[0] = clsLabel;
     int dx, dy;
-    Size windowSize = representation->windowSize(dx, dy);
+    Size windowSize = representation->windowSize(&dx, &dy);
     Mat integralImg(Size(windowSize.width + dx, windowSize.height + dy), data.type(), data.ptr<int>(idx));
     representation->preprocess(img, integralImg);
 }
@@ -232,7 +232,7 @@ void _CascadeClassifier::detectMultiScale(const Mat&amp; image, vector&lt;Rect&gt;&amp; object
     for (double factor = 1; ; factor *= scaleFactor) {
         int dx, dy;
-        Size originalWindowSize = representation->windowSize(dx, dy);
+        Size originalWindowSize = representation->windowSize(&dx, &dy);
         Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
         Size scaledImageSize(cvRound(image.cols/factor ), cvRound(image.rows/factor));
@@ -1407,7 +1407,7 @@ public:
     // and returned.
     virtual cv::Mat evaluate(const cv::Mat &image, const QList<int> &indices = QList<int>()) const = 0;
-    virtual cv::Size windowSize(int &dx, int &dy) const = 0; // dx and dy should indicate the change to the original window size after preprocessing
+    virtual cv::Size windowSize(int *dx = NULL, int *dy = NULL) const = 0; // dx and dy should indicate the change to the original window size after preprocessing
     virtual int numFeatures() const = 0;
     virtual int maxCatCount() const = 0;
 };
@@ -1422,11 +1422,11 @@ public:
     static Classifier *make(QString str, QObject *parent); /*!< \brief Make a classifier from a string. */
     virtual void train(const QList<cv::Mat> &images, const QList<float> &labels) = 0;
-    virtual float classify(const cv::Mat &image, float &confidence) const = 0;
+    virtual float classify(const cv::Mat &image, float *confidence = NULL) const = 0;
     // Slots for representation
     virtual cv::Mat preprocess(const cv::Mat &image) const = 0;
-    virtual cv::Size windowSize(int &dx, int &dy) const = 0;
+    virtual cv::Size windowSize(int *dx = NULL, int *dy = NULL) const = 0;
     // OpenCV compatibility
     virtual int numFeatures() const = 0;
@@ -129,9 +129,9 @@ class BoostedForestClassifier : public Classifier
         }
     }
-    float classify(const Mat &image, float &confidence) const
+    float classify(const Mat &image, float *confidence) const
     {
-        confidence = 0;
+        float sum = 0;
         for (int i = 0; i < classifiers.size(); i++) {
             Node *node = classifiers[i];
@@ -144,10 +144,12 @@ class BoostedForestClassifier : public Classifier
                     node = val <= node->threshold ? node->left : node->right;
                 }
             }
-            confidence += node->value;
+            sum += node->value;
         }
-        return confidence < threshold - THRESHOLD_EPS ? 0.0f : 1.0f;
+        if (confidence)
+            *confidence = sum;
+        return sum < threshold - THRESHOLD_EPS ? 0.0f : 1.0f;
     }
     int numFeatures() const
@@ -167,7 +169,7 @@ class BoostedForestClassifier : public Classifier
         return dst;
     }
-    Size windowSize(int &dx, int &dy) const
+    Size windowSize(int *dx, int *dy) const
     {
         return representation->windowSize(dx, dy);
     }
@@ -152,23 +152,18 @@ class CascadeClassifier : public Classifier
         }
     }
-    float classify(const Mat &image, float &confidence) const
+    float classify(const Mat &image, float *confidence) const
     {
-        if (stages.empty()) {
-            confidence = 0.0f;
-            return 1.0f;
-        }
-
-        for (int i = 0; i < stages.size(); i++) {
-            float result = stages[i]->classify(image, confidence);
+        float stageConf = 0.0f;
+        foreach (const Classifier *stage, stages) {
+            float result = stage->classify(image, &stageConf);
             if (result == 0.0f) {
-                //confidence *= i;
-                return i;
+                if (confidence)
+                    *confidence += stageConf;
+                return 0.0f;
             }
         }
-
-        //confidence *= stages.size();
-        return stages.size();
+        return 1.0f;
     }
     int numFeatures() const
@@ -186,7 +181,7 @@ class CascadeClassifier : public Classifier
         return stages.first()->preprocess(image);
     }
-    Size windowSize(int &dx, int &dy) const
+    Size windowSize(int *dx, int *dy) const
     {
         return stages.first()->windowSize(dx, dy);
     }
@@ -217,14 +212,14 @@ private:
     {
         imgHandler.restart();
-        float confidence = 0.0f; // not used;
+        float confidence = 0.0f;
         while (images.size() < numPos) {
             Mat pos(imgHandler.winSize, CV_8UC1);
             if (!imgHandler.getPos(pos))
                 qFatal("Cannot get another positive sample!");
-            if (classify(pos, confidence) > 0.0f) {
+            if (classify(pos, &confidence) > 0.0f) {
                 printf("POS current samples: %d\r", images.size());
                 images.append(pos);
                 labels.append(1.0f);
@@ -240,7 +235,7 @@ private:
             if (!imgHandler.getNeg(neg))
                 qFatal("Cannot get another negative sample!");
-            if (classify(neg, confidence) > 0.0f) {
+            if (classify(neg, &confidence) > 0.0f) {
                 printf("NEG current samples: %d\r", images.size() - posCount);
                 images.append(neg);
                 labels.append(0.0f);
@@ -100,7 +100,7 @@ class SlidingWindowTransform : public MetaTransform
                 for (double factor = 1; ; factor *= scaleFactor) {
                     int dx, dy;
-                    Size originalWindowSize = classifier->windowSize(dx, dy);
+                    Size originalWindowSize = classifier->windowSize(&dx, &dy);
                     Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
                     Size scaledImageSize(cvRound(m.cols/factor ), cvRound(m.rows/factor));
@@ -124,7 +124,7 @@ class SlidingWindowTransform : public MetaTransform
                             Mat window = repImage(Rect(Point(x, y), Size(originalWindowSize.width + dx, originalWindowSize.height + dy))).clone();
                             float gypWeight;
-                            int result = classifier->classify(window, gypWeight);
+                            int result = classifier->classify(window, &gypWeight);
                             if (12 - result < 4) {
                                 rects.push_back(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
@@ -89,7 +89,14 @@ class HaarRepresentation : public Representation
     }
     int numFeatures() const { return features.size(); }
-    Size windowSize(int &dx, int &dy) const { dx = dy = 1; return Size(winWidth, winHeight); }
+
+    Size windowSize(int *dx, int *dy) const
+    {
+        if (dx && dy)
+            *dx = *dy = 1;
+        return Size(winWidth, winHeight);
+    }
+
     int maxCatCount() const { return 0; }
     struct Feature
@@ -58,7 +58,13 @@ class MBLBPRepresentation : public Representation
         return result;
     }
-    Size windowSize(int &dx, int &dy) const { dx = dy = 1; return Size(winWidth, winHeight); }
+    Size windowSize(int *dx, int *dy) const
+    {
+        if (dx && dy)
+            *dx = *dy = 1;
+        return Size(winWidth, winHeight);
+    }
+
     int numFeatures() const { return features.size(); }
     int maxCatCount() const { return 256; }
+#include <openbr/plugins/openbr_internal.h>
+
+using namespace cv;
+
+namespace br
+{
+
+class NPDRepresentation : public Representation
+{
+    Q_OBJECT
+
+    Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)
+    Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)
+    BR_PROPERTY(int, winWidth, 24)
+    BR_PROPERTY(int, winHeight, 24)
+
+    void init()
+    {
+        for (int p1 = 0; p1 < (winWidth * winHeight); p1++)
+            for (int p2 = p1; p2 < (winWidth * winHeight); p2++)
+                features.append(Feature(p1, p2));
+    }
+
+    float evaluate(const Mat &image, int idx) const
+    {
+        return features[idx].calc(image);
+    }
+
+    Size windowSize(int *dx, int *dy) const
+    {
+        if (dx && dy)
+            *dx = *dy = 0;
+        return Size(winWidth, winHeight);
+    }
+
+    struct Feature
+    {
+        Feature() {}
+        Feature(int p1, int p2) { p[0] = p1; p[1] = p2; }
+        float calc(const Mat &image) const;
+
+        int p[2];
+    };
+    QList<Feature> features;
+};
+
+BR_REGISTER(Representation, NPDRepresentation)
+
+} // namespace br
+
+#include "representation/npd.moc"