Separate WaldBost and Stump

apps/icf/stump.cpp

@@ -0,0 +1,162 @@
+#include "stump.hpp"
+
+namespace cv
+{
+namespace adas
+{
+
+/* Cumulative sum by rows */
+static void cumsum(const Mat_<float>& src, Mat_<float> dst)
+{
+    CV_Assert(src.cols > 0);
+
+    for( int row = 0; row < src.rows; ++row )
+    {
+        dst(row, 0) = src(row, 0);
+        for( int col = 1; col < src.cols; ++col )
+        {
+            dst(row, col) = dst(row, col - 1) + src(row, col);
+        }
+    }
+}
+
+int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
+{
+    CV_Assert(labels.rows == 1 && labels.cols == data.cols);
+    CV_Assert(weights.rows == 1 && weights.cols == data.cols);
+    /* Assert that data and labels have int type */
+    /* Assert that weights have float type */
+
+
+    /* Prepare labels for each feature rearranged according to sorted order */
+    Mat sorted_labels(data.rows, data.cols, labels.type());
+    Mat sorted_weights(data.rows, data.cols, weights.type());
+    Mat indices;
+    sortIdx(data, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
+    for( int row = 0; row < indices.rows; ++row )
+    {
+        for( int col = 0; col < indices.cols; ++col )
+        {
+            sorted_labels.at<int>(row, col) =
+                labels.at<int>(0, indices.at<int>(row, col));
+            sorted_weights.at<float>(row, col) =
+                weights.at<float>(0, indices.at<float>(row, col));
+        }
+    }
+
+    /* Sort feature values */
+    Mat sorted_data(data.rows, data.cols, data.type());
+    sort(data, sorted_data, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
+
+    /* Split positive and negative weights */
+    Mat pos_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
+        sorted_weights.type());
+    Mat neg_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
+        sorted_weights.type());
+    for( int row = 0; row < data.rows; ++row )
+    {
+        for( int col = 0; col < data.cols; ++col )
+        {
+            if( sorted_labels.at<int>(row, col) == +1 )
+            {
+                pos_weights.at<float>(row, col) =
+                    sorted_weights.at<float>(row, col);
+            }
+            else
+            {
+                neg_weights.at<float>(row, col) =
+                    sorted_weights.at<float>(row, col);
+            }
+        }
+    }
+
+    /* Compute cumulative sums for fast stump error computation */
+    Mat pos_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
+        sorted_weights.type());
+    Mat neg_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
+        sorted_weights.type());
+    cumsum(pos_weights, pos_cum_weights);
+    cumsum(neg_weights, neg_cum_weights);
+
+    /* Compute total weights of positive and negative samples */
+    float pos_total_weight = pos_cum_weights.at<float>(0, weights.cols - 1);
+    float neg_total_weight = neg_cum_weights.at<float>(0, weights.cols - 1);
+
+
+    float eps = 1. / 4 * labels.cols;
+
+    /* Compute minimal error */
+    float min_err = FLT_MAX;
+    int min_row = -1;
+    int min_col = -1;
+    int min_polarity = 0;
+    float min_pos_value = 1, min_neg_value = -1;
+
+    for( int row = 0; row < sorted_weights.rows; ++row )
+    {
+        for( int col = 0; col < sorted_weights.cols - 1; ++col )
+        {
+            float err, h_pos, h_neg;
+
+            // Direct polarity
+
+            float pos_wrong = pos_cum_weights.at<float>(row, col);
+            float pos_right = pos_total_weight - pos_wrong;
+
+            float neg_right = neg_cum_weights.at<float>(row, col);
+            float neg_wrong = neg_total_weight - neg_right;
+
+            h_pos = .5 * log((pos_right + eps) / (pos_wrong + eps));
+            h_neg = .5 * log((neg_wrong + eps) / (neg_right + eps));
+
+            err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
+
+            if( err < min_err )
+            {
+                min_err = err;
+                min_row = row;
+                min_col = col;
+                min_polarity = +1;
+                min_pos_value = h_pos;
+                min_neg_value = h_neg;
+            }
+
+            // Opposite polarity
+            swap(pos_right, pos_wrong);
+            swap(neg_right, neg_wrong);
+
+            h_pos = -h_pos;
+            h_neg = -h_neg;
+
+            err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
+
+
+            if( err < min_err )
+            {
+                min_err = err;
+                min_row = row;
+                min_col = col;
+                min_polarity = -1;
+                min_pos_value = h_pos;
+                min_neg_value = h_neg;
+            }
+        }
+    }
+
+    /* Compute threshold, store found values in fields */
+    threshold_ = ( sorted_data.at<int>(min_row, min_col) +
+                   sorted_data.at<int>(min_row, min_col + 1) ) / 2;
+    polarity_ = min_polarity;
+    pos_value_ = min_pos_value;
+    neg_value_ = min_neg_value;
+
+    return min_row;
+}
+
+float Stump::predict(int value)
+{
+    return polarity_ * (value - threshold_) > 0 ? pos_value_ : neg_value_;
+}
+
+} /* namespace adas */
+} /* namespace cv */

apps/icf/stump.hpp

@@ -0,0 +1,58 @@
+#ifndef __OPENCV_ADAS_STUMP_HPP__
+#define __OPENCV_ADAS_STUMP_HPP__
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace adas
+{
+
+class Stump
+{
+public:
+
+    /* Initialize zero stump */
+    Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {}
+
+    /* Initialize stump with given threshold, polarity
+        and classification values */
+    Stump(int threshold, int polarity, float pos_value, float neg_value):
+        threshold_(threshold), polarity_(polarity),
+        pos_value_(pos_value), neg_value_(neg_value) {}
+
+    /* Train stump for given data
+
+        data — matrix of feature values, size M x N, one feature per row
+
+        labels — matrix of sample class labels, size 1 x N. Labels can be from
+            {-1, +1}
+
+        weights — matrix of sample weights, size 1 x N
+
+    Returns chosen feature index. Feature enumeration starts from 0
+    */
+    int train(const Mat& data, const Mat& labels, const Mat& weights);
+
+    /* Predict object class given
+
+        value — feature value. Feature must be the same as was chosen
+        during training stump
+
+    Returns real value, sign(value) means class
+    */
+    float predict(int value);
+
+private:
+    /* Stump decision threshold */
+    int threshold_;
+    /* Stump polarity, can be from {-1, +1} */
+    int polarity_;
+    /* Classification values for positive and negative classes  */
+    float pos_value_, neg_value_;
+};
+
+} /* namespace adas */
+} /* namespace cv */
+
+#endif /* __OPENCV_ADAS_STUMP_HPP__ */

apps/icf/waldboost.cpp

@@ -5,170 +5,12 @@ using std::swap;
 
 #include "waldboost.hpp"
 
-using cv::Mat;
-using cv::Mat_;
-using cv::sum;
-using cv::sort;
-using cv::sortIdx;
-using cv::adas::Stump;
-using cv::adas::WaldBoost;
-using cv::Ptr;
-using cv::adas::ACFFeatureEvaluator;
-
 using std::vector;
 
-/* Cumulative sum by rows */
+namespace cv
-static void cumsum(const Mat_<float>& src, Mat_<float> dst)
-{
-    CV_Assert(src.cols > 0);
-
-    for( int row = 0; row < src.rows; ++row )
-    {
-        dst(row, 0) = src(row, 0);
-        for( int col = 1; col < src.cols; ++col )
-        {
-            dst(row, col) = dst(row, col - 1) + src(row, col);
-        }
-    }
-}
-
-int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
-{
-    CV_Assert(labels.rows == 1 && labels.cols == data.cols);
-    CV_Assert(weights.rows == 1 && weights.cols == data.cols);
-    /* Assert that data and labels have int type */
-    /* Assert that weights have float type */
-
-
-    /* Prepare labels for each feature rearranged according to sorted order */
-    Mat sorted_labels(data.rows, data.cols, labels.type());
-    Mat sorted_weights(data.rows, data.cols, weights.type());
-    Mat indices;
-    sortIdx(data, indices, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
-    for( int row = 0; row < indices.rows; ++row )
-    {
-        for( int col = 0; col < indices.cols; ++col )
-        {
-            sorted_labels.at<int>(row, col) =
-                labels.at<int>(0, indices.at<int>(row, col));
-            sorted_weights.at<float>(row, col) =
-                weights.at<float>(0, indices.at<float>(row, col));
-        }
-    }
-
-    /* Sort feature values */
-    Mat sorted_data(data.rows, data.cols, data.type());
-    sort(data, sorted_data, cv::SORT_EVERY_ROW | cv::SORT_ASCENDING);
-
-    /* Split positive and negative weights */
-    Mat pos_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    Mat neg_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    for( int row = 0; row < data.rows; ++row )
-    {
-        for( int col = 0; col < data.cols; ++col )
 {
-            if( sorted_labels.at<int>(row, col) == +1 )
+namespace adas
 {
-                pos_weights.at<float>(row, col) =
-                    sorted_weights.at<float>(row, col);
-            }
-            else
-            {
-                neg_weights.at<float>(row, col) =
-                    sorted_weights.at<float>(row, col);
-            }
-        }
-    }
-
-    /* Compute cumulative sums for fast stump error computation */
-    Mat pos_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    Mat neg_cum_weights = Mat::zeros(sorted_weights.rows, sorted_weights.cols,
-        sorted_weights.type());
-    cumsum(pos_weights, pos_cum_weights);
-    cumsum(neg_weights, neg_cum_weights);
-
-    /* Compute total weights of positive and negative samples */
-    float pos_total_weight = pos_cum_weights.at<float>(0, weights.cols - 1);
-    float neg_total_weight = neg_cum_weights.at<float>(0, weights.cols - 1);
-
-
-    float eps = 1. / 4 * labels.cols;
-
-    /* Compute minimal error */
-    float min_err = FLT_MAX;
-    int min_row = -1;
-    int min_col = -1;
-    int min_polarity = 0;
-    float min_pos_value = 1, min_neg_value = -1;
-
-    for( int row = 0; row < sorted_weights.rows; ++row )
-    {
-        for( int col = 0; col < sorted_weights.cols - 1; ++col )
-        {
-            float err, h_pos, h_neg;
-
-            // Direct polarity
-
-            float pos_wrong = pos_cum_weights.at<float>(row, col);
-            float pos_right = pos_total_weight - pos_wrong;
-
-            float neg_right = neg_cum_weights.at<float>(row, col);
-            float neg_wrong = neg_total_weight - neg_right;
-
-            h_pos = .5 * log((pos_right + eps) / (pos_wrong + eps));
-            h_neg = .5 * log((neg_wrong + eps) / (neg_right + eps));
-
-            err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
-
-            if( err < min_err )
-            {
-                min_err = err;
-                min_row = row;
-                min_col = col;
-                min_polarity = +1;
-                min_pos_value = h_pos;
-                min_neg_value = h_neg;
-            }
-
-            // Opposite polarity
-            swap(pos_right, pos_wrong);
-            swap(neg_right, neg_wrong);
-
-            h_pos = -h_pos;
-            h_neg = -h_neg;
-
-            err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
-
-
-            if( err < min_err )
-            {
-                min_err = err;
-                min_row = row;
-                min_col = col;
-                min_polarity = -1;
-                min_pos_value = h_pos;
-                min_neg_value = h_neg;
-            }
-        }
-    }
-
-    /* Compute threshold, store found values in fields */
-    threshold_ = ( sorted_data.at<int>(min_row, min_col) +
-                   sorted_data.at<int>(min_row, min_col + 1) ) / 2;
-    polarity_ = min_polarity;
-    pos_value_ = min_pos_value;
-    neg_value_ = min_neg_value;
-
-    return min_row;
-}
-
-float Stump::predict(int value)
-{
-    return polarity_ * (value - threshold_) > 0 ? pos_value_ : neg_value_;
-}
 
 WaldBoost::WaldBoost(const WaldBoostParams& params): params_(params)
 {
@@ -275,3 +117,6 @@ float WaldBoost::predict(const Ptr<ACFFeatureEvaluator>& feature_evaluator)
     }
     return trace;
 }
+
+} /* namespace adas */
+} /* namespace cv */

apps/icf/waldboost.hpp

@@ -45,56 +45,13 @@ the use of this software, even if advised of the possibility of such damage.
 #include <opencv2/core.hpp>
 
 #include "acffeature.hpp"
+#include "stump.hpp"
 
 namespace cv
 {
 namespace adas
 {
 
-class Stump
-{
-public:
-
-    /* Initialize zero stump */
-    Stump(): threshold_(0), polarity_(1), pos_value_(1), neg_value_(-1) {}
-
-    /* Initialize stump with given threshold, polarity
-        and classification values */
-    Stump(int threshold, int polarity, float pos_value, float neg_value):
-        threshold_(threshold), polarity_(polarity),
-        pos_value_(pos_value), neg_value_(neg_value) {}
-
-    /* Train stump for given data
-
-        data — matrix of feature values, size M x N, one feature per row
-
-        labels — matrix of sample class labels, size 1 x N. Labels can be from
-            {-1, +1}
-
-        weights — matrix of sample weights, size 1 x N
-
-    Returns chosen feature index. Feature enumeration starts from 0
-    */
-    int train(const Mat& data, const Mat& labels, const Mat& weights);
-
-    /* Predict object class given
-
-        value — feature value. Feature must be the same as was chosen
-        during training stump
-
-    Returns real value, sign(value) means class
-    */
-    float predict(int value);
-
-private:
-    /* Stump decision threshold */
-    int threshold_;
-    /* Stump polarity, can be from {-1, +1} */
-    int polarity_;
-    /* Classification values for positive and negative classes  */
-    float pos_value_, neg_value_;
-};
-
 struct WaldBoostParams
 {
     int weak_count;