Add waldboost interface, stump implementation

apps/icf/waldboost.cpp

@@ -0,0 +1,166 @@
+#include "waldboost.hpp"
+
+using cv::Mat;
+using cv::Mat_;
+using cv::sort;
+using cv::sortIdx;
+using cv::adas::Stump;
+using cv::adas::WaldBoost;
+
+/* 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);
+        }
+    }
+}
+
+#include <iostream>
+using std::cout;
+using std::endl;
+
+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 */
+    int pos_total_weight = 0, neg_total_weight = 0;
+    for( int col = 0; col < labels.cols; ++col )
+    {
+        if( labels.at<int>(0, col) == +1)
+            pos_total_weight += weights.at<float>(0, col);
+        else
+            neg_total_weight += weights.at<float>(0, col);
+    }
+
+    cout << pos_total_weight << endl;
+    cout << neg_total_weight << endl;
+
+    cout << pos_weights << endl;
+    cout << neg_weights << endl;
+
+    cout << pos_cum_weights << endl;
+    cout << neg_cum_weights << endl;
+
+    /* Compute minimal error */
+    float min_err = FLT_MAX;
+    int min_row = -1;
+    int min_col = -1;
+    int min_polarity = 0;
+    for( int row = 0; row < sorted_weights.rows; ++row )
+    {
+        for( int col = 0; col < sorted_weights.cols - 1; ++col )
+        {
+            float err;
+
+            err = pos_cum_weights.at<float>(row, col) +
+                  (neg_total_weight - neg_cum_weights.at<float>(row, col));
+
+            cout << "row " << row << "err " << err << endl;
+
+            if( err < min_err )
+            {
+                min_err = err;
+                min_row = row;
+                min_col = col;
+                min_polarity = +1;
+            }
+
+
+            err = (pos_total_weight - pos_cum_weights.at<float>(row, col)) +
+                  neg_cum_weights.at<float>(row, col);
+
+            cout << "row " << row << "err " << err << endl;
+
+            if( err < min_err )
+            {
+                min_err = err;
+                min_row = row;
+                min_col = col;
+                min_polarity = -1;
+            }
+        }
+    }
+
+    cout << "min_err: " << min_err << endl;
+    /* 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;
+    cout << "threshold: " << threshold_ << endl;
+    polarity_ = min_polarity;
+
+    return min_row;
+}
+
+static inline int sign(int value)
+{
+    if (value > 0)
+        return +1;
+    return -1;
+}
+
+int Stump::predict(int value)
+{
+    return polarity_ * sign(value - threshold_);
+}

apps/icf/waldboost.hpp

@@ -0,0 +1,144 @@
+/*
+
+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) 2013, OpenCV Foundation, 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_ADAS_WALDBOOST_HPP__
+#define __OPENCV_ADAS_WALDBOOST_HPP__
+
+#include <opencv2/core.hpp>
+
+#include "acffeature.hpp"
+
+namespace cv
+{
+namespace adas
+{
+
+class Stump
+{
+public:
+
+    /* Initialize zero stump */
+    Stump(): threshold_(0), polarity_(1) {};
+
+    /* Initialize stump with given threshold and polarity */
+    Stump(int threshold, int polarity): threshold_(threshold),
+        polarity_(polarity) {};
+
+    /* 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 chose during training
+        stump
+
+    Returns object class from {-1, +1}
+    */
+    int predict(int value);
+
+private:
+    /* Stump decision threshold */
+    int threshold_;
+    /* Stump polarity, can be from {-1, +1} */
+    int polarity_;
+    /* Stump decision rule:
+        h(value) = polarity * sign(value - threshold)
+    */
+};
+
+struct WaldBoostParams
+{
+    int weak_count;
+};
+
+class WaldBoost
+{
+public:
+    /* Initialize WaldBoost cascade with default of specified parameters */
+    WaldBoost(const WaldBoostParams& params);
+
+    /* Train WaldBoost cascade 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}
+
+    Returns feature indices chosen for cascade.
+    Feature enumeration starts from 0
+    */
+    std::vector<int> train(const Mat& data,
+                           const Mat& labels);
+
+    /* Predict object class given object that can compute object features
+
+       feature_evaluator — object that can compute features by demand
+
+    Returns confidence_value — measure of confidense that object
+    is from class +1
+    */
+    float predict(const Ptr<ACFFeatureEvaluator>& feature_evaluator);
+
+private:
+    /* Parameters for cascade training */
+    WaldBoostParams params_;
+    /* Stumps in cascade */
+    std::vector<Stump> stumps_;
+    /* Weight for stumps in cascade linear combination */
+    std::vector<float> stump_weights_;
+    /* Rejection thresholds for linear combination at every stump evaluation */
+    std::vector<float> thresholds_;
+};
+
+} /* namespace adas */
+} /* namespace cv */
+
+#endif /* __OPENCV_ADAS_WALDBOOST_HPP__ */