added old=>new cascade conversion function

modules/objdetect/include/opencv2/objdetect.hpp

@@ -232,6 +232,8 @@ public:
     CV_WRAP int getFeatureType() const;
     void* getOldCascade();
 
+    CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
+
     void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
     Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
 

modules/objdetect/src/cascadedetect_convert.cpp

@@ -0,0 +1,276 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  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
+//
+// Copyright (C) 2013, 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:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's 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.
+//
+//   * The name of Intel Corporation may not 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 the Intel Corporation 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.
+//
+//M*/
+
+/* Haar features calculation */
+
+#include "precomp.hpp"
+#include <stdio.h>
+
+namespace cv
+{
+
+/* field names */
+
+#define ICV_HAAR_SIZE_NAME            "size"
+#define ICV_HAAR_STAGES_NAME          "stages"
+#define ICV_HAAR_TREES_NAME           "trees"
+#define ICV_HAAR_FEATURE_NAME         "feature"
+#define ICV_HAAR_RECTS_NAME           "rects"
+#define ICV_HAAR_TILTED_NAME          "tilted"
+#define ICV_HAAR_THRESHOLD_NAME       "threshold"
+#define ICV_HAAR_LEFT_NODE_NAME       "left_node"
+#define ICV_HAAR_LEFT_VAL_NAME        "left_val"
+#define ICV_HAAR_RIGHT_NODE_NAME      "right_node"
+#define ICV_HAAR_RIGHT_VAL_NAME       "right_val"
+#define ICV_HAAR_STAGE_THRESHOLD_NAME "stage_threshold"
+#define ICV_HAAR_PARENT_NAME          "parent"
+#define ICV_HAAR_NEXT_NAME            "next"
+
+namespace haar_cvt
+{
+
+struct HaarFeature
+{
+    enum { RECT_NUM = 3 };
+
+    HaarFeature()
+    {
+        tilted = false;
+        for( int i = 0; i < RECT_NUM; i++ )
+        {
+            rect[i].r = Rect(0,0,0,0);
+            rect[i].weight = 0.f;
+        }
+    }
+    bool tilted;
+    struct
+    {
+        Rect r;
+        float weight;
+    } rect[RECT_NUM];
+};
+
+struct HaarClassifierNode
+{
+    HaarClassifierNode()
+    {
+        f = left = right = 0;
+        threshold = 0.f;
+    }
+    int f, left, right;
+    float threshold;
+};
+
+struct HaarClassifier
+{
+    std::vector<HaarClassifierNode> nodes;
+    std::vector<float> leaves;
+};
+
+struct HaarStageClassifier
+{
+    double threshold;
+    std::vector<HaarClassifier> weaks;
+};
+
+static bool convert(const String& oldcascade, const String& newcascade)
+{
+    FileStorage oldfs(oldcascade, FileStorage::READ);
+    if( !oldfs.isOpened() )
+        return false;
+    FileNode oldroot = oldfs.getFirstTopLevelNode();
+
+    FileNode sznode = oldroot[ICV_HAAR_SIZE_NAME];
+    if( sznode.empty() )
+        return false;
+    int maxdepth = 0;
+    Size cascadesize;
+    cascadesize.width = (int)sznode[0];
+    cascadesize.height = (int)sznode[1];
+    std::vector<HaarFeature> features;
+
+    size_t i, j, k, n;
+
+    FileNode stages_seq = oldroot[ICV_HAAR_STAGES_NAME];
+    size_t nstages = stages_seq.size();
+    std::vector<HaarStageClassifier> stages(nstages);
+
+    for( i = 0; i < nstages; i++ )
+    {
+        FileNode stagenode = stages_seq[i];
+        HaarStageClassifier& stage = stages[i];
+        stage.threshold = (double)stagenode[ICV_HAAR_STAGE_THRESHOLD_NAME];
+        FileNode weaks_seq = stagenode[ICV_HAAR_TREES_NAME];
+        size_t nweaks = weaks_seq.size();
+        stage.weaks.resize(nweaks);
+
+        for( j = 0; j < nweaks; j++ )
+        {
+            HaarClassifier& weak = stage.weaks[j];
+            FileNode weaknode = weaks_seq[j];
+            size_t nnodes = weaknode.size();
+
+            for( n = 0; n < nnodes; n++ )
+            {
+                FileNode nnode = weaknode[n];
+                FileNode fnode = nnode[ICV_HAAR_FEATURE_NAME];
+                HaarFeature f;
+                HaarClassifierNode node;
+                node.f = (int)features.size();
+                f.tilted = (int)fnode[ICV_HAAR_TILTED_NAME] != 0;
+                FileNode rects_seq = fnode[ICV_HAAR_RECTS_NAME];
+                size_t nrects = rects_seq.size();
+
+                for( k = 0; k < nrects; k++ )
+                {
+                    FileNode rnode = rects_seq[k];
+                    f.rect[k].r.x = (int)rnode[0];
+                    f.rect[k].r.y = (int)rnode[1];
+                    f.rect[k].r.width = (int)rnode[2];
+                    f.rect[k].r.height = (int)rnode[3];
+                    f.rect[k].weight = (float)rnode[4];
+                }
+                features.push_back(f);
+                node.threshold = nnode[ICV_HAAR_THRESHOLD_NAME];
+                FileNode leftValNode = nnode[ICV_HAAR_LEFT_VAL_NAME];
+                if( !leftValNode.empty() )
+                {
+                    node.left = -(int)weak.leaves.size();
+                    weak.leaves.push_back((float)leftValNode);
+                }
+                else
+                {
+                    node.left = (int)nnode[ICV_HAAR_LEFT_NODE_NAME];
+                }
+                FileNode rightValNode = nnode[ICV_HAAR_RIGHT_VAL_NAME];
+                if( !rightValNode.empty() )
+                {
+                    node.right = -(int)weak.leaves.size();
+                    weak.leaves.push_back((float)rightValNode);
+                }
+                else
+                {
+                    node.right = (int)nnode[ICV_HAAR_RIGHT_NODE_NAME];
+                }
+                weak.nodes.push_back(node);
+            }
+        }
+    }
+
+    FileStorage newfs(newcascade, FileStorage::WRITE);
+    if( !newfs.isOpened() )
+        return false;
+
+    size_t maxWeakCount = 0, nfeatures = features.size();
+    for( i = 0; i < nstages; i++ )
+        maxWeakCount = std::max(maxWeakCount, stages[i].weaks.size());
+
+    newfs << "stageType" << "BOOST"
+    << "featureType" << "HAAR"
+    << "height" << cascadesize.width
+    << "width" << cascadesize.height
+    << "stageParams" << "{"
+        << "maxDepth" << maxdepth
+        << "maxWeakCount" << (int)maxWeakCount
+    << "}"
+    << "featureParams" << "{"
+        << "maxCatCount" << 0
+    << "}"
+    << "stageNum" << (int)nstages
+    << "stages" << "[";
+
+    for( i = 0; i < nstages; i++ )
+    {
+        size_t nweaks = stages[i].weaks.size();
+        newfs << "{" << "maxWeakCount" << (int)nweaks
+            << "stageThreshold" << stages[i].threshold
+            << "weakClassifiers" << "[";
+        for( j = 0; j < nweaks; j++ )
+        {
+            const HaarClassifier& c = stages[i].weaks[j];
+            newfs << "{" << "internalNodes" << "[";
+            size_t nnodes = c.nodes.size(), nleaves = c.leaves.size();
+            for( k = 0; k < nnodes; k++ )
+                newfs << c.nodes[k].left << c.nodes[k].right
+                    << c.nodes[k].f << c.nodes[k].threshold;
+            newfs << "]" << "leafValues" << "[";
+            for( k = 0; k < nleaves; k++ )
+                newfs << c.leaves[k];
+            newfs << "]" << "}";
+        }
+        newfs << "]" << "}";
+    }
+
+    newfs << "]"
+        << "features" << "[";
+
+    for( i = 0; i < nfeatures; i++ )
+    {
+        const HaarFeature& f = features[i];
+        newfs << "{" << "rects" << "[";
+        for( j = 0; j < (size_t)HaarFeature::RECT_NUM; j++ )
+        {
+            if( j >= 2 && fabs(f.rect[j].weight) < FLT_EPSILON )
+                break;
+            newfs << f.rect[j].r.x << f.rect[j].r.y <<
+                f.rect[j].r.width << f.rect[j].r.height << f.rect[j].weight;
+        }
+        newfs << "]";
+        if( f.tilted )
+            newfs << "tilted" << 1;
+        newfs << "}";
+    }
+
+    newfs << "]";
+    return true;
+}
+
+}
+
+bool CascadeClassifier::convert(const String& oldcascade, const String& newcascade)
+{
+    bool ok = haar_cvt::convert(oldcascade, newcascade);
+    if( !ok && newcascade.size() > 0 )
+        remove(newcascade.c_str());
+    return ok;
+}
+
+}