opencv/modules/legacy/src/kdtree.cpp

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// 2008-05-13, Xavier Delacour <xavier.delacour@gmail.com>

#include "precomp.hpp"

#if !defined _MSC_VER || defined __ICL || _MSC_VER >= 1300

#include "_kdtree.hpp"
#include "_featuretree.h"

class CvKDTreeWrap : public CvFeatureTree {
  template <class __scalartype, int __cvtype>
  struct deref {
    typedef __scalartype scalar_type;
    typedef double accum_type;

    CvMat* mat;
    deref(CvMat* _mat) : mat(_mat) {
      assert(CV_ELEM_SIZE1(__cvtype) == sizeof(__scalartype));
    }
    scalar_type operator() (int i, int j) const {
      return *((scalar_type*)(mat->data.ptr + i * mat->step) + j);
    }
  };

#define dispatch_cvtype(mat, c) \
    switch (CV_MAT_DEPTH((mat)->type)) { \
    case CV_32F: \
      { typedef CvKDTree<int, deref<float, CV_32F> > tree_type; c; break; } \
    case CV_64F: \
      { typedef CvKDTree<int, deref<double, CV_64F> > tree_type; c; break; } \
    default: assert(0); \
    }

  CvMat* mat;
  void* data;

  template <class __treetype>
  void find_nn(const CvMat* d, int k, int emax, CvMat* results, CvMat* dist) {
    __treetype* tr = (__treetype*) data;
    const uchar* dptr = d->data.ptr;
    uchar* resultsptr = results->data.ptr;
    uchar* distptr = dist->data.ptr;
    typename __treetype::bbf_nn_pqueue nn;

    assert(d->cols == tr->dims());
    assert(results->rows == d->rows);
    assert(results->rows == dist->rows);
    assert(results->cols == k);
    assert(dist->cols == k);

    for (int j = 0; j < d->rows; ++j)
    {
      const typename __treetype::scalar_type* dj = (const typename __treetype::scalar_type*) dptr;

      int* resultsj = (int*) resultsptr;
      double* distj = (double*) distptr;
      tr->find_nn_bbf(dj, k, emax, nn);

      assert((int)nn.size() <= k);
      for (unsigned int i = 0; i < nn.size(); ++i)
      {
        *resultsj++ = *nn[i].p;
        *distj++ = nn[i].dist;
      }
      std::fill(resultsj, resultsj + k - nn.size(), -1);
      std::fill(distj, distj + k - nn.size(), 0);

      dptr += d->step;
      resultsptr += results->step;
      distptr += dist->step;
    }
  }

  template <class __treetype>
  int find_ortho_range(CvMat* bounds_min, CvMat* bounds_max,
           CvMat* results) {
    int rn = results->rows * results->cols;
    std::vector<int> inbounds;
    assert(CV_MAT_DEPTH(mat->type) == CV_32F || CV_MAT_DEPTH(mat->type) == CV_64F);
    ((__treetype*)data)->find_ortho_range((typename __treetype::scalar_type*)bounds_min->data.ptr,
             (typename __treetype::scalar_type*)bounds_max->data.ptr,
             inbounds);
    std::copy(inbounds.begin(),
        inbounds.begin() + std::min((int)inbounds.size(), rn),
        (int*) results->data.ptr);
    return (int)inbounds.size();
  }

  CvKDTreeWrap(const CvKDTreeWrap& x);
  CvKDTreeWrap& operator= (const CvKDTreeWrap& rhs);
public:
  CvKDTreeWrap(CvMat* _mat) : mat(_mat) {
    // * a flag parameter should tell us whether
    // * (a) user ensures *mat outlives *this and is unchanged,
    // * (b) we take reference and user ensures mat is unchanged,
    // * (c) we copy data, (d) we own and release data.

    std::vector<int> tmp(mat->rows);
    for (unsigned int j = 0; j < tmp.size(); ++j)
      tmp[j] = j;

    dispatch_cvtype(mat, data = new tree_type
        (&tmp[0], &tmp[0] + tmp.size(), mat->cols,
         tree_type::deref_type(mat)));
  }
  ~CvKDTreeWrap() {
    dispatch_cvtype(mat, delete (tree_type*) data);
  }

  int dims() {
    int d = 0;
    dispatch_cvtype(mat, d = ((tree_type*) data)->dims());
    return d;
  }
  int type() {
    return mat->type;
  }

  void FindFeatures(const CvMat* desc, int k, int emax, CvMat* results, CvMat* dist) {
    cv::Ptr<CvMat> tmp_desc;

    if (desc->cols != dims())
      CV_Error(CV_StsUnmatchedSizes, "desc columns be equal feature dimensions");
    if (results->rows != desc->rows && results->cols != k)
      CV_Error(CV_StsUnmatchedSizes, "results and desc must be same height");
    if (dist->rows != desc->rows && dist->cols != k)
      CV_Error(CV_StsUnmatchedSizes, "dist and desc must be same height");
    if (CV_MAT_TYPE(results->type) != CV_32SC1)
      CV_Error(CV_StsUnsupportedFormat, "results must be CV_32SC1");
    if (CV_MAT_TYPE(dist->type) != CV_64FC1)
      CV_Error(CV_StsUnsupportedFormat, "dist must be CV_64FC1");

    if (CV_MAT_TYPE(type()) != CV_MAT_TYPE(desc->type)) {
      tmp_desc = cvCreateMat(desc->rows, desc->cols, type());
      cvConvert(desc, tmp_desc);
      desc = tmp_desc;
    }

    assert(CV_MAT_TYPE(desc->type) == CV_MAT_TYPE(mat->type));
    assert(CV_MAT_TYPE(dist->type) == CV_64FC1);
    assert(CV_MAT_TYPE(results->type) == CV_32SC1);

    dispatch_cvtype(mat, find_nn<tree_type>
        (desc, k, emax, results, dist));
  }
  int FindOrthoRange(CvMat* bounds_min, CvMat* bounds_max,
         CvMat* results) {
    bool free_bounds = false;
    int count = -1;

    if (bounds_min->cols * bounds_min->rows != dims() ||
  bounds_max->cols * bounds_max->rows != dims())
      CV_Error(CV_StsUnmatchedSizes, "bounds_{min,max} must 1 x dims or dims x 1");
    if (CV_MAT_TYPE(bounds_min->type) != CV_MAT_TYPE(bounds_max->type))
      CV_Error(CV_StsUnmatchedFormats, "bounds_{min,max} must have same type");
    if (CV_MAT_TYPE(results->type) != CV_32SC1)
      CV_Error(CV_StsUnsupportedFormat, "results must be CV_32SC1");

    if (CV_MAT_TYPE(bounds_min->type) != CV_MAT_TYPE(type())) {
      free_bounds = true;

      CvMat* old_bounds_min = bounds_min;
      bounds_min = cvCreateMat(bounds_min->rows, bounds_min->cols, type());
      cvConvert(old_bounds_min, bounds_min);

      CvMat* old_bounds_max = bounds_max;
      bounds_max = cvCreateMat(bounds_max->rows, bounds_max->cols, type());
      cvConvert(old_bounds_max, bounds_max);
    }

    assert(CV_MAT_TYPE(bounds_min->type) == CV_MAT_TYPE(mat->type));
    assert(CV_MAT_TYPE(bounds_min->type) == CV_MAT_TYPE(bounds_max->type));
    assert(bounds_min->rows * bounds_min->cols == dims());
    assert(bounds_max->rows * bounds_max->cols == dims());

    dispatch_cvtype(mat, count = find_ortho_range<tree_type>
        (bounds_min, bounds_max,results));

    if (free_bounds) {
      cvReleaseMat(&bounds_min);
      cvReleaseMat(&bounds_max);
    }

    return count;
  }
};

CvFeatureTree* cvCreateKDTree(CvMat* desc) {

  if (CV_MAT_TYPE(desc->type) != CV_32FC1 &&
      CV_MAT_TYPE(desc->type) != CV_64FC1)
    CV_Error(CV_StsUnsupportedFormat, "descriptors must be either CV_32FC1 or CV_64FC1");

  return new CvKDTreeWrap(desc);
}

#endif