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// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2013, University of Nizhny Novgorod, all rights reserved.
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#include "precomp.hpp"
#include "_lsvmc_matching.h"
#include <stdio.h>
#ifndef max
#define max(a,b) (((a) > (b)) ? (a) : (b))
#endif
#ifndef min
#define min(a,b) (((a) < (b)) ? (a) : (b))
#endif
namespace cv
{
namespace lsvm
{
void sort(int n, const float* x, int* indices);
/*
// Computation border size for feature map
//
// API
// int computeBorderSize(int maxXBorder, int maxYBorder, int *bx, int *by);
// INPUT
// maxXBorder - the largest root filter size (X-direction)
// maxYBorder - the largest root filter size (Y-direction)
// OUTPUT
// bx - border size (X-direction)
// by - border size (Y-direction)
// RESULT
// Error status
*/
int computeBorderSize(int maxXBorder, int maxYBorder, int *bx, int *by)
{
*bx = (int)ceilf(((float) maxXBorder) / 2.0f + 1.0f);
*by = (int)ceilf(((float) maxYBorder) / 2.0f + 1.0f);
return LATENT_SVM_OK;
}
/*
// Addition nullable border to the feature map
//
// API
// int addNullableBorder(featureMap *map, int bx, int by);
// INPUT
// map - feature map
// bx - border size (X-direction)
// by - border size (Y-direction)
// OUTPUT
// RESULT
// Error status
*/
int addNullableBorder(CvLSVMFeatureMapCaskade *map, int bx, int by)
{
int sizeX, sizeY, i, j, k;
float *new_map;
sizeX = map->sizeX + 2 * bx;
sizeY = map->sizeY + 2 * by;
new_map = (float *)malloc(sizeof(float) * sizeX * sizeY * map->numFeatures);
for (i = 0; i < sizeX * sizeY * map->numFeatures; i++)
{
new_map[i] = 0.0;
}
for (i = by; i < map->sizeY + by; i++)
{
for (j = bx; j < map->sizeX + bx; j++)
{
for (k = 0; k < map->numFeatures; k++)
{
new_map[(i * sizeX + j) * map->numFeatures + k] =
map->map[((i - by) * map->sizeX + j - bx) * map->numFeatures + k];
}
}
}
map->sizeX = sizeX;
map->sizeY = sizeY;
free(map->map);
map->map = new_map;
return LATENT_SVM_OK;
}
/*
// Computation maximum filter size for each dimension
//
// API
// int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents,
const int *kPartFilters,
unsigned int *maxXBorder, unsigned int *maxYBorder);
// INPUT
// filters - a set of filters (at first root filter, then part filters
and etc. for all components)
// kComponents - number of components
// kPartFilters - number of part filters for each component
// OUTPUT
// maxXBorder - maximum of filter size at the horizontal dimension
// maxYBorder - maximum of filter size at the vertical dimension
// RESULT
// Error status
*/
int getMaxFilterDims(const CvLSVMFilterObjectCaskade **filters, int kComponents,
const int *kPartFilters,
unsigned int *maxXBorder, unsigned int *maxYBorder)
{
int i, componentIndex;
*maxXBorder = filters[0]->sizeX;
*maxYBorder = filters[0]->sizeY;
componentIndex = kPartFilters[0] + 1;
for (i = 1; i < kComponents; i++)
{
if (unsigned(filters[componentIndex]->sizeX) > *maxXBorder)
{
*maxXBorder = filters[componentIndex]->sizeX;
}
if (unsigned(filters[componentIndex]->sizeY) > *maxYBorder)
{
*maxYBorder = filters[componentIndex]->sizeY;
}
componentIndex += (kPartFilters[i] + 1);
}
return LATENT_SVM_OK;
}
void sort(int n, const float* x, int* indices)
{
int i, j;
for (i = 0; i < n; i++)
for (j = i + 1; j < n; j++)
{
if (x[indices[j]] > x[indices[i]])
{
//float x_tmp = x[i];
int index_tmp = indices[i];
//x[i] = x[j];
indices[i] = indices[j];
//x[j] = x_tmp;
indices[j] = index_tmp;
}
}
}
/*
// Perform non-maximum suppression algorithm (described in original paper)
// to remove "similar" bounding boxes
//
// API
// int nonMaximumSuppression(int numBoxes, const CvPoint *points,
const CvPoint *oppositePoints, const float *score,
float overlapThreshold,
int *numBoxesOut, CvPoint **pointsOut,
CvPoint **oppositePointsOut, float **scoreOut);
// INPUT
// numBoxes - number of bounding boxes
// points - array of left top corner coordinates
// oppositePoints - array of right bottom corner coordinates
// score - array of detection scores
// overlapThreshold - threshold: bounding box is removed if overlap part
is greater than passed value
// OUTPUT
// numBoxesOut - the number of bounding boxes algorithm returns
// pointsOut - array of left top corner coordinates
// oppositePointsOut - array of right bottom corner coordinates
// scoreOut - array of detection scores
// RESULT
// Error status
*/
int nonMaximumSuppression(int numBoxes, const CvPoint *points,
const CvPoint *oppositePoints, const float *score,
float overlapThreshold,
int *numBoxesOut, CvPoint **pointsOut,
CvPoint **oppositePointsOut, float **scoreOut)
{
int i, j, index;
float* box_area = (float*)malloc(numBoxes * sizeof(float));
int* indices = (int*)malloc(numBoxes * sizeof(int));
int* is_suppressed = (int*)malloc(numBoxes * sizeof(int));
for (i = 0; i < numBoxes; i++)
{
indices[i] = i;
is_suppressed[i] = 0;
box_area[i] = (float)( (oppositePoints[i].x - points[i].x + 1) *
(oppositePoints[i].y - points[i].y + 1));
}
sort(numBoxes, score, indices);
for (i = 0; i < numBoxes; i++)
{
if (!is_suppressed[indices[i]])
{
for (j = i + 1; j < numBoxes; j++)
{
if (!is_suppressed[indices[j]])
{
int x1max = max(points[indices[i]].x, points[indices[j]].x);
int x2min = min(oppositePoints[indices[i]].x, oppositePoints[indices[j]].x);
int y1max = max(points[indices[i]].y, points[indices[j]].y);
int y2min = min(oppositePoints[indices[i]].y, oppositePoints[indices[j]].y);
int overlapWidth = x2min - x1max + 1;
int overlapHeight = y2min - y1max + 1;
if (overlapWidth > 0 && overlapHeight > 0)
{
float overlapPart = (overlapWidth * overlapHeight) / box_area[indices[j]];
if (overlapPart > overlapThreshold)
{
is_suppressed[indices[j]] = 1;
}
}
}
}
}
}
*numBoxesOut = 0;
for (i = 0; i < numBoxes; i++)
{
if (!is_suppressed[i]) (*numBoxesOut)++;
}
*pointsOut = (CvPoint *)malloc((*numBoxesOut) * sizeof(CvPoint));
*oppositePointsOut = (CvPoint *)malloc((*numBoxesOut) * sizeof(CvPoint));
*scoreOut = (float *)malloc((*numBoxesOut) * sizeof(float));
index = 0;
for (i = 0; i < numBoxes; i++)
{
if (!is_suppressed[indices[i]])
{
(*pointsOut)[index].x = points[indices[i]].x;
(*pointsOut)[index].y = points[indices[i]].y;
(*oppositePointsOut)[index].x = oppositePoints[indices[i]].x;
(*oppositePointsOut)[index].y = oppositePoints[indices[i]].y;
(*scoreOut)[index] = score[indices[i]];
index++;
}
}
free(indices);
free(box_area);
free(is_suppressed);
return LATENT_SVM_OK;
}
}
}