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FH98ViewOnPlaneSoftware/Src/CH91PayloadSoftware/fhog.cpp

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/*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) 2010-2013, University of Nizhny Novgorod, 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 the copyright holders 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*/
//Modified from latentsvm module's "lsvmc_featurepyramid.cpp".
//#include "precomp.hpp"
//#include "_lsvmc_latentsvm.h"
//#include "_lsvmc_resizeimg.h"
#include "stdafx.h"
#include "fhog.hpp"
#ifdef HAVE_TBB
#include <tbb/tbb.h>
#include "tbb/parallel_for.h"
#include "tbb/blocked_range.h"
#endif
#ifndef max
#define max(a,b) (((a) > (b)) ? (a) : (b))
#endif
#ifndef min
#define min(a,b) (((a) < (b)) ? (a) : (b))
#endif
/*
// Getting feature map for the selected subimage
//
// API
// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
// INPUT
// image - selected subimage
// k - size of cells
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCaskade **map)
{
int sizeX, sizeY;
int p, px, stringSize;
int height, width, numChannels;
int i, j, kk, c, ii, jj, d;
float * datadx, * datady;
int ch;
float magnitude, x, y, tx, ty;
IplImage * dx, * dy;
int *nearest;
float *w, a_x, b_x;
float kernel[3] = {-1.f, 0.f, 1.f};
CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
float * r;
int * alfa;
float boundary_x[NUM_SECTOR + 1];
float boundary_y[NUM_SECTOR + 1];
float max, dotProd;
int maxi;
height = image->height;
width = image->width ;
numChannels = image->nChannels;
dx = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
dy = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
sizeX = width / k;
sizeY = height / k;
px = 3 * NUM_SECTOR;
p = px;
stringSize = sizeX * p;
allocFeatureMapObject(map, sizeX, sizeY, p);
cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
float arg_vector;
for(i = 0; i <= NUM_SECTOR; i++)
{
arg_vector = ( (float) i ) * ( (float)(PI) / (float)(NUM_SECTOR) );
boundary_x[i] = cosf(arg_vector);
boundary_y[i] = sinf(arg_vector);
}/*for(i = 0; i <= NUM_SECTOR; i++) */
r = (float *)malloc( sizeof(float) * (width * height));
alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
for(j = 1; j < height - 1; j++)
{
datadx = (float*)(dx->imageData + dx->widthStep * j);
datady = (float*)(dy->imageData + dy->widthStep * j);
for(i = 1; i < width - 1; i++)
{
c = 0;
x = (datadx[i * numChannels + c]);
y = (datady[i * numChannels + c]);
r[j * width + i] =sqrtf(x * x + y * y);
for(ch = 1; ch < numChannels; ch++)
{
tx = (datadx[i * numChannels + ch]);
ty = (datady[i * numChannels + ch]);
magnitude = sqrtf(tx * tx + ty * ty);
if(magnitude > r[j * width + i])
{
r[j * width + i] = magnitude;
c = ch;
x = tx;
y = ty;
}
}/*for(ch = 1; ch < numChannels; ch++)*/
max = boundary_x[0] * x + boundary_y[0] * y;
maxi = 0;
for (kk = 0; kk < NUM_SECTOR; kk++)
{
dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
if (dotProd > max)
{
max = dotProd;
maxi = kk;
}
else
{
if (-dotProd > max)
{
max = -dotProd;
maxi = kk + NUM_SECTOR;
}
}
}
alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
alfa[j * width * 2 + i * 2 + 1] = maxi;
}/*for(i = 0; i < width; i++)*/
}/*for(j = 0; j < height; j++)*/
nearest = (int *)malloc(sizeof(int ) * k);
w = (float*)malloc(sizeof(float) * (k * 2));
for(i = 0; i < k / 2; i++)
{
nearest[i] = -1;
}/*for(i = 0; i < k / 2; i++)*/
for(i = k / 2; i < k; i++)
{
nearest[i] = 1;
}/*for(i = k / 2; i < k; i++)*/
for(j = 0; j < k / 2; j++)
{
b_x = k / 2 + j + 0.5f;
a_x = k / 2 - j - 0.5f;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = 0; j < k / 2; j++)*/
for(j = k / 2; j < k; j++)
{
a_x = j - k / 2 + 0.5f;
b_x =-j + k / 2 - 0.5f + k;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = k / 2; j < k; j++)*/
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
for(ii = 0; ii < k; ii++)
{
for(jj = 0; jj < k; jj++)
{
if ((i * k + ii > 0) &&
(i * k + ii < height - 1) &&
(j * k + jj > 0) &&
(j * k + jj < width - 1))
{
d = (k * i + ii) * width + (j * k + jj);
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
r[d] * w[ii * 2] * w[jj * 2];
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2];
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
}
if ((j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
}
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1) &&
(j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
}
}
}/*for(jj = 0; jj < k; jj++)*/
}/*for(ii = 0; ii < k; ii++)*/
}/*for(j = 1; j < sizeX - 1; j++)*/
}/*for(i = 1; i < sizeY - 1; i++)*/
cvReleaseImage(&dx);
cvReleaseImage(&dy);
free(w);
free(nearest);
free(r);
free(alfa);
return LATENT_SVM_OK;
}
/*
// Feature map Normalization and Truncation
//
// API
// int normalizeAndTruncate(featureMap *map, const float alfa);
// INPUT
// map - feature map
// alfa - truncation threshold
// OUTPUT
// map - truncated and normalized feature map
// RESULT
// Error status
*/
int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa)
{
int i,j, ii;
int sizeX, sizeY, p, pos, pp, xp, pos1, pos2;
float * partOfNorm; // norm of C(i, j)
float * newData;
float valOfNorm;
sizeX = map->sizeX;
sizeY = map->sizeY;
partOfNorm = (float *)malloc (sizeof(float) * (sizeX * sizeY));
p = NUM_SECTOR;
xp = NUM_SECTOR * 3;
pp = NUM_SECTOR * 12;
for(i = 0; i < sizeX * sizeY; i++)
{
valOfNorm = 0.0f;
pos = i * map->numFeatures;
for(j = 0; j < p; j++)
{
valOfNorm += map->map[pos + j] * map->map[pos + j];
}/*for(j = 0; j < p; j++)*/
partOfNorm[i] = valOfNorm;
}/*for(i = 0; i < sizeX * sizeY; i++)*/
sizeX -= 2;
sizeY -= 2;
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
//normalization
for(i = 1; i <= sizeY; i++)
{
for(j = 1; j <= sizeX; j++)
{
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
pos1 = (i ) * (sizeX + 2) * xp + (j ) * xp;
pos2 = (i-1) * (sizeX ) * pp + (j-1) * pp;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p * 2] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p * 3 ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 10] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
}/*for(j = 1; j <= sizeX; j++)*/
}/*for(i = 1; i <= sizeY; i++)*/
//truncation
for(i = 0; i < sizeX * sizeY * pp; i++)
{
if(newData [i] > alfa) newData [i] = alfa;
}/*for(i = 0; i < sizeX * sizeY * pp; i++)*/
//swop data
map->numFeatures = pp;
map->sizeX = sizeX;
map->sizeY = sizeY;
free (map->map);
free (partOfNorm);
map->map = newData;
return LATENT_SVM_OK;
}
/*
// Feature map reduction
// In each cell we reduce dimension of the feature vector
// according to original paper special procedure
//
// API
// int PCAFeatureMaps(featureMap *map)
// INPUT
// map - feature map
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map)
{
int i,j, ii, jj, k;
int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
float * newData;
float val;
float nx, ny;
sizeX = map->sizeX;
sizeY = map->sizeY;
p = map->numFeatures;
pp = NUM_SECTOR * 3 + 4;
yp = 4;
xp = NUM_SECTOR;
nx = 1.0f / sqrtf((float)(xp * 2));
ny = 1.0f / sqrtf((float)(yp ));
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
pos1 = ((i)*sizeX + j)*p;
pos2 = ((i)*sizeX + j)*pp;
k = 0;
for(jj = 0; jj < xp * 2; jj++)
{
val = 0;
for(ii = 0; ii < yp; ii++)
{
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
}/*for(ii = 0; ii < yp; ii++)*/
newData[pos2 + k] = val * ny;
k++;
}/*for(jj = 0; jj < xp * 2; jj++)*/
for(jj = 0; jj < xp; jj++)
{
val = 0;
for(ii = 0; ii < yp; ii++)
{
val += map->map[pos1 + ii * xp + jj];
}/*for(ii = 0; ii < yp; ii++)*/
newData[pos2 + k] = val * ny;
k++;
}/*for(jj = 0; jj < xp; jj++)*/
for(ii = 0; ii < yp; ii++)
{
val = 0;
for(jj = 0; jj < 2 * xp; jj++)
{
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
}/*for(jj = 0; jj < xp; jj++)*/
newData[pos2 + k] = val * nx;
k++;
} /*for(ii = 0; ii < yp; ii++)*/
}/*for(j = 0; j < sizeX; j++)*/
}/*for(i = 0; i < sizeY; i++)*/
//swop data
map->numFeatures = pp;
free (map->map);
map->map = newData;
return LATENT_SVM_OK;
}
//modified from "lsvmc_routine.cpp"
int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX,
const int sizeY, const int numFeatures)
{
int i;
(*obj) = (CvLSVMFeatureMapCaskade *)malloc(sizeof(CvLSVMFeatureMapCaskade));
(*obj)->sizeX = sizeX;
(*obj)->sizeY = sizeY;
(*obj)->numFeatures = numFeatures;
(*obj)->map = (float *) malloc(sizeof (float) *
(sizeX * sizeY * numFeatures));
for(i = 0; i < sizeX * sizeY * numFeatures; i++)
{
(*obj)->map[i] = 0.0f;
}
return LATENT_SVM_OK;
}
int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj)
{
if(*obj == NULL) return LATENT_SVM_MEM_NULL;
free((*obj)->map);
free(*obj);
(*obj) = NULL;
return LATENT_SVM_OK;
}
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