Merge pull request '提交代码' (#4) from wangmingqiang-branch into master
commit
a2edd5fdca
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2010-2013, University of Nizhny Novgorod, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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//Modified from latentsvm module's "lsvmc_featurepyramid.cpp".
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//#include "precomp.hpp"
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//#include "_lsvmc_latentsvm.h"
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//#include "_lsvmc_resizeimg.h"
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#include "fhog.hpp"
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#ifdef HAVE_TBB
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#include <tbb/tbb.h>
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#include "tbb/parallel_for.h"
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#include "tbb/blocked_range.h"
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#endif
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#ifndef max
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#define max(a,b) (((a) > (b)) ? (a) : (b))
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#endif
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#ifndef min
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#define min(a,b) (((a) < (b)) ? (a) : (b))
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#endif
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/*
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// Getting feature map for the selected subimage
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//
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// API
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// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
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// INPUT
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// image - selected subimage
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// k - size of cells
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// OUTPUT
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// map - feature map
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// RESULT
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// Error status
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*/
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int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCaskade **map)
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{
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int sizeX, sizeY;
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int p, px, stringSize;
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int height, width, numChannels;
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int i, j, kk, c, ii, jj, d;
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float * datadx, * datady;
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int ch;
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float magnitude, x, y, tx, ty;
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IplImage * dx, * dy;
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int *nearest;
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float *w, a_x, b_x;
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float kernel[3] = {-1.f, 0.f, 1.f};
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CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
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CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
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float * r;
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int * alfa;
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float boundary_x[NUM_SECTOR + 1];
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float boundary_y[NUM_SECTOR + 1];
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float max, dotProd;
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int maxi;
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height = image->height;
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width = image->width ;
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numChannels = image->nChannels;
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dx = cvCreateImage(cvSize(image->width, image->height),
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IPL_DEPTH_32F, 3);
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dy = cvCreateImage(cvSize(image->width, image->height),
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IPL_DEPTH_32F, 3);
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sizeX = width / k;
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sizeY = height / k;
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px = 3 * NUM_SECTOR;
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p = px;
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stringSize = sizeX * p;
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allocFeatureMapObject(map, sizeX, sizeY, p);
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cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
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cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
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float arg_vector;
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for(i = 0; i <= NUM_SECTOR; i++)
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{
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arg_vector = ( (float) i ) * ( (float)(PI) / (float)(NUM_SECTOR) );
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boundary_x[i] = cosf(arg_vector);
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boundary_y[i] = sinf(arg_vector);
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}/*for(i = 0; i <= NUM_SECTOR; i++) */
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r = (float *)malloc( sizeof(float) * (width * height));
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alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
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for(j = 1; j < height - 1; j++)
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{
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datadx = (float*)(dx->imageData + dx->widthStep * j);
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datady = (float*)(dy->imageData + dy->widthStep * j);
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for(i = 1; i < width - 1; i++)
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{
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c = 0;
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x = (datadx[i * numChannels + c]);
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y = (datady[i * numChannels + c]);
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r[j * width + i] =sqrtf(x * x + y * y);
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for(ch = 1; ch < numChannels; ch++)
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{
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tx = (datadx[i * numChannels + ch]);
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ty = (datady[i * numChannels + ch]);
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magnitude = sqrtf(tx * tx + ty * ty);
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if(magnitude > r[j * width + i])
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{
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r[j * width + i] = magnitude;
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c = ch;
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x = tx;
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y = ty;
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}
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}/*for(ch = 1; ch < numChannels; ch++)*/
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max = boundary_x[0] * x + boundary_y[0] * y;
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maxi = 0;
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for (kk = 0; kk < NUM_SECTOR; kk++)
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{
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dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
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if (dotProd > max)
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{
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max = dotProd;
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maxi = kk;
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}
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else
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{
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if (-dotProd > max)
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{
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max = -dotProd;
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maxi = kk + NUM_SECTOR;
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}
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}
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}
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alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
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alfa[j * width * 2 + i * 2 + 1] = maxi;
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}/*for(i = 0; i < width; i++)*/
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}/*for(j = 0; j < height; j++)*/
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nearest = (int *)malloc(sizeof(int ) * k);
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w = (float*)malloc(sizeof(float) * (k * 2));
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for(i = 0; i < k / 2; i++)
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{
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nearest[i] = -1;
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}/*for(i = 0; i < k / 2; i++)*/
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for(i = k / 2; i < k; i++)
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{
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nearest[i] = 1;
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}/*for(i = k / 2; i < k; i++)*/
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for(j = 0; j < k / 2; j++)
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{
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b_x = k / 2 + j + 0.5f;
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a_x = k / 2 - j - 0.5f;
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w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
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w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
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}/*for(j = 0; j < k / 2; j++)*/
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for(j = k / 2; j < k; j++)
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{
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a_x = j - k / 2 + 0.5f;
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b_x =-j + k / 2 - 0.5f + k;
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w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
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w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
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}/*for(j = k / 2; j < k; j++)*/
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for(i = 0; i < sizeY; i++)
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{
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for(j = 0; j < sizeX; j++)
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{
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for(ii = 0; ii < k; ii++)
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{
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for(jj = 0; jj < k; jj++)
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{
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if ((i * k + ii > 0) &&
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(i * k + ii < height - 1) &&
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(j * k + jj > 0) &&
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(j * k + jj < width - 1))
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{
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d = (k * i + ii) * width + (j * k + jj);
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(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
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r[d] * w[ii * 2] * w[jj * 2];
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(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2] * w[jj * 2];
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if ((i + nearest[ii] >= 0) &&
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(i + nearest[ii] <= sizeY - 1))
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{
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(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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}
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if ((j + nearest[jj] >= 0) &&
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(j + nearest[jj] <= sizeX - 1))
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{
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(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2] * w[jj * 2 + 1];
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(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2] * w[jj * 2 + 1];
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}
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if ((i + nearest[ii] >= 0) &&
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(i + nearest[ii] <= sizeY - 1) &&
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(j + nearest[jj] >= 0) &&
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(j + nearest[jj] <= sizeX - 1))
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{
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(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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}
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}
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}/*for(jj = 0; jj < k; jj++)*/
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}/*for(ii = 0; ii < k; ii++)*/
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}/*for(j = 1; j < sizeX - 1; j++)*/
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}/*for(i = 1; i < sizeY - 1; i++)*/
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cvReleaseImage(&dx);
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cvReleaseImage(&dy);
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free(w);
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free(nearest);
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free(r);
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free(alfa);
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return LATENT_SVM_OK;
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}
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/*
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// Feature map Normalization and Truncation
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//
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// API
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// int normalizeAndTruncate(featureMap *map, const float alfa);
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// INPUT
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// map - feature map
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// alfa - truncation threshold
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// OUTPUT
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// map - truncated and normalized feature map
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// RESULT
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// Error status
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*/
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int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa)
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{
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int i,j, ii;
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int sizeX, sizeY, p, pos, pp, xp, pos1, pos2;
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float * partOfNorm; // norm of C(i, j)
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float * newData;
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float valOfNorm;
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sizeX = map->sizeX;
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sizeY = map->sizeY;
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partOfNorm = (float *)malloc (sizeof(float) * (sizeX * sizeY));
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p = NUM_SECTOR;
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xp = NUM_SECTOR * 3;
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pp = NUM_SECTOR * 12;
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for(i = 0; i < sizeX * sizeY; i++)
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{
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valOfNorm = 0.0f;
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pos = i * map->numFeatures;
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for(j = 0; j < p; j++)
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{
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valOfNorm += map->map[pos + j] * map->map[pos + j];
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}/*for(j = 0; j < p; j++)*/
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partOfNorm[i] = valOfNorm;
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}/*for(i = 0; i < sizeX * sizeY; i++)*/
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sizeX -= 2;
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sizeY -= 2;
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newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
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//normalization
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for(i = 1; i <= sizeY; i++)
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{
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for(j = 1; j <= sizeX; j++)
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{
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
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pos1 = (i ) * (sizeX + 2) * xp + (j ) * xp;
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pos2 = (i-1) * (sizeX ) * pp + (j-1) * pp;
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii + p ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii + p * 2] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
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for(ii = 0; ii < p; ii++)
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||||
{
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newData[pos2 + ii + p * 3 ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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||||
for(ii = 0; ii < 2 * p; ii++)
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||||
{
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||||
newData[pos2 + ii + p * 10] = map->map[pos1 + ii + p] / valOfNorm;
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||||
}/*for(ii = 0; ii < 2 * p; ii++)*/
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||||
}/*for(j = 1; j <= sizeX; j++)*/
|
||||
}/*for(i = 1; i <= sizeY; i++)*/
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||||
//truncation
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||||
for(i = 0; i < sizeX * sizeY * pp; i++)
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||||
{
|
||||
if(newData [i] > alfa) newData [i] = alfa;
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||||
}/*for(i = 0; i < sizeX * sizeY * pp; i++)*/
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||||
//swop data
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map->numFeatures = pp;
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||||
map->sizeX = sizeX;
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map->sizeY = sizeY;
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free (map->map);
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free (partOfNorm);
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||||
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map->map = newData;
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||||
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||||
return LATENT_SVM_OK;
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||||
}
|
||||
/*
|
||||
// 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"
|
||||
//两个函数分别用于分配和释放CvLSVMFeatureMapCaskade结构体的内存。
|
||||
|
||||
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;
|
||||
}
|
||||
@ -0,0 +1,527 @@
|
||||
/*
|
||||
|
||||
Tracker based on Kernelized Correlation Filter (KCF) [1] and Circulant Structure with Kernels (CSK) [2].
|
||||
CSK is implemented by using raw gray level features, since it is a single-channel filter.
|
||||
KCF is implemented by using HOG features (the default), since it extends CSK to multiple channels.
|
||||
|
||||
[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
|
||||
"High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.
|
||||
|
||||
[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
|
||||
"Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.
|
||||
|
||||
Authors: Joao Faro, Christian Bailer, Joao F. Henriques
|
||||
Contacts: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt
|
||||
Institute of Systems and Robotics - University of Coimbra / Department Augmented Vision DFKI
|
||||
|
||||
|
||||
Constructor parameters, all boolean:
|
||||
hog: use HOG features (default), otherwise use raw pixels
|
||||
fixed_window: fix window size (default), otherwise use ROI size (slower but more accurate)
|
||||
multiscale: use multi-scale tracking (default; cannot be used with fixed_window = true)
|
||||
|
||||
Default values are set for all properties of the tracker depending on the above choices.
|
||||
Their values can be customized further before calling init():
|
||||
interp_factor: linear interpolation factor for adaptation
|
||||
sigma: gaussian kernel bandwidth
|
||||
lambda: regularization
|
||||
cell_size: HOG cell size
|
||||
padding: area surrounding the target, relative to its size
|
||||
output_sigma_factor: bandwidth of gaussian target
|
||||
template_size: template size in pixels, 0 to use ROI size
|
||||
scale_step: scale step for multi-scale estimation, 1 to disable it
|
||||
scale_weight: to downweight detection scores of other scales for added stability
|
||||
|
||||
For speed, the value (template_size/cell_size) should be a power of 2 or a product of small prime numbers.
|
||||
|
||||
Inputs to init():
|
||||
image is the initial frame.
|
||||
roi is a cv::Rect with the target positions in the initial frame
|
||||
|
||||
Inputs to update():
|
||||
image is the current frame.
|
||||
|
||||
Outputs of update():
|
||||
cv::Rect with target positions for the current frame
|
||||
|
||||
|
||||
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)
|
||||
|
||||
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.
|
||||
*/
|
||||
#include <iostream>
|
||||
#ifndef _KCFTRACKER_HEADERS
|
||||
#include "kcftracker.hpp"
|
||||
#include "ffttools.hpp"
|
||||
#include "recttools.hpp"
|
||||
#include "fhog.hpp"
|
||||
#include "labdata.hpp"
|
||||
#endif
|
||||
|
||||
// Constructor
|
||||
KCFTracker::KCFTracker(bool hog, bool fixed_window, bool multiscale, bool lab)
|
||||
{
|
||||
|
||||
// Parameters equal in all cases
|
||||
lambda = 0.0001;
|
||||
padding = 2.5;
|
||||
//output_sigma_factor = 0.1;
|
||||
output_sigma_factor = 0.125;
|
||||
|
||||
|
||||
if (hog) { // HOG
|
||||
// VOT
|
||||
interp_factor = 0.012;
|
||||
sigma = 0.6;
|
||||
// TPAMI
|
||||
//interp_factor = 0.02;
|
||||
//sigma = 0.5;
|
||||
cell_size = 4;
|
||||
_hogfeatures = true;
|
||||
|
||||
if (lab) {
|
||||
interp_factor = 0.005;
|
||||
sigma = 0.4;
|
||||
//output_sigma_factor = 0.025;
|
||||
output_sigma_factor = 0.1;
|
||||
|
||||
_labfeatures = true;
|
||||
_labCentroids = cv::Mat(nClusters, 3, CV_32FC1, &data);
|
||||
cell_sizeQ = cell_size*cell_size;
|
||||
}
|
||||
else{
|
||||
_labfeatures = false;
|
||||
}
|
||||
}
|
||||
else { // RAW
|
||||
interp_factor = 0.075;
|
||||
sigma = 0.2;
|
||||
cell_size = 1;
|
||||
_hogfeatures = false;
|
||||
|
||||
if (lab) {
|
||||
printf("Lab features are only used with HOG features.\n");
|
||||
_labfeatures = false;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if (multiscale) { // multiscale
|
||||
template_size = 96;
|
||||
//template_size = 100;
|
||||
scale_step = 1.05;
|
||||
scale_weight = 0.95;
|
||||
if (!fixed_window) {
|
||||
//printf("Multiscale does not support non-fixed window.\n");
|
||||
fixed_window = true;
|
||||
}
|
||||
}
|
||||
else if (fixed_window) { // fit correction without multiscale
|
||||
template_size = 96;
|
||||
//template_size = 100;
|
||||
scale_step = 1;
|
||||
}
|
||||
else {
|
||||
template_size = 1;
|
||||
scale_step = 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Initialize tracker
|
||||
void KCFTracker::init(const cv::Rect &roi, cv::Mat image)
|
||||
{
|
||||
_roi = roi;
|
||||
assert(roi.width >= 0 && roi.height >= 0);
|
||||
_tmpl = getFeatures(image, 1);
|
||||
_prob = createGaussianPeak(size_patch[0], size_patch[1]);
|
||||
_alphaf = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));
|
||||
//_num = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));
|
||||
//_den = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));
|
||||
train(_tmpl, 1.0); // train with initial frame
|
||||
}
|
||||
// Update position based on the new frame
|
||||
cv::Rect KCFTracker::update(cv::Mat image)
|
||||
{
|
||||
if (_roi.x + _roi.width <= 0) _roi.x = -_roi.width + 1;
|
||||
if (_roi.y + _roi.height <= 0) _roi.y = -_roi.height + 1;
|
||||
if (_roi.x >= image.cols - 1) _roi.x = image.cols - 2;
|
||||
if (_roi.y >= image.rows - 1) _roi.y = image.rows - 2;
|
||||
|
||||
float cx = _roi.x + _roi.width / 2.0f;
|
||||
float cy = _roi.y + _roi.height / 2.0f;
|
||||
|
||||
|
||||
float peak_value;
|
||||
cv::Point2f res = detect(_tmpl, getFeatures(image, 0, 1.0f), peak_value);
|
||||
|
||||
if (scale_step != 1) {
|
||||
// Test at a smaller _scale
|
||||
float new_peak_value;
|
||||
cv::Point2f new_res = detect(_tmpl, getFeatures(image, 0, 1.0f / scale_step), new_peak_value);
|
||||
|
||||
if (scale_weight * new_peak_value > peak_value) {
|
||||
res = new_res;
|
||||
peak_value = new_peak_value;
|
||||
_scale /= scale_step;
|
||||
_roi.width /= scale_step;
|
||||
_roi.height /= scale_step;
|
||||
}
|
||||
|
||||
// Test at a bigger _scale
|
||||
new_res = detect(_tmpl, getFeatures(image, 0, scale_step), new_peak_value);
|
||||
|
||||
if (scale_weight * new_peak_value > peak_value) {
|
||||
res = new_res;
|
||||
peak_value = new_peak_value;
|
||||
_scale *= scale_step;
|
||||
_roi.width *= scale_step;
|
||||
_roi.height *= scale_step;
|
||||
}
|
||||
}
|
||||
|
||||
// Adjust by cell size and _scale
|
||||
_roi.x = cx - _roi.width / 2.0f + ((float) res.x * cell_size * _scale);
|
||||
_roi.y = cy - _roi.height / 2.0f + ((float) res.y * cell_size * _scale);
|
||||
|
||||
if (_roi.x >= image.cols - 1) _roi.x = image.cols - 1;
|
||||
if (_roi.y >= image.rows - 1) _roi.y = image.rows - 1;
|
||||
if (_roi.x + _roi.width <= 0) _roi.x = -_roi.width + 2;
|
||||
if (_roi.y + _roi.height <= 0) _roi.y = -_roi.height + 2;
|
||||
|
||||
assert(_roi.width >= 0 && _roi.height >= 0);
|
||||
cv::Mat x = getFeatures(image, 0);
|
||||
train(x, interp_factor);
|
||||
|
||||
return _roi;
|
||||
}
|
||||
|
||||
|
||||
// Detect object in the current frame.
|
||||
cv::Point2f KCFTracker::detect(cv::Mat z, cv::Mat x, float &peak_value)
|
||||
{
|
||||
using namespace FFTTools;
|
||||
|
||||
cv::Mat k = gaussianCorrelation(x, z);
|
||||
cv::Mat res = (real(fftd(complexMultiplication(_alphaf, fftd(k)), true)));
|
||||
|
||||
//minMaxLoc only accepts doubles for the peak, and integer points for the coordinates
|
||||
cv::Point2i pi;
|
||||
double pv;
|
||||
|
||||
cv::Point2i pi_min;
|
||||
double pv_min;
|
||||
cv::minMaxLoc(res, &pv_min, &pv, &pi_min, &pi);
|
||||
peak_value = (float) pv;
|
||||
// std::cout << "min reponse : " << pv_min << " max response :" << pv << std::endl;
|
||||
|
||||
//subpixel peak estimation, coordinates will be non-integer
|
||||
cv::Point2f p((float)pi.x, (float)pi.y);
|
||||
|
||||
if (pi.x > 0 && pi.x < res.cols-1) {
|
||||
p.x += subPixelPeak(res.at<float>(pi.y, pi.x-1), peak_value, res.at<float>(pi.y, pi.x+1));
|
||||
}
|
||||
|
||||
if (pi.y > 0 && pi.y < res.rows-1) {
|
||||
p.y += subPixelPeak(res.at<float>(pi.y-1, pi.x), peak_value, res.at<float>(pi.y+1, pi.x));
|
||||
}
|
||||
|
||||
p.x -= (res.cols) / 2;
|
||||
p.y -= (res.rows) / 2;
|
||||
|
||||
return p;
|
||||
}
|
||||
|
||||
// train tracker with a single image
|
||||
void KCFTracker::train(cv::Mat x, float train_interp_factor)
|
||||
{
|
||||
using namespace FFTTools;
|
||||
|
||||
cv::Mat k = gaussianCorrelation(x, x);
|
||||
cv::Mat alphaf = complexDivision(_prob, (fftd(k) + lambda));
|
||||
|
||||
_tmpl = (1 - train_interp_factor) * _tmpl + (train_interp_factor) * x;
|
||||
_alphaf = (1 - train_interp_factor) * _alphaf + (train_interp_factor) * alphaf;
|
||||
|
||||
|
||||
/*cv::Mat kf = fftd(gaussianCorrelation(x, x));
|
||||
cv::Mat num = complexMultiplication(kf, _prob);
|
||||
cv::Mat den = complexMultiplication(kf, kf + lambda);
|
||||
|
||||
_tmpl = (1 - train_interp_factor) * _tmpl + (train_interp_factor) * x;
|
||||
_num = (1 - train_interp_factor) * _num + (train_interp_factor) * num;
|
||||
_den = (1 - train_interp_factor) * _den + (train_interp_factor) * den;
|
||||
|
||||
_alphaf = complexDivision(_num, _den);*/
|
||||
|
||||
}
|
||||
|
||||
// Evaluates a Gaussian kernel with bandwidth SIGMA for all relative shifts between input images X and Y, which must both be MxN. They must also be periodic (ie., pre-processed with a cosine window).
|
||||
cv::Mat KCFTracker::gaussianCorrelation(cv::Mat x1, cv::Mat x2)
|
||||
{
|
||||
using namespace FFTTools;
|
||||
cv::Mat c = cv::Mat( cv::Size(size_patch[1], size_patch[0]), CV_32F, cv::Scalar(0) );
|
||||
// HOG features
|
||||
if (_hogfeatures) {
|
||||
cv::Mat caux;
|
||||
cv::Mat x1aux;
|
||||
cv::Mat x2aux;
|
||||
for (int i = 0; i < size_patch[2]; i++) {
|
||||
x1aux = x1.row(i); // Procedure do deal with cv::Mat multichannel bug
|
||||
x1aux = x1aux.reshape(1, size_patch[0]);
|
||||
x2aux = x2.row(i).reshape(1, size_patch[0]);
|
||||
cv::mulSpectrums(fftd(x1aux), fftd(x2aux), caux, 0, true);
|
||||
caux = fftd(caux, true);
|
||||
rearrange(caux);
|
||||
caux.convertTo(caux,CV_32F);
|
||||
c = c + real(caux);
|
||||
}
|
||||
}
|
||||
// Gray features
|
||||
else {
|
||||
cv::mulSpectrums(fftd(x1), fftd(x2), c, 0, true);
|
||||
c = fftd(c, true);
|
||||
rearrange(c);
|
||||
c = real(c);
|
||||
}
|
||||
cv::Mat d;
|
||||
cv::max(cv::Mat(((cv::sum(cv::Mat(x1.mul(x1)))[0] + cv::sum(cv::Mat(x2.mul(x2)))[0])- 2. * c) / (size_patch[0]*size_patch[1]*size_patch[2])), 0, d);
|
||||
|
||||
cv::Mat k;
|
||||
cv::exp(cv::Mat(-d / (sigma * sigma)), k);
|
||||
return k;
|
||||
}
|
||||
|
||||
// Create Gaussian Peak. Function called only in the first frame.
|
||||
cv::Mat KCFTracker::createGaussianPeak(int sizey, int sizex)
|
||||
{
|
||||
cv::Mat_<float> res(sizey, sizex);
|
||||
|
||||
int syh = (sizey) / 2;
|
||||
int sxh = (sizex) / 2;
|
||||
|
||||
float output_sigma = std::sqrt((float) sizex * sizey) / padding * output_sigma_factor;
|
||||
float mult = -0.5 / (output_sigma * output_sigma);
|
||||
|
||||
for (int i = 0; i < sizey; i++)
|
||||
for (int j = 0; j < sizex; j++)
|
||||
{
|
||||
int ih = i - syh;
|
||||
int jh = j - sxh;
|
||||
res(i, j) = std::exp(mult * (float) (ih * ih + jh * jh));
|
||||
}
|
||||
return FFTTools::fftd(res);
|
||||
}
|
||||
|
||||
// Obtain sub-window from image, with replication-padding and extract features
|
||||
cv::Mat KCFTracker::getFeatures(const cv::Mat & image, bool inithann, float scale_adjust)
|
||||
{
|
||||
cv::Rect extracted_roi;
|
||||
|
||||
float cx = _roi.x + _roi.width / 2;
|
||||
float cy = _roi.y + _roi.height / 2;
|
||||
|
||||
if (inithann) {
|
||||
int padded_w = _roi.width * padding;
|
||||
int padded_h = _roi.height * padding;
|
||||
|
||||
if (template_size > 1) { // Fit largest dimension to the given template size
|
||||
if (padded_w >= padded_h) //fit to width
|
||||
_scale = padded_w / (float) template_size;
|
||||
else
|
||||
_scale = padded_h / (float) template_size;
|
||||
|
||||
_tmpl_sz.width = padded_w / _scale;
|
||||
_tmpl_sz.height = padded_h / _scale;
|
||||
}
|
||||
else { //No template size given, use ROI size
|
||||
_tmpl_sz.width = padded_w;
|
||||
_tmpl_sz.height = padded_h;
|
||||
_scale = 1;
|
||||
// original code from paper:
|
||||
/*if (sqrt(padded_w * padded_h) >= 100) { //Normal size
|
||||
_tmpl_sz.width = padded_w;
|
||||
_tmpl_sz.height = padded_h;
|
||||
_scale = 1;
|
||||
}
|
||||
else { //ROI is too big, track at half size
|
||||
_tmpl_sz.width = padded_w / 2;
|
||||
_tmpl_sz.height = padded_h / 2;
|
||||
_scale = 2;
|
||||
}*/
|
||||
}
|
||||
|
||||
if (_hogfeatures) {
|
||||
// Round to cell size and also make it even
|
||||
_tmpl_sz.width = ( ( (int)(_tmpl_sz.width / (2 * cell_size)) ) * 2 * cell_size ) + cell_size*2;
|
||||
_tmpl_sz.height = ( ( (int)(_tmpl_sz.height / (2 * cell_size)) ) * 2 * cell_size ) + cell_size*2;
|
||||
}
|
||||
else { //Make number of pixels even (helps with some logic involving half-dimensions)
|
||||
_tmpl_sz.width = (_tmpl_sz.width / 2) * 2;
|
||||
_tmpl_sz.height = (_tmpl_sz.height / 2) * 2;
|
||||
}
|
||||
}
|
||||
|
||||
extracted_roi.width = scale_adjust * _scale * _tmpl_sz.width;
|
||||
extracted_roi.height = scale_adjust * _scale * _tmpl_sz.height;
|
||||
|
||||
// center roi with new size
|
||||
extracted_roi.x = cx - extracted_roi.width / 2;
|
||||
extracted_roi.y = cy - extracted_roi.height / 2;
|
||||
|
||||
cv::Mat FeaturesMap;
|
||||
cv::Mat z = RectTools::subwindow(image, extracted_roi, cv::BORDER_REPLICATE);
|
||||
|
||||
if (z.cols != _tmpl_sz.width || z.rows != _tmpl_sz.height) {
|
||||
cv::resize(z, z, _tmpl_sz);
|
||||
}
|
||||
|
||||
// HOG features
|
||||
if (_hogfeatures) {
|
||||
#if CV_VERSION_MAJOR == 3 && CV_VERSION_MINOR > 3
|
||||
IplImage z_ipl = cvIplImage(z);
|
||||
#else
|
||||
IplImage z_ipl = z;
|
||||
#endif
|
||||
CvLSVMFeatureMapCaskade *map;
|
||||
getFeatureMaps(&z_ipl, cell_size, &map);
|
||||
normalizeAndTruncate(map,0.2f);
|
||||
PCAFeatureMaps(map);
|
||||
size_patch[0] = map->sizeY;
|
||||
size_patch[1] = map->sizeX;
|
||||
size_patch[2] = map->numFeatures;
|
||||
|
||||
FeaturesMap = cv::Mat(cv::Size(map->numFeatures,map->sizeX*map->sizeY), CV_32F, map->map); // Procedure do deal with cv::Mat multichannel bug
|
||||
FeaturesMap = FeaturesMap.t();
|
||||
freeFeatureMapObject(&map);
|
||||
|
||||
// Lab features
|
||||
if (_labfeatures) {
|
||||
cv::Mat imgLab;
|
||||
cvtColor(z, imgLab, CV_BGR2Lab);
|
||||
unsigned char *input = (unsigned char*)(imgLab.data);
|
||||
|
||||
// Sparse output vector
|
||||
cv::Mat outputLab = cv::Mat(_labCentroids.rows, size_patch[0]*size_patch[1], CV_32F, float(0));
|
||||
|
||||
int cntCell = 0;
|
||||
// Iterate through each cell
|
||||
for (int cY = cell_size; cY < z.rows-cell_size; cY+=cell_size){
|
||||
for (int cX = cell_size; cX < z.cols-cell_size; cX+=cell_size){
|
||||
// Iterate through each pixel of cell (cX,cY)
|
||||
for(int y = cY; y < cY+cell_size; ++y){
|
||||
for(int x = cX; x < cX+cell_size; ++x){
|
||||
// Lab components for each pixel
|
||||
float l = (float)input[(z.cols * y + x) * 3];
|
||||
float a = (float)input[(z.cols * y + x) * 3 + 1];
|
||||
float b = (float)input[(z.cols * y + x) * 3 + 2];
|
||||
|
||||
// Iterate trough each centroid
|
||||
float minDist = FLT_MAX;
|
||||
int minIdx = 0;
|
||||
float *inputCentroid = (float*)(_labCentroids.data);
|
||||
for(int k = 0; k < _labCentroids.rows; ++k){
|
||||
float dist = ( (l - inputCentroid[3*k]) * (l - inputCentroid[3*k]) )
|
||||
+ ( (a - inputCentroid[3*k+1]) * (a - inputCentroid[3*k+1]) )
|
||||
+ ( (b - inputCentroid[3*k+2]) * (b - inputCentroid[3*k+2]) );
|
||||
if(dist < minDist){
|
||||
minDist = dist;
|
||||
minIdx = k;
|
||||
}
|
||||
}
|
||||
// Store result at output
|
||||
outputLab.at<float>(minIdx, cntCell) += 1.0 / cell_sizeQ;
|
||||
//((float*) outputLab.data)[minIdx * (size_patch[0]*size_patch[1]) + cntCell] += 1.0 / cell_sizeQ;
|
||||
}
|
||||
}
|
||||
cntCell++;
|
||||
}
|
||||
}
|
||||
// Update size_patch[2] and add features to FeaturesMap
|
||||
size_patch[2] += _labCentroids.rows;
|
||||
FeaturesMap.push_back(outputLab);
|
||||
}
|
||||
}
|
||||
else {
|
||||
FeaturesMap = RectTools::getGrayImage(z);
|
||||
FeaturesMap -= (float) 0.5; // In Paper;
|
||||
size_patch[0] = z.rows;
|
||||
size_patch[1] = z.cols;
|
||||
size_patch[2] = 1;
|
||||
}
|
||||
|
||||
if (inithann) {
|
||||
createHanningMats();
|
||||
}
|
||||
FeaturesMap = hann.mul(FeaturesMap);
|
||||
return FeaturesMap;
|
||||
}
|
||||
|
||||
// Initialize Hanning window. Function called only in the first frame.
|
||||
void KCFTracker::createHanningMats()
|
||||
{
|
||||
cv::Mat hann1t = cv::Mat(cv::Size(size_patch[1],1), CV_32F, cv::Scalar(0));
|
||||
cv::Mat hann2t = cv::Mat(cv::Size(1,size_patch[0]), CV_32F, cv::Scalar(0));
|
||||
|
||||
for (int i = 0; i < hann1t.cols; i++)
|
||||
hann1t.at<float > (0, i) = 0.5 * (1 - std::cos(2 * 3.14159265358979323846 * i / (hann1t.cols - 1)));
|
||||
for (int i = 0; i < hann2t.rows; i++)
|
||||
hann2t.at<float > (i, 0) = 0.5 * (1 - std::cos(2 * 3.14159265358979323846 * i / (hann2t.rows - 1)));
|
||||
|
||||
cv::Mat hann2d = hann2t * hann1t;
|
||||
// HOG features
|
||||
if (_hogfeatures) {
|
||||
cv::Mat hann1d = hann2d.reshape(1,1); // Procedure do deal with cv::Mat multichannel bug
|
||||
|
||||
hann = cv::Mat(cv::Size(size_patch[0]*size_patch[1], size_patch[2]), CV_32F, cv::Scalar(0));
|
||||
for (int i = 0; i < size_patch[2]; i++) {
|
||||
for (int j = 0; j<size_patch[0]*size_patch[1]; j++) {
|
||||
hann.at<float>(i,j) = hann1d.at<float>(0,j);
|
||||
}
|
||||
}
|
||||
}
|
||||
// Gray features
|
||||
else {
|
||||
hann = hann2d;
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate sub-pixel peak for one dimension
|
||||
float KCFTracker::subPixelPeak(float left, float center, float right)
|
||||
{
|
||||
float divisor = 2 * center - right - left;
|
||||
|
||||
if (divisor == 0)
|
||||
return 0;
|
||||
|
||||
return 0.5 * (right - left) / divisor;
|
||||
}
|
||||
Loading…
Reference in new issue