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179 lines
5.5 KiB
C++
179 lines
5.5 KiB
C++
/*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_latentsvm.h".
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/*****************************************************************************/
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/* Latent SVM prediction API */
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/*****************************************************************************/
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#ifndef _FHOG_H_
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#define _FHOG_H_
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#include <stdio.h>
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//#include "_lsvmc_types.h"
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//#include "_lsvmc_error.h"
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//#include "_lsvmc_routine.h"
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//#include "opencv2/imgproc.hpp"
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#include "opencv2/imgproc/imgproc_c.h"
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//modified from "_lsvmc_types.h"
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// DataType: STRUCT featureMap
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// FEATURE MAP DESCRIPTION
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// Rectangular map (sizeX x sizeY),
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// every cell stores feature vector (dimension = numFeatures)
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// map - matrix of feature vectors
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// to set and get feature vectors (i,j)
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// used formula map[(j * sizeX + i) * p + k], where
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// k - component of feature vector in cell (i, j)
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typedef struct{
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int sizeX;
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int sizeY;
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int numFeatures;
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float *map;
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} CvLSVMFeatureMapCaskade;
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#include "float.h"
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#define PI CV_PI
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#define EPS 0.000001
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#define F_MAX FLT_MAX
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#define F_MIN -FLT_MAX
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// The number of elements in bin
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// The number of sectors in gradient histogram building
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#define NUM_SECTOR 9
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// The number of levels in image resize procedure
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// We need Lambda levels to resize image twice
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#define LAMBDA 10
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// Block size. Used in feature pyramid building procedure
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#define SIDE_LENGTH 8
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#define VAL_OF_TRUNCATE 0.2f
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//modified from "_lsvm_error.h"
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#define LATENT_SVM_OK 0
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#define LATENT_SVM_MEM_NULL 2
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#define DISTANCE_TRANSFORM_OK 1
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#define DISTANCE_TRANSFORM_GET_INTERSECTION_ERROR -1
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#define DISTANCE_TRANSFORM_ERROR -2
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#define DISTANCE_TRANSFORM_EQUAL_POINTS -3
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#define LATENT_SVM_GET_FEATURE_PYRAMID_FAILED -4
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#define LATENT_SVM_SEARCH_OBJECT_FAILED -5
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#define LATENT_SVM_FAILED_SUPERPOSITION -6
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#define FILTER_OUT_OF_BOUNDARIES -7
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#define LATENT_SVM_TBB_SCHEDULE_CREATION_FAILED -8
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#define LATENT_SVM_TBB_NUMTHREADS_NOT_CORRECT -9
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#define FFT_OK 2
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#define FFT_ERROR -10
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#define LSVM_PARSER_FILE_NOT_FOUND -11
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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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// Feature map Normalization and Truncation
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//
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// API
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// int normalizationAndTruncationFeatureMaps(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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// Feature map reduction
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// In each cell we reduce dimension of the feature vector
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// according to original paper special procedure
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//
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// API
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// int PCAFeatureMaps(featureMap *map)
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// INPUT
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// map - feature map
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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 PCAFeatureMaps(CvLSVMFeatureMapCaskade *map);
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//modified from "lsvmc_routine.h"
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int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX, const int sizeY,
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const int p);
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int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj);
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#endif
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