555 lines
17 KiB
C++
555 lines
17 KiB
C++
/******************************************************************************
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* Project: GDAL
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* Purpose: Correlator
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* Author: Andrew Migal, migal.drew@gmail.com
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*
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******************************************************************************
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* Copyright (c) 2012, Andrew Migal
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included
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* in all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
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* OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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****************************************************************************/
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/**
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* @file
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* @author Andrew Migal migal.drew@gmail.com
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* @brief Class for searching corresponding points on images.
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*/
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#ifndef GDALSIMPLESURF_H_
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#define GDALSIMPLESURF_H_
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#include "gdal_priv.h"
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#include "cpl_conv.h"
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#include <list>
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/**
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* @brief Class of "feature point" in raster. Used by SURF-based algorithm.
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*
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* @details This point presents coordinates of distinctive pixel in image.
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* In computer vision, feature points - the most "strong" and "unique"
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* pixels (or areas) in picture, which can be distinguished from others.
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* For more details, see FAST corner detector, SIFT, SURF and similar algorithms.
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*/
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class GDALFeaturePoint
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{
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public:
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/**
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* Standard constructor. Initializes all parameters with negative numbers
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* and allocates memory for descriptor.
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*/
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GDALFeaturePoint();
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/**
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* Copy constructor
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* @param fp Copied instance of GDALFeaturePoint class
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*/
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GDALFeaturePoint(const GDALFeaturePoint& fp);
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/**
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* Create instance of GDALFeaturePoint class
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*
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* @param nX X-coordinate (pixel)
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* @param nY Y-coordinate (line)
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* @param nScale Scale which contains this point (2, 4, 8, 16 and so on)
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* @param nRadius Half of the side of descriptor area
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* @param nSign Sign of Hessian determinant for this point
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*
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* @note This constructor normally is invoked by SURF-based algorithm,
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* which provides all necessary parameters.
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*/
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GDALFeaturePoint(int nX, int nY, int nScale, int nRadius, int nSign);
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virtual ~GDALFeaturePoint();
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GDALFeaturePoint& operator=(const GDALFeaturePoint& point);
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/**
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* Provide access to point's descriptor.
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*
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* @param nIndex Position of descriptor's value.
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* nIndex should be within range from 0 to DESC_SIZE (in current version - 64)
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*
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* @return Reference to value of descriptor in 'nIndex' position.
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* If index is out of range then behaviour is undefined.
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*/
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double& operator[](int nIndex);
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// Descriptor length
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static const int DESC_SIZE = 64;
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/**
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* Fetch X-coordinate (pixel) of point
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*
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* @return X-coordinate in pixels
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*/
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int GetX();
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/**
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* Set X coordinate of point
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*
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* @param nX X coordinate in pixels
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*/
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void SetX(int nX);
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/**
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* Fetch Y-coordinate (line) of point.
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*
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* @return Y-coordinate in pixels.
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*/
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int GetY();
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/**
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* Set Y coordinate of point.
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*
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* @param nY Y coordinate in pixels.
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*/
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void SetY(int nY);
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/**
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* Fetch scale of point.
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*
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* @return Scale for this point.
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*/
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int GetScale();
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/**
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* Set scale of point.
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*
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* @param nScale Scale for this point.
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*/
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void SetScale(int nScale);
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/**
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* Fetch radius of point.
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*
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* @return Radius for this point.
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*/
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int GetRadius();
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/**
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* Set radius of point.
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*
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* @param nRadius Radius for this point.
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*/
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void SetRadius(int nRadius);
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/**
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* Fetch sign of Hessian determinant of point.
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*
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* @return Sign for this point.
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*/
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int GetSign();
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/**
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* Set sign of point.
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*
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* @param nSign Sign of Hessian determinant for this point.
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*/
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void SetSign(int nSign);
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private:
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// Coordinates of point in image
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int nX;
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int nY;
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// --------------------
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int nScale;
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int nRadius;
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int nSign;
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// Descriptor array
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double *padfDescriptor;
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};
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/**
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* @author Andrew Migal migal.drew@gmail.com
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* @brief Integral image class (summed area table).
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* @details Integral image is a table for fast computing the sum of
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* values in rectangular subarea. In more detail, for 2-dimensional array
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* of numbers this class provides capability to get sum of values in
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* rectangular arbitrary area with any size in constant time.
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* Integral image is constructed from grayscale picture.
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*/
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class GDALIntegralImage
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{
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public:
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GDALIntegralImage();
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virtual ~GDALIntegralImage();
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/**
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* Compute integral image for specified array. Result is stored internally.
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*
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* @param padfImg Pointer to 2-dimensional array of values
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* @param nHeight Number of rows in array
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* @param nWidth Number of columns in array
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*/
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void Initialize(const double **padfImg, int nHeight, int nWidth);
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/**
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* Fetch value of specified position in integral image.
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*
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* @param nRow Row of this position
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* @param nCol Column of this position
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*
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* @return Value in specified position or zero if parameters are out of range.
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*/
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double GetValue(int nRow, int nCol);
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/**
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* Get sum of values in specified rectangular grid. Rectangle is constructed
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* from left top point.
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*
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* @param nRow Row of left top point of rectangle
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* @param nCol Column of left top point of rectangle
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* @param nWidth Width of rectangular area (number of columns)
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* @param nHeight Height of rectangular area (number of rows)
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*
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* @return Sum of values in specified grid.
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*/
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double GetRectangleSum(int nRow, int nCol, int nWidth, int nHeight);
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/**
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* Get value of horizontal Haar wavelet in specified square grid.
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*
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* @param nRow Row of left top point of square
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* @param nCol Column of left top point of square
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* @param nSize Side of the square
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*
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* @return Value of horizontal Haar wavelet in specified square grid.
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*/
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double HaarWavelet_X(int nRow, int nCol, int nSize);
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/**
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* Get value of vertical Haar wavelet in specified square grid.
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*
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* @param nRow Row of left top point of square
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* @param nCol Column of left top point of square
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* @param nSize Side of the square
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*
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* @return Value of vertical Haar wavelet in specified square grid.
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*/
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double HaarWavelet_Y(int nRow, int nCol, int nSize);
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/**
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* Fetch height of integral image.
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*
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* @return Height of integral image (number of rows).
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*/
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int GetHeight();
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/**
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* Fetch width of integral image.
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*
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* @return Width of integral image (number of columns).
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*/
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int GetWidth();
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private:
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double **pMatrix;
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int nWidth;
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int nHeight;
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};
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/**
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* @author Andrew Migal migal.drew@gmail.com
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* @brief Class for computation and storage of Hessian values in SURF-based algorithm.
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*
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* @details SURF-based algorithm normally uses this class for searching
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* feature points on raster images. Class also contains traces of Hessian matrices
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* to provide fast computations.
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*/
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class GDALOctaveLayer
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{
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public:
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GDALOctaveLayer();
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/**
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* Create instance with provided parameters.
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*
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* @param nOctave Number of octave which contains this layer
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* @param nInterval Number of position in octave
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*
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* @note Normally constructor is invoked only by SURF-based algorithm.
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*/
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GDALOctaveLayer(int nOctave, int nInterval);
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virtual ~GDALOctaveLayer();
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/**
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* Perform calculation of Hessian determinants and their signs
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* for specified integral image. Result is stored internally.
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*
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* @param poImg Integral image object, which provides all necessary
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* data for computation
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*
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* @note Normally method is invoked only by SURF-based algorithm.
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*/
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void ComputeLayer(GDALIntegralImage *poImg);
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/**
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* Octave which contains this layer (1,2,3...)
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*/
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int octaveNum;
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/**
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* Length of the side of filter
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*/
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int filterSize;
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/**
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* Length of the border
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*/
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int radius;
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/**
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* Scale for this layer
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*/
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int scale;
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/**
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* Image width in pixels
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*/
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int width;
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/**
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* Image height in pixels
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*/
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int height;
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/**
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* Hessian values for image pixels
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*/
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double **detHessians;
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/**
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* Hessian signs for speeded matching
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*/
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int **signs;
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};
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/**
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* @author Andrew Migal migal.drew@gmail.com
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* @brief Class for handling octave layers in SURF-based algorithm.
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* @details Class contains OctaveLayers and provides capability to construct octave space and distinguish
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* feature points. Normally this class is used only by SURF-based algorithm.
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*/
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class GDALOctaveMap
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{
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public:
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/**
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* Create octave space. Octave numbers are start with one. (1, 2, 3, 4, ... )
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*
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* @param nOctaveStart Number of bottom octave
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* @param nOctaveEnd Number of top octave. Should be equal or greater than OctaveStart
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*/
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GDALOctaveMap(int nOctaveStart, int nOctaveEnd);
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virtual ~GDALOctaveMap();
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/**
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* Calculate Hessian values for octave space
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* (for all stored octave layers) using specified integral image
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* @param poImg Integral image instance which provides necessary data
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* @see GDALOctaveLayer
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*/
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void ComputeMap(GDALIntegralImage *poImg);
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/**
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* Method makes decision that specified point
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* in middle octave layer is maximum among all points
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* from 3x3x3 neighbourhood (surrounding points in
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* bottom, middle and top layers). Provided layers should be from the same octave's interval.
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* Detects feature points.
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*
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* @param row Row of point, which is candidate to be feature point
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* @param col Column of point, which is candidate to be feature point
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* @param bot Bottom octave layer
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* @param mid Middle octave layer
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* @param top Top octave layer
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* @param threshold Threshold for feature point recognition. Detected feature point
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* will have Hessian value greater than this provided threshold.
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*
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* @return TRUE if candidate was evaluated as feature point or FALSE otherwise.
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*/
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bool PointIsExtremum(int row, int col, GDALOctaveLayer *bot,
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GDALOctaveLayer *mid, GDALOctaveLayer *top, double threshold);
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/**
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* 2-dimensional array of octave layers
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*/
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GDALOctaveLayer ***pMap;
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/**
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* Value for constructing internal octave space
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*/
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static const int INTERVALS = 4;
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/**
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* Number of bottom octave
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*/
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int octaveStart;
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/**
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* Number of top octave. Should be equal or greater than OctaveStart
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*/
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int octaveEnd;
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};
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/**
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* @author Andrew Migal migal.drew@gmail.com
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* @brief Class for searching corresponding points on images.
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* @details Provides capability for detection feature points
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* and finding equal points on different images.
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* Class implements simplified version of SURF algorithm (Speeded Up Robust Features).
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* As original, this realization is scale invariant, but sensitive to rotation.
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* Images should have similar rotation angles (maximum difference is up to 10-15 degrees),
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* otherwise algorithm produces incorrect and very unstable results.
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*/
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class GDALSimpleSURF
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{
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private:
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/**
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* Class stores indexes of pair of point
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* and distance between them.
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*/
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class MatchedPointPairInfo
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{
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public:
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MatchedPointPairInfo(int nInd_1, int nInd_2, double dfDist)
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{
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ind_1 = nInd_1;
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ind_2 = nInd_2;
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euclideanDist = dfDist;
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}
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int ind_1;
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int ind_2;
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double euclideanDist;
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};
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public:
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/**
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* Prepare class according to specified parameters. Octave numbers affects
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* to amount of detected points and their robustness.
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* Range between bottom and top octaves also affects to required time of detection points
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* (if range is large, algorithm should perform more operations).
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* @param nOctaveStart Number of bottom octave. Octave numbers starts with one
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* @param nOctaveEnd Number of top octave. Should be equal or greater than OctaveStart
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*
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* @note
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* Every octave finds points with specific size. For small images
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* use small octave numbers, for high resolution - large.
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* For 1024x1024 images it's normal to use any octave numbers from range 1-6.
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* (for example, octave start - 1, octave end - 3, or octave start - 2, octave end - 2.)
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* For larger images, try 1-10 range or even higher.
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* Pay attention that number of detected point decreases quickly per octave
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* for particular image. Algorithm finds more points in case of small octave numbers.
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* If method detects nothing, reduce bottom bound of octave range.
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*
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* NOTICE that every octave requires time to compute. Use a little range
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* or only one octave if execution time is significant.
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*/
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GDALSimpleSURF(int nOctaveStart, int nOctaveEnd);
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virtual ~GDALSimpleSURF();
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/**
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* Convert image with RGB channels to grayscale using "luminosity" method.
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* Result is used in SURF-based algorithm, but may be used anywhere where
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* grayscale images with nice contrast are required.
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*
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* @param red Image's red channel
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* @param green Image's green channel
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* @param blue Image's blue channel
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* @param nXSize Width of initial image
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* @param nYSize Height of initial image
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* @param padfImg Array for resulting grayscale image
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* @param nHeight Height of resulting image
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* @param nWidth Width of resulting image
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*
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* @return CE_None or CE_Failure if error occurs.
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*/
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static CPLErr ConvertRGBToLuminosity(
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GDALRasterBand *red,
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GDALRasterBand *green,
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GDALRasterBand *blue,
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int nXSize, int nYSize,
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double **padfImg, int nHeight, int nWidth);
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/**
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* Find feature points using specified integral image.
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*
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* @param poImg Integral image to be used
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* @param dfThreshold Threshold for feature point recognition. Detected feature point
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* will have Hessian value greater than this provided threshold.
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*
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* @note Typical threshold's value is 0,001. But this value
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* can be various in each case and depends on image's nature.
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* For example, value can be 0.002 or 0.005.
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* Fill free to experiment with it.
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* If threshold is high, than number of detected feature points is small,
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* and vice versa.
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*/
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std::vector<GDALFeaturePoint>*
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ExtractFeaturePoints(GDALIntegralImage *poImg, double dfThreshold);
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/**
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* Find corresponding points (equal points in two collections).
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*
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* @param poMatchPairs Resulting collection for matched points
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* @param poSecondCollect Points on the first image
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* @param poSecondCollect Points on the second image
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* @param dfThreshold Value from 0 to 1. Threshold affects to number of
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* matched points. If threshold is lower, amount of corresponding
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* points is larger, and vice versa
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*
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* @return CE_None or CE_Failure if error occurs.
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*/
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static CPLErr MatchFeaturePoints(
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std::vector<GDALFeaturePoint*> *poMatchPairs,
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std::vector<GDALFeaturePoint> *poFirstCollect,
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std::vector<GDALFeaturePoint> *poSecondCollect,
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double dfThreshold);
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private:
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/**
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* Compute euclidean distance between descriptors of two feature points.
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* It's used in comparison and matching of points.
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*
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* @param firstPoint First feature point to be compared
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* @param secondPoint Second feature point to be compared
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*
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* @return Euclidean distance between descriptors.
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*/
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static double GetEuclideanDistance(
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GDALFeaturePoint &firstPoint, GDALFeaturePoint &secondPoint);
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/**
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* Set provided distance values to range from 0 to 1.
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*
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* @param poList List of distances to be normalized
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*/
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static void NormalizeDistances(std::list<MatchedPointPairInfo> *poList);
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/**
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* Compute descriptor for specified feature point.
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*
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* @param poPoint Feature point instance
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* @param poImg image where feature point was found
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*/
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void SetDescriptor(GDALFeaturePoint *poPoint, GDALIntegralImage *poImg);
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private:
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int octaveStart;
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int octaveEnd;
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GDALOctaveMap *poOctMap;
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};
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#endif /* GDALSIMPLESURF_H_ */
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