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e439f26d37
Added markers to sinusoidal patterns computePhaseMap for Fourier transform profilometry computePhaseMap for Fourier transform profilometry added phase map computation for PSP. Changed the maskDftRegion to frequencyFiltering. It uses regions of interest and can filter symmetrically. Also added computeShadowMask and computeDataModulationTerm changed formatting in structured light module. First commit for the phase unwrapping module. So far, pixel reliabilities are computed and edges are sorted in a histogram. Fixed an error in edges sorting. Added the unwrap histogram method. It computes the number of 2*Pi that has to be added to each pixel to unwrap the phase map added an example for phase unwrapping and a test that unwraps a simple phase map Added documentation draft and a small example that can generate sinusoidal patterns removed unnecessary include added a few comments in sinusoidalpattern.cpp and histogramphaseunwrapping.cpp. Removed some redudancy about mask in the reliability computation. Changed formatting projector calibration as a sample bug fix calibration + cap sinus example doc and tutorials modified calibration example fix for pr fix for pr shadow mask for FTP as in the reference paper changed doc added test for faps dummy commit fixing warnings in test changed test to use jpeg dummy changed permissions and used atan2(x,y) instead of atan dummy commit dummy setting dmt to zero near shadow mask border. It reduces noise bug fix in dmt computation dummy commit for build bots dummy commit for build bots
61 lines
3.1 KiB
C++
61 lines
3.1 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) 2015, OpenCV Foundation, 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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// In no event shall the Intel Corporation or contributors be liable for any direct,
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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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//M*/
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#include "opencv2/phase_unwrapping/phase_unwrapping.hpp"
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#include "opencv2/phase_unwrapping/histogramphaseunwrapping.hpp"
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/** @defgroup phase_unwrapping Phase Unwrapping API
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Two-dimensional phase unwrapping is found in different applications like terrain elevation estimation
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in synthetic aperture radar (SAR), field mapping in magnetic resonance imaging or as a way of finding
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corresponding pixels in structured light reconstruction with sinusoidal patterns.
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Given a phase map, wrapped between [-pi; pi], phase unwrapping aims at finding the "true" phase map
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by adding the right number of 2*pi to each pixel.
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The problem is straightforward for perfect wrapped phase map, but real data are usually not noise-free.
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Among the different algorithms that were developed, quality-guided phase unwrapping methods are fast
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and efficient. They follow a path that unwraps high quality pixels first,
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avoiding error propagation from the start.
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In this module, a quality-guided phase unwrapping is implemented following the approach described in @cite histogramUnwrapping .
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*/ |