Super Resolution module
This commit is contained in:
Vladislav Vinogradov
parent
620c699456
commit
7a0d6f7733
@ -1,7 +1,7 @@
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#/usr/bin/env python
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import os, sys, re, string, fnmatch
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allmodules = ["core", "flann", "imgproc", "ml", "highgui", "video", "features2d", "calib3d", "objdetect", "legacy", "contrib", "gpu", "androidcamera", "java", "python", "stitching", "ts", "photo", "nonfree", "videostab", "ocl"]
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allmodules = ["core", "flann", "imgproc", "ml", "highgui", "video", "features2d", "calib3d", "objdetect", "legacy", "contrib", "gpu", "androidcamera", "java", "python", "stitching", "ts", "photo", "nonfree", "videostab", "ocl", "superres"]
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verbose = False
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show_warnings = True
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show_errors = True
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@ -380,7 +380,7 @@ class RstParser(object):
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@classmethod
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def parse_namespace(cls, func, section_name):
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known_namespaces = ["cv", "gpu", "flann"]
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known_namespaces = ["cv", "gpu", "flann", "superres"]
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l = section_name.strip()
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for namespace in known_namespaces:
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if l.startswith(namespace + "::"):
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33
modules/superres/CMakeLists.txt
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33
modules/superres/CMakeLists.txt
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if(ANDROID OR IOS)
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ocv_module_disable(superres)
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endif()
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set(the_description "Super Resolution")
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ocv_add_module(superres opencv_imgproc opencv_video OPTIONAL opencv_gpu opencv_highgui)
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ocv_module_include_directories()
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ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef /wd4127)
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if(HAVE_CUDA)
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string(REPLACE "-Wsign-promo" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
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ocv_source_group("Src\\Cuda" GLOB "src/cuda/*.cu")
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ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/include" ${CUDA_INCLUDE_DIRS})
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file(GLOB lib_cuda "src/cuda/*.cu")
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ocv_cuda_compile(cuda_objs ${lib_cuda})
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set(cuda_link_libs ${CUDA_LIBRARIES})
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else()
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set(lib_cuda "")
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set(cuda_objs "")
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set(cuda_link_libs "")
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endif()
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ocv_glob_module_sources(SOURCES ${lib_cuda} ${cuda_objs})
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ocv_create_module(${cuda_link_libs})
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ocv_add_precompiled_headers(${the_module})
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ocv_add_accuracy_tests()
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ocv_add_perf_tests()
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84
modules/superres/doc/super_resolution.rst
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84
modules/superres/doc/super_resolution.rst
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@ -0,0 +1,84 @@
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Super Resolution
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================
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.. highlight:: cpp
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The Super Resolution module contains a set of functions and classes that can be used to solve the problem of resolution enhancement. There are a few methods implemented, most of them are descibed in the papers [Farsiu03]_ and [Mitzel09]_.
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superres::SuperResolution
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-------------------------
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Base class for Super Resolution algorithms.
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.. ocv:class:: superres::SuperResolution : public Algorithm, public superres::FrameSource
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The class is only used to define the common interface for the whole family of Super Resolution algorithms.
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superres::SuperResolution::setInput
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-----------------------------------
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Set input frame source for Super Resolution algorithm.
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.. ocv:function:: void superres::SuperResolution::setInput(const Ptr<FrameSource>& frameSource)
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:param frameSource: Input frame source
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superres::SuperResolution::nextFrame
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------------------------------------
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Process next frame from input and return output result.
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.. ocv:function:: void superres::SuperResolution::nextFrame(OutputArray frame)
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:param frame: Output result
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superres::SuperResolution::collectGarbage
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-----------------------------------------
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Clear all inner buffers.
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.. ocv:function:: void superres::SuperResolution::collectGarbage()
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superres::createSuperResolution_BTVL1
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-------------------------------------
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Create Bilateral TV-L1 Super Resolution.
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.. ocv:function:: Ptr<SuperResolution> superres::createSuperResolution_BTVL1()
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.. ocv:function:: Ptr<SuperResolution> superres::createSuperResolution_BTVL1_GPU()
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This class implements Super Resolution algorithm described in the papers [Farsiu03]_ and [Mitzel09]_ .
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Here are important members of the class that control the algorithm, which you can set after constructing the class instance:
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* **int scale** Scale factor.
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* **int iterations** Iteration count.
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* **double tau** Asymptotic value of steepest descent method.
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* **double lambda** Weight parameter to balance data term and smoothness term.
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* **double alpha** Parameter of spacial distribution in Bilateral-TV.
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* **int btvKernelSize** Kernel size of Bilateral-TV filter.
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* **int blurKernelSize** Gaussian blur kernel size.
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* **double blurSigma** Gaussian blur sigma.
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* **int temporalAreaRadius** Radius of the temporal search area.
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* **Ptr<DenseOpticalFlowExt> opticalFlow** Dense optical flow algorithm.
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.. [Farsiu03] S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and robust Super-Resolution. Proc 2003 IEEE Int Conf on Image Process, pp. 291–294, 2003.
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.. [Mitzel09] D. Mitzel, T. Pock, T. Schoenemann, D. Cremers. Video super resolution using duality based TV-L1 optical flow. DAGM, 2009.
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8
modules/superres/doc/superres.rst
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8
modules/superres/doc/superres.rst
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**************************
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superres. Super Resolution
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**************************
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.. toctree::
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:maxdepth: 2
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super_resolution
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73
modules/superres/include/opencv2/superres/optical_flow.hpp
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73
modules/superres/include/opencv2/superres/optical_flow.hpp
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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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., 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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#ifndef __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__
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#define __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace superres
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{
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class CV_EXPORTS DenseOpticalFlowExt : public cv::Algorithm
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{
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public:
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virtual void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = noArray()) = 0;
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virtual void collectGarbage() = 0;
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};
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Farneback_GPU();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Simple();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_DualTVL1_GPU();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Brox_GPU();
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CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_PyrLK_GPU();
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}
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}
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#endif // __OPENCV_SUPERRES_OPTICAL_FLOW_HPP__
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98
modules/superres/include/opencv2/superres/superres.hpp
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98
modules/superres/include/opencv2/superres/superres.hpp
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@ -0,0 +1,98 @@
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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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., 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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#ifndef __OPENCV_SUPERRES_HPP__
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#define __OPENCV_SUPERRES_HPP__
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace superres
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{
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CV_EXPORTS bool initModule_superres();
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class CV_EXPORTS FrameSource
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{
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public:
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virtual ~FrameSource();
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virtual void nextFrame(OutputArray frame) = 0;
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virtual void reset() = 0;
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};
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Empty();
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Video(const std::string& fileName);
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Video_GPU(const std::string& fileName);
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CV_EXPORTS Ptr<FrameSource> createFrameSource_Camera(int deviceId = 0);
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class CV_EXPORTS SuperResolution : public cv::Algorithm, public FrameSource
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{
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public:
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void setInput(const Ptr<FrameSource>& frameSource);
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void nextFrame(OutputArray frame);
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void reset();
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virtual void collectGarbage();
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protected:
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SuperResolution();
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virtual void initImpl(Ptr<FrameSource>& frameSource) = 0;
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virtual void processImpl(Ptr<FrameSource>& frameSource, OutputArray output) = 0;
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private:
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Ptr<FrameSource> frameSource_;
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bool firstCall_;
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};
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// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
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// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
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CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1();
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CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_GPU();
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}
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}
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#endif // __OPENCV_SUPERRES_HPP__
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3
modules/superres/perf/perf_main.cpp
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3
modules/superres/perf/perf_main.cpp
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@ -0,0 +1,3 @@
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#include "perf_precomp.hpp"
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CV_PERF_TEST_MAIN(superres)
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1
modules/superres/perf/perf_precomp.cpp
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1
modules/superres/perf/perf_precomp.cpp
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@ -0,0 +1 @@
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#include "perf_precomp.hpp"
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26
modules/superres/perf/perf_precomp.hpp
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26
modules/superres/perf/perf_precomp.hpp
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#ifdef __GNUC__
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# pragma GCC diagnostic ignored "-Wmissing-declarations"
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# if defined __clang__ || defined __APPLE__
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# pragma GCC diagnostic ignored "-Wmissing-prototypes"
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# pragma GCC diagnostic ignored "-Wextra"
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# endif
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#endif
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#ifndef __OPENCV_PERF_PRECOMP_HPP__
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#define __OPENCV_PERF_PRECOMP_HPP__
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#ifdef HAVE_CVCONFIG_H
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#include "cvconfig.h"
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#endif
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#include "opencv2/core/core.hpp"
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#include "opencv2/core/gpumat.hpp"
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#include "opencv2/ts/ts_perf.hpp"
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#include "opencv2/superres/superres.hpp"
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#include "opencv2/superres/optical_flow.hpp"
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#ifdef GTEST_CREATE_SHARED_LIBRARY
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#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
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#endif
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#endif
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153
modules/superres/perf/perf_superres.cpp
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153
modules/superres/perf/perf_superres.cpp
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#include "perf_precomp.hpp"
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using namespace std;
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using namespace std::tr1;
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using namespace testing;
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using namespace perf;
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using namespace cv;
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using namespace cv::superres;
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using namespace cv::gpu;
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#define GPU_SANITY_CHECK(mat, ...) \
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do{ \
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Mat gpu_##mat(mat); \
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SANITY_CHECK(gpu_##mat, ## __VA_ARGS__); \
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} while(0)
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#define CPU_SANITY_CHECK(mat, ...) \
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do{ \
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Mat cpu_##mat(mat); \
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SANITY_CHECK(cpu_##mat, ## __VA_ARGS__); \
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} while(0)
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namespace
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{
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class OneFrameSource_CPU : public FrameSource
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{
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public:
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explicit OneFrameSource_CPU(const Mat& frame) : frame_(frame) {}
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void nextFrame(OutputArray frame)
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{
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frame.getMatRef() = frame_;
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}
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void reset()
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{
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}
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private:
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Mat frame_;
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};
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class OneFrameSource_GPU : public FrameSource
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{
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public:
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explicit OneFrameSource_GPU(const GpuMat& frame) : frame_(frame) {}
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void nextFrame(OutputArray frame)
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{
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frame.getGpuMatRef() = frame_;
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}
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void reset()
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{
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}
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private:
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GpuMat frame_;
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};
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class ZeroOpticalFlow : public DenseOpticalFlowExt
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{
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public:
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void calc(InputArray frame0, InputArray, OutputArray flow1, OutputArray flow2)
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{
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cv::Size size = frame0.size();
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if (!flow2.needed())
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{
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flow1.create(size, CV_32FC2);
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if (flow1.kind() == cv::_InputArray::GPU_MAT)
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flow1.getGpuMatRef().setTo(cv::Scalar::all(0));
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else
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flow1.getMatRef().setTo(cv::Scalar::all(0));
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}
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else
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{
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flow1.create(size, CV_32FC1);
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flow2.create(size, CV_32FC1);
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if (flow1.kind() == cv::_InputArray::GPU_MAT)
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flow1.getGpuMatRef().setTo(cv::Scalar::all(0));
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else
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flow1.getMatRef().setTo(cv::Scalar::all(0));
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if (flow2.kind() == cv::_InputArray::GPU_MAT)
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flow2.getGpuMatRef().setTo(cv::Scalar::all(0));
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else
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flow2.getMatRef().setTo(cv::Scalar::all(0));
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}
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}
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void collectGarbage()
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{
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}
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};
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}
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PERF_TEST_P(Size_MatType, SuperResolution_BTVL1,
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Combine(Values(szSmall64, szSmall128),
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Values(MatType(CV_8UC1), MatType(CV_8UC3))))
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{
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declare.time(5 * 60);
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const Size size = get<0>(GetParam());
|
||||
const int type = get<1>(GetParam());
|
||||
|
||||
Mat frame(size, type);
|
||||
declare.in(frame, WARMUP_RNG);
|
||||
|
||||
const int scale = 2;
|
||||
const int iterations = 50;
|
||||
const int temporalAreaRadius = 1;
|
||||
Ptr<DenseOpticalFlowExt> opticalFlow(new ZeroOpticalFlow);
|
||||
|
||||
if (PERF_RUN_GPU())
|
||||
{
|
||||
Ptr<SuperResolution> superRes = createSuperResolution_BTVL1_GPU();
|
||||
|
||||
superRes->set("scale", scale);
|
||||
superRes->set("iterations", iterations);
|
||||
superRes->set("temporalAreaRadius", temporalAreaRadius);
|
||||
superRes->set("opticalFlow", opticalFlow);
|
||||
|
||||
superRes->setInput(new OneFrameSource_GPU(GpuMat(frame)));
|
||||
|
||||
GpuMat dst;
|
||||
superRes->nextFrame(dst);
|
||||
|
||||
TEST_CYCLE_N(10) superRes->nextFrame(dst);
|
||||
|
||||
GPU_SANITY_CHECK(dst);
|
||||
}
|
||||
else
|
||||
{
|
||||
Ptr<SuperResolution> superRes = createSuperResolution_BTVL1();
|
||||
|
||||
superRes->set("scale", scale);
|
||||
superRes->set("iterations", iterations);
|
||||
superRes->set("temporalAreaRadius", temporalAreaRadius);
|
||||
superRes->set("opticalFlow", opticalFlow);
|
||||
|
||||
superRes->setInput(new OneFrameSource_CPU(frame));
|
||||
|
||||
Mat dst;
|
||||
superRes->nextFrame(dst);
|
||||
|
||||
TEST_CYCLE_N(10) superRes->nextFrame(dst);
|
||||
|
||||
CPU_SANITY_CHECK(dst);
|
||||
}
|
||||
}
|
||||
619
modules/superres/src/btv_l1.cpp
Normal file
619
modules/superres/src/btv_l1.cpp
Normal file
@ -0,0 +1,619 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
|
||||
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::superres;
|
||||
using namespace cv::superres::detail;
|
||||
|
||||
namespace
|
||||
{
|
||||
void calcRelativeMotions(const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions,
|
||||
vector<Mat>& relForwardMotions, vector<Mat>& relBackwardMotions,
|
||||
int baseIdx, Size size)
|
||||
{
|
||||
const int count = static_cast<int>(forwardMotions.size());
|
||||
|
||||
relForwardMotions.resize(count);
|
||||
relForwardMotions[baseIdx].create(size, CV_32FC2);
|
||||
relForwardMotions[baseIdx].setTo(Scalar::all(0));
|
||||
|
||||
relBackwardMotions.resize(count);
|
||||
relBackwardMotions[baseIdx].create(size, CV_32FC2);
|
||||
relBackwardMotions[baseIdx].setTo(Scalar::all(0));
|
||||
|
||||
for (int i = baseIdx - 1; i >= 0; --i)
|
||||
{
|
||||
add(relForwardMotions[i + 1], forwardMotions[i], relForwardMotions[i]);
|
||||
|
||||
add(relBackwardMotions[i + 1], backwardMotions[i + 1], relBackwardMotions[i]);
|
||||
}
|
||||
|
||||
for (int i = baseIdx + 1; i < count; ++i)
|
||||
{
|
||||
add(relForwardMotions[i - 1], backwardMotions[i], relForwardMotions[i]);
|
||||
|
||||
add(relBackwardMotions[i - 1], forwardMotions[i - 1], relBackwardMotions[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void upscaleMotions(const vector<Mat>& lowResMotions, vector<Mat>& highResMotions, int scale)
|
||||
{
|
||||
highResMotions.resize(lowResMotions.size());
|
||||
|
||||
for (size_t i = 0; i < lowResMotions.size(); ++i)
|
||||
{
|
||||
resize(lowResMotions[i], highResMotions[i], Size(), scale, scale, INTER_CUBIC);
|
||||
multiply(highResMotions[i], Scalar::all(scale), highResMotions[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void buildMotionMaps(const Mat& forwardMotion, const Mat& backwardMotion, Mat& forwardMap, Mat& backwardMap)
|
||||
{
|
||||
forwardMap.create(forwardMotion.size(), CV_32FC2);
|
||||
backwardMap.create(forwardMotion.size(), CV_32FC2);
|
||||
|
||||
for (int y = 0; y < forwardMotion.rows; ++y)
|
||||
{
|
||||
const Point2f* forwardMotionRow = forwardMotion.ptr<Point2f>(y);
|
||||
const Point2f* backwardMotionRow = backwardMotion.ptr<Point2f>(y);
|
||||
Point2f* forwardMapRow = forwardMap.ptr<Point2f>(y);
|
||||
Point2f* backwardMapRow = backwardMap.ptr<Point2f>(y);
|
||||
|
||||
for (int x = 0; x < forwardMotion.cols; ++x)
|
||||
{
|
||||
Point2f base(static_cast<float>(x), static_cast<float>(y));
|
||||
|
||||
forwardMapRow[x] = base + backwardMotionRow[x];
|
||||
backwardMapRow[x] = base + forwardMotionRow[x];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void upscaleImpl(const Mat& src, Mat& dst, int scale)
|
||||
{
|
||||
dst.create(src.rows * scale, src.cols * scale, src.type());
|
||||
dst.setTo(Scalar::all(0));
|
||||
|
||||
for (int y = 0, Y = 0; y < src.rows; ++y, Y += scale)
|
||||
{
|
||||
const T* srcRow = src.ptr<T>(y);
|
||||
T* dstRow = dst.ptr<T>(Y);
|
||||
|
||||
for (int x = 0, X = 0; x < src.cols; ++x, X += scale)
|
||||
dstRow[X] = srcRow[x];
|
||||
}
|
||||
}
|
||||
|
||||
void upscale(const Mat& src, Mat& dst, int scale)
|
||||
{
|
||||
typedef void (*func_t)(const Mat& src, Mat& dst, int scale);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0, upscaleImpl<float>, 0, upscaleImpl<Point3f>
|
||||
};
|
||||
|
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
|
||||
|
||||
const func_t func = funcs[src.channels()];
|
||||
|
||||
func(src, dst, scale);
|
||||
}
|
||||
|
||||
float diffSign(float a, float b)
|
||||
{
|
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
|
||||
}
|
||||
Point3f diffSign(Point3f a, Point3f b)
|
||||
{
|
||||
return Point3f(
|
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
|
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
|
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f
|
||||
);
|
||||
}
|
||||
|
||||
void diffSign(const Mat& src1, const Mat& src2, Mat& dst)
|
||||
{
|
||||
const int count = src1.cols * src1.channels();
|
||||
|
||||
dst.create(src1.size(), src1.type());
|
||||
|
||||
for (int y = 0; y < src1.rows; ++y)
|
||||
{
|
||||
const float* src1Ptr = src1.ptr<float>(y);
|
||||
const float* src2Ptr = src2.ptr<float>(y);
|
||||
float* dstPtr = dst.ptr<float>(y);
|
||||
|
||||
for (int x = 0; x < count; ++x)
|
||||
dstPtr[x] = diffSign(src1Ptr[x], src2Ptr[x]);
|
||||
}
|
||||
}
|
||||
|
||||
void calcBtvWeights(int btvKernelSize, double alpha, vector<float>& btvWeights)
|
||||
{
|
||||
const size_t size = btvKernelSize * btvKernelSize;
|
||||
|
||||
btvWeights.resize(size);
|
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2;
|
||||
const float alpha_f = static_cast<float>(alpha);
|
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m)
|
||||
{
|
||||
for (int l = ksize; l + m >= 0; --l, ++ind)
|
||||
btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l));
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
struct BtvRegularizationBody : ParallelLoopBody
|
||||
{
|
||||
void operator ()(const Range& range) const;
|
||||
|
||||
Mat src;
|
||||
mutable Mat dst;
|
||||
int ksize;
|
||||
const float* btvWeights;
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
void BtvRegularizationBody<T>::operator ()(const Range& range) const
|
||||
{
|
||||
for (int i = range.start; i < range.end; ++i)
|
||||
{
|
||||
const T* srcRow = src.ptr<T>(i);
|
||||
T* dstRow = dst.ptr<T>(i);
|
||||
|
||||
for(int j = ksize; j < src.cols - ksize; ++j)
|
||||
{
|
||||
const T srcVal = srcRow[j];
|
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m)
|
||||
{
|
||||
const T* srcRow2 = src.ptr<T>(i - m);
|
||||
const T* srcRow3 = src.ptr<T>(i + m);
|
||||
|
||||
for (int l = ksize; l + m >= 0; --l, ++ind)
|
||||
{
|
||||
dstRow[j] += btvWeights[ind] * (diffSign(srcVal, srcRow3[j + l]) - diffSign(srcRow2[j - l], srcVal));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void calcBtvRegularizationImpl(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights)
|
||||
{
|
||||
dst.create(src.size(), src.type());
|
||||
dst.setTo(Scalar::all(0));
|
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2;
|
||||
|
||||
BtvRegularizationBody<T> body;
|
||||
|
||||
body.src = src;
|
||||
body.dst = dst;
|
||||
body.ksize = ksize;
|
||||
body.btvWeights = &btvWeights[0];
|
||||
|
||||
parallel_for_(Range(ksize, src.rows - ksize), body);
|
||||
}
|
||||
|
||||
void calcBtvRegularization(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights)
|
||||
{
|
||||
typedef void (*func_t)(const Mat& src, Mat& dst, int btvKernelSize, const vector<float>& btvWeights);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0, calcBtvRegularizationImpl<float>, 0, calcBtvRegularizationImpl<Point3f>
|
||||
};
|
||||
|
||||
const func_t func = funcs[src.channels()];
|
||||
|
||||
func(src, dst, btvKernelSize, btvWeights);
|
||||
}
|
||||
|
||||
class BTVL1_Base
|
||||
{
|
||||
public:
|
||||
BTVL1_Base();
|
||||
|
||||
void process(const vector<Mat>& src, Mat& dst,
|
||||
const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions,
|
||||
int baseIdx);
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
int scale_;
|
||||
int iterations_;
|
||||
double tau_;
|
||||
double lambda_;
|
||||
double alpha_;
|
||||
int btvKernelSize_;
|
||||
int blurKernelSize_;
|
||||
double blurSigma_;
|
||||
Ptr<DenseOpticalFlowExt> opticalFlow_;
|
||||
|
||||
private:
|
||||
Ptr<FilterEngine> filter_;
|
||||
int curBlurKernelSize_;
|
||||
double curBlurSigma_;
|
||||
int curSrcType_;
|
||||
|
||||
vector<float> btvWeights_;
|
||||
int curBtvKernelSize_;
|
||||
double curAlpha_;
|
||||
|
||||
vector<Mat> lowResForwardMotions_;
|
||||
vector<Mat> lowResBackwardMotions_;
|
||||
|
||||
vector<Mat> highResForwardMotions_;
|
||||
vector<Mat> highResBackwardMotions_;
|
||||
|
||||
vector<Mat> forwardMaps_;
|
||||
vector<Mat> backwardMaps_;
|
||||
|
||||
Mat highRes_;
|
||||
|
||||
Mat diffTerm_, regTerm_;
|
||||
Mat a_, b_, c_;
|
||||
};
|
||||
|
||||
BTVL1_Base::BTVL1_Base()
|
||||
{
|
||||
scale_ = 4;
|
||||
iterations_ = 180;
|
||||
lambda_ = 0.03;
|
||||
tau_ = 1.3;
|
||||
alpha_ = 0.7;
|
||||
btvKernelSize_ = 7;
|
||||
blurKernelSize_ = 5;
|
||||
blurSigma_ = 0.0;
|
||||
opticalFlow_ = createOptFlow_Farneback();
|
||||
|
||||
curBlurKernelSize_ = -1;
|
||||
curBlurSigma_ = -1.0;
|
||||
curSrcType_ = -1;
|
||||
|
||||
curBtvKernelSize_ = -1;
|
||||
curAlpha_ = -1.0;
|
||||
}
|
||||
|
||||
void BTVL1_Base::process(const vector<Mat>& src, Mat& dst, const vector<Mat>& forwardMotions, const vector<Mat>& backwardMotions, int baseIdx)
|
||||
{
|
||||
CV_Assert( scale_ > 1 );
|
||||
CV_Assert( iterations_ > 0 );
|
||||
CV_Assert( tau_ > 0.0 );
|
||||
CV_Assert( alpha_ > 0.0 );
|
||||
CV_Assert( btvKernelSize_ > 0 );
|
||||
CV_Assert( blurKernelSize_ > 0 );
|
||||
CV_Assert( blurSigma_ >= 0.0 );
|
||||
|
||||
// update blur filter and btv weights
|
||||
|
||||
if (filter_.empty() || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
|
||||
{
|
||||
filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
|
||||
curBlurKernelSize_ = blurKernelSize_;
|
||||
curBlurSigma_ = blurSigma_;
|
||||
curSrcType_ = src[0].type();
|
||||
}
|
||||
|
||||
if (btvWeights_.empty() || btvKernelSize_ != curBtvKernelSize_ || alpha_ != curAlpha_)
|
||||
{
|
||||
calcBtvWeights(btvKernelSize_, alpha_, btvWeights_);
|
||||
curBtvKernelSize_ = btvKernelSize_;
|
||||
curAlpha_ = alpha_;
|
||||
}
|
||||
|
||||
// calc high res motions
|
||||
|
||||
calcRelativeMotions(forwardMotions, backwardMotions, lowResForwardMotions_, lowResBackwardMotions_, baseIdx, src[0].size());
|
||||
|
||||
upscaleMotions(lowResForwardMotions_, highResForwardMotions_, scale_);
|
||||
upscaleMotions(lowResBackwardMotions_, highResBackwardMotions_, scale_);
|
||||
|
||||
forwardMaps_.resize(highResForwardMotions_.size());
|
||||
backwardMaps_.resize(highResForwardMotions_.size());
|
||||
for (size_t i = 0; i < highResForwardMotions_.size(); ++i)
|
||||
buildMotionMaps(highResForwardMotions_[i], highResBackwardMotions_[i], forwardMaps_[i], backwardMaps_[i]);
|
||||
|
||||
// initial estimation
|
||||
|
||||
const Size lowResSize = src[0].size();
|
||||
const Size highResSize(lowResSize.width * scale_, lowResSize.height * scale_);
|
||||
|
||||
resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC);
|
||||
|
||||
// iterations
|
||||
|
||||
diffTerm_.create(highResSize, highRes_.type());
|
||||
a_.create(highResSize, highRes_.type());
|
||||
b_.create(highResSize, highRes_.type());
|
||||
c_.create(lowResSize, highRes_.type());
|
||||
|
||||
for (int i = 0; i < iterations_; ++i)
|
||||
{
|
||||
diffTerm_.setTo(Scalar::all(0));
|
||||
|
||||
for (size_t k = 0; k < src.size(); ++k)
|
||||
{
|
||||
// a = M * Ih
|
||||
remap(highRes_, a_, backwardMaps_[k], noArray(), INTER_NEAREST);
|
||||
// b = HM * Ih
|
||||
filter_->apply(a_, b_);
|
||||
// c = DHM * Ih
|
||||
resize(b_, c_, lowResSize, 0, 0, INTER_NEAREST);
|
||||
|
||||
diffSign(src[k], c_, c_);
|
||||
|
||||
// a = Dt * diff
|
||||
upscale(c_, a_, scale_);
|
||||
// b = HtDt * diff
|
||||
filter_->apply(a_, b_);
|
||||
// a = MtHtDt * diff
|
||||
remap(b_, a_, forwardMaps_[k], noArray(), INTER_NEAREST);
|
||||
|
||||
add(diffTerm_, a_, diffTerm_);
|
||||
}
|
||||
|
||||
if (lambda_ > 0)
|
||||
{
|
||||
calcBtvRegularization(highRes_, regTerm_, btvKernelSize_, btvWeights_);
|
||||
addWeighted(diffTerm_, 1.0, regTerm_, -lambda_, 0.0, diffTerm_);
|
||||
}
|
||||
|
||||
addWeighted(highRes_, 1.0, diffTerm_, tau_, 0.0, highRes_);
|
||||
}
|
||||
|
||||
Rect inner(btvKernelSize_, btvKernelSize_, highRes_.cols - 2 * btvKernelSize_, highRes_.rows - 2 * btvKernelSize_);
|
||||
highRes_(inner).copyTo(dst);
|
||||
}
|
||||
|
||||
void BTVL1_Base::collectGarbage()
|
||||
{
|
||||
filter_.release();
|
||||
|
||||
lowResForwardMotions_.clear();
|
||||
lowResBackwardMotions_.clear();
|
||||
|
||||
highResForwardMotions_.clear();
|
||||
highResBackwardMotions_.clear();
|
||||
|
||||
forwardMaps_.clear();
|
||||
backwardMaps_.clear();
|
||||
|
||||
highRes_.release();
|
||||
|
||||
diffTerm_.release();
|
||||
regTerm_.release();
|
||||
a_.release();
|
||||
b_.release();
|
||||
c_.release();
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////
|
||||
|
||||
class BTVL1 : public SuperResolution, private BTVL1_Base
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
BTVL1();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void initImpl(Ptr<FrameSource>& frameSource);
|
||||
void processImpl(Ptr<FrameSource>& frameSource, OutputArray output);
|
||||
|
||||
private:
|
||||
int temporalAreaRadius_;
|
||||
|
||||
void readNextFrame(Ptr<FrameSource>& frameSource);
|
||||
void processFrame(int idx);
|
||||
|
||||
Mat curFrame_;
|
||||
Mat prevFrame_;
|
||||
|
||||
vector<Mat> frames_;
|
||||
vector<Mat> forwardMotions_;
|
||||
vector<Mat> backwardMotions_;
|
||||
vector<Mat> outputs_;
|
||||
|
||||
int storePos_;
|
||||
int procPos_;
|
||||
int outPos_;
|
||||
|
||||
vector<Mat> srcFrames_;
|
||||
vector<Mat> srcForwardMotions_;
|
||||
vector<Mat> srcBackwardMotions_;
|
||||
Mat finalOutput_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(BTVL1, "SuperResolution.BTVL1",
|
||||
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor.");
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count.");
|
||||
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method.");
|
||||
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term.");
|
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV.");
|
||||
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter.");
|
||||
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
|
||||
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
|
||||
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
|
||||
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
|
||||
|
||||
BTVL1::BTVL1()
|
||||
{
|
||||
temporalAreaRadius_ = 4;
|
||||
}
|
||||
|
||||
void BTVL1::collectGarbage()
|
||||
{
|
||||
curFrame_.release();
|
||||
prevFrame_.release();
|
||||
|
||||
frames_.clear();
|
||||
forwardMotions_.clear();
|
||||
backwardMotions_.clear();
|
||||
outputs_.clear();
|
||||
|
||||
srcFrames_.clear();
|
||||
srcForwardMotions_.clear();
|
||||
srcBackwardMotions_.clear();
|
||||
finalOutput_.release();
|
||||
|
||||
SuperResolution::collectGarbage();
|
||||
BTVL1_Base::collectGarbage();
|
||||
}
|
||||
|
||||
void BTVL1::initImpl(Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
const int cacheSize = 2 * temporalAreaRadius_ + 1;
|
||||
|
||||
frames_.resize(cacheSize);
|
||||
forwardMotions_.resize(cacheSize);
|
||||
backwardMotions_.resize(cacheSize);
|
||||
outputs_.resize(cacheSize);
|
||||
|
||||
storePos_ = -1;
|
||||
|
||||
for (int t = -temporalAreaRadius_; t <= temporalAreaRadius_; ++t)
|
||||
readNextFrame(frameSource);
|
||||
|
||||
for (int i = 0; i <= temporalAreaRadius_; ++i)
|
||||
processFrame(i);
|
||||
|
||||
procPos_ = temporalAreaRadius_;
|
||||
outPos_ = -1;
|
||||
}
|
||||
|
||||
void BTVL1::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
|
||||
{
|
||||
if (outPos_ >= storePos_)
|
||||
{
|
||||
_output.release();
|
||||
return;
|
||||
}
|
||||
|
||||
readNextFrame(frameSource);
|
||||
|
||||
if (procPos_ < storePos_)
|
||||
{
|
||||
++procPos_;
|
||||
processFrame(procPos_);
|
||||
}
|
||||
|
||||
++outPos_;
|
||||
const Mat& curOutput = at(outPos_, outputs_);
|
||||
|
||||
if (_output.kind() < _InputArray::OPENGL_BUFFER)
|
||||
curOutput.convertTo(_output, CV_8U);
|
||||
else
|
||||
{
|
||||
curOutput.convertTo(finalOutput_, CV_8U);
|
||||
arrCopy(finalOutput_, _output);
|
||||
}
|
||||
}
|
||||
|
||||
void BTVL1::readNextFrame(Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
frameSource->nextFrame(curFrame_);
|
||||
|
||||
if (curFrame_.empty())
|
||||
return;
|
||||
|
||||
++storePos_;
|
||||
curFrame_.convertTo(at(storePos_, frames_), CV_32F);
|
||||
|
||||
if (storePos_ > 0)
|
||||
{
|
||||
opticalFlow_->calc(prevFrame_, curFrame_, at(storePos_ - 1, forwardMotions_));
|
||||
opticalFlow_->calc(curFrame_, prevFrame_, at(storePos_, backwardMotions_));
|
||||
}
|
||||
|
||||
curFrame_.copyTo(prevFrame_);
|
||||
}
|
||||
|
||||
void BTVL1::processFrame(int idx)
|
||||
{
|
||||
const int startIdx = max(idx - temporalAreaRadius_, 0);
|
||||
const int procIdx = idx;
|
||||
const int endIdx = min(startIdx + 2 * temporalAreaRadius_, storePos_);
|
||||
|
||||
const int count = endIdx - startIdx + 1;
|
||||
|
||||
srcFrames_.resize(count);
|
||||
srcForwardMotions_.resize(count);
|
||||
srcBackwardMotions_.resize(count);
|
||||
|
||||
int baseIdx = -1;
|
||||
|
||||
for (int i = startIdx, k = 0; i <= endIdx; ++i, ++k)
|
||||
{
|
||||
if (i == procIdx)
|
||||
baseIdx = k;
|
||||
|
||||
srcFrames_[k] = at(i, frames_);
|
||||
|
||||
if (i < endIdx)
|
||||
srcForwardMotions_[k] = at(i, forwardMotions_);
|
||||
if (i > startIdx)
|
||||
srcBackwardMotions_[k] = at(i, backwardMotions_);
|
||||
}
|
||||
|
||||
process(srcFrames_, at(idx, outputs_), srcForwardMotions_, srcBackwardMotions_, baseIdx);
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1()
|
||||
{
|
||||
return new BTVL1;
|
||||
}
|
||||
580
modules/superres/src/btv_l1_gpu.cpp
Normal file
580
modules/superres/src/btv_l1_gpu.cpp
Normal file
@ -0,0 +1,580 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
|
||||
// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
using namespace cv::superres;
|
||||
using namespace cv::superres::detail;
|
||||
|
||||
#ifndef HAVE_CUDA
|
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU()
|
||||
{
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<SuperResolution>();
|
||||
}
|
||||
|
||||
#else // HAVE_CUDA
|
||||
|
||||
namespace btv_l1_device
|
||||
{
|
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
|
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
|
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
|
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY);
|
||||
|
||||
template <int cn>
|
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
|
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream);
|
||||
|
||||
void loadBtvWeights(const float* weights, size_t count);
|
||||
template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
}
|
||||
|
||||
namespace
|
||||
{
|
||||
void calcRelativeMotions(const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions,
|
||||
vector<pair<GpuMat, GpuMat> >& relForwardMotions, vector<pair<GpuMat, GpuMat> >& relBackwardMotions,
|
||||
int baseIdx, Size size)
|
||||
{
|
||||
const int count = static_cast<int>(forwardMotions.size());
|
||||
|
||||
relForwardMotions.resize(count);
|
||||
relForwardMotions[baseIdx].first.create(size, CV_32FC1);
|
||||
relForwardMotions[baseIdx].first.setTo(Scalar::all(0));
|
||||
relForwardMotions[baseIdx].second.create(size, CV_32FC1);
|
||||
relForwardMotions[baseIdx].second.setTo(Scalar::all(0));
|
||||
|
||||
relBackwardMotions.resize(count);
|
||||
relBackwardMotions[baseIdx].first.create(size, CV_32FC1);
|
||||
relBackwardMotions[baseIdx].first.setTo(Scalar::all(0));
|
||||
relBackwardMotions[baseIdx].second.create(size, CV_32FC1);
|
||||
relBackwardMotions[baseIdx].second.setTo(Scalar::all(0));
|
||||
|
||||
for (int i = baseIdx - 1; i >= 0; --i)
|
||||
{
|
||||
gpu::add(relForwardMotions[i + 1].first, forwardMotions[i].first, relForwardMotions[i].first);
|
||||
gpu::add(relForwardMotions[i + 1].second, forwardMotions[i].second, relForwardMotions[i].second);
|
||||
|
||||
gpu::add(relBackwardMotions[i + 1].first, backwardMotions[i + 1].first, relBackwardMotions[i].first);
|
||||
gpu::add(relBackwardMotions[i + 1].second, backwardMotions[i + 1].second, relBackwardMotions[i].second);
|
||||
}
|
||||
|
||||
for (int i = baseIdx + 1; i < count; ++i)
|
||||
{
|
||||
gpu::add(relForwardMotions[i - 1].first, backwardMotions[i].first, relForwardMotions[i].first);
|
||||
gpu::add(relForwardMotions[i - 1].second, backwardMotions[i].second, relForwardMotions[i].second);
|
||||
|
||||
gpu::add(relBackwardMotions[i - 1].first, forwardMotions[i - 1].first, relBackwardMotions[i].first);
|
||||
gpu::add(relBackwardMotions[i - 1].second, forwardMotions[i - 1].second, relBackwardMotions[i].second);
|
||||
}
|
||||
}
|
||||
|
||||
void upscaleMotions(const vector<pair<GpuMat, GpuMat> >& lowResMotions, vector<pair<GpuMat, GpuMat> >& highResMotions, int scale)
|
||||
{
|
||||
highResMotions.resize(lowResMotions.size());
|
||||
|
||||
for (size_t i = 0; i < lowResMotions.size(); ++i)
|
||||
{
|
||||
gpu::resize(lowResMotions[i].first, highResMotions[i].first, Size(), scale, scale, INTER_CUBIC);
|
||||
gpu::resize(lowResMotions[i].second, highResMotions[i].second, Size(), scale, scale, INTER_CUBIC);
|
||||
|
||||
gpu::multiply(highResMotions[i].first, Scalar::all(scale), highResMotions[i].first);
|
||||
gpu::multiply(highResMotions[i].second, Scalar::all(scale), highResMotions[i].second);
|
||||
}
|
||||
}
|
||||
|
||||
void buildMotionMaps(const pair<GpuMat, GpuMat>& forwardMotion, const pair<GpuMat, GpuMat>& backwardMotion,
|
||||
pair<GpuMat, GpuMat>& forwardMap, pair<GpuMat, GpuMat>& backwardMap)
|
||||
{
|
||||
forwardMap.first.create(forwardMotion.first.size(), CV_32FC1);
|
||||
forwardMap.second.create(forwardMotion.first.size(), CV_32FC1);
|
||||
|
||||
backwardMap.first.create(forwardMotion.first.size(), CV_32FC1);
|
||||
backwardMap.second.create(forwardMotion.first.size(), CV_32FC1);
|
||||
|
||||
btv_l1_device::buildMotionMaps(forwardMotion.first, forwardMotion.second,
|
||||
backwardMotion.first, backwardMotion.second,
|
||||
forwardMap.first, forwardMap.second,
|
||||
backwardMap.first, backwardMap.second);
|
||||
}
|
||||
|
||||
void upscale(const GpuMat& src, GpuMat& dst, int scale, Stream& stream)
|
||||
{
|
||||
typedef void (*func_t)(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0, btv_l1_device::upscale<1>, 0, btv_l1_device::upscale<3>, btv_l1_device::upscale<4>
|
||||
};
|
||||
|
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
|
||||
|
||||
dst.create(src.rows * scale, src.cols * scale, src.type());
|
||||
dst.setTo(Scalar::all(0));
|
||||
|
||||
const func_t func = funcs[src.channels()];
|
||||
|
||||
func(src, dst, scale, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void diffSign(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
|
||||
{
|
||||
dst.create(src1.size(), src1.type());
|
||||
|
||||
btv_l1_device::diffSign(src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
void calcBtvWeights(int btvKernelSize, double alpha, vector<float>& btvWeights)
|
||||
{
|
||||
const size_t size = btvKernelSize * btvKernelSize;
|
||||
|
||||
btvWeights.resize(size);
|
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2;
|
||||
const float alpha_f = static_cast<float>(alpha);
|
||||
|
||||
for (int m = 0, ind = 0; m <= ksize; ++m)
|
||||
{
|
||||
for (int l = ksize; l + m >= 0; --l, ++ind)
|
||||
btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l));
|
||||
}
|
||||
|
||||
btv_l1_device::loadBtvWeights(&btvWeights[0], size);
|
||||
}
|
||||
|
||||
void calcBtvRegularization(const GpuMat& src, GpuMat& dst, int btvKernelSize)
|
||||
{
|
||||
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0,
|
||||
btv_l1_device::calcBtvRegularization<1>,
|
||||
0,
|
||||
btv_l1_device::calcBtvRegularization<3>,
|
||||
btv_l1_device::calcBtvRegularization<4>
|
||||
};
|
||||
|
||||
dst.create(src.size(), src.type());
|
||||
dst.setTo(Scalar::all(0));
|
||||
|
||||
const int ksize = (btvKernelSize - 1) / 2;
|
||||
|
||||
funcs[src.channels()](src, dst, ksize);
|
||||
}
|
||||
|
||||
class BTVL1_GPU_Base
|
||||
{
|
||||
public:
|
||||
BTVL1_GPU_Base();
|
||||
|
||||
void process(const vector<GpuMat>& src, GpuMat& dst,
|
||||
const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions,
|
||||
int baseIdx);
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
int scale_;
|
||||
int iterations_;
|
||||
double lambda_;
|
||||
double tau_;
|
||||
double alpha_;
|
||||
int btvKernelSize_;
|
||||
int blurKernelSize_;
|
||||
double blurSigma_;
|
||||
Ptr<DenseOpticalFlowExt> opticalFlow_;
|
||||
|
||||
private:
|
||||
vector<Ptr<FilterEngine_GPU> > filters_;
|
||||
int curBlurKernelSize_;
|
||||
double curBlurSigma_;
|
||||
int curSrcType_;
|
||||
|
||||
vector<float> btvWeights_;
|
||||
int curBtvKernelSize_;
|
||||
double curAlpha_;
|
||||
|
||||
vector<pair<GpuMat, GpuMat> > lowResForwardMotions_;
|
||||
vector<pair<GpuMat, GpuMat> > lowResBackwardMotions_;
|
||||
|
||||
vector<pair<GpuMat, GpuMat> > highResForwardMotions_;
|
||||
vector<pair<GpuMat, GpuMat> > highResBackwardMotions_;
|
||||
|
||||
vector<pair<GpuMat, GpuMat> > forwardMaps_;
|
||||
vector<pair<GpuMat, GpuMat> > backwardMaps_;
|
||||
|
||||
GpuMat highRes_;
|
||||
|
||||
vector<Stream> streams_;
|
||||
vector<GpuMat> diffTerms_;
|
||||
vector<GpuMat> a_, b_, c_;
|
||||
GpuMat regTerm_;
|
||||
};
|
||||
|
||||
BTVL1_GPU_Base::BTVL1_GPU_Base()
|
||||
{
|
||||
scale_ = 4;
|
||||
iterations_ = 180;
|
||||
lambda_ = 0.03;
|
||||
tau_ = 1.3;
|
||||
alpha_ = 0.7;
|
||||
btvKernelSize_ = 7;
|
||||
blurKernelSize_ = 5;
|
||||
blurSigma_ = 0.0;
|
||||
opticalFlow_ = createOptFlow_Farneback_GPU();
|
||||
|
||||
curBlurKernelSize_ = -1;
|
||||
curBlurSigma_ = -1.0;
|
||||
curSrcType_ = -1;
|
||||
|
||||
curBtvKernelSize_ = -1;
|
||||
curAlpha_ = -1.0;
|
||||
}
|
||||
|
||||
void BTVL1_GPU_Base::process(const vector<GpuMat>& src, GpuMat& dst,
|
||||
const vector<pair<GpuMat, GpuMat> >& forwardMotions, const vector<pair<GpuMat, GpuMat> >& backwardMotions,
|
||||
int baseIdx)
|
||||
{
|
||||
CV_Assert( scale_ > 1 );
|
||||
CV_Assert( iterations_ > 0 );
|
||||
CV_Assert( tau_ > 0.0 );
|
||||
CV_Assert( alpha_ > 0.0 );
|
||||
CV_Assert( btvKernelSize_ > 0 && btvKernelSize_ <= 16 );
|
||||
CV_Assert( blurKernelSize_ > 0 );
|
||||
CV_Assert( blurSigma_ >= 0.0 );
|
||||
|
||||
// update blur filter and btv weights
|
||||
|
||||
if (filters_.size() != src.size() || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
|
||||
{
|
||||
filters_.resize(src.size());
|
||||
for (size_t i = 0; i < src.size(); ++i)
|
||||
filters_[i] = createGaussianFilter_GPU(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
|
||||
curBlurKernelSize_ = blurKernelSize_;
|
||||
curBlurSigma_ = blurSigma_;
|
||||
curSrcType_ = src[0].type();
|
||||
}
|
||||
|
||||
if (btvWeights_.empty() || btvKernelSize_ != curBtvKernelSize_ || alpha_ != curAlpha_)
|
||||
{
|
||||
calcBtvWeights(btvKernelSize_, alpha_, btvWeights_);
|
||||
curBtvKernelSize_ = btvKernelSize_;
|
||||
curAlpha_ = alpha_;
|
||||
}
|
||||
|
||||
// calc motions between input frames
|
||||
|
||||
calcRelativeMotions(forwardMotions, backwardMotions, lowResForwardMotions_, lowResBackwardMotions_, baseIdx, src[0].size());
|
||||
|
||||
upscaleMotions(lowResForwardMotions_, highResForwardMotions_, scale_);
|
||||
upscaleMotions(lowResBackwardMotions_, highResBackwardMotions_, scale_);
|
||||
|
||||
forwardMaps_.resize(highResForwardMotions_.size());
|
||||
backwardMaps_.resize(highResForwardMotions_.size());
|
||||
for (size_t i = 0; i < highResForwardMotions_.size(); ++i)
|
||||
buildMotionMaps(highResForwardMotions_[i], highResBackwardMotions_[i], forwardMaps_[i], backwardMaps_[i]);
|
||||
|
||||
// initial estimation
|
||||
|
||||
const Size lowResSize = src[0].size();
|
||||
const Size highResSize(lowResSize.width * scale_, lowResSize.height * scale_);
|
||||
|
||||
gpu::resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC);
|
||||
|
||||
// iterations
|
||||
|
||||
streams_.resize(src.size());
|
||||
diffTerms_.resize(src.size());
|
||||
a_.resize(src.size());
|
||||
b_.resize(src.size());
|
||||
c_.resize(src.size());
|
||||
|
||||
for (int i = 0; i < iterations_; ++i)
|
||||
{
|
||||
for (size_t k = 0; k < src.size(); ++k)
|
||||
{
|
||||
// a = M * Ih
|
||||
gpu::remap(highRes_, a_[k], backwardMaps_[k].first, backwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
|
||||
// b = HM * Ih
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]);
|
||||
// c = DHF * Ih
|
||||
gpu::resize(b_[k], c_[k], lowResSize, 0, 0, INTER_NEAREST, streams_[k]);
|
||||
|
||||
diffSign(src[k], c_[k], c_[k], streams_[k]);
|
||||
|
||||
// a = Dt * diff
|
||||
upscale(c_[k], a_[k], scale_, streams_[k]);
|
||||
// b = HtDt * diff
|
||||
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]);
|
||||
// diffTerm = MtHtDt * diff
|
||||
gpu::remap(b_[k], diffTerms_[k], forwardMaps_[k].first, forwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
|
||||
}
|
||||
|
||||
if (lambda_ > 0)
|
||||
{
|
||||
calcBtvRegularization(highRes_, regTerm_, btvKernelSize_);
|
||||
gpu::addWeighted(highRes_, 1.0, regTerm_, -tau_ * lambda_, 0.0, highRes_);
|
||||
}
|
||||
|
||||
for (size_t k = 0; k < src.size(); ++k)
|
||||
{
|
||||
streams_[k].waitForCompletion();
|
||||
gpu::addWeighted(highRes_, 1.0, diffTerms_[k], tau_, 0.0, highRes_);
|
||||
}
|
||||
}
|
||||
|
||||
Rect inner(btvKernelSize_, btvKernelSize_, highRes_.cols - 2 * btvKernelSize_, highRes_.rows - 2 * btvKernelSize_);
|
||||
highRes_(inner).copyTo(dst);
|
||||
}
|
||||
|
||||
void BTVL1_GPU_Base::collectGarbage()
|
||||
{
|
||||
filters_.clear();
|
||||
|
||||
lowResForwardMotions_.clear();
|
||||
lowResBackwardMotions_.clear();
|
||||
|
||||
highResForwardMotions_.clear();
|
||||
highResBackwardMotions_.clear();
|
||||
|
||||
forwardMaps_.clear();
|
||||
backwardMaps_.clear();
|
||||
|
||||
highRes_.release();
|
||||
|
||||
diffTerms_.clear();
|
||||
a_.clear();
|
||||
b_.clear();
|
||||
c_.clear();
|
||||
regTerm_.release();
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////
|
||||
|
||||
class BTVL1_GPU : public SuperResolution, private BTVL1_GPU_Base
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
BTVL1_GPU();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void initImpl(Ptr<FrameSource>& frameSource);
|
||||
void processImpl(Ptr<FrameSource>& frameSource, OutputArray output);
|
||||
|
||||
private:
|
||||
int temporalAreaRadius_;
|
||||
|
||||
void readNextFrame(Ptr<FrameSource>& frameSource);
|
||||
void processFrame(int idx);
|
||||
|
||||
GpuMat curFrame_;
|
||||
GpuMat prevFrame_;
|
||||
|
||||
vector<GpuMat> frames_;
|
||||
vector<pair<GpuMat, GpuMat> > forwardMotions_;
|
||||
vector<pair<GpuMat, GpuMat> > backwardMotions_;
|
||||
vector<GpuMat> outputs_;
|
||||
|
||||
int storePos_;
|
||||
int procPos_;
|
||||
int outPos_;
|
||||
|
||||
vector<GpuMat> srcFrames_;
|
||||
vector<pair<GpuMat, GpuMat> > srcForwardMotions_;
|
||||
vector<pair<GpuMat, GpuMat> > srcBackwardMotions_;
|
||||
GpuMat finalOutput_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(BTVL1_GPU, "SuperResolution.BTVL1_GPU",
|
||||
obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor.");
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count.");
|
||||
obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method.");
|
||||
obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term.");
|
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV.");
|
||||
obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter.");
|
||||
obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
|
||||
obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
|
||||
obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
|
||||
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
|
||||
|
||||
BTVL1_GPU::BTVL1_GPU()
|
||||
{
|
||||
temporalAreaRadius_ = 4;
|
||||
}
|
||||
|
||||
void BTVL1_GPU::collectGarbage()
|
||||
{
|
||||
curFrame_.release();
|
||||
prevFrame_.release();
|
||||
|
||||
frames_.clear();
|
||||
forwardMotions_.clear();
|
||||
backwardMotions_.clear();
|
||||
outputs_.clear();
|
||||
|
||||
srcFrames_.clear();
|
||||
srcForwardMotions_.clear();
|
||||
srcBackwardMotions_.clear();
|
||||
finalOutput_.release();
|
||||
|
||||
SuperResolution::collectGarbage();
|
||||
BTVL1_GPU_Base::collectGarbage();
|
||||
}
|
||||
|
||||
void BTVL1_GPU::initImpl(Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
const int cacheSize = 2 * temporalAreaRadius_ + 1;
|
||||
|
||||
frames_.resize(cacheSize);
|
||||
forwardMotions_.resize(cacheSize);
|
||||
backwardMotions_.resize(cacheSize);
|
||||
outputs_.resize(cacheSize);
|
||||
|
||||
storePos_ = -1;
|
||||
|
||||
for (int t = -temporalAreaRadius_; t <= temporalAreaRadius_; ++t)
|
||||
readNextFrame(frameSource);
|
||||
|
||||
for (int i = 0; i <= temporalAreaRadius_; ++i)
|
||||
processFrame(i);
|
||||
|
||||
procPos_ = temporalAreaRadius_;
|
||||
outPos_ = -1;
|
||||
}
|
||||
|
||||
void BTVL1_GPU::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
|
||||
{
|
||||
if (outPos_ >= storePos_)
|
||||
{
|
||||
_output.release();
|
||||
return;
|
||||
}
|
||||
|
||||
readNextFrame(frameSource);
|
||||
|
||||
if (procPos_ < storePos_)
|
||||
{
|
||||
++procPos_;
|
||||
processFrame(procPos_);
|
||||
}
|
||||
|
||||
++outPos_;
|
||||
const GpuMat& curOutput = at(outPos_, outputs_);
|
||||
|
||||
if (_output.kind() == _InputArray::GPU_MAT)
|
||||
curOutput.convertTo(_output.getGpuMatRef(), CV_8U);
|
||||
else
|
||||
{
|
||||
curOutput.convertTo(finalOutput_, CV_8U);
|
||||
arrCopy(finalOutput_, _output);
|
||||
}
|
||||
}
|
||||
|
||||
void BTVL1_GPU::readNextFrame(Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
frameSource->nextFrame(curFrame_);
|
||||
|
||||
if (curFrame_.empty())
|
||||
return;
|
||||
|
||||
++storePos_;
|
||||
curFrame_.convertTo(at(storePos_, frames_), CV_32F);
|
||||
|
||||
if (storePos_ > 0)
|
||||
{
|
||||
pair<GpuMat, GpuMat>& forwardMotion = at(storePos_ - 1, forwardMotions_);
|
||||
pair<GpuMat, GpuMat>& backwardMotion = at(storePos_, backwardMotions_);
|
||||
|
||||
opticalFlow_->calc(prevFrame_, curFrame_, forwardMotion.first, forwardMotion.second);
|
||||
opticalFlow_->calc(curFrame_, prevFrame_, backwardMotion.first, backwardMotion.second);
|
||||
}
|
||||
|
||||
curFrame_.copyTo(prevFrame_);
|
||||
}
|
||||
|
||||
void BTVL1_GPU::processFrame(int idx)
|
||||
{
|
||||
const int startIdx = max(idx - temporalAreaRadius_, 0);
|
||||
const int procIdx = idx;
|
||||
const int endIdx = min(startIdx + 2 * temporalAreaRadius_, storePos_);
|
||||
|
||||
const int count = endIdx - startIdx + 1;
|
||||
|
||||
srcFrames_.resize(count);
|
||||
srcForwardMotions_.resize(count);
|
||||
srcBackwardMotions_.resize(count);
|
||||
|
||||
int baseIdx = -1;
|
||||
|
||||
for (int i = startIdx, k = 0; i <= endIdx; ++i, ++k)
|
||||
{
|
||||
if (i == procIdx)
|
||||
baseIdx = k;
|
||||
|
||||
srcFrames_[k] = at(i, frames_);
|
||||
|
||||
if (i < endIdx)
|
||||
srcForwardMotions_[k] = at(i, forwardMotions_);
|
||||
if (i > startIdx)
|
||||
srcBackwardMotions_[k] = at(i, backwardMotions_);
|
||||
}
|
||||
|
||||
process(srcFrames_, at(idx, outputs_), srcForwardMotions_, srcBackwardMotions_, baseIdx);
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU()
|
||||
{
|
||||
return new BTVL1_GPU;
|
||||
}
|
||||
|
||||
#endif // HAVE_CUDA
|
||||
234
modules/superres/src/cuda/btv_l1_gpu.cu
Normal file
234
modules/superres/src/cuda/btv_l1_gpu.cu
Normal file
@ -0,0 +1,234 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "opencv2/gpu/device/common.hpp"
|
||||
#include "opencv2/gpu/device/transform.hpp"
|
||||
#include "opencv2/gpu/device/vec_traits.hpp"
|
||||
#include "opencv2/gpu/device/vec_math.hpp"
|
||||
|
||||
using namespace cv::gpu;
|
||||
using namespace cv::gpu::device;
|
||||
|
||||
namespace btv_l1_device
|
||||
{
|
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
|
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
|
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
|
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY);
|
||||
|
||||
template <int cn>
|
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
|
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream);
|
||||
|
||||
void loadBtvWeights(const float* weights, size_t count);
|
||||
template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
}
|
||||
|
||||
namespace btv_l1_device
|
||||
{
|
||||
__global__ void buildMotionMapsKernel(const PtrStepSzf forwardMotionX, const PtrStepf forwardMotionY,
|
||||
PtrStepf backwardMotionX, PtrStepf backwardMotionY,
|
||||
PtrStepf forwardMapX, PtrStepf forwardMapY,
|
||||
PtrStepf backwardMapX, PtrStepf backwardMapY)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (x >= forwardMotionX.cols || y >= forwardMotionX.rows)
|
||||
return;
|
||||
|
||||
const float fx = forwardMotionX(y, x);
|
||||
const float fy = forwardMotionY(y, x);
|
||||
|
||||
const float bx = backwardMotionX(y, x);
|
||||
const float by = backwardMotionY(y, x);
|
||||
|
||||
forwardMapX(y, x) = x + bx;
|
||||
forwardMapY(y, x) = y + by;
|
||||
|
||||
backwardMapX(y, x) = x + fx;
|
||||
backwardMapY(y, x) = y + fy;
|
||||
}
|
||||
|
||||
void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
|
||||
PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
|
||||
PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
|
||||
PtrStepSzf backwardMapX, PtrStepSzf backwardMapY)
|
||||
{
|
||||
const dim3 block(32, 8);
|
||||
const dim3 grid(divUp(forwardMapX.cols, block.x), divUp(forwardMapX.rows, block.y));
|
||||
|
||||
buildMotionMapsKernel<<<grid, block>>>(forwardMotionX, forwardMotionY,
|
||||
backwardMotionX, bacwardMotionY,
|
||||
forwardMapX, forwardMapY,
|
||||
backwardMapX, backwardMapY);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
__global__ void upscaleKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int scale)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (x >= src.cols || y >= src.rows)
|
||||
return;
|
||||
|
||||
dst(y * scale, x * scale) = src(y, x);
|
||||
}
|
||||
|
||||
template <int cn>
|
||||
void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream)
|
||||
{
|
||||
typedef typename TypeVec<float, cn>::vec_type src_t;
|
||||
|
||||
const dim3 block(32, 8);
|
||||
const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
|
||||
|
||||
upscaleKernel<src_t><<<grid, block, 0, stream>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, scale);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template void upscale<1>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
template void upscale<3>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
template void upscale<4>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
|
||||
|
||||
__device__ __forceinline__ float diffSign(float a, float b)
|
||||
{
|
||||
return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
|
||||
}
|
||||
__device__ __forceinline__ float3 diffSign(const float3& a, const float3& b)
|
||||
{
|
||||
return make_float3(
|
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
|
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
|
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f
|
||||
);
|
||||
}
|
||||
__device__ __forceinline__ float4 diffSign(const float4& a, const float4& b)
|
||||
{
|
||||
return make_float4(
|
||||
a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
|
||||
a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
|
||||
a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f,
|
||||
0.0f
|
||||
);
|
||||
}
|
||||
|
||||
struct DiffSign : binary_function<float, float, float>
|
||||
{
|
||||
__device__ __forceinline__ float operator ()(float a, float b) const
|
||||
{
|
||||
return diffSign(a, b);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
namespace cv { namespace gpu { namespace device
|
||||
{
|
||||
template <> struct TransformFunctorTraits<btv_l1_device::DiffSign> : DefaultTransformFunctorTraits<btv_l1_device::DiffSign>
|
||||
{
|
||||
enum { smart_block_dim_y = 8 };
|
||||
enum { smart_shift = 4 };
|
||||
};
|
||||
}}}
|
||||
|
||||
namespace btv_l1_device
|
||||
{
|
||||
void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream)
|
||||
{
|
||||
transform(src1, src2, dst, DiffSign(), WithOutMask(), stream);
|
||||
}
|
||||
|
||||
__constant__ float c_btvRegWeights[16*16];
|
||||
|
||||
template <typename T>
|
||||
__global__ void calcBtvRegularizationKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int ksize)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x + ksize;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y + ksize;
|
||||
|
||||
if (y >= src.rows - ksize || x >= src.cols - ksize)
|
||||
return;
|
||||
|
||||
const T srcVal = src(y, x);
|
||||
|
||||
T dstVal = VecTraits<T>::all(0);
|
||||
|
||||
for (int m = 0, count = 0; m <= ksize; ++m)
|
||||
{
|
||||
for (int l = ksize; l + m >= 0; --l, ++count)
|
||||
dstVal = dstVal + c_btvRegWeights[count] * (diffSign(srcVal, src(y + m, x + l)) - diffSign(src(y - m, x - l), srcVal));
|
||||
}
|
||||
|
||||
dst(y, x) = dstVal;
|
||||
}
|
||||
|
||||
void loadBtvWeights(const float* weights, size_t count)
|
||||
{
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_btvRegWeights, weights, count * sizeof(float)) );
|
||||
}
|
||||
|
||||
template <int cn>
|
||||
void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize)
|
||||
{
|
||||
typedef typename TypeVec<float, cn>::vec_type src_t;
|
||||
|
||||
const dim3 block(32, 8);
|
||||
const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
|
||||
|
||||
calcBtvRegularizationKernel<src_t><<<grid, block>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, ksize);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
template void calcBtvRegularization<1>(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
template void calcBtvRegularization<3>(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
template void calcBtvRegularization<4>(PtrStepSzb src, PtrStepSzb dst, int ksize);
|
||||
}
|
||||
255
modules/superres/src/frame_source.cpp
Normal file
255
modules/superres/src/frame_source.cpp
Normal file
@ -0,0 +1,255 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
using namespace cv::superres;
|
||||
using namespace cv::superres::detail;
|
||||
|
||||
cv::superres::FrameSource::~FrameSource()
|
||||
{
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// EmptyFrameSource
|
||||
|
||||
namespace
|
||||
{
|
||||
class EmptyFrameSource : public FrameSource
|
||||
{
|
||||
public:
|
||||
void nextFrame(OutputArray frame);
|
||||
void reset();
|
||||
};
|
||||
|
||||
void EmptyFrameSource::nextFrame(OutputArray frame)
|
||||
{
|
||||
frame.release();
|
||||
}
|
||||
|
||||
void EmptyFrameSource::reset()
|
||||
{
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Empty()
|
||||
{
|
||||
return new EmptyFrameSource;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// VideoFrameSource & CameraFrameSource
|
||||
|
||||
#ifndef HAVE_OPENCV_HIGHGUI
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video(const string& fileName)
|
||||
{
|
||||
(void) fileName;
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<FrameSource>();
|
||||
}
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Camera(int deviceId)
|
||||
{
|
||||
(void) deviceId;
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<FrameSource>();
|
||||
}
|
||||
|
||||
#else // HAVE_OPENCV_HIGHGUI
|
||||
|
||||
namespace
|
||||
{
|
||||
class CaptureFrameSource : public FrameSource
|
||||
{
|
||||
public:
|
||||
void nextFrame(OutputArray frame);
|
||||
|
||||
protected:
|
||||
VideoCapture vc_;
|
||||
|
||||
private:
|
||||
Mat frame_;
|
||||
};
|
||||
|
||||
void CaptureFrameSource::nextFrame(OutputArray _frame)
|
||||
{
|
||||
if (_frame.kind() == _InputArray::MAT)
|
||||
{
|
||||
vc_ >> _frame.getMatRef();
|
||||
}
|
||||
else
|
||||
{
|
||||
vc_ >> frame_;
|
||||
arrCopy(frame_, _frame);
|
||||
}
|
||||
}
|
||||
|
||||
class VideoFrameSource : public CaptureFrameSource
|
||||
{
|
||||
public:
|
||||
VideoFrameSource(const string& fileName);
|
||||
|
||||
void reset();
|
||||
|
||||
private:
|
||||
string fileName_;
|
||||
};
|
||||
|
||||
VideoFrameSource::VideoFrameSource(const string& fileName) : fileName_(fileName)
|
||||
{
|
||||
reset();
|
||||
}
|
||||
|
||||
void VideoFrameSource::reset()
|
||||
{
|
||||
vc_.release();
|
||||
vc_.open(fileName_);
|
||||
CV_Assert( vc_.isOpened() );
|
||||
}
|
||||
|
||||
class CameraFrameSource : public CaptureFrameSource
|
||||
{
|
||||
public:
|
||||
CameraFrameSource(int deviceId);
|
||||
|
||||
void reset();
|
||||
|
||||
private:
|
||||
int deviceId_;
|
||||
};
|
||||
|
||||
CameraFrameSource::CameraFrameSource(int deviceId) : deviceId_(deviceId)
|
||||
{
|
||||
reset();
|
||||
}
|
||||
|
||||
void CameraFrameSource::reset()
|
||||
{
|
||||
vc_.release();
|
||||
vc_.open(deviceId_);
|
||||
CV_Assert( vc_.isOpened() );
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video(const string& fileName)
|
||||
{
|
||||
return new VideoFrameSource(fileName);
|
||||
}
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Camera(int deviceId)
|
||||
{
|
||||
return new CameraFrameSource(deviceId);
|
||||
}
|
||||
|
||||
#endif // HAVE_OPENCV_HIGHGUI
|
||||
|
||||
//////////////////////////////////////////////////////
|
||||
// VideoFrameSource_GPU
|
||||
|
||||
#ifndef HAVE_OPENCV_GPU
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video_GPU(const string& fileName)
|
||||
{
|
||||
(void) fileName;
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<FrameSource>();
|
||||
}
|
||||
|
||||
#else // HAVE_OPENCV_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class VideoFrameSource_GPU : public FrameSource
|
||||
{
|
||||
public:
|
||||
VideoFrameSource_GPU(const string& fileName);
|
||||
|
||||
void nextFrame(OutputArray frame);
|
||||
void reset();
|
||||
|
||||
private:
|
||||
string fileName_;
|
||||
VideoReader_GPU reader_;
|
||||
GpuMat frame_;
|
||||
};
|
||||
|
||||
VideoFrameSource_GPU::VideoFrameSource_GPU(const string& fileName) : fileName_(fileName)
|
||||
{
|
||||
reset();
|
||||
}
|
||||
|
||||
void VideoFrameSource_GPU::nextFrame(OutputArray _frame)
|
||||
{
|
||||
if (_frame.kind() == _InputArray::GPU_MAT)
|
||||
{
|
||||
bool res = reader_.read(_frame.getGpuMatRef());
|
||||
if (!res)
|
||||
_frame.release();
|
||||
}
|
||||
else
|
||||
{
|
||||
bool res = reader_.read(frame_);
|
||||
if (!res)
|
||||
_frame.release();
|
||||
else
|
||||
arrCopy(frame_, _frame);
|
||||
}
|
||||
}
|
||||
|
||||
void VideoFrameSource_GPU::reset()
|
||||
{
|
||||
reader_.close();
|
||||
reader_.open(fileName_);
|
||||
CV_Assert( reader_.isOpened() );
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<FrameSource> cv::superres::createFrameSource_Video_GPU(const string& fileName)
|
||||
{
|
||||
return new VideoFrameSource(fileName);
|
||||
}
|
||||
|
||||
#endif // HAVE_OPENCV_GPU
|
||||
273
modules/superres/src/input_array_utility.cpp
Normal file
273
modules/superres/src/input_array_utility.cpp
Normal file
@ -0,0 +1,273 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
|
||||
Mat cv::superres::arrGetMat(InputArray arr, Mat& buf)
|
||||
{
|
||||
switch (arr.kind())
|
||||
{
|
||||
case _InputArray::GPU_MAT:
|
||||
arr.getGpuMat().download(buf);
|
||||
return buf;
|
||||
|
||||
case _InputArray::OPENGL_BUFFER:
|
||||
arr.getOGlBuffer().copyTo(buf);
|
||||
return buf;
|
||||
|
||||
case _InputArray::OPENGL_TEXTURE:
|
||||
arr.getOGlTexture2D().copyTo(buf);
|
||||
return buf;
|
||||
|
||||
default:
|
||||
return arr.getMat();
|
||||
}
|
||||
}
|
||||
|
||||
GpuMat cv::superres::arrGetGpuMat(InputArray arr, GpuMat& buf)
|
||||
{
|
||||
switch (arr.kind())
|
||||
{
|
||||
case _InputArray::GPU_MAT:
|
||||
return arr.getGpuMat();
|
||||
|
||||
case _InputArray::OPENGL_BUFFER:
|
||||
arr.getOGlBuffer().copyTo(buf);
|
||||
return buf;
|
||||
|
||||
case _InputArray::OPENGL_TEXTURE:
|
||||
arr.getOGlTexture2D().copyTo(buf);
|
||||
return buf;
|
||||
|
||||
default:
|
||||
buf.upload(arr.getMat());
|
||||
return buf;
|
||||
}
|
||||
}
|
||||
|
||||
namespace
|
||||
{
|
||||
void mat2mat(InputArray src, OutputArray dst)
|
||||
{
|
||||
src.getMat().copyTo(dst);
|
||||
}
|
||||
void arr2buf(InputArray src, OutputArray dst)
|
||||
{
|
||||
dst.getOGlBufferRef().copyFrom(src);
|
||||
}
|
||||
void arr2tex(InputArray src, OutputArray dst)
|
||||
{
|
||||
dst.getOGlTexture2D().copyFrom(src);
|
||||
}
|
||||
void mat2gpu(InputArray src, OutputArray dst)
|
||||
{
|
||||
dst.getGpuMatRef().upload(src.getMat());
|
||||
}
|
||||
void buf2arr(InputArray src, OutputArray dst)
|
||||
{
|
||||
src.getOGlBuffer().copyTo(dst);
|
||||
}
|
||||
void tex2arr(InputArray src, OutputArray dst)
|
||||
{
|
||||
src.getOGlTexture2D().copyTo(dst);
|
||||
}
|
||||
void gpu2mat(InputArray src, OutputArray dst)
|
||||
{
|
||||
GpuMat d = src.getGpuMat();
|
||||
dst.create(d.size(), d.type());
|
||||
Mat m = dst.getMat();
|
||||
d.download(m);
|
||||
}
|
||||
void gpu2gpu(InputArray src, OutputArray dst)
|
||||
{
|
||||
src.getGpuMat().copyTo(dst.getGpuMatRef());
|
||||
}
|
||||
}
|
||||
|
||||
void cv::superres::arrCopy(InputArray src, OutputArray dst)
|
||||
{
|
||||
typedef void (*func_t)(InputArray src, OutputArray dst);
|
||||
static const func_t funcs[10][10] =
|
||||
{
|
||||
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, mat2mat, arr2buf, arr2tex, mat2gpu},
|
||||
{0, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr, buf2arr},
|
||||
{0, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr, tex2arr},
|
||||
{0, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, gpu2mat, arr2buf, arr2tex, gpu2gpu}
|
||||
};
|
||||
|
||||
const int src_kind = src.kind() >> _InputArray::KIND_SHIFT;
|
||||
const int dst_kind = dst.kind() >> _InputArray::KIND_SHIFT;
|
||||
|
||||
CV_DbgAssert( src_kind >= 0 && src_kind < 10 );
|
||||
CV_DbgAssert( dst_kind >= 0 && dst_kind < 10 );
|
||||
|
||||
const func_t func = funcs[src_kind][dst_kind];
|
||||
CV_DbgAssert( func != 0 );
|
||||
|
||||
func(src, dst);
|
||||
}
|
||||
|
||||
namespace
|
||||
{
|
||||
void convertToCn(InputArray src, OutputArray dst, int cn)
|
||||
{
|
||||
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
|
||||
CV_Assert( cn == 1 || cn == 3 || cn == 4 );
|
||||
|
||||
static const int codes[5][5] =
|
||||
{
|
||||
{-1, -1, -1, -1, -1},
|
||||
{-1, -1, -1, COLOR_GRAY2BGR, COLOR_GRAY2BGRA},
|
||||
{-1, -1, -1, -1, -1},
|
||||
{-1, COLOR_BGR2GRAY, -1, -1, COLOR_BGR2BGRA},
|
||||
{-1, COLOR_BGRA2GRAY, -1, COLOR_BGRA2BGR, -1},
|
||||
};
|
||||
|
||||
const int code = codes[src.channels()][cn];
|
||||
CV_DbgAssert( code >= 0 );
|
||||
|
||||
switch (src.kind())
|
||||
{
|
||||
case _InputArray::GPU_MAT:
|
||||
#ifdef HAVE_OPENCV_GPU
|
||||
gpu::cvtColor(src.getGpuMat(), dst.getGpuMatRef(), code, cn);
|
||||
#else
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
#endif
|
||||
break;
|
||||
|
||||
default:
|
||||
cvtColor(src, dst, code, cn);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
void convertToDepth(InputArray src, OutputArray dst, int depth)
|
||||
{
|
||||
CV_Assert( src.depth() <= CV_64F );
|
||||
CV_Assert( depth == CV_8U || depth == CV_32F );
|
||||
|
||||
static const double maxVals[] =
|
||||
{
|
||||
numeric_limits<uchar>::max(),
|
||||
numeric_limits<schar>::max(),
|
||||
numeric_limits<ushort>::max(),
|
||||
numeric_limits<short>::max(),
|
||||
numeric_limits<int>::max(),
|
||||
1.0,
|
||||
1.0,
|
||||
};
|
||||
|
||||
const double scale = maxVals[depth] / maxVals[src.depth()];
|
||||
|
||||
switch (src.kind())
|
||||
{
|
||||
case _InputArray::GPU_MAT:
|
||||
src.getGpuMat().convertTo(dst.getGpuMatRef(), depth, scale);
|
||||
break;
|
||||
|
||||
default:
|
||||
src.getMat().convertTo(dst, depth, scale);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Mat cv::superres::convertToType(const Mat& src, int type, Mat& buf0, Mat& buf1)
|
||||
{
|
||||
if (src.type() == type)
|
||||
return src;
|
||||
|
||||
const int depth = CV_MAT_DEPTH(type);
|
||||
const int cn = CV_MAT_CN(type);
|
||||
|
||||
if (src.depth() == depth)
|
||||
{
|
||||
convertToCn(src, buf0, cn);
|
||||
return buf0;
|
||||
}
|
||||
|
||||
if (src.channels() == cn)
|
||||
{
|
||||
convertToDepth(src, buf1, depth);
|
||||
return buf1;
|
||||
}
|
||||
|
||||
convertToCn(src, buf0, cn);
|
||||
convertToDepth(buf0, buf1, depth);
|
||||
return buf1;
|
||||
}
|
||||
|
||||
GpuMat cv::superres::convertToType(const GpuMat& src, int type, GpuMat& buf0, GpuMat& buf1)
|
||||
{
|
||||
if (src.type() == type)
|
||||
return src;
|
||||
|
||||
const int depth = CV_MAT_DEPTH(type);
|
||||
const int cn = CV_MAT_CN(type);
|
||||
|
||||
if (src.depth() == depth)
|
||||
{
|
||||
convertToCn(src, buf0, cn);
|
||||
return buf0;
|
||||
}
|
||||
|
||||
if (src.channels() == cn)
|
||||
{
|
||||
convertToDepth(src, buf1, depth);
|
||||
return buf1;
|
||||
}
|
||||
|
||||
convertToCn(src, buf0, cn);
|
||||
convertToDepth(buf0, buf1, depth);
|
||||
return buf1;
|
||||
}
|
||||
63
modules/superres/src/input_array_utility.hpp
Normal file
63
modules/superres/src/input_array_utility.hpp
Normal file
@ -0,0 +1,63 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__
|
||||
#define __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__
|
||||
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/core/gpumat.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace superres
|
||||
{
|
||||
CV_EXPORTS Mat arrGetMat(InputArray arr, Mat& buf);
|
||||
CV_EXPORTS gpu::GpuMat arrGetGpuMat(InputArray arr, gpu::GpuMat& buf);
|
||||
|
||||
CV_EXPORTS void arrCopy(InputArray src, OutputArray dst);
|
||||
|
||||
CV_EXPORTS Mat convertToType(const Mat& src, int type, Mat& buf0, Mat& buf1);
|
||||
CV_EXPORTS gpu::GpuMat convertToType(const gpu::GpuMat& src, int type, gpu::GpuMat& buf0, gpu::GpuMat& buf1);
|
||||
}
|
||||
}
|
||||
|
||||
#endif // __OPENCV_SUPERRES_INPUT_ARRAY_UTILITY_HPP__
|
||||
721
modules/superres/src/optical_flow.cpp
Normal file
721
modules/superres/src/optical_flow.cpp
Normal file
@ -0,0 +1,721 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
using namespace cv::superres;
|
||||
using namespace cv::superres::detail;
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// CpuOpticalFlow
|
||||
|
||||
namespace
|
||||
{
|
||||
class CpuOpticalFlow : public DenseOpticalFlowExt
|
||||
{
|
||||
public:
|
||||
explicit CpuOpticalFlow(int work_type);
|
||||
|
||||
void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2);
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
virtual void impl(const Mat& input0, const Mat& input1, OutputArray dst) = 0;
|
||||
|
||||
private:
|
||||
int work_type_;
|
||||
Mat buf_[6];
|
||||
Mat flow_;
|
||||
Mat flows_[2];
|
||||
};
|
||||
|
||||
CpuOpticalFlow::CpuOpticalFlow(int work_type) : work_type_(work_type)
|
||||
{
|
||||
}
|
||||
|
||||
void CpuOpticalFlow::calc(InputArray _frame0, InputArray _frame1, OutputArray _flow1, OutputArray _flow2)
|
||||
{
|
||||
Mat frame0 = arrGetMat(_frame0, buf_[0]);
|
||||
Mat frame1 = arrGetMat(_frame1, buf_[1]);
|
||||
|
||||
CV_Assert( frame1.type() == frame0.type() );
|
||||
CV_Assert( frame1.size() == frame0.size() );
|
||||
|
||||
Mat input0 = convertToType(frame0, work_type_, buf_[2], buf_[3]);
|
||||
Mat input1 = convertToType(frame1, work_type_, buf_[4], buf_[5]);
|
||||
|
||||
if (!_flow2.needed() && _flow1.kind() < _InputArray::OPENGL_BUFFER)
|
||||
{
|
||||
impl(input0, input1, _flow1);
|
||||
return;
|
||||
}
|
||||
|
||||
impl(input0, input1, flow_);
|
||||
|
||||
if (!_flow2.needed())
|
||||
{
|
||||
arrCopy(flow_, _flow1);
|
||||
}
|
||||
else
|
||||
{
|
||||
split(flow_, flows_);
|
||||
|
||||
arrCopy(flows_[0], _flow1);
|
||||
arrCopy(flows_[1], _flow2);
|
||||
}
|
||||
}
|
||||
|
||||
void CpuOpticalFlow::collectGarbage()
|
||||
{
|
||||
for (int i = 0; i < 6; ++i)
|
||||
buf_[i].release();
|
||||
flow_.release();
|
||||
flows_[0].release();
|
||||
flows_[1].release();
|
||||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Farneback
|
||||
|
||||
namespace
|
||||
{
|
||||
class Farneback : public CpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
Farneback();
|
||||
|
||||
protected:
|
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst);
|
||||
|
||||
private:
|
||||
double pyrScale_;
|
||||
int numLevels_;
|
||||
int winSize_;
|
||||
int numIters_;
|
||||
int polyN_;
|
||||
double polySigma_;
|
||||
int flags_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(Farneback, "DenseOpticalFlowExt.Farneback",
|
||||
obj.info()->addParam(obj, "pyrScale", obj.pyrScale_);
|
||||
obj.info()->addParam(obj, "numLevels", obj.numLevels_);
|
||||
obj.info()->addParam(obj, "winSize", obj.winSize_);
|
||||
obj.info()->addParam(obj, "numIters", obj.numIters_);
|
||||
obj.info()->addParam(obj, "polyN", obj.polyN_);
|
||||
obj.info()->addParam(obj, "polySigma", obj.polySigma_);
|
||||
obj.info()->addParam(obj, "flags", obj.flags_));
|
||||
|
||||
Farneback::Farneback() : CpuOpticalFlow(CV_8UC1)
|
||||
{
|
||||
pyrScale_ = 0.5;
|
||||
numLevels_ = 5;
|
||||
winSize_ = 13;
|
||||
numIters_ = 10;
|
||||
polyN_ = 5;
|
||||
polySigma_ = 1.1;
|
||||
flags_ = 0;
|
||||
}
|
||||
|
||||
void Farneback::impl(const Mat& input0, const Mat& input1, OutputArray dst)
|
||||
{
|
||||
calcOpticalFlowFarneback(input0, input1, dst, pyrScale_, numLevels_, winSize_, numIters_, polyN_, polySigma_, flags_);
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback()
|
||||
{
|
||||
return new Farneback;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Simple
|
||||
|
||||
namespace
|
||||
{
|
||||
class Simple : public CpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
Simple();
|
||||
|
||||
protected:
|
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst);
|
||||
|
||||
private:
|
||||
int layers_;
|
||||
int averagingBlockSize_;
|
||||
int maxFlow_;
|
||||
double sigmaDist_;
|
||||
double sigmaColor_;
|
||||
int postProcessWindow_;
|
||||
double sigmaDistFix_;
|
||||
double sigmaColorFix_;
|
||||
double occThr_;
|
||||
int upscaleAveragingRadius_;
|
||||
double upscaleSigmaDist_;
|
||||
double upscaleSigmaColor_;
|
||||
double speedUpThr_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(Simple, "DenseOpticalFlowExt.Simple",
|
||||
obj.info()->addParam(obj, "layers", obj.layers_);
|
||||
obj.info()->addParam(obj, "averagingBlockSize", obj.averagingBlockSize_);
|
||||
obj.info()->addParam(obj, "maxFlow", obj.maxFlow_);
|
||||
obj.info()->addParam(obj, "sigmaDist", obj.sigmaDist_);
|
||||
obj.info()->addParam(obj, "sigmaColor", obj.sigmaColor_);
|
||||
obj.info()->addParam(obj, "postProcessWindow", obj.postProcessWindow_);
|
||||
obj.info()->addParam(obj, "sigmaDistFix", obj.sigmaDistFix_);
|
||||
obj.info()->addParam(obj, "sigmaColorFix", obj.sigmaColorFix_);
|
||||
obj.info()->addParam(obj, "occThr", obj.occThr_);
|
||||
obj.info()->addParam(obj, "upscaleAveragingRadius", obj.upscaleAveragingRadius_);
|
||||
obj.info()->addParam(obj, "upscaleSigmaDist", obj.upscaleSigmaDist_);
|
||||
obj.info()->addParam(obj, "upscaleSigmaColor", obj.upscaleSigmaColor_);
|
||||
obj.info()->addParam(obj, "speedUpThr", obj.speedUpThr_));
|
||||
|
||||
Simple::Simple() : CpuOpticalFlow(CV_8UC3)
|
||||
{
|
||||
layers_ = 3;
|
||||
averagingBlockSize_ = 2;
|
||||
maxFlow_ = 4;
|
||||
sigmaDist_ = 4.1;
|
||||
sigmaColor_ = 25.5;
|
||||
postProcessWindow_ = 18;
|
||||
sigmaDistFix_ = 55.0;
|
||||
sigmaColorFix_ = 25.5;
|
||||
occThr_ = 0.35;
|
||||
upscaleAveragingRadius_ = 18;
|
||||
upscaleSigmaDist_ = 55.0;
|
||||
upscaleSigmaColor_ = 25.5;
|
||||
speedUpThr_ = 10;
|
||||
}
|
||||
|
||||
void Simple::impl(const Mat& _input0, const Mat& _input1, OutputArray dst)
|
||||
{
|
||||
Mat input0 = _input0;
|
||||
Mat input1 = _input1;
|
||||
calcOpticalFlowSF(input0, input1, dst.getMatRef(),
|
||||
layers_,
|
||||
averagingBlockSize_,
|
||||
maxFlow_,
|
||||
sigmaDist_,
|
||||
sigmaColor_,
|
||||
postProcessWindow_,
|
||||
sigmaDistFix_,
|
||||
sigmaColorFix_,
|
||||
occThr_,
|
||||
upscaleAveragingRadius_,
|
||||
upscaleSigmaDist_,
|
||||
upscaleSigmaColor_,
|
||||
speedUpThr_);
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Simple()
|
||||
{
|
||||
return new Simple;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// DualTVL1
|
||||
|
||||
namespace
|
||||
{
|
||||
class DualTVL1 : public CpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
DualTVL1();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const Mat& input0, const Mat& input1, OutputArray dst);
|
||||
|
||||
private:
|
||||
double tau_;
|
||||
double lambda_;
|
||||
double theta_;
|
||||
int nscales_;
|
||||
int warps_;
|
||||
double epsilon_;
|
||||
int iterations_;
|
||||
bool useInitialFlow_;
|
||||
|
||||
Ptr<DenseOpticalFlow> alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(DualTVL1, "DenseOpticalFlowExt.DualTVL1",
|
||||
obj.info()->addParam(obj, "tau", obj.tau_);
|
||||
obj.info()->addParam(obj, "lambda", obj.lambda_);
|
||||
obj.info()->addParam(obj, "theta", obj.theta_);
|
||||
obj.info()->addParam(obj, "nscales", obj.nscales_);
|
||||
obj.info()->addParam(obj, "warps", obj.warps_);
|
||||
obj.info()->addParam(obj, "epsilon", obj.epsilon_);
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_);
|
||||
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
|
||||
|
||||
DualTVL1::DualTVL1() : CpuOpticalFlow(CV_8UC1)
|
||||
{
|
||||
alg_ = cv::createOptFlow_DualTVL1();
|
||||
tau_ = alg_->getDouble("tau");
|
||||
lambda_ = alg_->getDouble("lambda");
|
||||
theta_ = alg_->getDouble("theta");
|
||||
nscales_ = alg_->getInt("nscales");
|
||||
warps_ = alg_->getInt("warps");
|
||||
epsilon_ = alg_->getDouble("epsilon");
|
||||
iterations_ = alg_->getInt("iterations");
|
||||
useInitialFlow_ = alg_->getBool("useInitialFlow");
|
||||
}
|
||||
|
||||
void DualTVL1::impl(const Mat& input0, const Mat& input1, OutputArray dst)
|
||||
{
|
||||
alg_->set("tau", tau_);
|
||||
alg_->set("lambda", lambda_);
|
||||
alg_->set("theta", theta_);
|
||||
alg_->set("nscales", nscales_);
|
||||
alg_->set("warps", warps_);
|
||||
alg_->set("epsilon", epsilon_);
|
||||
alg_->set("iterations", iterations_);
|
||||
alg_->set("useInitialFlow", useInitialFlow_);
|
||||
|
||||
alg_->calc(input0, input1, dst);
|
||||
}
|
||||
|
||||
void DualTVL1::collectGarbage()
|
||||
{
|
||||
alg_->collectGarbage();
|
||||
CpuOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1()
|
||||
{
|
||||
return new DualTVL1;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// GpuOpticalFlow
|
||||
|
||||
#ifndef HAVE_OPENCV_GPU
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback_GPU()
|
||||
{
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<DenseOpticalFlowExt>();
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_GPU()
|
||||
{
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<DenseOpticalFlowExt>();
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Brox_GPU()
|
||||
{
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<DenseOpticalFlowExt>();
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_PyrLK_GPU()
|
||||
{
|
||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||
return Ptr<DenseOpticalFlowExt>();
|
||||
}
|
||||
|
||||
#else // HAVE_OPENCV_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class GpuOpticalFlow : public DenseOpticalFlowExt
|
||||
{
|
||||
public:
|
||||
explicit GpuOpticalFlow(int work_type);
|
||||
|
||||
void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2);
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
virtual void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2) = 0;
|
||||
|
||||
private:
|
||||
int work_type_;
|
||||
GpuMat buf_[6];
|
||||
GpuMat u_, v_, flow_;
|
||||
};
|
||||
|
||||
GpuOpticalFlow::GpuOpticalFlow(int work_type) : work_type_(work_type)
|
||||
{
|
||||
}
|
||||
|
||||
void GpuOpticalFlow::calc(InputArray _frame0, InputArray _frame1, OutputArray _flow1, OutputArray _flow2)
|
||||
{
|
||||
GpuMat frame0 = arrGetGpuMat(_frame0, buf_[0]);
|
||||
GpuMat frame1 = arrGetGpuMat(_frame1, buf_[1]);
|
||||
|
||||
CV_Assert( frame1.type() == frame0.type() );
|
||||
CV_Assert( frame1.size() == frame0.size() );
|
||||
|
||||
GpuMat input0 = convertToType(frame0, work_type_, buf_[2], buf_[3]);
|
||||
GpuMat input1 = convertToType(frame1, work_type_, buf_[4], buf_[5]);
|
||||
|
||||
if (_flow2.needed() && _flow1.kind() == _InputArray::GPU_MAT && _flow2.kind() == _InputArray::GPU_MAT)
|
||||
{
|
||||
impl(input0, input1, _flow1.getGpuMatRef(), _flow2.getGpuMatRef());
|
||||
return;
|
||||
}
|
||||
|
||||
impl(input0, input1, u_, v_);
|
||||
|
||||
if (_flow2.needed())
|
||||
{
|
||||
arrCopy(u_, _flow1);
|
||||
arrCopy(v_, _flow2);
|
||||
}
|
||||
else
|
||||
{
|
||||
GpuMat src[] = {u_, v_};
|
||||
merge(src, 2, flow_);
|
||||
arrCopy(flow_, _flow1);
|
||||
}
|
||||
}
|
||||
|
||||
void GpuOpticalFlow::collectGarbage()
|
||||
{
|
||||
for (int i = 0; i < 6; ++i)
|
||||
buf_[i].release();
|
||||
u_.release();
|
||||
v_.release();
|
||||
flow_.release();
|
||||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Brox_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class Brox_GPU : public GpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
Brox_GPU();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2);
|
||||
|
||||
private:
|
||||
double alpha_;
|
||||
double gamma_;
|
||||
double scaleFactor_;
|
||||
int innerIterations_;
|
||||
int outerIterations_;
|
||||
int solverIterations_;
|
||||
|
||||
BroxOpticalFlow alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(Brox_GPU, "DenseOpticalFlowExt.Brox_GPU",
|
||||
obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Flow smoothness");
|
||||
obj.info()->addParam(obj, "gamma", obj.gamma_, false, 0, 0, "Gradient constancy importance");
|
||||
obj.info()->addParam(obj, "scaleFactor", obj.scaleFactor_, false, 0, 0, "Pyramid scale factor");
|
||||
obj.info()->addParam(obj, "innerIterations", obj.innerIterations_, false, 0, 0, "Number of lagged non-linearity iterations (inner loop)");
|
||||
obj.info()->addParam(obj, "outerIterations", obj.outerIterations_, false, 0, 0, "Number of warping iterations (number of pyramid levels)");
|
||||
obj.info()->addParam(obj, "solverIterations", obj.solverIterations_, false, 0, 0, "Number of linear system solver iterations"));
|
||||
|
||||
Brox_GPU::Brox_GPU() : GpuOpticalFlow(CV_32FC1), alg_(0.197f, 50.0f, 0.8f, 10, 77, 10)
|
||||
{
|
||||
alpha_ = alg_.alpha;
|
||||
gamma_ = alg_.gamma;
|
||||
scaleFactor_ = alg_.scale_factor;
|
||||
innerIterations_ = alg_.inner_iterations;
|
||||
outerIterations_ = alg_.outer_iterations;
|
||||
solverIterations_ = alg_.solver_iterations;
|
||||
}
|
||||
|
||||
void Brox_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2)
|
||||
{
|
||||
alg_.alpha = static_cast<float>(alpha_);
|
||||
alg_.gamma = static_cast<float>(gamma_);
|
||||
alg_.scale_factor = static_cast<float>(scaleFactor_);
|
||||
alg_.inner_iterations = innerIterations_;
|
||||
alg_.outer_iterations = outerIterations_;
|
||||
alg_.solver_iterations = solverIterations_;
|
||||
|
||||
alg_(input0, input1, dst1, dst2);
|
||||
}
|
||||
|
||||
void Brox_GPU::collectGarbage()
|
||||
{
|
||||
alg_.buf.release();
|
||||
GpuOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Brox_GPU()
|
||||
{
|
||||
return new Brox_GPU;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// PyrLK_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class PyrLK_GPU : public GpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
PyrLK_GPU();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2);
|
||||
|
||||
private:
|
||||
int winSize_;
|
||||
int maxLevel_;
|
||||
int iterations_;
|
||||
|
||||
PyrLKOpticalFlow alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(PyrLK_GPU, "DenseOpticalFlowExt.PyrLK_GPU",
|
||||
obj.info()->addParam(obj, "winSize", obj.winSize_);
|
||||
obj.info()->addParam(obj, "maxLevel", obj.maxLevel_);
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_));
|
||||
|
||||
PyrLK_GPU::PyrLK_GPU() : GpuOpticalFlow(CV_8UC1)
|
||||
{
|
||||
winSize_ = alg_.winSize.width;
|
||||
maxLevel_ = alg_.maxLevel;
|
||||
iterations_ = alg_.iters;
|
||||
}
|
||||
|
||||
void PyrLK_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2)
|
||||
{
|
||||
alg_.winSize.width = winSize_;
|
||||
alg_.winSize.height = winSize_;
|
||||
alg_.maxLevel = maxLevel_;
|
||||
alg_.iters = iterations_;
|
||||
|
||||
alg_.dense(input0, input1, dst1, dst2);
|
||||
}
|
||||
|
||||
void PyrLK_GPU::collectGarbage()
|
||||
{
|
||||
alg_.releaseMemory();
|
||||
GpuOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_PyrLK_GPU()
|
||||
{
|
||||
return new PyrLK_GPU;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// Farneback_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class Farneback_GPU : public GpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
Farneback_GPU();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2);
|
||||
|
||||
private:
|
||||
double pyrScale_;
|
||||
int numLevels_;
|
||||
int winSize_;
|
||||
int numIters_;
|
||||
int polyN_;
|
||||
double polySigma_;
|
||||
int flags_;
|
||||
|
||||
FarnebackOpticalFlow alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(Farneback_GPU, "DenseOpticalFlowExt.Farneback_GPU",
|
||||
obj.info()->addParam(obj, "pyrScale", obj.pyrScale_);
|
||||
obj.info()->addParam(obj, "numLevels", obj.numLevels_);
|
||||
obj.info()->addParam(obj, "winSize", obj.winSize_);
|
||||
obj.info()->addParam(obj, "numIters", obj.numIters_);
|
||||
obj.info()->addParam(obj, "polyN", obj.polyN_);
|
||||
obj.info()->addParam(obj, "polySigma", obj.polySigma_);
|
||||
obj.info()->addParam(obj, "flags", obj.flags_));
|
||||
|
||||
Farneback_GPU::Farneback_GPU() : GpuOpticalFlow(CV_8UC1)
|
||||
{
|
||||
pyrScale_ = alg_.pyrScale;
|
||||
numLevels_ = alg_.numLevels;
|
||||
winSize_ = alg_.winSize;
|
||||
numIters_ = alg_.numIters;
|
||||
polyN_ = alg_.polyN;
|
||||
polySigma_ = alg_.polySigma;
|
||||
flags_ = alg_.flags;
|
||||
}
|
||||
|
||||
void Farneback_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2)
|
||||
{
|
||||
alg_.pyrScale = pyrScale_;
|
||||
alg_.numLevels = numLevels_;
|
||||
alg_.winSize = winSize_;
|
||||
alg_.numIters = numIters_;
|
||||
alg_.polyN = polyN_;
|
||||
alg_.polySigma = polySigma_;
|
||||
alg_.flags = flags_;
|
||||
|
||||
alg_(input0, input1, dst1, dst2);
|
||||
}
|
||||
|
||||
void Farneback_GPU::collectGarbage()
|
||||
{
|
||||
alg_.releaseMemory();
|
||||
GpuOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_Farneback_GPU()
|
||||
{
|
||||
return new Farneback_GPU;
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
// DualTVL1_GPU
|
||||
|
||||
namespace
|
||||
{
|
||||
class DualTVL1_GPU : public GpuOpticalFlow
|
||||
{
|
||||
public:
|
||||
AlgorithmInfo* info() const;
|
||||
|
||||
DualTVL1_GPU();
|
||||
|
||||
void collectGarbage();
|
||||
|
||||
protected:
|
||||
void impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2);
|
||||
|
||||
private:
|
||||
double tau_;
|
||||
double lambda_;
|
||||
double theta_;
|
||||
int nscales_;
|
||||
int warps_;
|
||||
double epsilon_;
|
||||
int iterations_;
|
||||
bool useInitialFlow_;
|
||||
|
||||
OpticalFlowDual_TVL1_GPU alg_;
|
||||
};
|
||||
|
||||
CV_INIT_ALGORITHM(DualTVL1_GPU, "DenseOpticalFlowExt.DualTVL1_GPU",
|
||||
obj.info()->addParam(obj, "tau", obj.tau_);
|
||||
obj.info()->addParam(obj, "lambda", obj.lambda_);
|
||||
obj.info()->addParam(obj, "theta", obj.theta_);
|
||||
obj.info()->addParam(obj, "nscales", obj.nscales_);
|
||||
obj.info()->addParam(obj, "warps", obj.warps_);
|
||||
obj.info()->addParam(obj, "epsilon", obj.epsilon_);
|
||||
obj.info()->addParam(obj, "iterations", obj.iterations_);
|
||||
obj.info()->addParam(obj, "useInitialFlow", obj.useInitialFlow_));
|
||||
|
||||
DualTVL1_GPU::DualTVL1_GPU() : GpuOpticalFlow(CV_8UC1)
|
||||
{
|
||||
tau_ = alg_.tau;
|
||||
lambda_ = alg_.lambda;
|
||||
theta_ = alg_.theta;
|
||||
nscales_ = alg_.nscales;
|
||||
warps_ = alg_.warps;
|
||||
epsilon_ = alg_.epsilon;
|
||||
iterations_ = alg_.iterations;
|
||||
useInitialFlow_ = alg_.useInitialFlow;
|
||||
}
|
||||
|
||||
void DualTVL1_GPU::impl(const GpuMat& input0, const GpuMat& input1, GpuMat& dst1, GpuMat& dst2)
|
||||
{
|
||||
alg_.tau = tau_;
|
||||
alg_.lambda = lambda_;
|
||||
alg_.theta = theta_;
|
||||
alg_.nscales = nscales_;
|
||||
alg_.warps = warps_;
|
||||
alg_.epsilon = epsilon_;
|
||||
alg_.iterations = iterations_;
|
||||
alg_.useInitialFlow = useInitialFlow_;
|
||||
|
||||
alg_(input0, input1, dst1, dst2);
|
||||
}
|
||||
|
||||
void DualTVL1_GPU::collectGarbage()
|
||||
{
|
||||
alg_.collectGarbage();
|
||||
GpuOpticalFlow::collectGarbage();
|
||||
}
|
||||
}
|
||||
|
||||
Ptr<DenseOpticalFlowExt> cv::superres::createOptFlow_DualTVL1_GPU()
|
||||
{
|
||||
return new DualTVL1_GPU;
|
||||
}
|
||||
|
||||
#endif // HAVE_OPENCV_GPU
|
||||
43
modules/superres/src/precomp.cpp
Normal file
43
modules/superres/src/precomp.cpp
Normal file
@ -0,0 +1,43 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
78
modules/superres/src/precomp.hpp
Normal file
78
modules/superres/src/precomp.hpp
Normal file
@ -0,0 +1,78 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __OPENCV_PRECOMP_H__
|
||||
#define __OPENCV_PRECOMP_H__
|
||||
|
||||
#include <vector>
|
||||
#include <limits>
|
||||
|
||||
#ifdef HAVE_CVCONFIG_H
|
||||
#include "cvconfig.h"
|
||||
#endif
|
||||
|
||||
#include "opencv2/opencv_modules.hpp"
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/core/gpumat.hpp"
|
||||
#include "opencv2/core/opengl_interop.hpp"
|
||||
#include "opencv2/core/internal.hpp"
|
||||
#include "opencv2/imgproc/imgproc.hpp"
|
||||
#include "opencv2/video/tracking.hpp"
|
||||
|
||||
#ifdef HAVE_OPENCV_GPU
|
||||
#include "opencv2/gpu/gpu.hpp"
|
||||
#ifdef HAVE_CUDA
|
||||
#include "opencv2/gpu/stream_accessor.hpp"
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_OPENCV_HIGHGUI
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
#endif
|
||||
|
||||
#include "opencv2/superres/superres.hpp"
|
||||
#include "opencv2/superres/optical_flow.hpp"
|
||||
#include "input_array_utility.hpp"
|
||||
|
||||
#include "ring_buffer.hpp"
|
||||
|
||||
#endif /* __OPENCV_PRECOMP_H__ */
|
||||
79
modules/superres/src/ring_buffer.hpp
Normal file
79
modules/superres/src/ring_buffer.hpp
Normal file
@ -0,0 +1,79 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __RING_BUFFER_HPP__
|
||||
#define __RING_BUFFER_HPP__
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
namespace cv
|
||||
{
|
||||
namespace superres
|
||||
{
|
||||
namespace detail
|
||||
{
|
||||
template <typename T, class A>
|
||||
inline const T& at(int index, const std::vector<T, A>& items)
|
||||
{
|
||||
const int len = static_cast<int>(items.size());
|
||||
if (index < 0)
|
||||
index -= ((index - len + 1) / len) * len;
|
||||
if (index >= len)
|
||||
index %= len;
|
||||
return items[index];
|
||||
}
|
||||
|
||||
template <typename T, class A>
|
||||
inline T& at(int index, std::vector<T, A>& items)
|
||||
{
|
||||
const int len = static_cast<int>(items.size());
|
||||
if (index < 0)
|
||||
index -= ((index - len + 1) / len) * len;
|
||||
if (index >= len)
|
||||
index %= len;
|
||||
return items[index];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#endif // __RING_BUFFER_HPP__
|
||||
85
modules/superres/src/super_resolution.cpp
Normal file
85
modules/superres/src/super_resolution.cpp
Normal file
@ -0,0 +1,85 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::superres;
|
||||
|
||||
bool cv::superres::initModule_superres()
|
||||
{
|
||||
return !createSuperResolution_BTVL1().empty();
|
||||
}
|
||||
|
||||
cv::superres::SuperResolution::SuperResolution()
|
||||
{
|
||||
frameSource_ = createFrameSource_Empty();
|
||||
firstCall_ = true;
|
||||
}
|
||||
|
||||
void cv::superres::SuperResolution::setInput(const Ptr<FrameSource>& frameSource)
|
||||
{
|
||||
frameSource_ = frameSource;
|
||||
firstCall_ = true;
|
||||
}
|
||||
|
||||
void cv::superres::SuperResolution::nextFrame(OutputArray frame)
|
||||
{
|
||||
if (firstCall_)
|
||||
{
|
||||
initImpl(frameSource_);
|
||||
firstCall_ = false;
|
||||
}
|
||||
|
||||
processImpl(frameSource_, frame);
|
||||
}
|
||||
|
||||
void cv::superres::SuperResolution::reset()
|
||||
{
|
||||
frameSource_->reset();
|
||||
firstCall_ = true;
|
||||
}
|
||||
|
||||
void cv::superres::SuperResolution::collectGarbage()
|
||||
{
|
||||
}
|
||||
3
modules/superres/test/test_main.cpp
Normal file
3
modules/superres/test/test_main.cpp
Normal file
@ -0,0 +1,3 @@
|
||||
#include "test_precomp.hpp"
|
||||
|
||||
CV_TEST_MAIN("superres")
|
||||
1
modules/superres/test/test_precomp.cpp
Normal file
1
modules/superres/test/test_precomp.cpp
Normal file
@ -0,0 +1 @@
|
||||
#include "test_precomp.hpp"
|
||||
23
modules/superres/test/test_precomp.hpp
Normal file
23
modules/superres/test/test_precomp.hpp
Normal file
@ -0,0 +1,23 @@
|
||||
#ifdef __GNUC__
|
||||
# pragma GCC diagnostic ignored "-Wmissing-declarations"
|
||||
# if defined __clang__ || defined __APPLE__
|
||||
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
|
||||
# pragma GCC diagnostic ignored "-Wextra"
|
||||
# endif
|
||||
#endif
|
||||
|
||||
#ifndef __OPENCV_TEST_PRECOMP_HPP__
|
||||
#define __OPENCV_TEST_PRECOMP_HPP__
|
||||
|
||||
#ifdef HAVE_CVCONFIG_H
|
||||
#include "cvconfig.h"
|
||||
#endif
|
||||
|
||||
#include "opencv2/opencv_modules.hpp"
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/imgproc/imgproc.hpp"
|
||||
#include "opencv2/ts/ts.hpp"
|
||||
#include "opencv2/superres/superres.hpp"
|
||||
#include "input_array_utility.hpp"
|
||||
|
||||
#endif
|
||||
236
modules/superres/test/test_superres.cpp
Normal file
236
modules/superres/test/test_superres.cpp
Normal file
@ -0,0 +1,236 @@
|
||||
#include "test_precomp.hpp"
|
||||
|
||||
class AllignedFrameSource : public cv::superres::FrameSource
|
||||
{
|
||||
public:
|
||||
AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
|
||||
|
||||
void nextFrame(cv::OutputArray frame);
|
||||
void reset();
|
||||
|
||||
private:
|
||||
cv::Ptr<cv::superres::FrameSource> base_;
|
||||
cv::Mat origFrame_;
|
||||
int scale_;
|
||||
};
|
||||
|
||||
AllignedFrameSource::AllignedFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
|
||||
base_(base), scale_(scale)
|
||||
{
|
||||
CV_Assert( !base_.empty() );
|
||||
}
|
||||
|
||||
void AllignedFrameSource::nextFrame(cv::OutputArray frame)
|
||||
{
|
||||
base_->nextFrame(origFrame_);
|
||||
|
||||
if (origFrame_.rows % scale_ == 0 && origFrame_.cols % scale_ == 0)
|
||||
{
|
||||
cv::superres::arrCopy(origFrame_, frame);
|
||||
}
|
||||
else
|
||||
{
|
||||
cv::Rect ROI(0, 0, (origFrame_.cols / scale_) * scale_, (origFrame_.rows / scale_) * scale_);
|
||||
cv::superres::arrCopy(origFrame_(ROI), frame);
|
||||
}
|
||||
}
|
||||
|
||||
void AllignedFrameSource::reset()
|
||||
{
|
||||
base_->reset();
|
||||
}
|
||||
|
||||
class DegradeFrameSource : public cv::superres::FrameSource
|
||||
{
|
||||
public:
|
||||
DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale);
|
||||
|
||||
void nextFrame(cv::OutputArray frame);
|
||||
void reset();
|
||||
|
||||
private:
|
||||
cv::Ptr<cv::superres::FrameSource> base_;
|
||||
cv::Mat origFrame_;
|
||||
cv::Mat blurred_;
|
||||
cv::Mat deg_;
|
||||
double iscale_;
|
||||
};
|
||||
|
||||
DegradeFrameSource::DegradeFrameSource(const cv::Ptr<cv::superres::FrameSource>& base, int scale) :
|
||||
base_(base), iscale_(1.0 / scale)
|
||||
{
|
||||
CV_Assert( !base_.empty() );
|
||||
}
|
||||
|
||||
void addGaussNoise(cv::Mat& image, double sigma)
|
||||
{
|
||||
cv::Mat noise(image.size(), CV_32FC(image.channels()));
|
||||
cvtest::TS::ptr()->get_rng().fill(noise, cv::RNG::NORMAL, 0.0, sigma);
|
||||
|
||||
cv::addWeighted(image, 1.0, noise, 1.0, 0.0, image, image.depth());
|
||||
}
|
||||
|
||||
void addSpikeNoise(cv::Mat& image, int frequency)
|
||||
{
|
||||
cv::Mat_<uchar> mask(image.size(), 0);
|
||||
|
||||
for (int y = 0; y < mask.rows; ++y)
|
||||
{
|
||||
for (int x = 0; x < mask.cols; ++x)
|
||||
{
|
||||
if (cvtest::TS::ptr()->get_rng().uniform(0, frequency) < 1)
|
||||
mask(y, x) = 255;
|
||||
}
|
||||
}
|
||||
|
||||
image.setTo(cv::Scalar::all(255), mask);
|
||||
}
|
||||
|
||||
void DegradeFrameSource::nextFrame(cv::OutputArray frame)
|
||||
{
|
||||
base_->nextFrame(origFrame_);
|
||||
|
||||
cv::GaussianBlur(origFrame_, blurred_, cv::Size(5, 5), 0);
|
||||
cv::resize(blurred_, deg_, cv::Size(), iscale_, iscale_, cv::INTER_NEAREST);
|
||||
|
||||
addGaussNoise(deg_, 10.0);
|
||||
addSpikeNoise(deg_, 500);
|
||||
|
||||
cv::superres::arrCopy(deg_, frame);
|
||||
}
|
||||
|
||||
void DegradeFrameSource::reset()
|
||||
{
|
||||
base_->reset();
|
||||
}
|
||||
|
||||
double MSSIM(const cv::Mat& i1, const cv::Mat& i2)
|
||||
{
|
||||
const double C1 = 6.5025;
|
||||
const double C2 = 58.5225;
|
||||
|
||||
const int depth = CV_32F;
|
||||
|
||||
cv::Mat I1, I2;
|
||||
i1.convertTo(I1, depth);
|
||||
i2.convertTo(I2, depth);
|
||||
|
||||
cv::Mat I2_2 = I2.mul(I2); // I2^2
|
||||
cv::Mat I1_2 = I1.mul(I1); // I1^2
|
||||
cv::Mat I1_I2 = I1.mul(I2); // I1 * I2
|
||||
|
||||
cv::Mat mu1, mu2;
|
||||
cv::GaussianBlur(I1, mu1, cv::Size(11, 11), 1.5);
|
||||
cv::GaussianBlur(I2, mu2, cv::Size(11, 11), 1.5);
|
||||
|
||||
cv::Mat mu1_2 = mu1.mul(mu1);
|
||||
cv::Mat mu2_2 = mu2.mul(mu2);
|
||||
cv::Mat mu1_mu2 = mu1.mul(mu2);
|
||||
|
||||
cv::Mat sigma1_2, sigma2_2, sigma12;
|
||||
|
||||
cv::GaussianBlur(I1_2, sigma1_2, cv::Size(11, 11), 1.5);
|
||||
sigma1_2 -= mu1_2;
|
||||
|
||||
cv::GaussianBlur(I2_2, sigma2_2, cv::Size(11, 11), 1.5);
|
||||
sigma2_2 -= mu2_2;
|
||||
|
||||
cv::GaussianBlur(I1_I2, sigma12, cv::Size(11, 11), 1.5);
|
||||
sigma12 -= mu1_mu2;
|
||||
|
||||
cv::Mat t1, t2;
|
||||
cv::Mat numerator;
|
||||
cv::Mat denominator;
|
||||
|
||||
// t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
|
||||
t1 = 2 * mu1_mu2 + C1;
|
||||
t2 = 2 * sigma12 + C2;
|
||||
numerator = t1.mul(t2);
|
||||
|
||||
// t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
|
||||
t1 = mu1_2 + mu2_2 + C1;
|
||||
t2 = sigma1_2 + sigma2_2 + C2;
|
||||
denominator = t1.mul(t2);
|
||||
|
||||
// ssim_map = numerator./denominator;
|
||||
cv::Mat ssim_map;
|
||||
cv::divide(numerator, denominator, ssim_map);
|
||||
|
||||
// mssim = average of ssim map
|
||||
cv::Scalar mssim = cv::mean(ssim_map);
|
||||
|
||||
if (i1.channels() == 1)
|
||||
return mssim[0];
|
||||
|
||||
return (mssim[0] + mssim[1] + mssim[3]) / 3;
|
||||
}
|
||||
|
||||
class SuperResolution : public testing::Test
|
||||
{
|
||||
public:
|
||||
void RunTest(cv::Ptr<cv::superres::SuperResolution> superRes);
|
||||
};
|
||||
|
||||
void SuperResolution::RunTest(cv::Ptr<cv::superres::SuperResolution> superRes)
|
||||
{
|
||||
const std::string inputVideoName = cvtest::TS::ptr()->get_data_path() + "car.avi";
|
||||
const int scale = 2;
|
||||
const int iterations = 100;
|
||||
const int temporalAreaRadius = 2;
|
||||
|
||||
ASSERT_FALSE( superRes.empty() );
|
||||
|
||||
const int btvKernelSize = superRes->getInt("btvKernelSize");
|
||||
|
||||
superRes->set("scale", scale);
|
||||
superRes->set("iterations", iterations);
|
||||
superRes->set("temporalAreaRadius", temporalAreaRadius);
|
||||
|
||||
cv::Ptr<cv::superres::FrameSource> goldSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale));
|
||||
cv::Ptr<cv::superres::FrameSource> lowResSource(new DegradeFrameSource(new AllignedFrameSource(cv::superres::createFrameSource_Video(inputVideoName), scale), scale));
|
||||
|
||||
// skip first frame
|
||||
cv::Mat frame;
|
||||
|
||||
lowResSource->nextFrame(frame);
|
||||
goldSource->nextFrame(frame);
|
||||
|
||||
cv::Rect inner(btvKernelSize, btvKernelSize, frame.cols - 2 * btvKernelSize, frame.rows - 2 * btvKernelSize);
|
||||
|
||||
superRes->setInput(lowResSource);
|
||||
|
||||
double srAvgMSSIM = 0.0;
|
||||
const int count = 10;
|
||||
|
||||
cv::Mat goldFrame, superResFrame;
|
||||
for (int i = 0; i < count; ++i)
|
||||
{
|
||||
goldSource->nextFrame(goldFrame);
|
||||
ASSERT_FALSE( goldFrame.empty() );
|
||||
|
||||
superRes->nextFrame(superResFrame);
|
||||
ASSERT_FALSE( superResFrame.empty() );
|
||||
|
||||
const double srMSSIM = MSSIM(goldFrame(inner), superResFrame);
|
||||
|
||||
srAvgMSSIM += srMSSIM;
|
||||
}
|
||||
|
||||
srAvgMSSIM /= count;
|
||||
|
||||
EXPECT_GE( srAvgMSSIM, 0.5 );
|
||||
}
|
||||
|
||||
TEST_F(SuperResolution, BTVL1)
|
||||
{
|
||||
RunTest(cv::superres::createSuperResolution_BTVL1());
|
||||
}
|
||||
|
||||
#if defined(HAVE_OPENCV_GPU) && defined(HAVE_CUDA)
|
||||
|
||||
TEST_F(SuperResolution, BTVL1_GPU)
|
||||
{
|
||||
RunTest(cv::superres::createSuperResolution_BTVL1_GPU());
|
||||
}
|
||||
|
||||
#endif
|
||||
@ -1,7 +1,7 @@
|
||||
SET(OPENCV_GPU_SAMPLES_REQUIRED_DEPS opencv_core opencv_flann opencv_imgproc opencv_highgui
|
||||
opencv_ml opencv_video opencv_objdetect opencv_features2d
|
||||
opencv_calib3d opencv_legacy opencv_contrib opencv_gpu
|
||||
opencv_nonfree)
|
||||
opencv_nonfree opencv_superres)
|
||||
|
||||
ocv_check_dependencies(${OPENCV_GPU_SAMPLES_REQUIRED_DEPS})
|
||||
|
||||
|
||||
152
samples/gpu/super_resolution.cpp
Normal file
152
samples/gpu/super_resolution.cpp
Normal file
@ -0,0 +1,152 @@
|
||||
#include <iostream>
|
||||
#include <iomanip>
|
||||
#include <string>
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
#include "opencv2/imgproc/imgproc.hpp"
|
||||
#include "opencv2/contrib/contrib.hpp"
|
||||
#include "opencv2/superres/superres.hpp"
|
||||
#include "opencv2/superres/optical_flow.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::superres;
|
||||
|
||||
#define MEASURE_TIME(op) \
|
||||
{ \
|
||||
TickMeter tm; \
|
||||
tm.start(); \
|
||||
op; \
|
||||
tm.stop(); \
|
||||
cout << tm.getTimeSec() << " sec" << endl; \
|
||||
}
|
||||
|
||||
static Ptr<DenseOpticalFlowExt> createOptFlow(const string& name, bool useGpu)
|
||||
{
|
||||
if (name == "farneback")
|
||||
{
|
||||
if (useGpu)
|
||||
return createOptFlow_Farneback_GPU();
|
||||
else
|
||||
return createOptFlow_Farneback();
|
||||
}
|
||||
else if (name == "simple")
|
||||
return createOptFlow_Simple();
|
||||
else if (name == "tvl1")
|
||||
{
|
||||
if (useGpu)
|
||||
return createOptFlow_DualTVL1_GPU();
|
||||
else
|
||||
return createOptFlow_DualTVL1();
|
||||
}
|
||||
else if (name == "brox")
|
||||
return createOptFlow_Brox_GPU();
|
||||
else if (name == "pyrlk")
|
||||
return createOptFlow_PyrLK_GPU();
|
||||
else
|
||||
{
|
||||
cerr << "Incorrect Optical Flow algorithm - " << name << endl;
|
||||
exit(-1);
|
||||
}
|
||||
|
||||
return Ptr<DenseOpticalFlowExt>();
|
||||
}
|
||||
|
||||
int main(int argc, const char* argv[])
|
||||
{
|
||||
CommandLineParser cmd(argc, argv,
|
||||
"{ v | video | | Input video }"
|
||||
"{ o | output | | Output video }"
|
||||
"{ s | scale | 4 | Scale factor }"
|
||||
"{ i | iterations | 180 | Iteration count }"
|
||||
"{ t | temporal | 4 | Radius of the temporal search area }"
|
||||
"{ f | flow | farneback | Optical flow algorithm (farneback, simple, tvl1, brox, pyrlk) }"
|
||||
"{ gpu | gpu | false | Use GPU }"
|
||||
"{ h | help | false | Print help message }"
|
||||
);
|
||||
|
||||
if (cmd.get<bool>("help"))
|
||||
{
|
||||
cout << "This sample demonstrates Super Resolution algorithms for video sequence" << endl;
|
||||
cmd.printParams();
|
||||
return 0;
|
||||
}
|
||||
|
||||
const string inputVideoName = cmd.get<string>("video");
|
||||
const string outputVideoName = cmd.get<string>("output");
|
||||
const int scale = cmd.get<int>("scale");
|
||||
const int iterations = cmd.get<int>("iterations");
|
||||
const int temporalAreaRadius = cmd.get<int>("temporal");
|
||||
const string optFlow = cmd.get<string>("flow");
|
||||
const bool useGpu = cmd.get<bool>("gpu");
|
||||
|
||||
Ptr<SuperResolution> superRes;
|
||||
if (useGpu)
|
||||
superRes = createSuperResolution_BTVL1_GPU();
|
||||
else
|
||||
superRes = createSuperResolution_BTVL1();
|
||||
|
||||
superRes->set("scale", scale);
|
||||
superRes->set("iterations", iterations);
|
||||
superRes->set("temporalAreaRadius", temporalAreaRadius);
|
||||
superRes->set("opticalFlow", createOptFlow(optFlow, useGpu));
|
||||
|
||||
Ptr<FrameSource> frameSource;
|
||||
if (useGpu)
|
||||
{
|
||||
// Try to use gpu Video Decoding
|
||||
try
|
||||
{
|
||||
frameSource = createFrameSource_Video_GPU(inputVideoName);
|
||||
Mat frame;
|
||||
frameSource->nextFrame(frame);
|
||||
}
|
||||
catch (const cv::Exception&)
|
||||
{
|
||||
frameSource.release();
|
||||
}
|
||||
}
|
||||
if (frameSource.empty())
|
||||
frameSource = createFrameSource_Video(inputVideoName);
|
||||
|
||||
// skip first frame, it is usually corrupted
|
||||
{
|
||||
Mat frame;
|
||||
frameSource->nextFrame(frame);
|
||||
cout << "Input : " << inputVideoName << " " << frame.size() << endl;
|
||||
cout << "Scale factor : " << scale << endl;
|
||||
cout << "Iterations : " << iterations << endl;
|
||||
cout << "Temporal radius : " << temporalAreaRadius << endl;
|
||||
cout << "Optical Flow : " << optFlow << endl;
|
||||
cout << "Mode : " << (useGpu ? "GPU" : "CPU") << endl;
|
||||
}
|
||||
|
||||
superRes->setInput(frameSource);
|
||||
|
||||
VideoWriter writer;
|
||||
|
||||
for (int i = 0;; ++i)
|
||||
{
|
||||
cout << '[' << setw(3) << i << "] : ";
|
||||
|
||||
Mat result;
|
||||
MEASURE_TIME(superRes->nextFrame(result));
|
||||
|
||||
if (result.empty())
|
||||
break;
|
||||
|
||||
imshow("Super Resolution", result);
|
||||
|
||||
if (waitKey(1000) > 0)
|
||||
break;
|
||||
|
||||
if (!outputVideoName.empty())
|
||||
{
|
||||
if (!writer.isOpened())
|
||||
writer.open(outputVideoName, CV_FOURCC('X', 'V', 'I', 'D'), 25.0, result.size());
|
||||
writer << result;
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user