put all old stitching API into detail namespace
This commit is contained in:
Alexey Spizhevoy
parent
bf0081a850
commit
95a3ffd0c5
@ -144,7 +144,7 @@ macro(define_opencv_module name)
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file(GLOB lib_srcs "src/*.cpp")
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file(GLOB lib_int_hdrs "src/*.h*")
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file(GLOB lib_hdrs "include/opencv2/${name}/*.h*")
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file(GLOB lib_hdrs "include/opencv2/${name}/*.h*" "include/opencv2/${name}/detail/*.h*")
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if(COMMAND get_module_external_sources)
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get_module_external_sources(${name})
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@ -45,8 +45,8 @@
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#include "opencv2/core/core.hpp"
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#include "matchers.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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// See "Construction of Panoramic Image Mosaics with Global and Local Alignment"
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// by Heung-Yeung Shum and Richard Szeliski.
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@ -58,6 +58,7 @@ void CV_EXPORTS estimateFocal(const std::vector<ImageFeatures> &features,
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bool CV_EXPORTS calibrateRotatingCamera(const std::vector<Mat> &Hs, Mat &K);
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_AUTOCALIB_HPP__
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@ -44,8 +44,8 @@
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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// Simple blender which puts one image over another
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class CV_EXPORTS Blender
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@ -119,6 +119,7 @@ void CV_EXPORTS createLaplacePyrGpu(const Mat &img, int num_levels, std::vector<
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// Restores source image
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void CV_EXPORTS restoreImageFromLaplacePyr(std::vector<Mat>& pyr);
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_BLENDERS_HPP__
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@ -44,8 +44,8 @@
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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struct CV_EXPORTS CameraParams
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{
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@ -58,6 +58,7 @@ struct CV_EXPORTS CameraParams
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Mat t; // Translation
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};
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} // namespace detail
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} // namespace cv
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#endif // #ifndef __OPENCV_STITCHING_CAMERA_HPP__
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@ -44,8 +44,8 @@
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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class CV_EXPORTS ExposureCompensator
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{
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@ -99,6 +99,7 @@ private:
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std::vector<Mat_<float> > gain_maps_;
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};
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP__
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@ -45,8 +45,8 @@
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#include "opencv2/core/core.hpp"
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#include "opencv2/features2d/features2d.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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struct CV_EXPORTS ImageFeatures
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{
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@ -140,6 +140,7 @@ protected:
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cv::Ptr<FeaturesMatcher> impl_;
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};
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_MATCHERS_HPP__
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@ -47,8 +47,8 @@
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#include "util.hpp"
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#include "camera.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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class CV_EXPORTS Estimator
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{
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@ -126,6 +126,7 @@ std::vector<int> CV_EXPORTS leaveBiggestComponent(std::vector<ImageFeatures> &fe
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void CV_EXPORTS findMaxSpanningTree(int num_images, const std::vector<MatchesInfo> &pairwise_matches,
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Graph &span_tree, std::vector<int> ¢ers);
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_MOTION_ESTIMATORS_HPP__
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@ -44,8 +44,8 @@
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#include "opencv2/core/core.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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class CV_EXPORTS SeamFinder
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{
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@ -103,6 +103,7 @@ private:
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Ptr<Impl> impl_;
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};
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_SEAM_FINDERS_HPP__
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@ -56,8 +56,8 @@
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#define LOGLN(msg) LOG(msg << std::endl)
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namespace cv
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{
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namespace cv {
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namespace detail {
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class CV_EXPORTS DisjointSets
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{
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@ -114,6 +114,7 @@ Point CV_EXPORTS resultTl(const std::vector<Point> &corners);
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// Returns random 'count' element subset of the {0,1,...,size-1} set
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void CV_EXPORTS selectRandomSubset(int count, int size, std::vector<int> &subset);
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} // namespace detail
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} // namespace cv
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#include "util_inl.hpp"
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@ -46,8 +46,8 @@
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#include "opencv2/core/core.hpp"
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#include "util.hpp" // Make your IDE see declarations
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namespace cv
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{
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namespace cv {
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namespace detail {
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template <typename B>
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B Graph::forEach(B body) const
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@ -120,6 +120,7 @@ static inline int sqr(int x) { return x * x; }
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static inline float sqr(float x) { return x * x; }
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static inline double sqr(double x) { return x * x; }
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_UTIL_INL_HPP__
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@ -46,8 +46,8 @@
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/gpu/gpu.hpp"
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namespace cv
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{
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namespace cv {
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namespace detail {
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class CV_EXPORTS Warper
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{
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@ -194,6 +194,7 @@ private:
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gpu::GpuMat d_xmap_, d_ymap_, d_dst_, d_src_;
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};
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} // namespace detail
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} // namespace cv
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#include "warpers_inl.hpp"
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@ -45,8 +45,8 @@
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#include "opencv2/core/core.hpp"
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#include "warpers.hpp" // Make your IDE see declarations
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namespace cv
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{
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namespace cv {
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namespace detail {
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template <class P>
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Point WarperBase<P>::warp(const Mat &src, float focal, const Mat &R, Mat &dst,
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@ -256,6 +256,7 @@ void CylindricalProjector::mapBackward(float u, float v, float &x, float &y)
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y = focal * y / z + size.height * 0.5f;
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}
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} // namespace detail
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} // namespace cv
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#endif // __OPENCV_STITCHING_WARPERS_INL_HPP__
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@ -1,54 +0,0 @@
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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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// Intel License Agreement
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//
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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_STITCHING_HPP__
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#define __OPENCV_STITCHING_HPP__
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#include "opencv2/stitching/autocalib.hpp"
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#include "opencv2/stitching/blenders.hpp"
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#include "opencv2/stitching/camera.hpp"
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#include "opencv2/stitching/exposure_compensate.hpp"
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#include "opencv2/stitching/matchers.hpp"
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#include "opencv2/stitching/motion_estimators.hpp"
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#include "opencv2/stitching/seam_finders.hpp"
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#include "opencv2/stitching/util.hpp"
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#include "opencv2/stitching/warpers.hpp"
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#endif // __OPENCV_STITCHING_HPP__
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@ -39,27 +39,32 @@
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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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#include "precomp.hpp"
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using namespace std;
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using namespace cv;
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namespace
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namespace {
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template<typename _Tp> static inline bool
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decomposeCholesky(_Tp* A, size_t astep, int m)
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{
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template<typename _Tp> static inline bool
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decomposeCholesky(_Tp* A, size_t astep, int m)
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{
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if (!Cholesky(A, astep, m, 0, 0, 0))
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return false;
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astep /= sizeof(A[0]);
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for (int i = 0; i < m; ++i)
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A[i*astep + i] = (_Tp)(1./A[i*astep + i]);
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return true;
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}
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if (!Cholesky(A, astep, m, 0, 0, 0))
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return false;
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astep /= sizeof(A[0]);
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for (int i = 0; i < m; ++i)
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A[i*astep + i] = (_Tp)(1./A[i*astep + i]);
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return true;
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}
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} // namespace
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void cv::focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, bool &f1_ok)
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namespace cv {
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namespace detail {
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void focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, bool &f1_ok)
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{
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CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));
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@ -90,7 +95,7 @@ void cv::focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok,
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}
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void cv::estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
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void estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
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vector<double> &focals)
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{
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const int num_images = static_cast<int>(features.size());
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@ -131,7 +136,7 @@ void cv::estimateFocal(const vector<ImageFeatures> &features, const vector<Match
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}
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bool cv::calibrateRotatingCamera(const vector<Mat> &Hs, Mat &K)
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bool calibrateRotatingCamera(const vector<Mat> &Hs, Mat &K)
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{
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int m = static_cast<int>(Hs.size());
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CV_Assert(m >= 1);
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@ -181,3 +186,6 @@ bool cv::calibrateRotatingCamera(const vector<Mat> &Hs, Mat &K)
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K = W.t();
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return true;
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}
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} // namespace detail
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} // namespace cv
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@ -39,14 +39,17 @@
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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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#include "precomp.hpp"
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using namespace std;
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using namespace cv;
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namespace cv {
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namespace detail {
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static const float WEIGHT_EPS = 1e-5f;
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Ptr<Blender> cv::Blender::createDefault(int type, bool try_gpu)
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Ptr<Blender> Blender::createDefault(int type, bool try_gpu)
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{
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if (type == NO)
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return new Blender();
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@ -59,13 +62,13 @@ Ptr<Blender> cv::Blender::createDefault(int type, bool try_gpu)
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}
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void cv::Blender::prepare(const vector<Point> &corners, const vector<Size> &sizes)
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void Blender::prepare(const vector<Point> &corners, const vector<Size> &sizes)
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{
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prepare(resultRoi(corners, sizes));
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}
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void cv::Blender::prepare(Rect dst_roi)
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void Blender::prepare(Rect dst_roi)
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{
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dst_.create(dst_roi.size(), CV_16SC3);
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dst_.setTo(Scalar::all(0));
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@ -75,7 +78,7 @@ void cv::Blender::prepare(Rect dst_roi)
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}
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void cv::Blender::feed(const Mat &img, const Mat &mask, Point tl)
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void Blender::feed(const Mat &img, const Mat &mask, Point tl)
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{
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CV_Assert(img.type() == CV_16SC3);
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CV_Assert(mask.type() == CV_8U);
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@ -99,7 +102,7 @@ void cv::Blender::feed(const Mat &img, const Mat &mask, Point tl)
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}
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void cv::Blender::blend(Mat &dst, Mat &dst_mask)
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void Blender::blend(Mat &dst, Mat &dst_mask)
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{
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dst_.setTo(Scalar::all(0), dst_mask_ == 0);
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dst = dst_;
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@ -109,7 +112,7 @@ void cv::Blender::blend(Mat &dst, Mat &dst_mask)
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}
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void cv::FeatherBlender::prepare(Rect dst_roi)
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void FeatherBlender::prepare(Rect dst_roi)
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{
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Blender::prepare(dst_roi);
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dst_weight_map_.create(dst_roi.size(), CV_32F);
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@ -117,7 +120,7 @@ void cv::FeatherBlender::prepare(Rect dst_roi)
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}
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void cv::FeatherBlender::feed(const Mat &img, const Mat &mask, Point tl)
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void FeatherBlender::feed(const Mat &img, const Mat &mask, Point tl)
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{
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CV_Assert(img.type() == CV_16SC3);
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CV_Assert(mask.type() == CV_8U);
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@ -144,7 +147,7 @@ void cv::FeatherBlender::feed(const Mat &img, const Mat &mask, Point tl)
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}
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void cv::FeatherBlender::blend(Mat &dst, Mat &dst_mask)
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void FeatherBlender::blend(Mat &dst, Mat &dst_mask)
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{
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normalizeUsingWeightMap(dst_weight_map_, dst_);
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dst_mask_ = dst_weight_map_ > WEIGHT_EPS;
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@ -152,14 +155,14 @@ void cv::FeatherBlender::blend(Mat &dst, Mat &dst_mask)
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}
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cv::MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands)
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MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands)
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{
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setNumBands(num_bands);
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can_use_gpu_ = try_gpu && gpu::getCudaEnabledDeviceCount();
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}
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void cv::MultiBandBlender::prepare(Rect dst_roi)
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void MultiBandBlender::prepare(Rect dst_roi)
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{
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dst_roi_final_ = dst_roi;
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@ -192,7 +195,7 @@ void cv::MultiBandBlender::prepare(Rect dst_roi)
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}
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void cv::MultiBandBlender::feed(const Mat &img, const Mat &mask, Point tl)
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void MultiBandBlender::feed(const Mat &img, const Mat &mask, Point tl)
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{
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CV_Assert(img.type() == CV_16SC3);
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CV_Assert(mask.type() == CV_8U);
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@ -277,7 +280,7 @@ void cv::MultiBandBlender::feed(const Mat &img, const Mat &mask, Point tl)
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}
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void cv::MultiBandBlender::blend(Mat &dst, Mat &dst_mask)
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void MultiBandBlender::blend(Mat &dst, Mat &dst_mask)
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{
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for (int i = 0; i <= num_bands_; ++i)
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normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]);
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@ -298,7 +301,7 @@ void cv::MultiBandBlender::blend(Mat &dst, Mat &dst_mask)
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//////////////////////////////////////////////////////////////////////////////
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// Auxiliary functions
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void cv::normalizeUsingWeightMap(const Mat& weight, Mat& src)
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void normalizeUsingWeightMap(const Mat& weight, Mat& src)
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{
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CV_Assert(weight.type() == CV_32F);
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CV_Assert(src.type() == CV_16SC3);
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@ -317,7 +320,7 @@ void cv::normalizeUsingWeightMap(const Mat& weight, Mat& src)
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}
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void cv::createWeightMap(const Mat &mask, float sharpness, Mat &weight)
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||||
void createWeightMap(const Mat &mask, float sharpness, Mat &weight)
|
||||
{
|
||||
CV_Assert(mask.type() == CV_8U);
|
||||
distanceTransform(mask, weight, CV_DIST_L1, 3);
|
||||
@ -325,7 +328,7 @@ void cv::createWeightMap(const Mat &mask, float sharpness, Mat &weight)
|
||||
}
|
||||
|
||||
|
||||
void cv::createLaplacePyr(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
void createLaplacePyr(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
{
|
||||
pyr.resize(num_levels + 1);
|
||||
pyr[0] = img;
|
||||
@ -340,7 +343,7 @@ void cv::createLaplacePyr(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
}
|
||||
|
||||
|
||||
void cv::createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
void createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
{
|
||||
pyr.resize(num_levels + 1);
|
||||
|
||||
@ -361,7 +364,7 @@ void cv::createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
|
||||
}
|
||||
|
||||
|
||||
void cv::restoreImageFromLaplacePyr(vector<Mat> &pyr)
|
||||
void restoreImageFromLaplacePyr(vector<Mat> &pyr)
|
||||
{
|
||||
if (pyr.size() == 0)
|
||||
return;
|
||||
@ -372,3 +375,6 @@ void cv::restoreImageFromLaplacePyr(vector<Mat> &pyr)
|
||||
add(tmp, pyr[i - 1], pyr[i - 1]);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -1,16 +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, 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;
|
||||
|
||||
cv::CameraParams::CameraParams() : focal(1), R(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
cv::CameraParams::CameraParams(const CameraParams &other) { *this = other; }
|
||||
CameraParams::CameraParams() : focal(1), R(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
|
||||
|
||||
const cv::CameraParams& CameraParams::operator =(const CameraParams &other)
|
||||
CameraParams::CameraParams(const CameraParams &other) { *this = other; }
|
||||
|
||||
const CameraParams& CameraParams::operator =(const CameraParams &other)
|
||||
{
|
||||
focal = other.focal;
|
||||
R = other.R.clone();
|
||||
t = other.t.clone();
|
||||
return *this;
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -39,13 +39,16 @@
|
||||
// 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;
|
||||
|
||||
Ptr<ExposureCompensator> cv::ExposureCompensator::createDefault(int type)
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type)
|
||||
{
|
||||
if (type == NO)
|
||||
return new NoExposureCompensator();
|
||||
@ -58,8 +61,8 @@ Ptr<ExposureCompensator> cv::ExposureCompensator::createDefault(int type)
|
||||
}
|
||||
|
||||
|
||||
void cv::ExposureCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
const vector<Mat> &masks)
|
||||
void ExposureCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
const vector<Mat> &masks)
|
||||
{
|
||||
vector<pair<Mat,uchar> > level_masks;
|
||||
for (size_t i = 0; i < masks.size(); ++i)
|
||||
@ -68,8 +71,8 @@ void cv::ExposureCompensator::feed(const vector<Point> &corners, const vector<Ma
|
||||
}
|
||||
|
||||
|
||||
void cv::GainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
const vector<pair<Mat,uchar> > &masks)
|
||||
void GainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
const vector<pair<Mat,uchar> > &masks)
|
||||
{
|
||||
LOGLN("Exposure compensation...");
|
||||
int64 t = getTickCount();
|
||||
@ -143,13 +146,13 @@ void cv::GainCompensator::feed(const vector<Point> &corners, const vector<Mat> &
|
||||
}
|
||||
|
||||
|
||||
void cv::GainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
|
||||
void GainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
|
||||
{
|
||||
image *= gains_(index, 0);
|
||||
}
|
||||
|
||||
|
||||
vector<double> cv::GainCompensator::gains() const
|
||||
vector<double> GainCompensator::gains() const
|
||||
{
|
||||
vector<double> gains_vec(gains_.rows);
|
||||
for (int i = 0; i < gains_.rows; ++i)
|
||||
@ -158,7 +161,7 @@ vector<double> cv::GainCompensator::gains() const
|
||||
}
|
||||
|
||||
|
||||
void cv::BlocksGainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
void BlocksGainCompensator::feed(const vector<Point> &corners, const vector<Mat> &images,
|
||||
const vector<pair<Mat,uchar> > &masks)
|
||||
{
|
||||
CV_Assert(corners.size() == images.size() && images.size() == masks.size());
|
||||
@ -218,7 +221,7 @@ void cv::BlocksGainCompensator::feed(const vector<Point> &corners, const vector<
|
||||
}
|
||||
|
||||
|
||||
void cv::BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
|
||||
void BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, const Mat &/*mask*/)
|
||||
{
|
||||
CV_Assert(image.type() == CV_8UC3);
|
||||
|
||||
@ -240,3 +243,6 @@ void cv::BlocksGainCompensator::apply(int index, Point /*corner*/, Mat &image, c
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -39,13 +39,305 @@
|
||||
// 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::detail;
|
||||
using namespace cv::gpu;
|
||||
|
||||
void cv::FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
|
||||
namespace {
|
||||
|
||||
class CpuSurfFeaturesFinder : public FeaturesFinder
|
||||
{
|
||||
public:
|
||||
CpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
|
||||
int num_octaves_descr, int num_layers_descr)
|
||||
{
|
||||
detector_ = new SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
|
||||
extractor_ = new SurfDescriptorExtractor(num_octaves_descr, num_layers_descr);
|
||||
}
|
||||
|
||||
protected:
|
||||
void find(const Mat &image, ImageFeatures &features);
|
||||
|
||||
private:
|
||||
Ptr<FeatureDetector> detector_;
|
||||
Ptr<DescriptorExtractor> extractor_;
|
||||
};
|
||||
|
||||
|
||||
class GpuSurfFeaturesFinder : public FeaturesFinder
|
||||
{
|
||||
public:
|
||||
GpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
|
||||
int num_octaves_descr, int num_layers_descr)
|
||||
{
|
||||
surf_.keypointsRatio = 0.1f;
|
||||
surf_.hessianThreshold = hess_thresh;
|
||||
surf_.extended = false;
|
||||
num_octaves_ = num_octaves;
|
||||
num_layers_ = num_layers;
|
||||
num_octaves_descr_ = num_octaves_descr;
|
||||
num_layers_descr_ = num_layers_descr;
|
||||
}
|
||||
|
||||
void releaseMemory();
|
||||
|
||||
protected:
|
||||
void find(const Mat &image, ImageFeatures &features);
|
||||
|
||||
private:
|
||||
GpuMat image_;
|
||||
GpuMat gray_image_;
|
||||
SURF_GPU surf_;
|
||||
GpuMat keypoints_;
|
||||
GpuMat descriptors_;
|
||||
int num_octaves_, num_layers_;
|
||||
int num_octaves_descr_, num_layers_descr_;
|
||||
};
|
||||
|
||||
|
||||
void CpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
Mat gray_image;
|
||||
CV_Assert(image.depth() == CV_8U);
|
||||
cvtColor(image, gray_image, CV_BGR2GRAY);
|
||||
detector_->detect(gray_image, features.keypoints);
|
||||
extractor_->compute(gray_image, features.keypoints, features.descriptors);
|
||||
}
|
||||
|
||||
|
||||
void GpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
CV_Assert(image.depth() == CV_8U);
|
||||
|
||||
ensureSizeIsEnough(image.size(), image.type(), image_);
|
||||
image_.upload(image);
|
||||
|
||||
ensureSizeIsEnough(image.size(), CV_8UC1, gray_image_);
|
||||
cvtColor(image_, gray_image_, CV_BGR2GRAY);
|
||||
|
||||
surf_.nOctaves = num_octaves_;
|
||||
surf_.nOctaveLayers = num_layers_;
|
||||
surf_.upright = false;
|
||||
surf_(gray_image_, GpuMat(), keypoints_);
|
||||
|
||||
surf_.nOctaves = num_octaves_descr_;
|
||||
surf_.nOctaveLayers = num_layers_descr_;
|
||||
surf_.upright = true;
|
||||
surf_(gray_image_, GpuMat(), keypoints_, descriptors_, true);
|
||||
surf_.downloadKeypoints(keypoints_, features.keypoints);
|
||||
|
||||
descriptors_.download(features.descriptors);
|
||||
}
|
||||
|
||||
void GpuSurfFeaturesFinder::releaseMemory()
|
||||
{
|
||||
surf_.releaseMemory();
|
||||
image_.release();
|
||||
gray_image_.release();
|
||||
keypoints_.release();
|
||||
descriptors_.release();
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
struct DistIdxPair
|
||||
{
|
||||
bool operator<(const DistIdxPair &other) const { return dist < other.dist; }
|
||||
double dist;
|
||||
int idx;
|
||||
};
|
||||
|
||||
|
||||
struct MatchPairsBody
|
||||
{
|
||||
MatchPairsBody(const MatchPairsBody& other)
|
||||
: matcher(other.matcher), features(other.features),
|
||||
pairwise_matches(other.pairwise_matches), near_pairs(other.near_pairs) {}
|
||||
|
||||
MatchPairsBody(FeaturesMatcher &matcher, const vector<ImageFeatures> &features,
|
||||
vector<MatchesInfo> &pairwise_matches, vector<pair<int,int> > &near_pairs)
|
||||
: matcher(matcher), features(features),
|
||||
pairwise_matches(pairwise_matches), near_pairs(near_pairs) {}
|
||||
|
||||
void operator ()(const BlockedRange &r) const
|
||||
{
|
||||
const int num_images = static_cast<int>(features.size());
|
||||
for (int i = r.begin(); i < r.end(); ++i)
|
||||
{
|
||||
int from = near_pairs[i].first;
|
||||
int to = near_pairs[i].second;
|
||||
int pair_idx = from*num_images + to;
|
||||
|
||||
matcher(features[from], features[to], pairwise_matches[pair_idx]);
|
||||
pairwise_matches[pair_idx].src_img_idx = from;
|
||||
pairwise_matches[pair_idx].dst_img_idx = to;
|
||||
|
||||
size_t dual_pair_idx = to*num_images + from;
|
||||
|
||||
pairwise_matches[dual_pair_idx] = pairwise_matches[pair_idx];
|
||||
pairwise_matches[dual_pair_idx].src_img_idx = to;
|
||||
pairwise_matches[dual_pair_idx].dst_img_idx = from;
|
||||
|
||||
if (!pairwise_matches[pair_idx].H.empty())
|
||||
pairwise_matches[dual_pair_idx].H = pairwise_matches[pair_idx].H.inv();
|
||||
|
||||
for (size_t j = 0; j < pairwise_matches[dual_pair_idx].matches.size(); ++j)
|
||||
std::swap(pairwise_matches[dual_pair_idx].matches[j].queryIdx,
|
||||
pairwise_matches[dual_pair_idx].matches[j].trainIdx);
|
||||
LOG(".");
|
||||
}
|
||||
}
|
||||
|
||||
FeaturesMatcher &matcher;
|
||||
const vector<ImageFeatures> &features;
|
||||
vector<MatchesInfo> &pairwise_matches;
|
||||
vector<pair<int,int> > &near_pairs;
|
||||
|
||||
private:
|
||||
void operator =(const MatchPairsBody&);
|
||||
};
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
typedef set<pair<int,int> > MatchesSet;
|
||||
|
||||
// These two classes are aimed to find features matches only, not to
|
||||
// estimate homography
|
||||
|
||||
class CpuMatcher : public FeaturesMatcher
|
||||
{
|
||||
public:
|
||||
CpuMatcher(float match_conf) : FeaturesMatcher(true), match_conf_(match_conf) {}
|
||||
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
|
||||
|
||||
private:
|
||||
float match_conf_;
|
||||
};
|
||||
|
||||
|
||||
class GpuMatcher : public FeaturesMatcher
|
||||
{
|
||||
public:
|
||||
GpuMatcher(float match_conf) : match_conf_(match_conf) {}
|
||||
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
|
||||
|
||||
void releaseMemory();
|
||||
|
||||
private:
|
||||
float match_conf_;
|
||||
GpuMat descriptors1_, descriptors2_;
|
||||
GpuMat train_idx_, distance_, all_dist_;
|
||||
vector< vector<DMatch> > pair_matches;
|
||||
};
|
||||
|
||||
|
||||
void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
|
||||
{
|
||||
matches_info.matches.clear();
|
||||
FlannBasedMatcher matcher;
|
||||
vector< vector<DMatch> > pair_matches;
|
||||
MatchesSet matches;
|
||||
|
||||
// Find 1->2 matches
|
||||
matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
{
|
||||
matches_info.matches.push_back(m0);
|
||||
matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
|
||||
}
|
||||
}
|
||||
|
||||
// Find 2->1 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
|
||||
matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
|
||||
{
|
||||
matches_info.matches.clear();
|
||||
|
||||
ensureSizeIsEnough(features1.descriptors.size(), features1.descriptors.type(), descriptors1_);
|
||||
ensureSizeIsEnough(features2.descriptors.size(), features2.descriptors.type(), descriptors2_);
|
||||
|
||||
descriptors1_.upload(features1.descriptors);
|
||||
descriptors2_.upload(features2.descriptors);
|
||||
|
||||
BruteForceMatcher_GPU< L2<float> > matcher;
|
||||
MatchesSet matches;
|
||||
|
||||
// Find 1->2 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(descriptors1_, descriptors2_, train_idx_, distance_, all_dist_, 2);
|
||||
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
{
|
||||
matches_info.matches.push_back(m0);
|
||||
matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
|
||||
}
|
||||
}
|
||||
|
||||
// Find 2->1 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(descriptors2_, descriptors1_, train_idx_, distance_, all_dist_, 2);
|
||||
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
|
||||
matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
|
||||
}
|
||||
}
|
||||
|
||||
void GpuMatcher::releaseMemory()
|
||||
{
|
||||
descriptors1_.release();
|
||||
descriptors2_.release();
|
||||
train_idx_.release();
|
||||
distance_.release();
|
||||
all_dist_.release();
|
||||
vector< vector<DMatch> >().swap(pair_matches);
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
find(image, features);
|
||||
features.img_size = image.size();
|
||||
@ -53,104 +345,7 @@ void cv::FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
|
||||
}
|
||||
|
||||
|
||||
namespace
|
||||
{
|
||||
class CpuSurfFeaturesFinder : public FeaturesFinder
|
||||
{
|
||||
public:
|
||||
CpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
|
||||
int num_octaves_descr, int num_layers_descr)
|
||||
{
|
||||
detector_ = new SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
|
||||
extractor_ = new SurfDescriptorExtractor(num_octaves_descr, num_layers_descr);
|
||||
}
|
||||
|
||||
protected:
|
||||
void find(const Mat &image, ImageFeatures &features);
|
||||
|
||||
private:
|
||||
Ptr<FeatureDetector> detector_;
|
||||
Ptr<DescriptorExtractor> extractor_;
|
||||
};
|
||||
|
||||
|
||||
class GpuSurfFeaturesFinder : public FeaturesFinder
|
||||
{
|
||||
public:
|
||||
GpuSurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
|
||||
int num_octaves_descr, int num_layers_descr)
|
||||
{
|
||||
surf_.keypointsRatio = 0.1f;
|
||||
surf_.hessianThreshold = hess_thresh;
|
||||
surf_.extended = false;
|
||||
num_octaves_ = num_octaves;
|
||||
num_layers_ = num_layers;
|
||||
num_octaves_descr_ = num_octaves_descr;
|
||||
num_layers_descr_ = num_layers_descr;
|
||||
}
|
||||
|
||||
void releaseMemory();
|
||||
|
||||
protected:
|
||||
void find(const Mat &image, ImageFeatures &features);
|
||||
|
||||
private:
|
||||
GpuMat image_;
|
||||
GpuMat gray_image_;
|
||||
SURF_GPU surf_;
|
||||
GpuMat keypoints_;
|
||||
GpuMat descriptors_;
|
||||
int num_octaves_, num_layers_;
|
||||
int num_octaves_descr_, num_layers_descr_;
|
||||
};
|
||||
|
||||
|
||||
void CpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
Mat gray_image;
|
||||
CV_Assert(image.depth() == CV_8U);
|
||||
cvtColor(image, gray_image, CV_BGR2GRAY);
|
||||
detector_->detect(gray_image, features.keypoints);
|
||||
extractor_->compute(gray_image, features.keypoints, features.descriptors);
|
||||
}
|
||||
|
||||
|
||||
void GpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
CV_Assert(image.depth() == CV_8U);
|
||||
|
||||
ensureSizeIsEnough(image.size(), image.type(), image_);
|
||||
image_.upload(image);
|
||||
|
||||
ensureSizeIsEnough(image.size(), CV_8UC1, gray_image_);
|
||||
cvtColor(image_, gray_image_, CV_BGR2GRAY);
|
||||
|
||||
surf_.nOctaves = num_octaves_;
|
||||
surf_.nOctaveLayers = num_layers_;
|
||||
surf_.upright = false;
|
||||
surf_(gray_image_, GpuMat(), keypoints_);
|
||||
|
||||
surf_.nOctaves = num_octaves_descr_;
|
||||
surf_.nOctaveLayers = num_layers_descr_;
|
||||
surf_.upright = true;
|
||||
surf_(gray_image_, GpuMat(), keypoints_, descriptors_, true);
|
||||
surf_.downloadKeypoints(keypoints_, features.keypoints);
|
||||
|
||||
descriptors_.download(features.descriptors);
|
||||
}
|
||||
|
||||
void GpuSurfFeaturesFinder::releaseMemory()
|
||||
{
|
||||
surf_.releaseMemory();
|
||||
image_.release();
|
||||
gray_image_.release();
|
||||
keypoints_.release();
|
||||
descriptors_.release();
|
||||
}
|
||||
} // namespace
|
||||
|
||||
|
||||
cv::SurfFeaturesFinder::SurfFeaturesFinder(bool try_use_gpu, double hess_thresh, int num_octaves, int num_layers,
|
||||
SurfFeaturesFinder::SurfFeaturesFinder(bool try_use_gpu, double hess_thresh, int num_octaves, int num_layers,
|
||||
int num_octaves_descr, int num_layers_descr)
|
||||
{
|
||||
if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
|
||||
@ -160,12 +355,13 @@ cv::SurfFeaturesFinder::SurfFeaturesFinder(bool try_use_gpu, double hess_thresh,
|
||||
}
|
||||
|
||||
|
||||
void cv::SurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
void SurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
|
||||
{
|
||||
(*impl_)(image, features);
|
||||
}
|
||||
|
||||
void cv::SurfFeaturesFinder::releaseMemory()
|
||||
|
||||
void SurfFeaturesFinder::releaseMemory()
|
||||
{
|
||||
impl_->releaseMemory();
|
||||
}
|
||||
@ -173,11 +369,11 @@ void cv::SurfFeaturesFinder::releaseMemory()
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
cv::MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0), confidence(0) {}
|
||||
MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0), confidence(0) {}
|
||||
|
||||
cv::MatchesInfo::MatchesInfo(const MatchesInfo &other) { *this = other; }
|
||||
MatchesInfo::MatchesInfo(const MatchesInfo &other) { *this = other; }
|
||||
|
||||
const cv::MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
|
||||
const MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
|
||||
{
|
||||
src_img_idx = other.src_img_idx;
|
||||
dst_img_idx = other.dst_img_idx;
|
||||
@ -192,69 +388,7 @@ const cv::MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
struct DistIdxPair
|
||||
{
|
||||
bool operator<(const DistIdxPair &other) const { return dist < other.dist; }
|
||||
double dist;
|
||||
int idx;
|
||||
};
|
||||
|
||||
|
||||
struct MatchPairsBody
|
||||
{
|
||||
MatchPairsBody(const MatchPairsBody& other)
|
||||
: matcher(other.matcher), features(other.features),
|
||||
pairwise_matches(other.pairwise_matches), near_pairs(other.near_pairs) {}
|
||||
|
||||
MatchPairsBody(FeaturesMatcher &matcher, const vector<ImageFeatures> &features,
|
||||
vector<MatchesInfo> &pairwise_matches, vector<pair<int,int> > &near_pairs)
|
||||
: matcher(matcher), features(features),
|
||||
pairwise_matches(pairwise_matches), near_pairs(near_pairs) {}
|
||||
|
||||
void operator ()(const BlockedRange &r) const
|
||||
{
|
||||
const int num_images = static_cast<int>(features.size());
|
||||
for (int i = r.begin(); i < r.end(); ++i)
|
||||
{
|
||||
int from = near_pairs[i].first;
|
||||
int to = near_pairs[i].second;
|
||||
int pair_idx = from*num_images + to;
|
||||
|
||||
matcher(features[from], features[to], pairwise_matches[pair_idx]);
|
||||
pairwise_matches[pair_idx].src_img_idx = from;
|
||||
pairwise_matches[pair_idx].dst_img_idx = to;
|
||||
|
||||
size_t dual_pair_idx = to*num_images + from;
|
||||
|
||||
pairwise_matches[dual_pair_idx] = pairwise_matches[pair_idx];
|
||||
pairwise_matches[dual_pair_idx].src_img_idx = to;
|
||||
pairwise_matches[dual_pair_idx].dst_img_idx = from;
|
||||
|
||||
if (!pairwise_matches[pair_idx].H.empty())
|
||||
pairwise_matches[dual_pair_idx].H = pairwise_matches[pair_idx].H.inv();
|
||||
|
||||
for (size_t j = 0; j < pairwise_matches[dual_pair_idx].matches.size(); ++j)
|
||||
std::swap(pairwise_matches[dual_pair_idx].matches[j].queryIdx,
|
||||
pairwise_matches[dual_pair_idx].matches[j].trainIdx);
|
||||
LOG(".");
|
||||
}
|
||||
}
|
||||
|
||||
FeaturesMatcher &matcher;
|
||||
const vector<ImageFeatures> &features;
|
||||
vector<MatchesInfo> &pairwise_matches;
|
||||
vector<pair<int,int> > &near_pairs;
|
||||
|
||||
private:
|
||||
void operator =(const MatchPairsBody&);
|
||||
};
|
||||
} // namespace
|
||||
|
||||
|
||||
void cv::FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches)
|
||||
void FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches)
|
||||
{
|
||||
const int num_images = static_cast<int>(features.size());
|
||||
|
||||
@ -276,138 +410,7 @@ void cv::FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vec
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace
|
||||
{
|
||||
typedef set<pair<int,int> > MatchesSet;
|
||||
|
||||
// These two classes are aimed to find features matches only, not to
|
||||
// estimate homography
|
||||
|
||||
class CpuMatcher : public FeaturesMatcher
|
||||
{
|
||||
public:
|
||||
CpuMatcher(float match_conf) : FeaturesMatcher(true), match_conf_(match_conf) {}
|
||||
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
|
||||
|
||||
private:
|
||||
float match_conf_;
|
||||
};
|
||||
|
||||
|
||||
class GpuMatcher : public FeaturesMatcher
|
||||
{
|
||||
public:
|
||||
GpuMatcher(float match_conf) : match_conf_(match_conf) {}
|
||||
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
|
||||
|
||||
void releaseMemory();
|
||||
|
||||
private:
|
||||
float match_conf_;
|
||||
GpuMat descriptors1_, descriptors2_;
|
||||
GpuMat train_idx_, distance_, all_dist_;
|
||||
vector< vector<DMatch> > pair_matches;
|
||||
};
|
||||
|
||||
|
||||
void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
|
||||
{
|
||||
matches_info.matches.clear();
|
||||
FlannBasedMatcher matcher;
|
||||
vector< vector<DMatch> > pair_matches;
|
||||
MatchesSet matches;
|
||||
|
||||
// Find 1->2 matches
|
||||
matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
{
|
||||
matches_info.matches.push_back(m0);
|
||||
matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
|
||||
}
|
||||
}
|
||||
|
||||
// Find 2->1 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
|
||||
matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
|
||||
{
|
||||
matches_info.matches.clear();
|
||||
|
||||
ensureSizeIsEnough(features1.descriptors.size(), features1.descriptors.type(), descriptors1_);
|
||||
ensureSizeIsEnough(features2.descriptors.size(), features2.descriptors.type(), descriptors2_);
|
||||
|
||||
descriptors1_.upload(features1.descriptors);
|
||||
descriptors2_.upload(features2.descriptors);
|
||||
|
||||
BruteForceMatcher_GPU< L2<float> > matcher;
|
||||
MatchesSet matches;
|
||||
|
||||
// Find 1->2 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(descriptors1_, descriptors2_, train_idx_, distance_, all_dist_, 2);
|
||||
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
{
|
||||
matches_info.matches.push_back(m0);
|
||||
matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
|
||||
}
|
||||
}
|
||||
|
||||
// Find 2->1 matches
|
||||
pair_matches.clear();
|
||||
matcher.knnMatch(descriptors2_, descriptors1_, train_idx_, distance_, all_dist_, 2);
|
||||
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
|
||||
for (size_t i = 0; i < pair_matches.size(); ++i)
|
||||
{
|
||||
if (pair_matches[i].size() < 2)
|
||||
continue;
|
||||
const DMatch& m0 = pair_matches[i][0];
|
||||
const DMatch& m1 = pair_matches[i][1];
|
||||
if (m0.distance < (1.f - match_conf_) * m1.distance)
|
||||
if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
|
||||
matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
|
||||
}
|
||||
}
|
||||
|
||||
void GpuMatcher::releaseMemory()
|
||||
{
|
||||
descriptors1_.release();
|
||||
descriptors2_.release();
|
||||
train_idx_.release();
|
||||
distance_.release();
|
||||
all_dist_.release();
|
||||
vector< vector<DMatch> >().swap(pair_matches);
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
|
||||
cv::BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
|
||||
BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
|
||||
{
|
||||
if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
|
||||
impl_ = new GpuMatcher(match_conf);
|
||||
@ -420,7 +423,7 @@ cv::BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_c
|
||||
}
|
||||
|
||||
|
||||
void cv::BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
|
||||
void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
|
||||
MatchesInfo &matches_info)
|
||||
{
|
||||
(*impl_)(features1, features2, matches_info);
|
||||
@ -498,7 +501,10 @@ void cv::BestOf2NearestMatcher::match(const ImageFeatures &features1, const Imag
|
||||
matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
|
||||
}
|
||||
|
||||
void cv::BestOf2NearestMatcher::releaseMemory()
|
||||
void BestOf2NearestMatcher::releaseMemory()
|
||||
{
|
||||
impl_->releaseMemory();
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -39,56 +39,69 @@
|
||||
// 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::detail;
|
||||
|
||||
namespace
|
||||
namespace {
|
||||
|
||||
struct IncDistance
|
||||
{
|
||||
IncDistance(vector<int> &dists) : dists(&dists[0]) {}
|
||||
void operator ()(const GraphEdge &edge) { dists[edge.to] = dists[edge.from] + 1; }
|
||||
int* dists;
|
||||
};
|
||||
|
||||
struct IncDistance
|
||||
|
||||
struct CalcRotation
|
||||
{
|
||||
CalcRotation(int num_images, const vector<MatchesInfo> &pairwise_matches, vector<CameraParams> &cameras)
|
||||
: num_images(num_images), pairwise_matches(&pairwise_matches[0]), cameras(&cameras[0]) {}
|
||||
|
||||
void operator ()(const GraphEdge &edge)
|
||||
{
|
||||
IncDistance(vector<int> &dists) : dists(&dists[0]) {}
|
||||
void operator ()(const GraphEdge &edge) { dists[edge.to] = dists[edge.from] + 1; }
|
||||
int* dists;
|
||||
};
|
||||
int pair_idx = edge.from * num_images + edge.to;
|
||||
|
||||
double f_from = cameras[edge.from].focal;
|
||||
double f_to = cameras[edge.to].focal;
|
||||
|
||||
Mat K_from = Mat::eye(3, 3, CV_64F);
|
||||
K_from.at<double>(0, 0) = f_from;
|
||||
K_from.at<double>(1, 1) = f_from;
|
||||
|
||||
Mat K_to = Mat::eye(3, 3, CV_64F);
|
||||
K_to.at<double>(0, 0) = f_to;
|
||||
K_to.at<double>(1, 1) = f_to;
|
||||
|
||||
Mat R = K_from.inv() * pairwise_matches[pair_idx].H.inv() * K_to;
|
||||
cameras[edge.to].R = cameras[edge.from].R * R;
|
||||
}
|
||||
|
||||
int num_images;
|
||||
const MatchesInfo* pairwise_matches;
|
||||
CameraParams* cameras;
|
||||
};
|
||||
|
||||
|
||||
struct CalcRotation
|
||||
{
|
||||
CalcRotation(int num_images, const vector<MatchesInfo> &pairwise_matches, vector<CameraParams> &cameras)
|
||||
: num_images(num_images), pairwise_matches(&pairwise_matches[0]), cameras(&cameras[0]) {}
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void operator ()(const GraphEdge &edge)
|
||||
{
|
||||
int pair_idx = edge.from * num_images + edge.to;
|
||||
|
||||
double f_from = cameras[edge.from].focal;
|
||||
double f_to = cameras[edge.to].focal;
|
||||
|
||||
Mat K_from = Mat::eye(3, 3, CV_64F);
|
||||
K_from.at<double>(0, 0) = f_from;
|
||||
K_from.at<double>(1, 1) = f_from;
|
||||
|
||||
Mat K_to = Mat::eye(3, 3, CV_64F);
|
||||
K_to.at<double>(0, 0) = f_to;
|
||||
K_to.at<double>(1, 1) = f_to;
|
||||
|
||||
Mat R = K_from.inv() * pairwise_matches[pair_idx].H.inv() * K_to;
|
||||
cameras[edge.to].R = cameras[edge.from].R * R;
|
||||
}
|
||||
|
||||
int num_images;
|
||||
const MatchesInfo* pairwise_matches;
|
||||
CameraParams* cameras;
|
||||
};
|
||||
void calcDeriv(const Mat &err1, const Mat &err2, double h, Mat res)
|
||||
{
|
||||
for (int i = 0; i < err1.rows; ++i)
|
||||
res.at<double>(i, 0) = (err2.at<double>(i, 0) - err1.at<double>(i, 0)) / h;
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
|
||||
void cv::HomographyBasedEstimator::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
|
||||
vector<CameraParams> &cameras)
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
void HomographyBasedEstimator::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
|
||||
vector<CameraParams> &cameras)
|
||||
{
|
||||
LOGLN("Estimating rotations...");
|
||||
int64 t = getTickCount();
|
||||
@ -135,8 +148,8 @@ void cv::HomographyBasedEstimator::estimate(const vector<ImageFeatures> &feature
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void cv::BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
|
||||
vector<CameraParams> &cameras)
|
||||
void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
|
||||
vector<CameraParams> &cameras)
|
||||
{
|
||||
if (cost_space_ == NO)
|
||||
return;
|
||||
@ -250,7 +263,7 @@ void cv::BundleAdjuster::estimate(const vector<ImageFeatures> &features, const v
|
||||
}
|
||||
|
||||
|
||||
void cv::BundleAdjuster::calcError(Mat &err)
|
||||
void BundleAdjuster::calcError(Mat &err)
|
||||
{
|
||||
err.create(total_num_matches_ * 3, 1, CV_64F);
|
||||
|
||||
@ -314,19 +327,7 @@ void cv::BundleAdjuster::calcError(Mat &err)
|
||||
}
|
||||
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
void calcDeriv(const Mat &err1, const Mat &err2, double h, Mat res)
|
||||
{
|
||||
for (int i = 0; i < err1.rows; ++i)
|
||||
res.at<double>(i, 0) = (err2.at<double>(i, 0) - err1.at<double>(i, 0)) / h;
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
|
||||
void cv::BundleAdjuster::calcJacobian()
|
||||
void BundleAdjuster::calcJacobian()
|
||||
{
|
||||
J_.create(total_num_matches_ * 3, num_images_ * 4, CV_64F);
|
||||
|
||||
@ -374,7 +375,7 @@ void cv::BundleAdjuster::calcJacobian()
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// TODO replace SVD with eigen
|
||||
void cv::waveCorrect(vector<Mat> &rmats)
|
||||
void waveCorrect(vector<Mat> &rmats)
|
||||
{
|
||||
LOGLN("Wave correcting...");
|
||||
int64 t = getTickCount();
|
||||
@ -415,7 +416,7 @@ void cv::waveCorrect(vector<Mat> &rmats)
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
string cv::matchesGraphAsString(vector<string> &pathes, vector<MatchesInfo> &pairwise_matches,
|
||||
string matchesGraphAsString(vector<string> &pathes, vector<MatchesInfo> &pairwise_matches,
|
||||
float conf_threshold)
|
||||
{
|
||||
stringstream str;
|
||||
@ -481,7 +482,7 @@ string cv::matchesGraphAsString(vector<string> &pathes, vector<MatchesInfo> &pai
|
||||
return str.str();
|
||||
}
|
||||
|
||||
vector<int> cv::leaveBiggestComponent(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
|
||||
vector<int> leaveBiggestComponent(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
|
||||
float conf_threshold)
|
||||
{
|
||||
const int num_images = static_cast<int>(features.size());
|
||||
@ -539,7 +540,7 @@ vector<int> cv::leaveBiggestComponent(vector<ImageFeatures> &features, vector<M
|
||||
}
|
||||
|
||||
|
||||
void cv::findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise_matches,
|
||||
void findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise_matches,
|
||||
Graph &span_tree, vector<int> ¢ers)
|
||||
{
|
||||
Graph graph(num_images);
|
||||
@ -608,3 +609,7 @@ void cv::findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise
|
||||
centers.push_back(i);
|
||||
CV_Assert(centers.size() > 0 && centers.size() <= 2);
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
|
||||
@ -52,15 +52,15 @@
|
||||
#include <set>
|
||||
#include <functional>
|
||||
#include <sstream>
|
||||
#include "opencv2/stitching/autocalib.hpp"
|
||||
#include "opencv2/stitching/blenders.hpp"
|
||||
#include "opencv2/stitching/camera.hpp"
|
||||
#include "opencv2/stitching/exposure_compensate.hpp"
|
||||
#include "opencv2/stitching/matchers.hpp"
|
||||
#include "opencv2/stitching/motion_estimators.hpp"
|
||||
#include "opencv2/stitching/seam_finders.hpp"
|
||||
#include "opencv2/stitching/util.hpp"
|
||||
#include "opencv2/stitching/warpers.hpp"
|
||||
#include "opencv2/stitching/detail/autocalib.hpp"
|
||||
#include "opencv2/stitching/detail/blenders.hpp"
|
||||
#include "opencv2/stitching/detail/camera.hpp"
|
||||
#include "opencv2/stitching/detail/exposure_compensate.hpp"
|
||||
#include "opencv2/stitching/detail/matchers.hpp"
|
||||
#include "opencv2/stitching/detail/motion_estimators.hpp"
|
||||
#include "opencv2/stitching/detail/seam_finders.hpp"
|
||||
#include "opencv2/stitching/detail/util.hpp"
|
||||
#include "opencv2/stitching/detail/warpers.hpp"
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/core/internal.hpp"
|
||||
#include "opencv2/imgproc/imgproc.hpp"
|
||||
|
||||
@ -39,12 +39,15 @@
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
Ptr<SeamFinder> cv::SeamFinder::createDefault(int type)
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
Ptr<SeamFinder> SeamFinder::createDefault(int type)
|
||||
{
|
||||
if (type == NO)
|
||||
return new NoSeamFinder();
|
||||
@ -59,7 +62,7 @@ Ptr<SeamFinder> cv::SeamFinder::createDefault(int type)
|
||||
}
|
||||
|
||||
|
||||
void cv::PairwiseSeamFinder::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
void PairwiseSeamFinder::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
vector<Mat> &masks)
|
||||
{
|
||||
LOGLN("Finding seams...");
|
||||
@ -86,7 +89,7 @@ void cv::PairwiseSeamFinder::find(const vector<Mat> &src, const vector<Point> &c
|
||||
}
|
||||
|
||||
|
||||
void cv::VoronoiSeamFinder::findInPair(size_t first, size_t second, Rect roi)
|
||||
void VoronoiSeamFinder::findInPair(size_t first, size_t second, Rect roi)
|
||||
{
|
||||
const int gap = 10;
|
||||
Mat submask1(roi.height + 2 * gap, roi.width + 2 * gap, CV_8U);
|
||||
@ -140,7 +143,7 @@ void cv::VoronoiSeamFinder::findInPair(size_t first, size_t second, Rect roi)
|
||||
}
|
||||
|
||||
|
||||
class cv::GraphCutSeamFinder::Impl : public PairwiseSeamFinder
|
||||
class GraphCutSeamFinder::Impl : public PairwiseSeamFinder
|
||||
{
|
||||
public:
|
||||
Impl(int cost_type, float terminal_cost, float bad_region_penalty)
|
||||
@ -163,7 +166,7 @@ private:
|
||||
};
|
||||
|
||||
|
||||
void cv::GraphCutSeamFinder::Impl::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
void GraphCutSeamFinder::Impl::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
vector<Mat> &masks)
|
||||
{
|
||||
// Compute gradients
|
||||
@ -194,7 +197,7 @@ void cv::GraphCutSeamFinder::Impl::find(const vector<Mat> &src, const vector<Poi
|
||||
}
|
||||
|
||||
|
||||
void cv::GraphCutSeamFinder::Impl::setGraphWeightsColor(const Mat &img1, const Mat &img2,
|
||||
void GraphCutSeamFinder::Impl::setGraphWeightsColor(const Mat &img1, const Mat &img2,
|
||||
const Mat &mask1, const Mat &mask2, GCGraph<float> &graph)
|
||||
{
|
||||
const Size img_size = img1.size();
|
||||
@ -242,7 +245,7 @@ void cv::GraphCutSeamFinder::Impl::setGraphWeightsColor(const Mat &img1, const M
|
||||
}
|
||||
|
||||
|
||||
void cv::GraphCutSeamFinder::Impl::setGraphWeightsColorGrad(
|
||||
void GraphCutSeamFinder::Impl::setGraphWeightsColorGrad(
|
||||
const Mat &img1, const Mat &img2, const Mat &dx1, const Mat &dx2,
|
||||
const Mat &dy1, const Mat &dy2, const Mat &mask1, const Mat &mask2,
|
||||
GCGraph<float> &graph)
|
||||
@ -296,7 +299,7 @@ void cv::GraphCutSeamFinder::Impl::setGraphWeightsColorGrad(
|
||||
}
|
||||
|
||||
|
||||
void cv::GraphCutSeamFinder::Impl::findInPair(size_t first, size_t second, Rect roi)
|
||||
void GraphCutSeamFinder::Impl::findInPair(size_t first, size_t second, Rect roi)
|
||||
{
|
||||
Mat img1 = images_[first], img2 = images_[second];
|
||||
Mat dx1 = dx_[first], dx2 = dx_[second];
|
||||
@ -394,12 +397,16 @@ void cv::GraphCutSeamFinder::Impl::findInPair(size_t first, size_t second, Rect
|
||||
}
|
||||
|
||||
|
||||
cv::GraphCutSeamFinder::GraphCutSeamFinder(int cost_type, float terminal_cost, float bad_region_penalty)
|
||||
GraphCutSeamFinder::GraphCutSeamFinder(int cost_type, float terminal_cost, float bad_region_penalty)
|
||||
: impl_(new Impl(cost_type, terminal_cost, bad_region_penalty)) {}
|
||||
|
||||
|
||||
void cv::GraphCutSeamFinder::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
void GraphCutSeamFinder::find(const vector<Mat> &src, const vector<Point> &corners,
|
||||
vector<Mat> &masks)
|
||||
{
|
||||
impl_->find(src, corners, masks);
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
|
||||
@ -42,9 +42,11 @@
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
void cv::DisjointSets::createOneElemSets(int n)
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
void DisjointSets::createOneElemSets(int n)
|
||||
{
|
||||
rank_.assign(n, 0);
|
||||
size.assign(n, 1);
|
||||
@ -54,7 +56,7 @@ void cv::DisjointSets::createOneElemSets(int n)
|
||||
}
|
||||
|
||||
|
||||
int cv::DisjointSets::findSetByElem(int elem)
|
||||
int DisjointSets::findSetByElem(int elem)
|
||||
{
|
||||
int set = elem;
|
||||
while (set != parent[set])
|
||||
@ -70,7 +72,7 @@ int cv::DisjointSets::findSetByElem(int elem)
|
||||
}
|
||||
|
||||
|
||||
int cv::DisjointSets::mergeSets(int set1, int set2)
|
||||
int DisjointSets::mergeSets(int set1, int set2)
|
||||
{
|
||||
if (rank_[set1] < rank_[set2])
|
||||
{
|
||||
@ -91,13 +93,13 @@ int cv::DisjointSets::mergeSets(int set1, int set2)
|
||||
}
|
||||
|
||||
|
||||
void cv::Graph::addEdge(int from, int to, float weight)
|
||||
void Graph::addEdge(int from, int to, float weight)
|
||||
{
|
||||
edges_[from].push_back(GraphEdge(from, to, weight));
|
||||
}
|
||||
|
||||
|
||||
bool cv::overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi)
|
||||
bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi)
|
||||
{
|
||||
int x_tl = max(tl1.x, tl2.x);
|
||||
int y_tl = max(tl1.y, tl2.y);
|
||||
@ -112,7 +114,7 @@ bool cv::overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi)
|
||||
}
|
||||
|
||||
|
||||
Rect cv::resultRoi(const vector<Point> &corners, const vector<Mat> &images)
|
||||
Rect resultRoi(const vector<Point> &corners, const vector<Mat> &images)
|
||||
{
|
||||
vector<Size> sizes(images.size());
|
||||
for (size_t i = 0; i < images.size(); ++i)
|
||||
@ -121,7 +123,7 @@ Rect cv::resultRoi(const vector<Point> &corners, const vector<Mat> &images)
|
||||
}
|
||||
|
||||
|
||||
Rect cv::resultRoi(const vector<Point> &corners, const vector<Size> &sizes)
|
||||
Rect resultRoi(const vector<Point> &corners, const vector<Size> &sizes)
|
||||
{
|
||||
CV_Assert(sizes.size() == corners.size());
|
||||
Point tl(numeric_limits<int>::max(), numeric_limits<int>::max());
|
||||
@ -137,7 +139,7 @@ Rect cv::resultRoi(const vector<Point> &corners, const vector<Size> &sizes)
|
||||
}
|
||||
|
||||
|
||||
Point cv::resultTl(const vector<Point> &corners)
|
||||
Point resultTl(const vector<Point> &corners)
|
||||
{
|
||||
Point tl(numeric_limits<int>::max(), numeric_limits<int>::max());
|
||||
for (size_t i = 0; i < corners.size(); ++i)
|
||||
@ -149,7 +151,7 @@ Point cv::resultTl(const vector<Point> &corners)
|
||||
}
|
||||
|
||||
|
||||
void cv::selectRandomSubset(int count, int size, vector<int> &subset)
|
||||
void selectRandomSubset(int count, int size, vector<int> &subset)
|
||||
{
|
||||
subset.clear();
|
||||
for (int i = 0; i < size; ++i)
|
||||
@ -161,3 +163,6 @@ void cv::selectRandomSubset(int count, int size, vector<int> &subset)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -39,12 +39,15 @@
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "precomp.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
Ptr<Warper> cv::Warper::createByCameraFocal(float focal, int type, bool try_gpu)
|
||||
namespace cv {
|
||||
namespace detail {
|
||||
|
||||
Ptr<Warper> Warper::createByCameraFocal(float focal, int type, bool try_gpu)
|
||||
{
|
||||
bool can_use_gpu = try_gpu && gpu::getCudaEnabledDeviceCount();
|
||||
if (type == PLANE)
|
||||
@ -58,7 +61,7 @@ Ptr<Warper> cv::Warper::createByCameraFocal(float focal, int type, bool try_gpu)
|
||||
}
|
||||
|
||||
|
||||
void cv::ProjectorBase::setTransformation(const Mat &R)
|
||||
void ProjectorBase::setTransformation(const Mat &R)
|
||||
{
|
||||
CV_Assert(R.size() == Size(3, 3));
|
||||
CV_Assert(R.type() == CV_32F);
|
||||
@ -73,7 +76,7 @@ void cv::ProjectorBase::setTransformation(const Mat &R)
|
||||
}
|
||||
|
||||
|
||||
void cv::PlaneWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
void PlaneWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
{
|
||||
float tl_uf = numeric_limits<float>::max();
|
||||
float tl_vf = numeric_limits<float>::max();
|
||||
@ -105,14 +108,14 @@ void cv::PlaneWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
}
|
||||
|
||||
|
||||
Point cv::PlaneWarperGpu::warp(const Mat &src, float focal, const cv::Mat &R, cv::Mat &dst, int interp_mode, int border_mode)
|
||||
Point PlaneWarperGpu::warp(const Mat &src, float focal, const Mat &R, Mat &dst, int interp_mode, int border_mode)
|
||||
{
|
||||
src_size_ = src.size();
|
||||
projector_.size = src.size();
|
||||
projector_.focal = focal;
|
||||
projector_.setTransformation(R);
|
||||
|
||||
cv::Point dst_tl, dst_br;
|
||||
Point dst_tl, dst_br;
|
||||
detectResultRoi(dst_tl, dst_br);
|
||||
|
||||
gpu::buildWarpPlaneMaps(src.size(), Rect(dst_tl, Point(dst_br.x+1, dst_br.y+1)),
|
||||
@ -131,7 +134,7 @@ Point cv::PlaneWarperGpu::warp(const Mat &src, float focal, const cv::Mat &R, cv
|
||||
}
|
||||
|
||||
|
||||
void cv::SphericalWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
void SphericalWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
{
|
||||
detectResultRoiByBorder(dst_tl, dst_br);
|
||||
|
||||
@ -175,7 +178,7 @@ void cv::SphericalWarper::detectResultRoi(Point &dst_tl, Point &dst_br)
|
||||
}
|
||||
|
||||
|
||||
Point cv::SphericalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Mat &dst,
|
||||
Point SphericalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Mat &dst,
|
||||
int interp_mode, int border_mode)
|
||||
{
|
||||
src_size_ = src.size();
|
||||
@ -183,7 +186,7 @@ Point cv::SphericalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Ma
|
||||
projector_.focal = focal;
|
||||
projector_.setTransformation(R);
|
||||
|
||||
cv::Point dst_tl, dst_br;
|
||||
Point dst_tl, dst_br;
|
||||
detectResultRoi(dst_tl, dst_br);
|
||||
|
||||
gpu::buildWarpSphericalMaps(src.size(), Rect(dst_tl, Point(dst_br.x+1, dst_br.y+1)),
|
||||
@ -202,7 +205,7 @@ Point cv::SphericalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Ma
|
||||
}
|
||||
|
||||
|
||||
Point cv::CylindricalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Mat &dst,
|
||||
Point CylindricalWarperGpu::warp(const Mat &src, float focal, const Mat &R, Mat &dst,
|
||||
int interp_mode, int border_mode)
|
||||
{
|
||||
src_size_ = src.size();
|
||||
@ -210,7 +213,7 @@ Point cv::CylindricalWarperGpu::warp(const Mat &src, float focal, const Mat &R,
|
||||
projector_.focal = focal;
|
||||
projector_.setTransformation(R);
|
||||
|
||||
cv::Point dst_tl, dst_br;
|
||||
Point dst_tl, dst_br;
|
||||
detectResultRoi(dst_tl, dst_br);
|
||||
|
||||
gpu::buildWarpCylindricalMaps(src.size(), Rect(dst_tl, Point(dst_br.x+1, dst_br.y+1)),
|
||||
@ -228,3 +231,5 @@ Point cv::CylindricalWarperGpu::warp(const Mat &src, float focal, const Mat &R,
|
||||
return dst_tl;
|
||||
}
|
||||
|
||||
} // namespace detail
|
||||
} // namespace cv
|
||||
|
||||
@ -49,11 +49,20 @@
|
||||
// Matthew Brown and David G. Lowe. 2007.
|
||||
|
||||
#include <fstream>
|
||||
#include "opencv2/stitching/stitching.hpp"
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
#include "opencv2/stitching/detail/autocalib.hpp"
|
||||
#include "opencv2/stitching/detail/blenders.hpp"
|
||||
#include "opencv2/stitching/detail/camera.hpp"
|
||||
#include "opencv2/stitching/detail/exposure_compensate.hpp"
|
||||
#include "opencv2/stitching/detail/matchers.hpp"
|
||||
#include "opencv2/stitching/detail/motion_estimators.hpp"
|
||||
#include "opencv2/stitching/detail/seam_finders.hpp"
|
||||
#include "opencv2/stitching/detail/util.hpp"
|
||||
#include "opencv2/stitching/detail/warpers.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::detail;
|
||||
|
||||
void printUsage()
|
||||
{
|
||||
|
||||
Loading…
Reference in New Issue
Block a user