Ethan Rublee
15 years ago
parent
c6e43c385d
commit
f6b0818996
4 changed files with 247 additions and 148 deletions
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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) 2009-2010, 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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#include "precomp.hpp" |
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namespace cv { |
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DynamicDetector::DynamicDetector(int min_features, |
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int max_features, int max_iters, const Ptr<AdjusterAdapter>& a) : |
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escape_iters_(max_iters), min_features_(min_features), max_features_( |
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max_features), adjuster_(a) { |
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} |
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void DynamicDetector::detectImpl(const cv::Mat& image, std::vector< |
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cv::KeyPoint>& keypoints, const cv::Mat& mask) const { |
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//for oscillation testing
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bool down = false; |
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bool up = false; |
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//flag for whether the correct threshhold has been reached
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bool thresh_good = false; |
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//this is bad but adjuster should persist from detection to detection
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AdjusterAdapter& adjuster = const_cast<AdjusterAdapter&> (*adjuster_); |
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//break if the desired number hasn't been reached.
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int iter_count = escape_iters_; |
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do { |
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keypoints.clear(); |
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//the adjuster takes care of calling the detector with updated parameters
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adjuster.detect(image, keypoints,mask); |
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if (int(keypoints.size()) < min_features_) { |
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down = true; |
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adjuster.tooFew(min_features_, keypoints.size()); |
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} else if (int(keypoints.size()) > max_features_) { |
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up = true; |
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adjuster.tooMany(max_features_, keypoints.size()); |
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} else |
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thresh_good = true; |
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} while (--iter_count >= 0 && !(down && up) && !thresh_good |
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&& adjuster.good()); |
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} |
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FastAdjuster::FastAdjuster(int init_thresh, bool nonmax) : |
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thresh_(init_thresh), nonmax_(nonmax) { |
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} |
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void FastAdjuster::detectImpl(const cv::Mat& image, |
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std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const { |
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FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask); |
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} |
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void FastAdjuster::tooFew(int min, int n_detected) { |
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//fast is easy to adjust
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thresh_--; |
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} |
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void FastAdjuster::tooMany(int max, int n_detected) { |
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//fast is easy to adjust
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thresh_++; |
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} |
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//return whether or not the threshhold is beyond
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//a useful point
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bool FastAdjuster::good() const { |
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return (thresh_ > 1) && (thresh_ < 200); |
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} |
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StarAdjuster::StarAdjuster(double initial_thresh) : |
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thresh_(initial_thresh) { |
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} |
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void StarAdjuster::detectImpl(const cv::Mat& image, |
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std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const { |
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StarFeatureDetector detector_tmp(16, thresh_, 10, 8, 3); |
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detector_tmp.detect(image, keypoints, mask); |
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} |
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void StarAdjuster::tooFew(int min, int n_detected) { |
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thresh_ *= 0.9; |
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if (thresh_ < 1.1) |
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thresh_ = 1.1; |
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} |
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void StarAdjuster::tooMany(int max, int n_detected) { |
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thresh_ *= 1.1; |
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} |
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bool StarAdjuster::good() const { |
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return (thresh_ > 2) && (thresh_ < 200); |
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} |
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SurfAdjuster::SurfAdjuster() : |
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thresh_(400.0) { |
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} |
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void SurfAdjuster::detectImpl(const cv::Mat& image, |
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std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const { |
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SurfFeatureDetector detector_tmp(thresh_); |
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detector_tmp.detect(image, keypoints, mask); |
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} |
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void SurfAdjuster::tooFew(int min, int n_detected) { |
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thresh_ *= 0.9; |
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if (thresh_ < 1.1) |
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thresh_ = 1.1; |
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} |
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void SurfAdjuster::tooMany(int max, int n_detected) { |
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thresh_ *= 1.1; |
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} |
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//return whether or not the threshhold is beyond
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//a useful point
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bool SurfAdjuster::good() const { |
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return (thresh_ > 2) && (thresh_ < 1000); |
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} |
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} |
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