added hybrid (feature-based & meanshift-based) tracker (by Pushkar)
This commit is contained in:
Vadim Pisarevsky
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modules/contrib/src/featuretracker.cpp
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219
modules/contrib/src/featuretracker.cpp
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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) 2008-2011, Willow Garage Inc., all rights reserved.
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// Redistribution and use in source and binary forms, with or without modification,
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// * Redistribution's of source code must retain the above copyright notice,
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// * The name of Intel Corporation may not be used to endorse or promote products
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// This software is provided by the copyright holders and contributors "as is" and
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//M*/
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#include "precomp.hpp"
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#include <stdio.h>
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#include <iostream>
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#include <highgui.h>
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#include "opencv2/calib3d/calib3d.hpp"
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#include "opencv2/contrib/hybridtracker.hpp"
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using namespace cv;
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CvFeatureTracker::CvFeatureTracker(CvFeatureTrackerParams _params) :
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params(_params)
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{
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switch (params.feature_type)
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{
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case CvFeatureTrackerParams::SIFT:
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detector = new SiftFeatureDetector(
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SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
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SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD() + 0.7,
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SIFT::CommonParams::DEFAULT_NOCTAVES + 4,
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SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS + 2,
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SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
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SIFT::CommonParams::FIRST_ANGLE);
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case CvFeatureTrackerParams::SURF:
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detector = new SurfFeatureDetector(400, 3, 4);
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default:
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detector = new GoodFeaturesToTrackDetector();
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}
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descriptor = new SurfDescriptorExtractor(3, 4, false);
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matcher = new BruteForceMatcher<L2<float> > ();
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}
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CvFeatureTracker::~CvFeatureTracker()
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{
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}
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void CvFeatureTracker::newTrackingWindow(Mat image, Rect selection)
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{
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image.copyTo(prev_image);
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cvtColor(prev_image, prev_image_bw, CV_BGR2GRAY);
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prev_trackwindow = selection;
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prev_center.x = selection.x;
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prev_center.y = selection.y;
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ittr = 0;
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}
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Rect CvFeatureTracker::updateTrackingWindow(Mat image)
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{
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if(params.feature_type == CvFeatureTrackerParams::OPTICAL_FLOW)
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return updateTrackingWindowWithFlow(image);
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else
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updateTrackingWindowWithSIFT(image);
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}
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Rect CvFeatureTracker::updateTrackingWindowWithSIFT(Mat image)
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{
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ittr++;
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vector<KeyPoint> prev_keypoints, curr_keypoints;
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vector<Point2f> prev_keys, curr_keys;
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Mat prev_desc, curr_desc;
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Rect window = prev_trackwindow;
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Mat mask = Mat::zeros(image.size(), CV_8UC1);
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rectangle(mask, Point(window.x, window.y), Point(window.x + window.width,
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window.y + window.height), Scalar(255), CV_FILLED);
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detector->detect(prev_image, prev_keypoints, mask);
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window.x -= params.window_size;
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window.y -= params.window_size;
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window.width += params.window_size;
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window.height += params.window_size;
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rectangle(mask, Point(window.x, window.y), Point(window.x + window.width,
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window.y + window.height), Scalar(255), CV_FILLED);
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detector->detect(image, curr_keypoints, mask);
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if (prev_keypoints.size() > 4 && curr_keypoints.size() > 4)
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{
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descriptor->compute(prev_image, prev_keypoints, prev_desc);
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descriptor->compute(image, curr_keypoints, curr_desc);
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matcher->match(prev_desc, curr_desc, matches);
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for (int i = 0; i < matches.size(); i++)
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{
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prev_keys.push_back(prev_keypoints[matches[i].queryIdx].pt);
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curr_keys.push_back(curr_keypoints[matches[i].trainIdx].pt);
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}
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Mat T = findHomography(prev_keys, curr_keys, CV_LMEDS);
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prev_trackwindow.x += T.at<double> (0, 2);
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prev_trackwindow.y += T.at<double> (1, 2);
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}
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prev_center.x = prev_trackwindow.x;
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prev_center.y = prev_trackwindow.y;
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prev_image = image;
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return prev_trackwindow;
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}
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Rect CvFeatureTracker::updateTrackingWindowWithFlow(Mat image)
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{
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ittr++;
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Size subPixWinSize(10,10), winSize(31,31);
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Mat image_bw;
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TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
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vector<uchar> status;
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vector<float> err;
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cvtColor(image, image_bw, CV_BGR2GRAY);
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cvtColor(prev_image, prev_image_bw, CV_BGR2GRAY);
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if (ittr == 1)
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{
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Mat mask = Mat::zeros(image.size(), CV_8UC1);
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rectangle(mask, Point(prev_trackwindow.x, prev_trackwindow.y), Point(
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prev_trackwindow.x + prev_trackwindow.width, prev_trackwindow.y
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+ prev_trackwindow.height), Scalar(255), CV_FILLED);
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goodFeaturesToTrack(image_bw, features[1], 500, 0.01, 20, mask, 3, 0, 0.04);
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cornerSubPix(image_bw, features[1], subPixWinSize, Size(-1, -1), termcrit);
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}
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else
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{
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calcOpticalFlowPyrLK(prev_image_bw, image_bw, features[0], features[1],
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status, err, winSize, 3, termcrit);
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Point2f feature0_center(0, 0);
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Point2f feature1_center(0, 0);
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int goodtracks = 0;
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for (int i = 0; i < features[1].size(); i++)
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{
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if (status[i] == 1)
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{
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feature0_center.x += features[0][i].x;
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feature0_center.y += features[0][i].y;
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feature1_center.x += features[1][i].x;
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feature1_center.y += features[1][i].y;
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goodtracks++;
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}
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}
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feature0_center.x /= goodtracks;
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feature0_center.y /= goodtracks;
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feature1_center.x /= goodtracks;
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feature1_center.y /= goodtracks;
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prev_center.x += (feature1_center.x - feature0_center.x);
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prev_center.y += (feature1_center.y - feature0_center.y);
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prev_trackwindow.x = (int)prev_center.x;
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prev_trackwindow.y = (int)prev_center.y;
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}
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swap(features[0], features[1]);
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image.copyTo(prev_image);
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}
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void CvFeatureTracker::setTrackingWindow(Rect _window)
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{
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prev_trackwindow = _window;
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}
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Rect CvFeatureTracker::getTrackingWindow()
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{
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return prev_trackwindow;
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}
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Point2f CvFeatureTracker::getTrackingCenter()
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{
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Point2f center(0, 0);
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center.x = prev_center.x + prev_trackwindow.width/2.0;
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center.y = prev_center.y + prev_trackwindow.height/2.0;
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return center;
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}
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