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remove goturn related code

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Wanli
2024-04-28 21:41:24 +08:00
parent fe06856b0c
commit 02e8476cc8
7 changed files with 1 additions and 291 deletions
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39
modules/tracking/include/opencv2/tracking/tracking_legacy.hpp
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@@ -286,45 +286,6 @@ public:
virtual ~TrackerKCF() CV_OVERRIDE {}
};
#if 0 // legacy variant is not available
/** @brief the GOTURN (Generic Object Tracking Using Regression Networks) tracker
* GOTURN (@cite GOTURN) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers,
* GOTURN is much faster due to offline training without online fine-tuning nature.
* GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video,
* we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly
* robust to viewpoint changes, lighting changes, and deformations.
* Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227.
* Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2.
* Original paper is here: <http://davheld.github.io/GOTURN/GOTURN.pdf>
* As long as original authors implementation: <https://github.com/davheld/GOTURN#train-the-tracker>
* Implementation of training algorithm is placed in separately here due to 3d-party dependencies:
* <https://github.com/Auron-X/GOTURN_Training_Toolkit>
* GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository.
*/
class CV_EXPORTS_W TrackerGOTURN : public cv::legacy::Tracker
{
public:
struct CV_EXPORTS Params
{
Params();
void read(const FileNode& /*fn*/);
void write(FileStorage& /*fs*/) const;
String modelTxt;
String modelBin;
};
/** @brief Constructor
@param parameters GOTURN parameters TrackerGOTURN::Params
*/
static Ptr<legacy::TrackerGOTURN> create(const TrackerGOTURN::Params &parameters);
CV_WRAP static Ptr<legacy::TrackerGOTURN> create();
virtual ~TrackerGOTURN() CV_OVERRIDE {}
};
#endif
/** @brief the MOSSE (Minimum Output Sum of Squared %Error) tracker
The implementation is based on @cite MOSSE Visual Object Tracking using Adaptive Correlation Filters

11
modules/tracking/misc/java/test/TrackerCreateTest.java
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@@ -5,7 +5,6 @@ import org.opencv.core.CvException;
import org.opencv.test.OpenCVTestCase;
import org.opencv.video.Tracker;
import org.opencv.video.TrackerGOTURN;
import org.opencv.tracking.TrackerKCF;
import org.opencv.video.TrackerMIL;
@@ -16,16 +15,6 @@ public class TrackerCreateTest extends OpenCVTestCase {
super.setUp();
}
public void testCreateTrackerGOTURN() {
try {
Tracker tracker = TrackerGOTURN.create();
assert(tracker != null);
} catch (CvException e) {
// expected, model files may be missing
}
}
public void testCreateTrackerKCF() {
Tracker tracker = TrackerKCF.create();
}

5
modules/tracking/misc/python/test/test_tracking_contrib.py
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@@ -11,10 +11,6 @@ class tracking_contrib_test(NewOpenCVTests):
t = cv.TrackerMIL_create()
t = cv.TrackerKCF_create()
try:
t = cv.TrackerGOTURN_create()
except cv.error as e:
pass # may fail due to missing DL model files
def test_createLegacyTracker(self):
@@ -22,7 +18,6 @@ class tracking_contrib_test(NewOpenCVTests):
t = cv.legacy.TrackerMIL_create()
t = cv.legacy.TrackerKCF_create()
t = cv.legacy.TrackerMedianFlow_create()
#t = cv.legacy.TrackerGOTURN_create()
t = cv.legacy.TrackerMOSSE_create()
t = cv.legacy.TrackerCSRT_create()

230
modules/tracking/samples/goturnTracker.cpp
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@@ -1,230 +0,0 @@
/*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) 2013, OpenCV Foundation, 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*/
//Demo of GOTURN tracker
//In order to use GOTURN tracker, GOTURN architecture goturn.prototxt and goturn.caffemodel are required to exist in root folder.
//There are 2 ways to get caffemodel:
//1 - Train you own GOTURN model using <https://github.com/Auron-X/GOTURN_Training_Toolkit>
//2 - Download pretrained caffemodel from <https://github.com/opencv/opencv_extra>
#include "opencv2/opencv_modules.hpp"
#if defined(HAVE_OPENCV_DNN) && defined(HAVE_OPENCV_DATASETS)
#include "opencv2/datasets/track_alov.hpp"
#include <opencv2/core/utility.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/tracking.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::datasets;
#define NUM_TEST_FRAMES 1000
static Mat image;
static bool paused;
static bool selectObjects = false;
static bool startSelection = false;
static Rect boundingBox;
static const char* keys =
{ "{@dataset_path || Dataset path }"
"{@dataset_id |1| Dataset ID }"
};
static void onMouse(int event, int x, int y, int, void*)
{
if (!selectObjects)
{
switch (event)
{
case EVENT_LBUTTONDOWN:
//set origin of the bounding box
startSelection = true;
boundingBox.x = x;
boundingBox.y = y;
boundingBox.width = boundingBox.height = 0;
break;
case EVENT_LBUTTONUP:
//sei with and height of the bounding box
boundingBox.width = std::abs(x - boundingBox.x);
boundingBox.height = std::abs(y - boundingBox.y);
paused = false;
selectObjects = true;
startSelection = false;
break;
case EVENT_MOUSEMOVE:
if (startSelection && !selectObjects)
{
//draw the bounding box
Mat currentFrame;
image.copyTo(currentFrame);
rectangle(currentFrame, Point((int)boundingBox.x, (int)boundingBox.y), Point(x, y), Scalar(255, 0, 0), 2, 1);
imshow("GOTURN Tracking", currentFrame);
}
break;
}
}
}
static void help()
{
cout << "\nThis example is a simple demo of GOTURN tracking on ALOV300++ dataset"
"ALOV dataset contains videos with ID range: 1~314\n"
"-- pause video [p] and draw a bounding boxes around the targets to start the tracker\n"
"Example:\n"
"./goturnTracker <dataset_path> <dataset_id>\n"
<< endl;
cout << "\n\nHot keys: \n"
"\tq - quit the program\n"
"\tp - pause video\n";
}
int main(int argc, char *argv[])
{
CommandLineParser parser(argc, argv, keys);
string datasetRootPath = parser.get<string>(0);
int datasetID = parser.get<int>(1);
if (datasetRootPath.empty())
{
help();
return -1;
}
Mat frame;
paused = false;
namedWindow("GOTURN Tracking", 0);
setMouseCallback("GOTURN Tracking", onMouse, 0);
//Create GOTURN tracker
auto tracker = TrackerGOTURN::create();
//Load and init full ALOV300++ dataset with a given datasetID, as alternative you can use loadAnnotatedOnly(..)
//to load only frames with labelled ground truth ~ every 5-th frame
Ptr<cv::datasets::TRACK_alov> dataset = TRACK_alov::create();
dataset->load(datasetRootPath);
dataset->initDataset(datasetID);
//Read first frame
dataset->getNextFrame(frame);
if (frame.empty())
{
cout << "invalid dataset: " << datasetRootPath << endl;
return -2;
}
frame.copyTo(image);
rectangle(image, boundingBox, Scalar(255, 0, 0), 2, 1);
imshow("GOTURN Tracking", image);
bool initialized = false;
paused = true;
int frameCounter = 0;
//Time measurment
int64 e3 = getTickCount();
for (;;)
{
if (!paused)
{
//Time measurment
int64 e1 = getTickCount();
if (initialized){
if (!dataset->getNextFrame(frame))
break;
frame.copyTo(image);
}
if (!initialized && selectObjects)
{
//Initialize the tracker and add targets
tracker->init(frame, boundingBox);
rectangle(frame, boundingBox, Scalar(0, 0, 255), 2, 1);
initialized = true;
}
else if (initialized)
{
//Update all targets
if (tracker->update(frame, boundingBox))
{
rectangle(frame, boundingBox, Scalar(0, 0, 255), 2, 1);
}
}
imshow("GOTURN Tracking", frame);
frameCounter++;
//Time measurment
int64 e2 = getTickCount();
double t1 = (e2 - e1) / getTickFrequency();
cout << frameCounter << "\tframe : " << t1 * 1000.0 << "ms" << endl;
}
char c = (char)waitKey(2);
if (c == 'q')
break;
if (c == 'p')
paused = !paused;
}
//Time measurment
int64 e4 = getTickCount();
double t2 = (e4 - e3) / getTickFrequency();
cout << "Average Time for Frame: " << t2 * 1000.0 / frameCounter << "ms" << endl;
cout << "Average FPS: " << 1.0 / t2*frameCounter << endl;
waitKey(0);
return 0;
}
#else // ! HAVE_OPENCV_DNN && HAVE_OPENCV_DATASETS
#include <opencv2/core.hpp>
int main() {
CV_Error(cv::Error::StsNotImplemented , "this sample needs to be built with opencv_datasets and opencv_dnn !");
return -1;
}
#endif

4
modules/tracking/samples/samples_utility.hpp
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@@ -20,8 +20,6 @@ inline cv::Ptr<cv::Tracker> createTrackerByName(const std::string& name)
tracker = legacy::upgradeTrackingAPI(legacy::TrackerMedianFlow::create());
else if (name == "MIL")
tracker = cv::TrackerMIL::create();
else if (name == "GOTURN")
tracker = cv::TrackerGOTURN::create();
else if (name == "MOSSE")
tracker = legacy::upgradeTrackingAPI(legacy::TrackerMOSSE::create());
else if (name == "CSRT")
@@ -48,8 +46,6 @@ inline cv::Ptr<cv::legacy::Tracker> createTrackerByName_legacy(const std::string
tracker = legacy::TrackerMedianFlow::create();
else if (name == "MIL")
tracker = legacy::TrackerMIL::create();
else if (name == "GOTURN")
CV_Error(cv::Error::StsNotImplemented, "FIXIT: migration on new API is required");
else if (name == "MOSSE")
tracker = legacy::TrackerMOSSE::create();
else if (name == "CSRT")

2
modules/tracking/samples/tracker.cpp
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@@ -22,7 +22,7 @@ static void help()
"Example of <video_name> is in opencv_extra/testdata/cv/tracking/\n"
"Call:\n"
"./tracker <tracker_algorithm> <video_name> <start_frame> [<bounding_frame>]\n"
"tracker_algorithm can be: MIL, BOOSTING, MEDIANFLOW, TLD, KCF, GOTURN, MOSSE.\n"
"tracker_algorithm can be: MIL, BOOSTING, MEDIANFLOW, TLD, KCF, MOSSE.\n"
<< endl;
cout << "\n\nHot keys: \n"

1
modules/tracking/tutorials/tutorial_introduction_to_tracker.markdown
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@@ -50,7 +50,6 @@ Explanation
+ MEDIANFLOW
+ TLD
+ KCF
+ GOTURN
+ MOSSE
Each tracker algorithm has their own advantages and disadvantages, please refer the documentation of @ref cv::Tracker for more detailed information.