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Merge pull request #25503 from WanliZhong:remove_goturn

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Remove goturn caffe model #25503

**Merged with:** https://github.com/opencv/opencv_extra/pull/1174
**Merged with:** https://github.com/opencv/opencv_contrib/pull/3729

Part of https://github.com/opencv/opencv/issues/25314

This PR aims to remove goturn tracking model because Caffe importer will be remove in 5.0

The GOTURN model will take **388 MB** of traffic for each download if converted to onnx. If the user wants to use the tracking method, we can recommend they use Vit or dasimRPN.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Wanli
2024-05-06 16:57:30 +08:00
committed by GitHub
parent 94f4678d3a
commit d231b4e362
10 changed files with 8 additions and 259 deletions
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modules/video/include/opencv2/video/tracking.hpp
-41
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@@ -789,47 +789,6 @@ public:
//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
};
/** @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 Tracker
{
protected:
TrackerGOTURN(); // use ::create()
public:
virtual ~TrackerGOTURN() CV_OVERRIDE;
struct CV_EXPORTS_W_SIMPLE Params
{
CV_WRAP Params();
CV_PROP_RW std::string modelTxt;
CV_PROP_RW std::string modelBin;
};
/** @brief Constructor
@param parameters GOTURN parameters TrackerGOTURN::Params
*/
static CV_WRAP
Ptr<TrackerGOTURN> create(const TrackerGOTURN::Params& parameters = TrackerGOTURN::Params());
//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
};
class CV_EXPORTS_W TrackerDaSiamRPN : public Tracker
{
protected: