jaco
10 years ago
parent
6aff2d878c
commit
ccde01a671
20 changed files with 139 additions and 387 deletions
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<name>saliency</name> |
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Common Interfaces of Saliency |
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============================ |
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.. highlight:: cpp |
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Saliency : Algorithm |
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------------------- |
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.. ocv:class:: Saliency |
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Base abstract class for Saliency algorithms:: |
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class CV_EXPORTS_W Saliency : public virtual Algorithm |
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{ |
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public: |
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virtual ~Saliency(); |
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static Ptr<Saliency> create( const String& saliencyType ); |
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bool computeSaliency( const InputArray image, OutputArray saliencyMap ); |
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String getClassName() const; |
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}; |
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Saliency::create |
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--------------- |
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Creates a specialized saliency algorithm by its name. |
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.. ocv:function:: static Ptr<Saliency> create( const String& saliencyType ); |
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:param saliencyType: saliency Type |
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The following saliency types are now supported: |
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* ``"SPECTRAL_RESIDUAL"`` -- :ocv:class:`StaticSaliencySpectralResidual` |
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* ``"BING"`` -- :ocv:class:`ObjectnessBING` |
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Saliency::computeSaliency |
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------------------------ |
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Performs all the operations, according to the specific algorithm created, to obtain the saliency map. |
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.. ocv:function:: bool computeSaliency( const InputArray image, OutputArray saliencyMap ); |
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:param image: image or set of input images. According to InputArray proxy and to the needs of different algorithms (currently plugged), the param image may be Mat or vector<Mat> |
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:param saliencyMap: saliency map. According to OutputArray proxy and to the results given by different algorithms (currently plugged), the saliency map may be a Mat or vector<Vec4i> (BING results). |
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Saliency::getClassName |
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--------------------- |
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Get the name of the specific Saliency Algorithm. |
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.. ocv:function:: String getClassName() const; |
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Subproject commit 14511ac1cc8564e1aee153efae4bf0453d3e645e |
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Subproject commit 16789e8dcad170f502c81ea2b9714a4dd6bff0c1 |
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Motion Saliency Algorithms |
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============================ |
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.. highlight:: cpp |
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Algorithms belonging to this category, are particularly focused to detect salient objects over time (hence also over frame), then there is a temporal component sealing cosider that allows to detect "moving" objects as salient, meaning therefore also the more general sense of detection the changes in the scene. |
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Objectness Algorithms |
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============================ |
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.. highlight:: cpp |
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Saliency API |
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============ |
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.. highlight:: cpp |
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Many computer vision applications may benefit from understanding where humans focus given a scene. Other than cognitively understanding the way human perceive images and scenes, finding salient regions and objects in the images helps various tasks such as speeding up object detection, object recognition, object tracking and content-aware image editing. |
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|
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About the saliency, there is a rich literature but the development is very fragmented. The principal purpose of this API is to give a |
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unique interface, a unique framework for use and plug sever saliency algorithms, also with very different nature and methodology, but they share the same purpose, organizing algorithms into three main categories: |
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**Static Saliency**: algorithms belonging to this category, exploit different image features that allow to detect salient objects in a non dynamic scenarios. |
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**Motion Saliency**: algorithms belonging to this category, are particularly focused to detect salient objects over time (hence also over frame), then there is a temporal component sealing cosider that allows to detect "moving" objects as salient, meaning therefore also the more general sense of detection the changes in the scene. |
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**Objectness**: Objectness is usually represented as a value which reflects how likely an image window covers an object of any category. Algorithms belonging to this category, avoid making decisions early on, by proposing a small number of category-independent proposals, that are expected to cover all objects in an image. Being able to perceive objects before identifying them is closely related to bottom up visual attention (saliency). |
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UML design: |
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----------- |
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**Saliency diagram** |
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.. image:: pics/saliency.png |
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:width: 80% |
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:alt: Saliency diagram |
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:align: center |
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To see how API works, try tracker demo: https://github.com/fpuja/opencv_contrib/blob/saliencyModuleDevelop/modules/saliency/samples/computeSaliency.cpp |
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.. note:: This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/misc/ |
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The following reference was used in the API |
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.. [SR] Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007. |
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.. [BING] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014. |
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Saliency classes: |
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---------------- |
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.. toctree:: |
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:maxdepth: 2 |
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saliency_categories |
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static_saliency_algorithms |
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motion_saliency_algorithms |
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objectness_algorithms |
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Saliency API |
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============ |
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.. highlight:: cpp |
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UML design: |
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----------- |
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**Saliency diagram** |
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.. image:: pics/saliency.png |
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:width: 80% |
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:alt: Tracker diagram |
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:align: center |
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|
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To see how API works, try tracker demo: |
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|
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|
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.. note:: This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/misc/ |
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The following reference was used in the API |
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.. [SR] Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007. |
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.. [BING] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014. |
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Saliency classes: |
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---------------- |
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.. toctree:: |
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:maxdepth: 2 |
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Saliency categories |
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============================ |
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.. highlight:: cpp |
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Base classes which give a general interface for each specialized type of saliency algorithm and provide utility methods for each algorithm in its class. |
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StaticSaliency |
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-------------- |
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.. ocv:class:: TrackerSampler |
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StaticSaliency class:: |
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class CV_EXPORTS_W StaticSaliency : public virtual Saliency |
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{ |
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public: |
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bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap ); |
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protected: |
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virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0; |
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}; |
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StaticSaliency::computeBinaryMap |
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------------------------------- |
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This function perform a binary map of given saliency map. This is obtained in this way: |
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In a first step, to improve the definition of interest areas and facilitate identification of targets, a segmentation |
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by clustering is performed, using *K-means algorithm*. Then, to gain a binary representation of clustered saliency map, since values of the map can vary according to the characteristics of frame under analysis, it is not convenient to use a fixed threshold. |
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So, *Otsu’s algorithm* is used, which assumes that the image to be thresholded contains two classes of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the algorithm calculates the optimal threshold separating those two classes, so that their |
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intra-class variance is minimal. |
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.. ocv:function:: bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap ); |
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:param saliencyMap: the saliency map obtained through one of the specialized algorithms |
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:param binaryMap : the binary map |
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MotionSaliency |
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-------------- |
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.. ocv:class:: MotionSaliency |
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MotionSaliency class:: |
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class CV_EXPORTS_W MotionSaliency : public virtual Saliency |
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{ |
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protected: |
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virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0; |
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}; |
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Objectness |
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-------------- |
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.. ocv:class:: Objectness |
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Objectness class:: |
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class CV_EXPORTS_W Objectness : public virtual Saliency |
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{ |
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protected: |
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virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0; |
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}; |
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Static Saliency algorithms |
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============================ |
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.. highlight:: cpp |
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Algorithms belonging to this category, exploit different image features that allow to detect salient objects in a non dynamic scenarios. |
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Presently, the Spectral Residual approach [SR]_ has been implemented. |
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.. [SR] Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007. |
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StaticSaliencySpectralResidual |
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------------------------------ |
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Starting from the principle of natural image statistics, this method simulate the behavior of pre-attentive visual search. The algorithm analyze the log spectrum of each image and obtain the spectral residual. Then transform the spectral residual to spatial domain to obtain the saliency map, which suggests the positions of proto-objects. |
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.. ocv:class:: StaticSaliencySpectralResidual |
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Implementation of SpectralResidual from :ocv:class:`StaticSaliency`:: |
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class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency |
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{ |
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public: |
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StaticSaliencySpectralResidual(); |
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~StaticSaliencySpectralResidual(); |
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typedef Ptr<Size> (Algorithm::*SizeGetter)(); |
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typedef void (Algorithm::*SizeSetter)( const Ptr<Size> & ); |
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Ptr<Size> getWsize(); |
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void setWsize( const Ptr<Size> &arrPtr ); |
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void read( const FileNode& fn ); |
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void write( FileStorage& fs ) const; |
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protected: |
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bool computeSaliencyImpl( const InputArray src, OutputArray dst ); |
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}; |
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Subproject commit aa0a3275061fc1bc8c0d6464ff21b2c7b41c6179 |
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