dispev | matching algorithm for stereoscopic DISParity EValuation | Computer Vision library

by matteobustreo C++ Version: Current License: MIT

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kandi X-RAY | dispev Summary

dispev is a C++ library typically used in Artificial Intelligence, Computer Vision, Deep Learning, OpenCV, Example Codes applications. dispev has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

dispev is a C++ implementation of the matching algorithm for stereoscopic DISParity EValuation described in A two-stage correlation method for stereoscopic depth estimation by Nils Einecke, and Julian Eggert (DICTA, page 227-234. IEEE Computer Society, 2010). Algorithm implementation uses OpenCV 2.4.10 and it is preconfigured for being compiled with Visual Studio 2010. All the required dependancy files are included in the zip file (version x64, vc10). "bin/" folder contains a precompiled version of the code and a .bat file for running the algorithm in some test images. A Matlab file is provided for analysing the results.

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dispev has a low active ecosystem.

It has 5 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 110 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dispev is current.

Quality

dispev has no bugs reported.

Security

dispev has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

dispev is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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dispev releases are not available. You will need to build from source code and install.

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dispev Key Features

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dispev Examples and Code Snippets

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Community Discussions

Trending Discussions on dispev

Stream re-partitioning on DSL toplogy with selectKey and transform

QUESTION

Stream re-partitioning on DSL toplogy with selectKey and transform

Asked 2019-Feb-14 at 07:54

I feel like I am probably missing something very basic, but I'll ask anyway.

There is input topic with multiple partitions. I'm using selectKey as part of DSL topology. The selectKey always returns the same value. My expectation is that after internal re-partitioning triggered by selectKey() the next processor in the topology will be called on the same partition for the same key. However the next processor that is transform() is called on different partitions for the same key.

Topology:

...

ANSWER

Answered 2019-Feb-14 at 07:54

For process(), transform(), and transformValues() there is no auto-repartitioning. You will need to insert a manual through() call to repartition the data. If you compare the JavaDocs (with groupBy() or join() that support auto-repartitioning) you see that auto-repartitioning is not mentioned for them.

The reason is, that those three methods are part of Processor API integration into the DSL, and thus no DSL operators. Their semantics are unknown and thus we cannot tell if they require repartitioning if the key was change or not. To avoid unnecessary repartitioning, auto-repartitioning is not performed.

There is also a corresponding Jira: https://issues.apache.org/jira/browse/KAFKA-7608

Source https://stackoverflow.com/questions/54680774

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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Install dispev

You can download it from GitHub.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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