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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:44:20 UTC
[jira] [Resolved] (SPARK-22723) Add support for other data types
and add mode info to ImageSchema
[ https://issues.apache.org/jira/browse/SPARK-22723?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-22723.
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Resolution: Incomplete
> Add support for other data types and add mode info to ImageSchema
> ------------------------------------------------------------------
>
> Key: SPARK-22723
> URL: https://issues.apache.org/jira/browse/SPARK-22723
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Tomas Nykodym
> Priority: Minor
> Labels: bulk-closed
>
> When working with ImageSchema, I came across two shortcomings I had to address in our code for spark-deep-learning and I feel like it would be a good idea to add this functionality directly to ImageSchema.
> Firstly, ImageSchema code currently handles only images stored as uint8 and since we produce float-based images in some of our use cases I had to write alternatives to ImageSchema.toImage and ImageSchema.toNDArray.
> Secondly, there is no description of what open cv modes mean. It would be useful to have a data structure describing properties such as number of channels and data type for each mode.
> The aim of this ticket is to add support for these into the ImageSchema. To be more specific, I would like to add the following:
> 1. support for images stored as floats (CV_F32C* formats)
> ImageSchema.toImage, ImageSchema.toNDArray would need to be updated
> 2. include description of supported open cv modes, in particular number of channels and data type.
> This ticket is based on our implementation in spark-deep-learning. See https://github.com/tomasatdatabricks/spark-deep-learning/blob/537d1b125355955dbf9d9cc06c2615f0e30138dc/python/sparkdl/image/imageIO.py#L35-L93
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