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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/01/05 18:34:00 UTC
[jira] [Commented] (SPARK-22730) Add support for non-integer image
formats
[ https://issues.apache.org/jira/browse/SPARK-22730?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16313615#comment-16313615 ]
Apache Spark commented on SPARK-22730:
--------------------------------------
User 'tomasatdatabricks' has created a pull request for this issue:
https://github.com/apache/spark/pull/20168
> Add support for non-integer image formats
> -----------------------------------------
>
> Key: SPARK-22730
> URL: https://issues.apache.org/jira/browse/SPARK-22730
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Tomas Nykodym
>
> The conversion functions toImage and toNDArray provided by ImageSchema currently do not support non-integer image formats.
> Therefore, users who want to work with both integer and floating point formats have to write their own versions.
> Related to this problem is the lack of description of supported openCV modes (e.g. number of channels, data type).
> This tickets is based on our implementation in spark-deep learning and aims to bring this functionality to the ImageSchema.
> To be more specific, we want to
> 1. update toImage and toNDArray functions to handle float32(64) based images.
> See https://github.com/tomasatdatabricks/spark-deep-learning/blob/92217afcfdb3f0a42540f396d9018d75ffa6ba7c/python/sparkdl/image/imageIO.py#L61-L87
> 2. add information about individual OpenCv modes, e.g.
> See https://github.com/tomasatdatabricks/spark-deep-learning/blob/92217afcfdb3f0a42540f396d9018d75ffa6ba7c/python/sparkdl/image/imageIO.py#L31-L46
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