{"action":"created","issue":{"url":"https://api.github.com/repos/Azure/mmlspark/issues/360","repository_url":"https://api.github.com/repos/Azure/mmlspark","labels_url":"https://api.github.com/repos/Azure/mmlspark/issues/360/labels{/name}","comments_url":"https://api.github.com/repos/Azure/mmlspark/issues/360/comments","events_url":"https://api.github.com/repos/Azure/mmlspark/issues/360/events","html_url":"https://github.com/Azure/mmlspark/issues/360","id":353930503,"node_id":"MDU6SXNzdWUzNTM5MzA1MDM=","number":360,"title":"Support higher bit depth images with ImageSchema","user":{"login":"luzlab","id":9924835,"node_id":"MDQ6VXNlcjk5MjQ4MzU=","avatar_url":"https://avatars1.githubusercontent.com/u/9924835?v=4","gravatar_id":"","url":"https://api.github.com/users/luzlab","html_url":"https://github.com/luzlab","followers_url":"https://api.github.com/users/luzlab/followers","following_url":"https://api.github.com/users/luzlab/following{/other_user}","gists_url":"https://api.github.com/users/luzlab/gists{/gist_id}","starred_url":"https://api.github.com/users/luzlab/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/luzlab/subscriptions","organizations_url":"https://api.github.com/users/luzlab/orgs","repos_url":"https://api.github.com/users/luzlab/repos","events_url":"https://api.github.com/users/luzlab/events{/privacy}","received_events_url":"https://api.github.com/users/luzlab/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-08-24T21:16:03Z","updated_at":"2018-08-28T03:42:08Z","closed_at":null,"author_association":"NONE","body":"We're developing machine vision solutions for in-process monitoring of manufacturing processes, including collecting wide-field absorbance maps. The camera is 16-bit and optimized for collecting low-noise quantitative data suitable for feeding deconvolution/superresolution analysis pipelines.\r\n\r\nWe currently use OpenCV to process the images, and generate 16-bit unsigned single channel and 32-bit floating point single channel data. Both formats are supported by OpenCV.\r\n\r\nThe current implementation of ImageSchema creates only 8bit image dataframes.\r\n\r\nHow challenging would it be to support additional types? If someone could provide guidance, I'd be willing to work on this."},"comment":{"url":"https://api.github.com/repos/Azure/mmlspark/issues/comments/416441104","html_url":"https://github.com/Azure/mmlspark/issues/360#issuecomment-416441104","issue_url":"https://api.github.com/repos/Azure/mmlspark/issues/360","id":416441104,"node_id":"MDEyOklzc3VlQ29tbWVudDQxNjQ0MTEwNA==","user":{"login":"luzlab","id":9924835,"node_id":"MDQ6VXNlcjk5MjQ4MzU=","avatar_url":"https://avatars1.githubusercontent.com/u/9924835?v=4","gravatar_id":"","url":"https://api.github.com/users/luzlab","html_url":"https://github.com/luzlab","followers_url":"https://api.github.com/users/luzlab/followers","following_url":"https://api.github.com/users/luzlab/following{/other_user}","gists_url":"https://api.github.com/users/luzlab/gists{/gist_id}","starred_url":"https://api.github.com/users/luzlab/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/luzlab/subscriptions","organizations_url":"https://api.github.com/users/luzlab/orgs","repos_url":"https://api.github.com/users/luzlab/repos","events_url":"https://api.github.com/users/luzlab/events{/privacy}","received_events_url":"https://api.github.com/users/luzlab/received_events","type":"User","site_admin":false},"created_at":"2018-08-28T03:42:08Z","updated_at":"2018-08-28T03:42:08Z","author_association":"NONE","body":"It's definitely failing when I try to import some images. I've gotten errors loading 8bit RGBA images and 16bit gray (1 channel) images.\r\n\r\n```\r\njava.lang.Exception: Results too large\r\n\tat com.databricks.backend.daemon.driver.OutputAggregator$.maybeApplyOutputAggregation(OutputAggregator.scala:193)\r\n\tat com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation0(OutputAggregator.scala:122)\r\n\tat com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation(OutputAggregator.scala:51)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal$$anonfun$getResultBufferInternal$1.apply(PythonDriverLocal.scala:553)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal$$anonfun$getResultBufferInternal$1.apply(PythonDriverLocal.scala:518)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal.withInterpLock(PythonDriverLocal.scala:471)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal.getResultBufferInternal(PythonDriverLocal.scala:518)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal.getResultBuffer(DriverLocal.scala:321)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal.com$databricks$backend$daemon$driver$PythonDriverLocal$$outputSuccess(PythonDriverLocal.scala:505)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal$$anonfun$repl$3.apply(PythonDriverLocal.scala:221)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal$$anonfun$repl$3.apply(PythonDriverLocal.scala:218)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal.withInterpLock(PythonDriverLocal.scala:471)\r\n\tat com.databricks.backend.daemon.driver.PythonDriverLocal.repl(PythonDriverLocal.scala:218)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$3.apply(DriverLocal.scala:248)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$3.apply(DriverLocal.scala:228)\r\n\tat com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:188)\r\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)\r\n\tat com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:183)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:40)\r\n\tat com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:221)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:40)\r\n\tat com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:228)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:595)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:595)\r\n\tat scala.util.Try$.apply(Try.scala:192)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:590)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.getCommandOutputAndError(DriverWrapper.scala:474)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:548)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:380)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:327)\r\n\tat com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:215)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\njava.lang.Ex\r\n```"}}
{"actor":{"display_login":"luzlab"}}