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Is your feature request related to a problem? Please describe.
It is either time intensive or imprecise to use the pcv.roi.custom function currently since you need to provide a list of vertices, and the length of this list can be long depending on the complexity of the ROI getting created.
Describe the solution you'd like
We wish to utilize interactive point and click capabilities within Jupyter to streamline this process. Matplotlib pyplot has event handling that we can use to allow users to spit out the list of vertices that would be needed to run pcv.roi.custom. This is useful for those that are analyzing data within Jupyter at a lower throughput (going image by image as is often the case with hyperspectral image workflows) but will also hopefully make the process of customizing parameters in workflows that are going to be ran in parallel.
Describe alternatives you've considered
I've tried to think about ways that it would be possible to have the point collection be built into the custom ROI function. It would be possible to know when points have been finished being appended since we could close a plot or use another event for signifying when to move on to the ROI portion of the function. This would not be particularly helpful for using in parallel though.
Additional context
Add any other context, sample data, or code relevant to the feature request here.
The text was updated successfully, but these errors were encountered:
Is your feature request related to a problem? Please describe.
It is either time intensive or imprecise to use the
pcv.roi.custom
function currently since you need to provide a list of vertices, and the length of this list can be long depending on the complexity of the ROI getting created.Describe the solution you'd like
We wish to utilize interactive point and click capabilities within Jupyter to streamline this process. Matplotlib pyplot has event handling that we can use to allow users to spit out the list of vertices that would be needed to run
pcv.roi.custom
. This is useful for those that are analyzing data within Jupyter at a lower throughput (going image by image as is often the case with hyperspectral image workflows) but will also hopefully make the process of customizing parameters in workflows that are going to be ran in parallel.Describe alternatives you've considered
I've tried to think about ways that it would be possible to have the point collection be built into the custom ROI function. It would be possible to know when points have been finished being appended since we could close a plot or use another event for signifying when to move on to the ROI portion of the function. This would not be particularly helpful for using in parallel though.
Additional context
Add any other context, sample data, or code relevant to the feature request here.
The text was updated successfully, but these errors were encountered: