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I have an application where I need to label time-series imagery, I cannot simply convert the images to single images and label those as I am trying to track differences between images in the time series stack (even between pairs of images would be fine for now).
I envision an interface like (https://www.cssscript.com/demo/view-before-after-images-reveal/) where the two images can be compared, with annotation taking place on top of that interface. So far the issue in implementing this seems to be that LOST only fetches a single image from a folder at a time, and the interface only expects a single image.
I am willing to have a go at implementing this, but could you provide some guidance on the best approach and relevant parts of LOST which will need to be updated? I was thinking that instead of relying on flask to fetch the data directly from a folder, perhaps a data loader like interface (as PyTorch etc have) might be the most flexible way to go.
The text was updated successfully, but these errors were encountered:
I have an application where I need to label time-series imagery, I cannot simply convert the images to single images and label those as I am trying to track differences between images in the time series stack (even between pairs of images would be fine for now).
I envision an interface like (https://www.cssscript.com/demo/view-before-after-images-reveal/) where the two images can be compared, with annotation taking place on top of that interface. So far the issue in implementing this seems to be that LOST only fetches a single image from a folder at a time, and the interface only expects a single image.
I am willing to have a go at implementing this, but could you provide some guidance on the best approach and relevant parts of LOST which will need to be updated? I was thinking that instead of relying on flask to fetch the data directly from a folder, perhaps a data loader like interface (as PyTorch etc have) might be the most flexible way to go.
The text was updated successfully, but these errors were encountered: