Getting data shapes right when classifying a large # of images
I'm following the image classification tutorial, but having to modify it a bit to account for the fact that I'm dealing with ~200,000 images.
I have used File > Import > Image Sequence as a work-around, and I think it would suffice for my purposes but my training data loads in R with 101 columns (slice1, slice2, ... slice101) and the only way I can figure to send the test data to R results in a list of matrices with the correct number of rows, but > 3,000 columns (X1, X2,...X3000).
Can someone help me figure this out?
I also posted this question with more detail on StackOverflow and created a new tag for bio7 on that site:
In addition you can also transfer images (imported image sequence as a stack) with the Java API and then
classify and store images one by one, e.g. with a Groovy or Jython script calling the Rserve API.
I think the last step in the tutorial can be misleading for a hughe number of images because it uses only one image.
And with this method you can simply omit a signature whereas the 'Pixel RM Stack' method always create a signature column which of course can be deleted afterwards (I used the 'Pixel' transfer in the video to make it as simple as possible).