ScanCol24-train&data:
---------------------
This example should be run once you understand how to prepare and analyze image
coming from a flatbed scanner (see ScanCol24-example). These data are provided
by Xabier Irigoien from AZTI. This is plancton from net tows in the Bay of
Biscay. It is stained and digitized with an HP scanner in reflective mode at
600dpi.

There is also an example of a very simple training set in _train subdirectory
(only five groups are used). You can explore it with XnView (Apps -> Image
viewer (XnView)) and try the different classification algorithms on it.

Once you have a satisfactory training set, you can save it for further reuse
(Objects -> Save), and you can process the 2 samples provided too.

To run this example:
--------------------
- Download and unzip "ScanCol24-train&data.zip" in your 'ZooPhytoImage Examples'
  directory, or anywhere you like to place it.
  
- Start Zoo/PhytoImage and import the training set (sixth button). Select the
  _train subdirectory. At the end of the importation, you see a table of summary
  statistics about this training set.
  
- Train a classifier with it (seventh button). Experiment with the different
  algorithms provided, and display the "confusion matrix" using the eighth
  button after the training.
  
- Train a classifier with this training set and save it (Objects -> save; to
  reuse it later, or on another computer, reopen the corresponding file with
  Objects -> load). Inspect its performances. For the sake of this tutorial,
  we will assume that you are satisfied with it.
  
- Now that you have a classifier, you can predict all particules in your two
  samples, and calculate total/partial abundances, total/partial biomasses and
  total/partial size spectra. Usually, you need to document your series in a
  'description.zis' file (nineth button) before you can process it. In the
  present case, we provide an example description file. Inspect it to learn what
  you are supposed to put in it.
  
- For processing the two samples, click on the tenth button and select the
  provided 'description.zis' file. Each sample is processed in turn. If you
  checked the option for saving individual measurements, a table for each sample
  with the ECD, identification and calculation of individual biomass of each
  particle is saved on the same directory as the one where the 'description.zis'
  file resides. Once this is done, you can visualize the results by clicking on
  the eleventh button.
  
- Finally, you can further analyse your series by using all the statistical or
  graphical tools available in R. See Help -> Manuals -> An Introduction to R
  from the 'R Console' menu, if you are not familiar with R. All the data is in
  a 'ZIRes' object (called 'ZIres' by default), and it is a 'data frame' in R's
  terminology. Since this is the basic component in R, you can easily use any
  function (like plot(), for instance) on it to continue your analysis with R.

- If you prefer to use another software for you analysis (Matlab? Excel? other?)
  you just need to export your data (twelveth button). Data are exported as tab
  separated ASCII files, a format easily readable by any external software.