Zoo/PhytoImage Examples:
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There is a series of example datasets available to demontrate Zoo/PhytoImage
features. These can be downloaded separately from http://www.sciviews.org/zooimage
In any case, you should start practicing with these datasets, following respective
short tutorials provided. They demonstrate how to analyze images of different
origins:

- MicroCol24-example: micro/macrophotographies with any kind of digital camera
  with no calibration, or only spatial calibration and 24bit color.

- ScanCol24-example: stained plankton scanned at low resolution (600/800dpi)
  with any kind of flat-bed scanner; 24bit color.
  
- ScanG16-example: high resolution (1200/2400dpi) 16bit gray images scanned in
  transparency mode with an Epson 4870/4990 scanner, using Vuescan. Full
  calibration of the background (done automatically by the scanner), gray levels
  using neutral gray photographic filters, and spatial calibration (fixed
  resolution).
  
- MacroG16-example: high resolution (1000-3000dpi) 12bit gray raw images obtained
  with a reflex digital camera and a professional macro lens. These photographies
  were acquired with a Canon 20D (8 megapixels) camera, a Canon 100mm f/2.8 Macro
  lens, a Canon 580Ex flash, and two white reflectors at 45° each to light the
  samples from behind (transparency mode). Full calibration of the images through
  a blank-field image (calibration of the background), a picture of neutral gray
  photographic filters (calibration of gray levels) and a picture of a 2mm scale
  (spatial calibration).
  
- FlowCAM-example-FIT-IMS: data acquired with an old FlowCAM analog model.

- FlowCAM-example-FIT-VIS: data acquired with a recent digital FlowCAM and the
  Visual Spreadsheet software.
  
- Scan24-train&data: an example of a simple training set; color scanned images.

- ScanG16-train&data: an example of a more detailed training set, and a second
  version partly reworked.

- MacroG16-train&data: another training set, done with macrophotographies.

- PlanktonJ-example: a .zid (Zoo/PhytoImage Data) file to be used with PlanktonJ.


Explore Zoo/PhytoImage:
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Start with the 'ScanCol24-example' datasets, which is the simplest analysis
one can do (one picture per sample, one .zim file -metadata file- per image).
Image processing should be straightforward and finalize it by building the two
corresponding .zid files. It is also a good illustration of images you can get
with almost any flatbed scanner available out there, using a Petri dish to hold
your sample and haematoxylin staining to better separate the silhouettes.

You should try also 'ScanCol24-train&data' which contains a simple training set.
Learn how to import it, train a classifier and then, use the classifier to
calculate variables of interest for your samples, display and export final results.

Next, 'MicroCol24-example' is a small dataset that pretty well illustrates the
use of the Zoo/PhytoImage compiler to create a large number of .zim metadata files.
It uses both a template in 'ImportTemplate.zie', and a table of characteristics
for each sample stored in an ASCII *.txt file to automatize importation of images
and data into a suitable format. The .xls Excel version of the table is also
provided. It is easier to edit, but you must export it into a text file
(the .txt version) before you can use it in the current version of Zoo/PhytoImage.
The photographies are taken with a microscope and a digital camera (a widely
available digitizing system).

Once you can run these three examples and understand the logic behind, you should
be ready to use Zoo/PhytoImage with your own data. However, it is worth studying
the other, more complex, examples dealing with high-resolution and fully
calibrated pictures: 'MacroG16-example' and 'ScanG16-example'. Corresponding
example training sets and series of samples are in 'MacroG16-train&data' and
'ScanG16-train&data'. Switch to them, after you understand how to create .zid
files from your images.