MacroG16-example: ------------------- A few pictures taken with a digital reflex camera and a macro lens (namely, Canon 20D with Canon 100mm f/2.8 macro lens), and recorded at higher quality as possible (RAW images with .CR2 extension). The sample comes from the same series of 'ScanG16-example': neritic samples taken out off Tulear, Madagascar. The preprocess of this samples is one of the most complex and time-consumming calculation. Digital cameras record images in color thanks to a sensor whose individual cells record red, green or blue component. It is called the Bayer matrix. It means that we do not have all color information for each pixel, but a regular mosaic of color. To get the final image, one needs to "demosaicing" the image. Zoo/PhytoImage uses one of the best algorithm today for demosaicing: AHD, "adaptative homogeneity-directed demosaicing". Other preprocessings of the pictures include automatic graylevel calibration using images of neutral gray filters (see picture 'Image_3823.CR2'). Spatial calibration must be done manually by measuring the size of a given object or scale in a reference picture. Picture 'Image_3822.CR2' is a calibration slide with a 2mm scale that should be measured manually (do it in ImageJ) in order to provide the spatial calibration in the metadata (PixelSize/PixelUnit, that is size and size unit of one pixel in the image). Picture 'Image_3824.CR2' is the blank-field image. It is eliminated from all images just after demosaicing before recording the final picture at the end of the importation process. The last step done here, is to create metada (.zim file) associated with these images. It is a mixture between metadata you provide (see 'ImportTemplate.zie', including spatial calibration (PixelSize) discussed hereabove. Metadata fields that vary from image to image, or from sample to sample are included in the 'MacroG16.txt' file. Another source of information is included in the EXIF field of each RAW file. Major EXIF fields regarding how the image was take (speed, aperture, etc.) are automatically extracted and included in a place tagged by '' in the 'ImportTemplate.zie' file, that is, last line of the '[Image]' section. Last items added in the .zim file are data measured during preprocess and importation, mainly, graylevel calibration. This complex importation and preprocessing of the data requires ImageMagick, see the web site to download and install it, if you have not done it yet. To run this example: -------------------- - Download and unzip "MacroG16-example.zip" in your 'ZooPhytoImage Examples' directory, or anywhere you like to place it. - Start Zoo/PhytoImage and import the data (second button, select 'Table and ImportTemplate.zie' file type at the bottom of the dialog box and then, select MacroG16.txt file). The import mechanism compiles the .zim files (metadata) for each sample and rework the images as explained hereabove. - Once files are ready, they can be analyzed (third button, switch to ImageJ). In ImageJ, select (Plugins -> ZooPhytoImage -> Macrophoto Gray16). Select the .zim file you just created and watch the analysis live on screen. You can examine the mask and other temporary files in the _work subdirectory. - When the analysis is done, you can close ImageJ and switch back to the Zoo/PhytoImage assistant. Click on the fourth button to finalize the two .zid file corresponding to this analysis. You should end up with one .zid file (Zoo/PhytoImage Data) plus all the original files moved to the _raw subdirectory. What next...: ------------- The .zid files are key files in Zoo/PhytoImage. They contain everything you need for manual and automatic identification of the particules and for the calculation of summary statistics (abundances, biomasses, size spectra, etc.). At this point, you can archive (on DVD, external hard disks, etc.) the original data that are now in the _raw subdirectory o free space on your machine, and safely work with the .zid files only. With only one sample, you cannot do much more, but you are supposed to collect more data, then to train a classifier, check its performances and finally use it to identify particles in your samples. For examples with more data, switch to 'MacroColG16-train&data', and also to 'ScanG16-train&data', with similar data but originating from scanned images.