Script uitleg erbij gezet en uitleg hoe ons filemap systeem moet werken voor...

Script uitleg erbij gezet en uitleg hoe ons filemap systeem moet werken voor (nja mijne in elk geval, weet niet welke nog meer) enzo.

docs/report.tex

@@ -351,7 +351,25 @@ it as good as possible because all occurrences are in the learning set.
 \subsection{Supporting Scripts}
 
 To be able to use the code efficiently, we wrote a number of scripts. This
-section describes the purpose and usage of each script.
+section describes the purpose and usage of each script. For each script it is
+essential that you use the correct folder and subfolder naming scheme. The scheme
+is as follows:
+
+\begin{enumerate}
+
+\item A main folder called 'images' placed in the current directory as the src folder.
+\item In the images folder there have to be three folders. Images, Infos and LearningSet.
+\item The Images and Infos folder contain subfolders which are numbered ($0001$ to possibly $9999$).
+\item In each of the subfolders the data (i.e the images or xml files) can be placed.
+And have to be named $00991_XXXXX.ext$, where XXXXX can be $00000 to 99999$.
+\item For loops in the script currently only go up to 9 subfolders, with a maximum
+of containing 100 images or xml files. These numbers have to be adjusted if the scripts 
+are being used, but with a bigger dataset.
+\end{enumerate}
+
+It is ofcourse possible to use your own naming scheme. A search for the $filename$ variable will most
+likely find the occurences where the naming scheme is implemented.
+
 
 \subsection*{\texttt{create\_characters.py}}
 
@@ -367,8 +385,16 @@ section describes the purpose and usage of each script.
 
 \subsection*{\texttt{generate\_learning\_set.py}}
 
-
-
+Usage of this script could be minimal, since you only need to extract the letters carefully
+and succesfully once. Then other scripts in this list can use the extracted images. Most
+likely the other scripts will use caching to speed up the system to. But in short, the script will create images of a single character based on a given dataset
+of license plate images and corresponding xml files. If the xml files give correct
+locations of the characters they can be extracted. The workhorse of this script is
+$plate = xml_to_LicensePlate(filename, save_character=1)$. Where save_character is
+an optional variable. If set it will save the image in the LearningSet folder and
+pick the correct subfolder based on the character value. So if the XML says a character
+is an 'A' it will be placed in the 'A' folder. These folders will be created automatically
+if they do not exist yet.
 \subsection*{\texttt{load\_learning\_set.py}}