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+\usepackage[colorlinks=true, urlcolor=cyan]{hyperref}
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\begin{document}
\subsection{Hentai and Thighdeology} \label{sec:hentai}
For the purpose of this study and future studies in Project Hentai AI, the data in the datasets are categorised in three definitions: \emph{Hentai}, \emph{Ecchi} and \emph{Lewd}.
-In its simplest definition, Hentai is anime and manga pornography and can be seen as the highest tier out of the three. Ecchi on the other hand, when used as an adjective, translates to ``sexy'', ``dirty'' or ``naughty'', and has been used to describe anime and manga with \emph{sexual overtones} (playful sexuality or softcore). Lewd in these studies is defined as \emph{sexual undertones}.
-Project Hentai AI includes hentai, ecchi and lewd but groups them together in the name of the project under the term hentai for simplicity.
-% Todo: add images to demonstrate?
+In its simplest definition, Hentai is simply anime and manga pornography and can be seen as the most extreme out of the three. Ecchi on the other hand, when used as an adjective, translates to ``sexy'', ``dirty'' or ``naughty'', and has been used to describe anime and manga with \emph{sexual overtones} (playful sexuality or softcore). Lewd in these studies is defined as \emph{sexual undertones}.
+Contrary to its name, Project Hentai AI includes ecchi and lewd as well but the name of the project uses the term hentai for simplicity.
Thighdeology is the worship of thick anime thighs which has its Mecca on the Thighdeology subreddit\footnote{\url{https://www.reddit.com/r/thighdeology/}}.
The top two rules on the subreddit are: (1) All images must be thigh-focused and (2) No Pictures of Sex (Nudity is allowed). The second rule is a clear demonstration of the distinction between hentai and ecchi described above.
\emph{``Blessed is the man that walketh not in the counsel of the ungodly, nor standeth in the way of sinners, nor sitteth in the seat of the scornful. But his delight is in the law of the THICC anime thighs.''}
\end{quote}
-
-
-
-
\section{Method} \label{sec:method}
\subsection{Data Collection} \label{sec:datacollection}
\item Private Donations
\end{itemize}
-~\\\noindent After collection, the data was manually screened for (A) presence of thighs (B) image quality and (C) image ``cropability''. The presence of thighs simply implies that the image in question contains a section of the lower body of a humanoid character. The vast majority of the characters depicted in the images collected were of the feminine nature, although this was most likely due to the skewed ratio of feminine/masculine thighs from the sources themselves and not due to any discrimination during the collecting. This is further discussed within limitations in Section~\ref{sec:limitations}.
+~\\\noindent After collection, the data was manually screened for (A) presence of thighs (B) image quality and (C) image ``cropability''. The presence of thighs simply implies that the image in question contains a section of the lower body of a humanoid character. The vast majority of the characters depicted in the images collected were of the feminine nature, although this was most likely due to the skewed ratio of feminine/masculine thighs from the sources themselves and not due to any discrimination during the manual collecting. This is further discussed within limitations in Section~\ref{sec:limitations}.
Image quality refers to the resolution of the picture. When finding duplicates, the one with higher resolution was kept. Some images where included in the dataset even if the quality of the resolution was below average due to either its content or source.
The images were then cropped to contain as little as possible apart from the topic at hand (thighs). This was done with the intention of focusing both the manual labeling process as well as the machine learning training on the thighs. If the character on the image would have a certain hair color this could potentially influence the user when labeling the dataset, and later might be picked up during the learning and thus distorting the focus on the subject matter for this study.
-The cropping was performed by leveraging an open source module called \emph{interactivecrop}\footnote{\url{https://openbits.app/posts/python-interactive-cropping/}} installed via pip. A custom callback method was used to save the cropped subsection of the image.
After cropping the original non-cropped images are kept with their original name, while the newly cropped images get an appended notation of having undergone the procedure (e.g., \textbf{thighs-0001-crop.png}).
-The cropping was done manually by hand, using the interface provided in interactivecrop which resulted in that the cropped images were rectangles approximating squares. The implications of this when training the machine learning model with the dataset is further described in Section~\ref{sec:limitations}.
-
The cropped images were placed in a separate directory from the original images. By keeping both datasets, this study provides the possibility of utilizing the non-cropped images for future work.
+The data transformation implementation is detailed in Section~\ref{sec:datatfms}.
\subsection{Data Labeling} \label{sec:datalabeling}
-The labeling of the data is categorised in three different methods:
+The labeling of datasets in wAiFu is categorised in three different methods:
\begin{itemize}
\item Boolean labeling
\item Score labeling
\item Multi-labeling
\end{itemize}
-The \emph{Boolean labeling} consist of two disjunctive values (e.g., True/False, Yes/No, Approved/Disapproved, 1/0) which is the closest to the reviews previously gotten when brokering pictures of anime thighs manually. An image would be sent and an Approved/Disapproved would be received in return.
-% Todo: add model
+The \emph{Boolean labeling} consist of two disjunctive values (e.g., True/False, Yes/No, Approved/Disapproved, 1/0) which is the closest to the reviews previously gotten when brokering pictures of anime thighs manually. An image would be sent and an Approved/Disapproved would be received in return. A diagram example is seen in Figure~\ref{fig:protocol}.
+\begin{figure}
+ \includegraphics[width=.5\textwidth]{img/thighs_diagram.drawio.pdf}
+ \caption{The protocol of reviewing thighs using boolean labeling}
+ \label{fig:protocol}
+\end{figure}
The \emph{Score labeling} ranks the images on a scale (e.g., 0-10, 1-5, A-F). This could be considered to be a more advanced implementation of Boolean labeling (which would be viewed as a scale of 0-1) by adding more values in between.
The \emph{Multi-labeling} is an additional application area outside of just ranking thighs. Tags could be marked as labels (multiple labels per image) in order to recognise and identify these patterns. This could be related to clothes (e.g., thigh highs, panties, skirt) or body features (e.g., muscle, tattoo, tanned).
+The data labeling implementation is detailed in Section~\ref{sec:impl_labelapp}
+
\subsection{fast.ai} \label{sec:fastai}
% Todo
The AI implementation was using fast.ai, a layered API for deep learning~\cite{fastai}.
\section{Design} \label{sec:design}
\subsection{wAiFu Framework} \label{sec:waifu}
+Witty Artificial Intelligence Framework Utilization.
% Talk about the overview of the framework, the main idea
\section{Implementation} \label{sec:implementation}
-All code is open source and can be found on GitHub\footnote{\url{https://github.com/hentai-ai}}
+The code of all tools in Project Hentai AI is open source and can be found at \url{https://git.hentai-ai.org}.
\subsection{Data Transformations} \label{sec:datatfms}
-\begin{itemize}
- \item Converting to PNG
- \item Renaming
- \item Cropping with interactivecrop
-\end{itemize}
+
+\subsubsection{Convert and Rename}
+Talk about the script for making the dataset homogeneous.
+
+\subsubsection{Cropping Images}
+The cropping was performed using a python script extended from an open source image viewer, using a tile system for performance when zooming an panning\footnote{\url{https://github.com/foobar167/junkyard/blob/master/zoom_advanced.py}}. This was extended by adding a parameter for a target directory of images as well as a keybinding to crop and cycle through the images.
\subsection{Label App: Hentai Tinder} \label{sec:impl_labelapp}
+TODO: Update this section!!\\
The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#8866).
\begin{itemize}
\item Tkinter is a Python binding to the Tk GUI toolkit\footnote{\url{https://docs.python.org/3/library/tkinter.html}}
\section{Conclusion}
+\section*{Acknowledgement}
+We would like to thank Kittey for coming up with the name of the project: \emph{wAiFu}. We would also like to thank Hood Classic\#8866 for coming up with the name of the labeling app: \emph{Hentai Tinder}.
+
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