diff --git a/.vscode/ltex.dictionary.de-DE.txt b/.vscode/ltex.dictionary.de-DE.txt
index 85699716c141b50230134a00077b3e9362b9d3d9..51871205ab0b6095e06cecdae2257e99c35f4927 100644
--- a/.vscode/ltex.dictionary.de-DE.txt
+++ b/.vscode/ltex.dictionary.de-DE.txt
@@ -39,3 +39,6 @@ jtop
Debug-Nachricht
Operating
JetBot
+Canny-Edge-Detektor
+Gradientenbetrag
+Gradientenrichtung
diff --git a/.vscode/ltex.hiddenFalsePositives.de-DE.txt b/.vscode/ltex.hiddenFalsePositives.de-DE.txt
index e483b873b6d319aa7bc9077b0270bf45a2c02897..90ce5fda704b46bb7e06f629cb9986a44696f9ac 100644
--- a/.vscode/ltex.hiddenFalsePositives.de-DE.txt
+++ b/.vscode/ltex.hiddenFalsePositives.de-DE.txt
@@ -11,3 +11,4 @@
{"rule":"GERMAN_SPELLER_RULE","sentence":"^\\QBeim Start der \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q wird die main() Funktion aufgerufen, welche die notwendigen \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q Funktionen zur Initialisierung aufruft, das benötigt \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q abonniert, ein \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q anhängt und die eigenen Dummies veröffentlicht.\\E$"}
{"rule":"GERMAN_SPELLER_RULE","sentence":"^\\QDie Beziehung der Dummies ist in \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q Grafisch dargestellt.\\E$"}
{"rule":"GERMAN_SPELLER_RULE","sentence":"^\\QUm die Laufzeit der \\E(?:Dummy|Ina|Jimmy-)[0-9]+\\Q zu bestimmen wird die aktuelle Zeit wie sie von der Funktion ros::Time::now() zurückgegeben wird verwendet.\\E$"}
+{"rule":"DE_CASE","sentence":"^\\QUm die Datenmenge gering und die Laufzeit schnell zu halten, werden lediglich die vier Klassen Vertikal, Horizontal, Diagonal 1 und Diagonal 2 verwendend.\\E$"}
diff --git a/Bachelorarbeit.pdf b/Bachelorarbeit.pdf
index 6381cdcf5a1fae5e6330a063b63e5c81d962b13b..e5d0b05962ae5a999d9dda89b4d0b1952039a7f1 100644
Binary files a/Bachelorarbeit.pdf and b/Bachelorarbeit.pdf differ
diff --git "a/bib/Glossareintr\303\244ge.tex" "b/bib/Glossareintr\303\244ge.tex"
index d18aeb71ed2318623f4b2365d938657e0df86db7..59067b1c988d3ec3e2e409bb8a682c4f7fe48a35 100644
--- "a/bib/Glossareintr\303\244ge.tex"
+++ "b/bib/Glossareintr\303\244ge.tex"
@@ -74,6 +74,14 @@
}
}
+\newglossaryentry{JetBot}{
+ name={JetBot},
+ description={
+ Der in deser Arbeite verwendete Roboter. Gesteuert wird er von einem NVIDIA Jetson Nano Entwicklerboard, auf welchem die hier erstellten
+ Programme laufen. Weiteres ist in \autoref{sec: JetBot} beschrieben.
+ }
+}
+
\newglossaryentry{Welt-coords}{
name={Weltkoordinaten},
description={
@@ -100,12 +108,33 @@
name={ROI, kurz für Region of Interest},
text={ROI},
description={
- Ein Bildbereich, der für die derzeitige Anwendung relevant ist. Das restlcihe Bild wird vernachlässigt.
+ Ein Bildbereich, der für die derzeitige Anwendung relevant ist. Das restliche Bild wird vernachlässigt.
+ }
+}
+
+\newglossaryentry{gauss-filter}{
+ name={Gaussscher Filter},
+ description={
+ Eine Filterfunktion um Bilder zu glätten. Sie biltet den mittels einer 2D-Gaußfunktion gewichten Mittelwert einer Nachbarschschaft.
}
}
+\newglossaryentry{canny}{
+ name={Canny-Edge-Detektor},
+ description={
+ Ein Algorithmus zur Erkennung von Kanten in Bildern.
+ }
+}
+
+\newglossaryentry{Gradientenorientierung}{
+ name={Gradientenorientierung},
+ description={
+ Richtung des Farbgradienten in einer Pixelnachbarschaft. Verläuft von dunklen Bildbereichen zu hellen Bildbereichen.
+ }
+}
\newglossaryentry{Kernel}{
name={Kernel},
+ plural={Kerneln},
description={\todo{Beschreibung}}
}
diff --git a/chap/implementation.tex b/chap/implementation.tex
index 64ae998dbff8ff0f7897300786d55dd6bce07eb1..5830dab184623b7991fba4fec178cd2908125bda 100644
--- a/chap/implementation.tex
+++ b/chap/implementation.tex
@@ -1,16 +1,285 @@
\chapter{Fahrspurerkennung} \label{chap: implementation}
- \section{Bild Vorbereitung} \label{sec: bild vorbereitung}
-
- \section{Canny-Edge-Detector} \label{sec: canny edge}
-
- \section{Orientierungserfassung mittels Sobel} \label{sec: sobel}
+ Dieses Kapitel thematisiert, wie die Erkennung der Fahrspurmarkierungen umgesetzt wir. Begonnen wird mit einer konzeptionellen Version in
+ Python, mit der der Ablauf des Algorithmus geplant und getestet wird. Danach wird die Logik in einer \gls{C++} \gls{ROS Node} umgesetzt, um die
+ best möglichst Performance zu erhalten.
\begin{figure}
- \includegraphics[width=.4\textwidth]{svg/CannyEdge_Orientation.pdf}
- \caption{Klassifizierung der Gradientenorientierung (nach \cite{Vorlesung:Homann})}
- \label{fig: gadienten orientierung}
+ \includegraphics[page=1,scale=.85]{svg/Topics_makerDetection.pdf}
+ \caption{Zusammenhang der Fahrspurmarkierung-Erkennungs \gls{ROS Node} mit den bestehenden \glspl{ROS Node}}
+ \label{fig: topics marker detection}
\end{figure}
- \section{Linienbildung} \label{sec: linien finden}
+ Wie diese neuen \glspl{ROS Node} mit den bestehenden \glspl{ROS Node} in Beziehung stehen soll, ist in \autoref{fig: topics marker detection}
+ grafisch dargestellt. Neu ist dabei, dass diese \gls{ROS Node} das korrigierte Schwarz-Weiß Bild von der in \autoref{sec: undistort Node}
+ beschriebenen entzerrer \gls{ROS Node} abonniert und die eigenen Ergebnis als neues \gls{Topic} zur Verfügung stellt.
+
+ \section{Konzeptionierung in Python}
+
+ Die Entwicklung und Konzeptionierung des Algorithmus Erfolg in \gls{python}, da diese Sprache nicht kompiliert werden muss, was das Testen
+ beschleunigt, und generell einfacher zu verwenden ist.
+
+ Der Algorithmus lässt sich in mehre Einzelschritte aufteilen und wird daher in den folgen Unterkapitel beschreiben. Zur Übersicht
+ ist aber der gesamte Ablauf in \autoref{fig: PAP} vereinfacht skizziert. Angefangen wird dort mit dem Erhalten des Bildes, womit sowohl
+ manuelles laden eines Beispielbildes, als auch das Erhalten des Bildes über ein \gls{Topic} gemeint ist.
+
+ \begin{figure}
+ \includegraphics[scale=.85]{svg/PAP_marker_erkennung.pdf}
+ \caption{Ablauf des Algorithmus zur Erkennung von Fahrspurmarkierungen}
+ \label{fig: PAP}
+ \end{figure}
+
+ Während einer Testfahrt des \glspl{JetBot} wurden von der entzerrer \gls{ROS Node} veröffentlichte Bilder abgespeichert, sodass sie zum
+ lokalen Testen zur Verfügung stehen. Diese wurden unter \cite{git:dataset-strassen} abgelegt. In \autoref{fig: beispiel bild} ist eines dieser
+ Bilder gezeigt, mit dem im Folgenden die Einzelschritte demonstriert werden.
+
+ \begin{figure}
+ \includegraphics[width=.6\textwidth]{img/Marks_original.png}
+ \caption{Ein Beispiel Bild an dem der Ablauf demonstriert wird}
+ \label{fig: beispiel bild}
+ \end{figure}
+
+
+ \pagebreak
+ \subsection{Kantenerkennung mittels Canny-Edge-Detektor}
+
+ Begonnen wird mit der Detektion von Kante im Bild. Dazu wird das Bild zuerst mit \gls{OpenCV} geladen. \todo{Absatz Ja/Nein?}
+
+ Um kleine Störungen im Bild, welche bestehende Kanten verzerren oder als falsche Kante erkannt werden könnten, zu reduzieren, wird das
+ Bild mit einem \glslink{gauss-filter}{Gaußschen Filter} geglättet. Es wird ein $3\!\times\!3$ \gls{Kernel} mit einer Normalverteilung von
+ $\sigma=1,5$ verwendet. \gls{OpenCV} stell hierzu die Funktion \lstinline{GaussianBlur()} zur Verfügung, der das geladene Bild, die
+ Kegelgröße und der Wert für $\sigma$ übergeben wird.
+
+ Die eigentliche Kantenerkennung wird mittels eines \glspl{canny} durchgeführt. Dabei handelt es sich um einen von John Canny 19983
+ entwickelten und in \cite{Canny:computationAlapproachEdgeDetection} veröffentlichten Algorithmus. Dieser bestimmt für jeden Pixel den
+ Gradientenbetrag der Gradienten in X- und Y-Richtung. Dann werden diejenigen Pixel unterdrückt, welche entlang der Gradientenrichtung kein
+ Maximum darstellen. Zum Abschluss wird das Bild mit einem Hysterese-Schwellwert binarisiert. Das bedeutet, dass alle Pixel über einem
+ initialen, oberen Schwellwert als Kanten gesetzt werden und mittels eines zweiten, niedrigeren Schwellwerte, Lücken zwischen diesen Pixeln
+ geschlossen werden. \cite{Nischwitz:Computergrafik2}
+
+ Auch dieser Algorithmus ist in \gls{OpenCV} bereits implementiert und wird für den ersten Entwurf verwendet. Die Funktion bekommt das
+ geladene und geglättet Bild sowie die beiden Hysterese-Schwellwerte übergeben. Diese ist auch in \autoref{code: canny} gezeigt.
+
+ \begin{lstlisting}[
+ float,
+ style=example,
+ caption={Laden, glätten eines Bildes und durchführen der Kantenerkennung mit \gls{OpenCV}},
+ label=code: canny,
+ language=Python
+ ]
+ # load image (should be gray, so convert)
+ img = cv2.imread("./image.png")
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+ # edge detection
+ img = cv2.GaussianBlur(img, (3,3), 1.5)
+ canny = cv2.Canny(img, 180, 40)
+ \end{lstlisting}
+
+ Wird dieser Code auf das Beispielbild \ref{fig: beispiel bild} angewendet und das Ergebnis des \glspl{canny} ausgegeben, ergibt sich
+ \autoref{fig: canny edges}. Im Gegensatz zu alternativen, wie einer reinen Grabentenbetrachtung, liefert der \gls{canny}
+ Kantenmarkierungen (hier in weiß) die nur ein\todo{einen?} Pixel breit sind. Dies ermöglicht die in den folgenden Unterkapiteln
+ beschriebenen Schritte.
+
+ \begin{figure}
+ \includegraphics[width=.6\textwidth]{img/Marks_cannyedges.png}
+ \caption{Vom Canny-Edge-Detector gefundene Kanten}
+ \label{fig: canny edges}
+ \end{figure}
+
+
+ \subsection{Klassifizierung der Kantenpixel}
+
+ Nur die Identifikation von Pixeln als Kantenpixeln reicht nicht aus, um bereits Linienmarkierungen zu erkennen. Uns Menschen fällt es zwar
+ Leicht in \autoref{fig: canny edges} die gesuchten Linien zu identifizieren, für den Algorithmus handelt es sich aber nur um eine
+ "zufällige"\todo{"zufällige" OK?} Ansammlung von weißen Pixeln. Es werden weiter Informationen benötigt.
+
+ Deshalb werden jedem Kantenpixel eine Klasse entsprechend seiner Orientierung zugeordnet. Um die Datenmenge gering und die Laufzeit
+ schnell zu halten, werden lediglich die vier Klassen \emph{Vertikal}, \emph{Horizontal}, \emph{Diagonal 1} und \emph{Diagonal 2}
+ verwendend. Zusätzlich wird noch die Richtungsinformation als Vorzeichen abgespeichert.
+
+ Die Klassifizierte erfolgt anhand der \gls{Gradientenorientierung} eines Pixels. Dazu werden mit $3\!\times\!3$ Sobel-\glspl{Kernel} die
+ Gradienten $d_x$ und $d_y$ bestimmt. Mit der \lstinline{atan2()} Funktion kann aus diesen beiden Größen der Winkel des Gradientenvektors
+ $\vec{G}$ berechnet werden. Mit diesem Winkel kann nun entsprechen der \autoref{fig: gadienten orientierung} die Klasse bestimmt werden.
+ Dabei ist zu beachten das $\vec{G}$ immer orthogonal auf der eigentlichen Kante steht, deshalb ist die Klasse \emph{Vertikal} auch auf der
+ links-rechts Achse der Abbildung zu finden.
+
+ \begin{figure}
+ \includegraphics[width=.4\textwidth]{svg/CannyEdge_Orientation.pdf}
+ \caption{Klassifizierung der Gradientenorientierung (nach \cite{Homann:VorlesungBildverarbeitung})}
+ \label{fig: gadienten orientierung}
+ \end{figure}
+
+ \pagebreak
+ Die Klassifizierung wird in einer 8-Bit Variable abgespeichert, da so ein normales Graustufen-Bild als Datenstruktur verwendet werden
+ kann. Jeder Klasse wird dabei ein Bit wie folgt zugeordnet:
+
+ \begin{table}
+ \caption{Zuordnung der Klassen zu Bits}
+ \begin{tabular}{r l}
+ Bit & Klasse \\
+ 1 & \emph{Vertikal} \\
+ 2 & \emph{Diagonal 1} \\
+ 3 & \emph{Diagonal 2} \\
+ 4 & \emph{Horizontal} \\
+ 5 & Vorzeichen-Bit \\
+ \end{tabular}
+ \end{table}
+
+ Um die Klassifizierung in \gls{python} durchzuführen, wird zuerst ein weiteres, leere 8-Bit Bild mit identischer Größe angelegt. Dann wird
+ erneut über alle Pixel des Bildes iteriert. Da allerdings die meisten Pixel schwarz und damit uninteressant sind, können diese direkt
+ verworfen werden. Für alle verbleibenden, weißen Pixel wird die Klassifizierung durchgeführt.
+
+ \begin{lstlisting}[
+ float,
+ style=example,
+ caption={Schleife über das vom \gls{canny} gelieferte Bild},
+ language=Python
+ ]
+ for (u, v), e in np.ndenumerate(canny[1:-1, 1:-1]):
+ if not e:
+ continue
+ u += 1
+ v += 1
+ \end{lstlisting}
+
+ \begin{equation}\label{eq: dx dy}
+ \begin{split}
+ d_x &=
+ \begin{bmatrix}
+ p_{\text{-}1\text{-}1} & p_{\text{-}10} & p_{\text{-}11} \\
+ p_{0\text{-}1} & p_{00} & p_{01} \\
+ p_{1\text{-}1} & p_{10} & p_{11} \\
+ \end{bmatrix}
+ \circ
+ \begin{bmatrix}
+ 0 & 0 & 0 \\
+ -1 & 0 & +1 \\
+ 0 & 0 & 0 \\
+ \end{bmatrix}
+ \\
+ d_y &=
+ \begin{bmatrix}
+ p_{\text{-}1\text{-}1} & p_{\text{-}10} & p_{\text{-}11} \\
+ p_{0\text{-}1} & p_{00} & p_{01} \\
+ p_{1\text{-}1} & p_{10} & p_{11} \\
+ \end{bmatrix}
+ \circ
+ \begin{bmatrix}
+ 0 & -1 & 0 \\
+ 0 & 0 & 0 \\
+ 0 & +1 & 0 \\
+ \end{bmatrix}
+ \end{split}
+ \end{equation}
+
+ Zuerst die Gradienten $d_x$ und $d_y$ ermittel. Dazu wird die $3\!\times\!3$ Pixelnachbarschaft des aktuellen Pixels elementweise mit dem
+ jeweiligen Sobel-\gls{Kernel} multipliziert und die Summe der Ergebnismatrix gebildet (siehe \autoref{eq: dx dy}). Das Pythonpaket
+ \lstinline{numpy} stellt hierfür sehr hilfreiche Funktion zum Arbeiten mit Matrizen zu Verfügung. Dadurch lässt sich diese Operation in
+ wenigen Zeilen durchführen, wie \autoref{code dx dy} gezeigt.
+
+ \begin{lstlisting}[
+ float,
+ style=example,
+ caption={Bestimmung der Gradienten $d_x$ und $d_y$},
+ label=code: dx dy,
+ language=Python
+ ]
+ nh = img[u-1:u+2, v-1:v+2]
+ dx = np.sum(nh * SOBEL_X)
+ dy = np.sum(nh * SOBEL_Y)
+ \end{lstlisting}
+
+ Mit diesen werden wird nun die \lstinline{atan2(dy,dx)} Funktion aufgerufen. Diese gibt einen Winkel in rad zurück, welcher zur besseren
+ Nachvollziehbarkeit in Grad umgerechnet wird.
+
+ Nun werden durch eine Folge von Bedingungen die Klasse des aktuellen Pixels bestimmt. Zuerst wird das Vorzeichen bestimmt und im 5. Bit
+ abgespeichert. Dies vereinfacht die folgenden Abfragen, da für die \emph{Vertikale} und \emph{Horizontale} Klasse der Betrag des Winkels
+ ausreicht.
+
+ Ist die Klasse bestimmt, wird das entsprechende Bit des Pixels gesetzt. Die Umsetzung in Python ist in
+ \autoref{code: python Klassifizierung} gezeigt.
+
+ \begin{lstlisting}[
+ float,
+ style=example,
+ caption={Durchführen der Klassifizierung mittel des bestimmten Winkels},
+ label=code: python Klassifizierung,
+ language=Python
+ ]
+ arc = atan2(dy, dx) / pi * 180
+
+ if arc < 0:
+ pixel_info[u, v] |= 0x10
+ arc = abs(arc)
+ if arc >= 157.5 or 22.5 > arc:
+ pixel_info[u, v] |= V
+ elif 22.5 <= arc < 67.5:
+ pixel_info[u, v] |= D1 if not pixel_info[u, v] else D2
+ elif 67.5 <= arc < 112.5:
+ pixel_info[u, v] |= H
+ elif 112.5 <= arc < 157.5:
+ pixel_info[u, v] |= D2 if not pixel_info[u, v] else D1
+ \end{lstlisting}
+
+ Wurde jeder Kantenpixel klassifiziert, ist der Vorgang beendet. Zur Veranschaulichung wurde ein Bild erstellt, wo jeder Klasse und
+ Vorzeichen eine eindeutige Farbe zugeordnet ist. So ist genau zu erkennen, welche Kanten derselben Klasse zugeordnet wurden. Diese Bild
+ ist in \autoref{fig: classified edges} gezeigt.
+
+ \begin{figure}
+ \includegraphics[width=.6\textwidth]{img/Marks_classification.png}
+ \caption{
+ Klassifizierte Kanten mit farblicher Markierung der unterschiedlichen Klassen
+ (Farben sind nicht identisch mit \autoref{fig: gadienten orientierung})
+ }
+ \label{fig: classified edges}
+ \end{figure}
+
+ \subsubsection{Genauigkeit der Klassifizierung}
+
+ Die Klassifizierung erfolgt leider nicht immer völlig zuverlässig. Gut klassifizierte Kanten haben für die gesamte Länge der Linie
+ dieselbe Klasse erhalten wie es im vergrößerten Bildausschnitt \autoref{fig: gute kante} zu sehen ist. Im Gegensatz dazu haben
+ unzuverlässig klassifizierte Kanten mehrere Klassen in einem engen Bereich und wechseln häufig sogar mehrfach zwischen mehreren
+ Klassen, wie das Beispiel in \autoref{fig: schlechte kante} zeigt.
+
+ \begin{figure}
+ \subfigure[Beispiel guter Kantenklassifizierung]{
+ \label{fig: gute kante}
+ \includegraphics[scale=4]{img/Marks_classification_cut1.png}
+ }
+ \subfigure[Beispiel schlechter Kantenklassifizierung]{
+ \label{fig: schlechte kante}
+ \includegraphics[scale=4]{img/Marks_classification_cut2.png}
+ }
+ \caption{Vergleich gut und schlecht klassifizierte Bildbereiche}
+ \end{figure}
+
+ Durch Störungen und generell schlechtere Bildqualität in weiter von der Kamera entfernten Bildbereichen weisen vor allem die äußeren
+ Linien viele dieser Ungenauigkeiten auf. Das führt dazu, das die nachfolgende Logik viele einzelne, kleine Linien anstatt der
+ vollständigen, durchgängigen Linie, erkennt.
+
+ Die zentralen Linienmarkierungen der eigenen Spur werden aber zuverlässig genug klassifiziert.
+
+
+ \subsection{Linienbildung} \label{sec: linien finden}
+
+ \begin{figure}
+ \includegraphics[width=.6\textwidth]{img/Marks_found-lines.png}
+ \caption{Umrisse und Mittellinien der gefundenen Fahrspurmarkierungen}
+ \label{fig: found markings}
+ \end{figure}
+
+
+ \subsection{Performance Betrachtung}
+
+ sehr schlecht offensichtlich...
+
+
+ \section{Implementierung in eine ROS Node}
+
+ \subsection{Performance Betrachtung}
+
+
+ \section{Optimierung durch eigene Implementierung des Canny-Edge-Detectors}
+ \subsection{Performance Betrachtung}
diff --git a/img/Marks_cannyedges.png b/img/Marks_cannyedges.png
new file mode 100644
index 0000000000000000000000000000000000000000..1a6975c0ca411a5c165b74bd3ebd2fdda3005b53
Binary files /dev/null and b/img/Marks_cannyedges.png differ
diff --git a/img/Marks_classification.png b/img/Marks_classification.png
new file mode 100644
index 0000000000000000000000000000000000000000..5569f41f0ddc666f85a69c568c28fc70428e98ad
Binary files /dev/null and b/img/Marks_classification.png differ
diff --git a/img/Marks_classification_cut1.png b/img/Marks_classification_cut1.png
new file mode 100644
index 0000000000000000000000000000000000000000..8f4af79019390ae9184de13b343077d392c8da73
Binary files /dev/null and b/img/Marks_classification_cut1.png differ
diff --git a/img/Marks_classification_cut2.png b/img/Marks_classification_cut2.png
new file mode 100644
index 0000000000000000000000000000000000000000..855aca5eb37dba27f7815c4cfcdcfc038aeac2e4
Binary files /dev/null and b/img/Marks_classification_cut2.png differ
diff --git a/img/Marks_original.png b/img/Marks_original.png
new file mode 100644
index 0000000000000000000000000000000000000000..ea0f05a948fe19721f6bc6f4600203d39119a4a6
Binary files /dev/null and b/img/Marks_original.png differ
diff --git a/svg/CannyEdge_Orientation.svg b/svg/CannyEdge_Orientation.svg
index b019de263fc3357fced3cfdcdababe1099c91b32..b24be786922f5242adb1227893fc6edb45561b39 100644
--- a/svg/CannyEdge_Orientation.svg
+++ b/svg/CannyEdge_Orientation.svg
@@ -7,7 +7,7 @@
viewBox="0 0 17.633106 17.633106"
version="1.1"
id="svg5"
- inkscape:version="1.1.2 (b8e25be833, 2022-02-05)"
+ inkscape:version="1.2.1 (9c6d41e410, 2022-07-14)"
sodipodi:docname="CannyEdge_Orientation.svg"
xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
@@ -28,28 +28,111 @@
inkscape:snap-smooth-nodes="true"
showguides="true"
inkscape:guide-bbox="true"
- inkscape:zoom="2.8284272"
- inkscape:cx="94.221978"
- inkscape:cy="60.457629"
+ inkscape:zoom="16"
+ inkscape:cx="25.374999"
+ inkscape:cy="44.437499"
inkscape:window-width="1920"
inkscape:window-height="1017"
inkscape:window-x="-8"
inkscape:window-y="-8"
inkscape:window-maximized="1"
- inkscape:current-layer="layer1" />
+ inkscape:current-layer="layer1"
+ inkscape:showpageshadow="2"
+ inkscape:deskcolor="#d1d1d1" />
<defs
- id="defs2" />
+ id="defs2">
+ <defs
+ id="id-c5e6cdc8-5ede-4051-aa2a-d2190714a883">
+ <g
+ id="id-291aad23-09f8-414d-8fa3-16fd809c7e80">
+ <g
+ id="id-2a18a33d-8733-4d65-8792-06e0ba98a019" />
+ <g
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diff --git a/svg/Topics_makerDetection.svg b/svg/Topics_makerDetection.svg
new file mode 100644
index 0000000000000000000000000000000000000000..32e11d6668826e801ff2220527886772591a7b90
--- /dev/null
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