fixed graphics to fit new format

12 files changed, 16 insertions, 23 deletions

diff --git a/data/combined/graphs/plots.py b/data/combined/graphs/plots.py
index 8e855da..7b3940e 100755
--- a/data/combined/graphs/plots.py
+++ b/data/combined/graphs/plots.py
@@ -20,7 +20,7 @@ legend_width = 0.2
 out_dir = sys.argv[1]
 
 #limbs distribution
-plt.figure(figsize=(5.6,1.7))
+plt.figure(figsize=(6,1.8))
 data = np.loadtxt("../limbs-avg-zdiff/data.csv",delimiter=",")
 data = data[#(data[:,1] == 25) 
              ((data != -1).all(1))
@@ -40,7 +40,7 @@ for i in range(len(mean)):
 plt.savefig(os.path.join(out_dir,"limbs.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #frames distribution
-plt.figure(figsize=(4.5,2))
+plt.figure(figsize=(3,1.3))
 x = np.loadtxt("frames.txt",usecols=(0,))
 y = range(1,len(x)+1)
 width=0.8
@@ -55,7 +55,7 @@ plt.savefig(os.path.join(out_dir,"frames.pdf"),bbox_inches="tight",pad_inches=0.
 l = ["3","5","10","all"]
 
 #10-fold, naive
-plt.figure()
+plt.figure(figsize=(3,2.2))
 for i in l:
     x,y = np.loadtxt(i+"_nb_off.mat",unpack=True)
     plt.plot(100*x,100*y,label="$n_p=$ "+i)
@@ -64,11 +64,11 @@ plt.ylabel("Precision [%]")
 leg =plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"offline-nb.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #10-fold, SHT
-plt.figure()
+plt.figure(figsize=(3,2.2))
 for i in l:
     x,y = np.loadtxt(i+"_sht_off.mat",unpack=True)
     plt.plot(100*x,100*y,label="$n_p=$ "+i)
@@ -77,11 +77,11 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="lower left")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"offline-sht.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #online,NB
-plt.figure()
+plt.figure(figsize=(3,2.2))
 for i in l:
     x,y = np.loadtxt(i+"_nb_on.mat",unpack=True)
     plt.plot(100*x,100*y,label="$n_p=$ "+i)
@@ -90,11 +90,11 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(2)
+#plt.gca().set_aspect(2)
 plt.savefig(os.path.join(out_dir,"online-nb.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #online,SHT
-plt.figure()
+plt.figure(figsize=(3,2.2))
 for i in l:
     x,y = np.loadtxt(i+"_sht_on.mat",unpack=True)
     plt.plot(100*x,100*y,label="$n_p=$ "+i)
@@ -103,11 +103,11 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"online-sht.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #face
-plt.figure()
+plt.figure(figsize=(3,2.2))
 x,y = np.loadtxt("5_nb_split.mat",unpack=True)
 a,b = np.loadtxt("face.csv",delimiter=",", unpack=True)
 plt.plot(100*x,100*y,label="Skeleton")
@@ -117,11 +117,11 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"face.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #back
-plt.figure()
+plt.figure(figsize=(3,2.2))
 x,y = np.loadtxt("back_all_sht_on.mat",unpack=True)
 a,b = np.loadtxt("all_sht_off.mat",unpack=True)
 c,d = np.loadtxt("front_back_all_sht.mat",unpack=True)
@@ -133,11 +133,11 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"back.pdf"),bbox_inches="tight",pad_inches=0.05)
 
 #variance-reduction
-plt.figure()
+plt.figure(figsize=(3,2.2))
 x,y = np.loadtxt("half-var-all_sht_on.mat",unpack=True)
 a,b = np.loadtxt("all_sht_on.mat",unpack=True)
 plt.plot(100*x,100*y,label="Reduced noise")
@@ -147,5 +147,5 @@ plt.ylabel("Precision [%]")
 leg = plt.legend(loc="best")
 leg.get_frame().set_linewidth(legend_width)
 plt.axis([20,100,50,100])
-plt.gca().set_aspect(8./5)
+#plt.gca().set_aspect(8./5)
 plt.savefig(os.path.join(out_dir,"var.pdf"),bbox_inches="tight",pad_inches=0.05)
diff --git a/experimental.tex b/experimental.tex
index fde1395..f59be72 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -146,7 +146,6 @@ happens if the noise from the Kinect is reduced.
   \begin{center}
     \includegraphics[width=0.49\textwidth]{graphics/frames.pdf}
   \end{center}
-  \vspace{-1.5\baselineskip}
   \caption{Distribution of the frequency of each individual in the
   dataset}
   \label{fig:frames}
@@ -202,7 +201,6 @@ we reach 90\% accuracy at 60\% recall for a group size of 10 people.
   \caption{Results with 10-fold cross-validation for the top $n_p$ most present people}
 \label{fig:offline}
 \end{center}
-  \vspace{-1.5\baselineskip}
 \end{figure*}
 
 %\begin{figure}[t]
@@ -251,7 +249,6 @@ recognition rates mostly above 90\% for group sizes of 3 and 5.
 \begin{center}
     \includegraphics[width=0.49\textwidth]{graphics/online-sht.pdf}
 \end{center}
-\vspace{-1.5\baselineskip}
     \caption{Results for the online setting, where $n_p$ is the size of
     the group as in Figure~\ref{fig:offline}}
     \label{fig:online}
@@ -260,7 +257,6 @@ recognition rates mostly above 90\% for group sizes of 3 and 5.
   \begin{center}
     \includegraphics[width=0.49\textwidth]{graphics/face.pdf}
   \end{center}
-\vspace{-1.5\baselineskip}
   \caption{Results for face recognition versus skeleton recognition
   with $n_p=5$ people}
   \label{fig:face}
@@ -297,7 +293,6 @@ less than 10\% at most thresholds.
 \begin{center}
   \includegraphics[width=0.49\textwidth]{graphics/back.pdf}
 \end{center}
-\vspace{-1.5\baselineskip}
 \caption{Results with people walking away from and toward the camera}
 \label{fig:back}
 \end{figure}
@@ -305,7 +300,6 @@ less than 10\% at most thresholds.
 \begin{center}
   \includegraphics[width=0.49\textwidth]{graphics/var.pdf}
 \end{center}
-\vspace{-1.5\baselineskip}
 \caption{Results with and without halving the variance of the noise}
 \label{fig:var}
 \end{figure}
diff --git a/graphics/back.pdf b/graphics/back.pdf
index ec3345e..6086ad3 100644
--- a/graphics/back.pdf
+++ b/graphics/back.pdf
diff --git a/graphics/face.pdf b/graphics/face.pdf
index 58f50b8..02162b4 100644
--- a/graphics/face.pdf
+++ b/graphics/face.pdf
diff --git a/graphics/frames.pdf b/graphics/frames.pdf
index d5e2166..6b95abd 100644
--- a/graphics/frames.pdf
+++ b/graphics/frames.pdf
diff --git a/graphics/limbs.pdf b/graphics/limbs.pdf
index 0bbaad5..c3c023b 100644
--- a/graphics/limbs.pdf
+++ b/graphics/limbs.pdf
diff --git a/graphics/offline-nb.pdf b/graphics/offline-nb.pdf
index 7b22078..78283c9 100644
--- a/graphics/offline-nb.pdf
+++ b/graphics/offline-nb.pdf
diff --git a/graphics/offline-sht.pdf b/graphics/offline-sht.pdf
index 5be8762..42e461f 100644
--- a/graphics/offline-sht.pdf
+++ b/graphics/offline-sht.pdf
diff --git a/graphics/online-nb.pdf b/graphics/online-nb.pdf
index ec06cdc..3b59692 100644
--- a/graphics/online-nb.pdf
+++ b/graphics/online-nb.pdf
diff --git a/graphics/online-sht.pdf b/graphics/online-sht.pdf
index c8f7554..0034cca 100644
--- a/graphics/online-sht.pdf
+++ b/graphics/online-sht.pdf
diff --git a/graphics/var.pdf b/graphics/var.pdf
index 2983361..f4f75e2 100644
--- a/graphics/var.pdf
+++ b/graphics/var.pdf
diff --git a/uniqueness.tex b/uniqueness.tex
index 3d91dc8..ca89e85 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -70,7 +70,6 @@ output of the algorithm for the threshold $\delta$ is defined as:
   \begin{center}
     \includegraphics[width=0.49\textwidth]{graphics/roc.pdf}
   \end{center}
-  \vspace{-1.5\baselineskip}
   \caption{ROC curve for several standard deviations of the noise and
   for the state-of-the-art \emph{Associate-Predict} face detection
   algorithm. The standard deviation $\sigma$ is shown in millimeters}