Small fixes here and there.

README.md

@@ -1,15 +1,19 @@
 #Installation Instructions:
-Download scripts in an existing ROS-Node
+Download scripts in an existing ROS package
+
 Download needed libraries:
 
 	sudo apt-get install python-scipy python-sklearn
 
 Tested on R.O.S. Hydro, Ubuntu 12.04
+
 Python v. 2.7.3
+
 Scipy v. 0.9.0 
+
 Sklearn v. 0.10
 
-laser scanner f.o.v : +- 45 deg. , intensity publishing enabled(not yet used)
+laser scanner f.o.v : +- 45 deg. , intensity publishing enabled(not used yet)
 
 # Human-Pattern-Recognition
 Real time recongition of humans through laser scans

hpr.py

@@ -145,8 +145,9 @@ def laser_listener():
                 break
             else:
                 print 'Try again'
+
         metrics = int(sys.argv[6])
-        while not (RepresentsInt(metrics)):
+        while not (RepresentsInt(metrics) and metrics != '1' and metrics != '0'):
             metrics=input('Set maximum scan range in m: ')
             if RepresentsInt(metrics):
                 break

hpr_mat.py

@@ -126,8 +126,8 @@ def offline_test():
                 print 'Try again'
 
         metrics = int(sys.argv[6])
-        while not (RepresentsInt(metrics) and metrics != 1 and metrics != 0):
-            wall_end=input('Create files that will be used for metrics? (1/0)')
+        while not (RepresentsInt(metrics) and metrics != '1' and metrics != '0'):
+            metrics=input('Set maximum scan range in m: ')
             if RepresentsInt(metrics):
                 break
             else:

merge_train.py

@@ -75,7 +75,7 @@ def DisplayClassifier():
     counts = np.bincount(np_ann_files)
     annot["humans"] = counts[1]
     annot["obstacles"] = counts[0]
-    sio.savemat('full_set_annotations', annot);
+    sio.savemat(path+"full_set_annotations", annot);
 
     #Create z-scored data
     temp = zscore(t_files)

video/Gaussian_NB_classifier_merged.p

@@ -0,0 +1,116 @@
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