#Installation Instructions: Download scripts in an existing ROS-Node 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)
Real time recongition of humans through laser scans
#Sample Run 1)Record bag file with laser scans 2)Convert it to .mat format 3)Manually annotate the data 4)Create a classifier, and P.C.A obj 5)Test online with the previously created classifier
#a)Convert R.O.S. bagfiles to suitable .mat files using 'bag2mat.py':
Enter desired destination with file ending in .mat
Command line use:
$rosrun <package_name> bag2mat.py <bag_file_path> <.mat_file_path> <laser_scan_rostopic> <scan_duration>
#b)Annotate with annotate.py :
Either provide command line arguments with the same order as below, or run the script without arguments and provide them when prompted
*Enter timewindow (int)
*Enter frames to set wall (int)
*Enter filename (string, no quotes)
trained data will be saved as : <input>.<trainingdata>
Command line use:
$python annotate.py <time_window> <wall_set_frames> <mat_file_to_use>
#c)create classifier with merge_train.py:
merge_train will create a classifier in the specified folder
$python merge_train <folder of annotated .mat files>
#d)Test on live data with hpr_with_metrics.py:
Publish laser scans on topic /scan, enable intensities, set min_angle, max_angle to -45,45 degrees
respectively (to be changed).
Command line use : $rosrun <package_name> hpr.py
#e)Test with data files instead of live data : $python offline_test.py <data_file_path> <annotation_data> <classifier_path> <pca_object_path>
RECOMMENDATION:
Use same timewindow, and wall set time for each training set, and use the same values when
evaluating