baseline_classifier.py
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00001 #
00002 # Copyright (c) 2010, Georgia Tech Research Corporation
00003 # All rights reserved.
00004 #
00005 # Redistribution and use in source and binary forms, with or without
00006 # modification, are permitted provided that the following conditions are met:
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00008 #       notice, this list of conditions and the following disclaimer.
00009 #     * Redistributions in binary form must reproduce the above copyright
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00012 #     * Neither the name of the Georgia Tech Research Corporation nor the
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00014 #       derived from this software without specific prior written permission.
00015 #
00016 # THIS SOFTWARE IS PROVIDED BY GEORGIA TECH RESEARCH CORPORATION ''AS IS'' AND
00017 # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
00018 # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
00019 # DISCLAIMED. IN NO EVENT SHALL GEORGIA TECH BE LIABLE FOR ANY DIRECT, INDIRECT,
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00021 # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
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00025 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00026 #
00027 
00028 #  \author Martin Schuster (Healthcare Robotics Lab, Georgia Tech.)
00029 
00030 from classifier import classifier
00031 
00032 import hrl_lib.util as ut
00033 import numpy as np
00034 
00035 from hrl_lib.util import getTime
00036 
00037 import processor
00038 
00039 class baseline_classifier(classifier):
00040     '''
00041     classdocs
00042     '''
00043 
00044 
00045     #def __init__(selfparams):
00046     #    '''
00047     #    Constructor
00048     #    '''
00049         
00050     
00051     def test(self, feature_data = None):
00052         #test on current scan:
00053         print getTime(), 'test on:', self.processor.scan_dataset.id    
00054             
00055         if feature_data == None:
00056             filename = self.processor.get_features_filename()
00057             dict = ut.load_pickle(filename)
00058         else:
00059             dict = feature_data
00060         
00061         baseline_labels = self.classify_baseline_code()
00062     
00063         return baseline_labels, self.test_results(dict, baseline_labels)  
00064     
00065     
00066     def classify_baseline_code(self):
00067         import hrl_tilting_hokuyo.processing_3d as p3d
00068         import hrl_tilting_hokuyo.occupancy_grid_3d as og3d
00069         import hrl_tilting_hokuyo.display_3d_mayavi as d3m
00070         pt = np.matrix(self.processor.point_of_interest).T
00071         #define VOI
00072         width_half = self.processor.voi_width / 2.0
00073         brf = pt+np.matrix([-width_half,-width_half,-width_half]).T
00074         tlb = pt+np.matrix([width_half, width_half, width_half]).T
00075         resolution = np.matrix([0.1,0.1,0.0025]).T
00076         max_dist = 15
00077         min_dist = -15
00078         gr = og3d.occupancy_grid_3d(brf,tlb,resolution)
00079         print 'filling grid...'
00080         gr.fill_grid(self.processor.pts3d_bound)
00081         print '...filled.'
00082         gr.to_binary(1)
00083         l = gr.find_plane_indices(assume_plane=True,hmin=0.3,hmax=2)
00084         z_min = min(l)*gr.resolution[2,0]+gr.brf[2,0]
00085         z_max = max(l)*gr.resolution[2,0]+gr.brf[2,0]
00086         
00087         pts = np.asarray(self.processor.pts3d_bound)
00088         conditions_surface = np.multiply(pts[2,:] > z_min, pts[2,:] < z_max)
00089         print 'cf',conditions_surface
00090         conditions_clutter = np.invert(conditions_surface)
00091         conditions_surface = np.multiply(conditions_surface, np.array(self.processor.map_polys) > 0)
00092         print 'cf',conditions_surface
00093         idx_surface = np.where(conditions_surface)
00094         conditions_clutter = np.multiply(conditions_clutter, np.array(self.processor.map_polys) > 0)
00095         idx_clutter = np.where(conditions_clutter)
00096         
00097         n, m = np.shape(self.processor.pts3d_bound)
00098         print n,m
00099         labels = np.zeros(m)
00100         print np.shape(labels), labels
00101         print np.shape(idx_surface), idx_surface
00102         labels[idx_surface] = processor.LABEL_SURFACE
00103         labels[idx_clutter] = processor.LABEL_CLUTTER
00104         
00105         print labels
00106         
00107         return labels     
00108          
00109     


laser_camera_segmentation
Author(s): Martin Schuster, Advisor: Prof. Charlie Kemp, Lab: Healthcare Robotics Lab at Georgia Tech
autogenerated on Wed Nov 27 2013 11:56:44