def do_cluster(raw_data, mode):
cluster_labels=[]
filter_zeros=np.where(raw_data[:, 1] != 0)[0]
#% OPTIMIZE DATA QUALITY
clear_data = raw_data[filter_zeros,:] # ignore the zeros on X
filter_zeros=np.where(clear_data[:, 2] != 0)[0]
clear_data = clear_data[filter_zeros,:] # ignore the zeros on Y
rospy.loginfo('filtered data')
#filter2=np.where(clear_data[:, 1] > 0.5)[0]
#clear_data = clear_data[filter2, :] # ignore mishits
#% TRAINING
if mode == 0:
cluster_labels =np.zeros((len(clear_data),1),int)
eps = 0.5
min_points = 3
rospy.loginfo('call DBscan ')
[core_samples,cluster_labels, n_clusters, human]=scikit_dbscan.dbscan(clear_data, eps, min_points,mode)
#% TESTING
if mode == 1:
rospy.loginfo('call DBscan ')
[core_samples,cluster_labels,n_clusters, human]=scikit_dbscan.dbscan(clear_data,eps, min_points)
return core_samples,cluster_labels,human
def main_cb(cloud_msg):
#DECLARE GLOBAL VARIABLES
global slot_count
global final_data
global all_hogs
global train_surfaces
global surfacesX
global all_surf
global labels
#CONVERT TO XYZ
rospy.loginfo('converting pointcloud %d to XYZ array ',slot_count)
raw_data = pointclouds.pointcloud2_to_xyz_array(cloud_msg, remove_nans=True)
#
mode=0 # mode=0 -->COLLECT DATA mode=1 --> TRAIN AND TEST
#BUILD CLUSTER
filter_zeros=np.where(raw_data[:, 0] != 0)[0]
clear_data = raw_data[filter_zeros,:] # ignore the zeros on X
filter_zeros=np.where(clear_data[:, 1] != 0)[0]
clear_data = clear_data[filter_zeros,:] # ignore the zeros on Y
cluster_labels =np.zeros((len(clear_data),1),int)
#% TRAINING
eps = 0.5
min_points = 5
rospy.loginfo('call DBscan ')
[core_samples,cluster_labels, n_clusters, human, surfacesX]=scikit_dbscan.dbscan(clear_data, eps, min_points,mode,False)
clear_data=clear_data[core_samples,:]
# SURFACE & HOG Features EXTRACTION
all_surf.append(surfacesX)
rospy.loginfo('Done.')
#EXTRACT AND SAVE HOGS FEATURES
rospy.loginfo('extract hogs for timeslot %d',slot_count)
[hogs,hog_image] = myhog.hog(surfacesX)
#pl.plot(hog_image)
#pl.show(0.5)
final_data.append(clear_data)
labels.append(cluster_labels)
human_detection.append(human)
all_hogs.append(hogs)
rospy.loginfo('all_hogs length %d',len(all_hogs))
rospy.loginfo('final_data length %d',len(final_data))
rospy.loginfo('human_detection length %d',len(human_detection))
if mode==0:
f = open("train_hogs.txt","a")
simplejson.dump(all_hogs,f)
f.close()
f = open("train_classifications.txt","a")
simplejson.dump(all_human_detection,f)
f.close()
if mode==1:
with open("train_hogs.txt") as f:
train_hogs = simplejson.load(f)
with open("train_classifications.txt") as f2:
train_labels = simplejson.load(f2)
X=array(train_hogs)
y=array(train_labels)
clf = svm.SVC()
clf.fit(X, y)
#[p0V,p1V,pAb]= trainNB0(array(train_hogs),array(train_labels))
#print testEntry,'classified as: ',classifyNB(array(hogs),p0V,p1V,pAb)
slot_count = slot_count + 1