def setUp(self):
self.gesture_db = GestureDatabase()
self.gesture = self.gesture_db.get_gesture_entry("come_here")
self.D = self.gesture.dimensionality
self.gesture.process_signal_accommodation()
self.coordinates = self.gesture.get_merged_training_data()
for i in xrange(len(self.coordinates)):
for j in xrange(len(self.coordinates[i])):
self.coordinates[i][j] = self.coordinates[i][j] * 100.0
self.__learning_system = None
help=
"models to evaluate. if no specified, all that are evaluated. Otherwise provide comma separated models names ie: -m HMMGestureModel,GMMGestureModel,Time-GMMGestureModel,DTWGestureModel"
)
(options, args) = parser.parse_args()
if options.gesture_entries != None:
gesture_entries_names = options.gesture_entries.split(",")
else:
#default gestures to evaluate
rospy.loginfo(
"gestures must be specified using the parameter -g or (--gestures-entries). See help."
)
exit()
rospy.loginfo("gestures to be analyzed: %s", str(gesture_entries_names))
gesture_db = GestureDatabase()
#gesture_entries_names=["come_here"]
print "models to load: %s" % str(options.models_types)
if options.models_types == None:
models_names = gesture_db.get_models_names()
else:
models_types_name = options.models_types.split(",")
models_names = []
for mt in models_types_name:
models_names = models_names + gesture_db.get_models_names(
model_type_name=mt)
print "models found: %s" % str(models_names)
#cache for avoiding load models
def generate_images(gestures_to_paint, time_wrapping=False):
gesture_db = GestureDatabase()
images = []
max_number_demos = 24
functions = get_plot_functions()
image_index = 0
for gesture_entry_name in gestures_to_paint:
#load gesture
gesture = gesture_db.get_gesture_entry(gesture_entry_name)
print "loading gesture: %s" % gesture_entry_name
try:
#data preprocessing
gesture.process_signal_accommodation(
offset_accomodate=True,
demo_longitude_accomodate=True,
regular_sampling_acomodation=True,
time_wrapping=False)
for f in functions:
#create plotting infrastructure
fig1 = matplotlib.pyplot.figure(image_index)
fig1.clear()
leg = []
#plot
number_of_demos = min(max_number_demos,
gesture.demonstration_count)
m = None
(plot_key_name, leg) = f(gesture, m, fig1, number_of_demos)
if leg != None:
legend(tuple(leg))
images.append((gesture.name, fig1))
if m != None:
imageurl = REPORT_DIR + "/" + f.__name__ + "/" + gesture_entry_name + "-" + plot_key_name + ".png"
else:
imageurl = REPORT_DIR + "/" + f.__name__ + "/" + gesture_entry_name + ".png"
fig1.savefig(imageurl)
image_index += 1
except:
continue
'''models=gesture_db.get_models_by_gesture_name(gesture.name+".time.*k2time_wrapped\.gmm")
print "loading models: %s"+str(models)
models= [None]+models
for m in models:
for f in functions:
#create plotting infrastructure
fig1 = matplotlib.pyplot.figure(image_index)
fig1.clear()
leg=[]
#plot
number_of_demos=min(max_number_demos,gesture.demonstration_count)
(plot_key_name,leg)=f(gesture,m,fig1,number_of_demos)
if leg!=None:
legend(tuple(leg))
images.append((gesture.name,fig1))
imageurl=REPORT_DIR+"/"+f.__name__+"/"+gesture_entry_name+"-"+plot_key_name+".png"
fig1.savefig(imageurl)
image_index+=1
'''
return images