def classify_object(self, object):
# class_dict = {'red_cube': 1, 'green_cylinder': 2, 'blue_handle': 0, 'obstacle': 0}
# load object
unclassified_object = object.unclassifiedObject
if self.logging >= 2 : print("Got Object to Classify: \r\n %s" %unclassified_object)
height = unclassified_object.c_height
# h = unclassified_object.c_avg_col_h
# s = unclassified_object.c_avg_col_s
# v = unclassified_object.c_avg_col_v
color_class = unclassified_object.c_color_class
# Classifier steps:
# 1) Consider the color
# 2) Consider the height of the object. This should be close to one of the dimensions of one object
# 3) Consider the volume (this may be optional if we already get good results)
# 1)
object_candidates = self.original_objects
for obj in object_candidates:
print obj.color
print "vs."
print color_class
print obj.color == color_class
object_candidates = [obj for obj in object_candidates if obj.color == color_class]
# if self.logging >= 1 : print("Remaining object candidates after color filtering {0}:".format(len(object_candidates) ) )
if self.logging >= 1 : print("Remaining object candidates after color filtering:" )
if self.logging >= 1 :
for c in object_candidates:
print c.name
# 2)
object_candidates = [obj for obj in object_candidates if self.check_dimension_tolerance(obj, height) ]
if self.logging >= 1 : print("Remaining object candidates after height filtering:")
if self.logging >= 1 :
for c in object_candidates:
print c.name
# 3)
# Not implemented yet
# Evaluate the remaining object_candidates
if len(object_candidates) > 1:
# We couldn't filter the data to get only one object. Set the class name to ambiguous
unclassified_object.object.id = "ambiguous"
unclassified_object.c_type = 255 # Classified as UNKNOWN(255)
elif len(object_candidates) == 1:
unclassified_object.object.id = object_candidates[0].name
unclassified_object.c_type = 1 # Classified as object, otherwise 0
else:
unclassified_object.object.id = "unknown"
unclassified_object.c_type = 255 # Classified as UNKNOWN(255)
# unclassified_object.c_shape = class_dict[class_name[0]] # TODO is this neccessary?
# unclassified_object.object.id = class_name
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1: print("Classified Object as: %s"%unclassified_object.object.id)
self.publish_marker_for_object(unclassified_object, unclassified_object.object.id + "\nheight: " + str(height))
return resp
def classify_object(self, cobject):
# load object
unclassified_object = cobject.unclassifiedObject
if self.logging >= 1:
rospy.loginfo("Got Object to Classify: \r\n %s" % unclassified_object)
height = unclassified_object.c_height
h = unclassified_object.c_avg_col_h
s = unclassified_object.c_avg_col_s
v = unclassified_object.c_avg_col_v
# Convert c++ hsv room to python one (take care only to 180 to fit in 8 bit)
h = int(h / 360. * 180)
s = int(s * 255)
v = int(v * 255)
hsv_color = np.uint8([[[h, s, v]]])
bgr_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)
r = bgr_color[0][0][2]
g = bgr_color[0][0][1]
b = bgr_color[0][0][0]
color_class = self.__color_class_number_mapping[unclassified_object.c_color_class]
volume = unclassified_object.c_volume
# build classifyable object and classify it
classifyable_unclassified_object = []
if 'color_class' in self.__selected_attributes:
classifyable_unclassified_object.append(color_class)
if 'height' in self.__selected_attributes:
classifyable_unclassified_object.append(height)
if 'volume' in self.__selected_attributes:
classifyable_unclassified_object.append(volume)
if 'cuboid' in self.__selected_attributes:
classifyable_unclassified_object += unclassified_object.object.primitives[0].dimensions
if 'color_hsv' in self.__selected_attributes:
classifyable_unclassified_object += [h, s, v]
if 'color_rgb' in self.__selected_attributes:
classifyable_unclassified_object += [r, g, b]
if self.logging >= 1:
rospy.loginfo("Object to Classify: \r\n %s" % classifyable_unclassified_object)
class_name = self.__classifier.predict(classifyable_unclassified_object)[0]
if class_name == "obstacle":
c_type = EurocObject.OBSTACLE
else:
c_type = EurocObject.OBJECT
# print class_name
# unclassified_object.c_shape = self.get_shape_of_class(class_name[0])
unclassified_object.object.id = class_name
unclassified_object.c_type = c_type
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1:
rospy.loginfo("Classified Object as: %s" % class_name)
self.__publish_marker_for_object(unclassified_object, unclassified_object.object.id + "\nheight: " + str(height))
return resp
def classify_object(self, object):
class_dict = {
'red_cube': 1,
'green_cylinder': 2,
'blue_handle': 0,
'obstacle': 0
}
# load object
unclassified_object = object.unclassifiedObject
if self.logging >= 2:
print("Got Object to Classify: \r\n %s" % unclassified_object)
height = unclassified_object.c_height
volume = unclassified_object.c_volume
h = unclassified_object.c_avg_col_h
s = unclassified_object.c_avg_col_s
v = unclassified_object.c_avg_col_v
#Convert c++ hsv room to python one (take care only to 180 to fit in 8 bit)
h = int(h / 360. * 180)
s = int(s * 255)
v = int(v * 255)
hsv_color = np.uint8([[[h, s, v]]])
# bgr_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)
# r = bgr_color[0][0][2]
# g = bgr_color[0][0][1]
# b = bgr_color[0][0][0]
#sort edges
if unclassified_object.c_cuboid_success:
edges = list(unclassified_object.object.primitives[0].dimensions)
edges.sort()
#build classifyable object and classify it
classifyable_unclassified_object = [h, s, v, height] # + edges
if self.logging >= 1:
print("Object to Classify: \r\n %s" %
classifyable_unclassified_object)
class_name = self.clf.predict(classifyable_unclassified_object)
# print class_name
# class_name = self.lolloosed(r,g,b)
if (volume > 0.0001) or (height >
(self.max_height + self.max_height * 0.05)):
class_name[0] = 'obstacle'
unclassified_object.c_shape = class_dict[class_name[0]]
unclassified_object.object.id = class_name[0]
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1:
print("Classified Object as: %s" % class_name)
self.publish_marker_for_object(
unclassified_object, class_name[0] + "\nheight: " + str(height))
return resp
def classify_object(self, object):
class_dict = {'red_cube': 1, 'green_cylinder': 2, 'blue_handle': 0, 'obstacle': 0}
# load object
unclassified_object = object.unclassifiedObject
if self.logging >= 2:
print("Got Object to Classify: \r\n %s" %unclassified_object)
height = unclassified_object.c_height
volume = unclassified_object.c_volume
h = unclassified_object.c_avg_col_h
s = unclassified_object.c_avg_col_s
v = unclassified_object.c_avg_col_v
#Convert c++ hsv room to python one (take care only to 180 to fit in 8 bit)
h = int(h / 360. * 180)
s = int(s * 255)
v = int(v * 255)
hsv_color = np.uint8([[[h, s, v]]])
# bgr_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)
# r = bgr_color[0][0][2]
# g = bgr_color[0][0][1]
# b = bgr_color[0][0][0]
#sort edges
if unclassified_object.c_cuboid_success:
edges = list(unclassified_object.object.primitives[0].dimensions)
edges.sort()
#build classifyable object and classify it
classifyable_unclassified_object = [h, s, v, height]# + edges
if self.logging >= 1:
print("Object to Classify: \r\n %s"%classifyable_unclassified_object)
class_name = self.clf.predict(classifyable_unclassified_object)
# print class_name
# class_name = self.lolloosed(r,g,b)
if (volume > 0.0001) or (height > (self.max_height + self.max_height*0.05)):
class_name[0] = 'obstacle'
unclassified_object.c_shape = class_dict[class_name[0]]
unclassified_object.object.id = class_name[0]
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1:
print("Classified Object as: %s"%class_name)
self.publish_marker_for_object(unclassified_object, class_name[0] + "\nheight: " + str(height))
return resp
def classify_object(self, cobject):
# load object
unclassified_object = cobject.unclassifiedObject
if self.logging >= 1:
rospy.loginfo("Got Object to Classify: \r\n %s" %
unclassified_object)
height = unclassified_object.c_height
h = unclassified_object.c_avg_col_h
s = unclassified_object.c_avg_col_s
v = unclassified_object.c_avg_col_v
# Convert c++ hsv room to python one (take care only to 180 to fit in 8 bit)
h = int(h / 360. * 180)
s = int(s * 255)
v = int(v * 255)
hsv_color = np.uint8([[[h, s, v]]])
bgr_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)
r = bgr_color[0][0][2]
g = bgr_color[0][0][1]
b = bgr_color[0][0][0]
color_class = self.__color_class_number_mapping[
unclassified_object.c_color_class]
volume = unclassified_object.c_volume
# build classifyable object and classify it
classifyable_unclassified_object = []
if 'color_class' in self.__selected_attributes:
classifyable_unclassified_object.append(color_class)
if 'height' in self.__selected_attributes:
classifyable_unclassified_object.append(height)
if 'volume' in self.__selected_attributes:
classifyable_unclassified_object.append(volume)
if 'cuboid' in self.__selected_attributes:
classifyable_unclassified_object += unclassified_object.object.primitives[
0].dimensions
if 'color_hsv' in self.__selected_attributes:
classifyable_unclassified_object += [h, s, v]
if 'color_rgb' in self.__selected_attributes:
classifyable_unclassified_object += [r, g, b]
if self.logging >= 1:
rospy.loginfo("Object to Classify: \r\n %s" %
classifyable_unclassified_object)
class_name = self.__classifier.predict(
classifyable_unclassified_object)[0]
if class_name == "obstacle":
c_type = EurocObject.OBSTACLE
else:
c_type = EurocObject.OBJECT
# print class_name
# unclassified_object.c_shape = self.get_shape_of_class(class_name[0])
unclassified_object.object.id = class_name
unclassified_object.c_type = c_type
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1:
rospy.loginfo("Classified Object as: %s" % class_name)
self.__publish_marker_for_object(
unclassified_object,
unclassified_object.object.id + "\nheight: " + str(height))
return resp
def classify_object(self, object):
# class_dict = {'red_cube': 1, 'green_cylinder': 2, 'blue_handle': 0, 'obstacle': 0}
# load object
unclassified_object = object.unclassifiedObject
if self.logging >= 2:
print("Got Object to Classify: \r\n %s" % unclassified_object)
height = unclassified_object.c_height
# h = unclassified_object.c_avg_col_h
# s = unclassified_object.c_avg_col_s
# v = unclassified_object.c_avg_col_v
color_class = unclassified_object.c_color_class
# Classifier steps:
# 1) Consider the color
# 2) Consider the height of the object. This should be close to one of the dimensions of one object
# 3) Consider the volume (this may be optional if we already get good results)
# 1)
object_candidates = self.original_objects
for obj in object_candidates:
print obj.color
print "vs."
print color_class
print obj.color == color_class
object_candidates = [
obj for obj in object_candidates if obj.color == color_class
]
# if self.logging >= 1 : print("Remaining object candidates after color filtering {0}:".format(len(object_candidates) ) )
if self.logging >= 1:
print("Remaining object candidates after color filtering:")
if self.logging >= 1:
for c in object_candidates:
print c.name
# 2)
object_candidates = [
obj for obj in object_candidates
if self.check_dimension_tolerance(obj, height)
]
if self.logging >= 1:
print("Remaining object candidates after height filtering:")
if self.logging >= 1:
for c in object_candidates:
print c.name
# 3)
# Not implemented yet
# Evaluate the remaining object_candidates
if len(object_candidates) > 1:
# We couldn't filter the data to get only one object. Set the class name to ambiguous
unclassified_object.object.id = "ambiguous"
unclassified_object.c_type = 255 # Classified as UNKNOWN(255)
elif len(object_candidates) == 1:
unclassified_object.object.id = object_candidates[0].name
unclassified_object.c_type = 1 # Classified as object, otherwise 0
else:
unclassified_object.object.id = "unknown"
unclassified_object.c_type = 255 # Classified as UNKNOWN(255)
# unclassified_object.c_shape = class_dict[class_name[0]] # TODO is this neccessary?
# unclassified_object.object.id = class_name
resp = ClassifierResponse()
resp.classifiedObject = unclassified_object
if self.logging >= 1:
print("Classified Object as: %s" % unclassified_object.object.id)
self.publish_marker_for_object(
unclassified_object,
unclassified_object.object.id + "\nheight: " + str(height))
return resp
