def detect_object(self, tagged_text: TaggedText) -> db.onto.Object:
"""
Detects an object mentioned in the input text, extracts its properties and saves it
in the object placeholder.
Parameters
----------
tagged_text an input text
Returns
-------
an object placeholder to be grounded later
"""
obj = None
text = tagged_text.get_text()
# try to detect one of the known objects in text
for obj_str in self.class_map.keys():
if tagged_text.contains_pos_token(obj_str, "NN"):
obj = self.detect_explicit_object(tagged_text, obj_str)
break
# try to detect a coreference to an object
if obj is None and tagged_text.contains_pos_token("it", "PRP"):
obj = self.detect_coreferenced_object()
self.logger.debug(f"Object detected for \"{text}\": {obj}")
return obj
def detect_object(self, tagged_text : TaggedText) -> db.onto.Object:
"""
Detects an object mentioned in the input text, extracts its properties and saves it
in the object placeholder.
Parameters
----------
tagged_text an input text
Returns
-------
an object placeholder to be grounded later
"""
obj = None
text = tagged_text.get_text()
# try to detect one of the known objects in text
for obj_str in self.class_map.keys():
try:
#TODO should be only NN, but we have a problem that kostka is detected as VB/VBD
obj_str_lang = self.templ_det[self.lang][obj_str]
if tagged_text.contains_pos_token(obj_str_lang, "NN") or tagged_text.contains_pos_token(obj_str_lang, "VBD") or tagged_text.contains_pos_token(obj_str_lang, "VB") or tagged_text.contains_pos_token(obj_str_lang, "NNS") :
obj = self.detect_explicit_object(tagged_text, obj_str)
break
except:
pass
# try to detect a coreference to an object
if obj is None and tagged_text.contains_pos_token("it", "PRP"):
obj = self.detect_coreferenced_object()
self.logger.debug(f"Object detected for \"{text}\": {obj}")
self.ui.say(self.guidance_file[self.lang]["object_matched"]+" "+ text)
return obj
def detect_relative_location(
self, tagged_text: TaggedText) -> db.onto.RelativeLocation:
# for preventing cyclic imports in previous Python versions
import nlp_crow.modules.ObjectDetector as ObjectDetector
loc = None
obj_rel_locs = ["center", "left", "right", "top", "bottom"]
for rel_loc in obj_rel_locs:
if tagged_text.contains_text(self.templ_det[self.lang][rel_loc]):
# TODO temporary solution: detect the object which the location refers to in the part of the sentence *after* the location
index = tagged_text.indices_of(rel_loc)[0]
tagged_text_cut = tagged_text.cut(index + 1, None)
# detect to which object the location refers
od = ObjectDetector.ObjectDetector()
relative_to = od.detect_object(tagged_text_cut)
if not relative_to:
ad = AreaDetector()
relative_to = ad.detect_area(tagged_text_cut)
if relative_to:
loc = db.onto.RelativeLocation()
loc.loc_type = rel_loc
loc.relative_to = relative_to
return loc
def detect_absolute_location(self, tagged_text: TaggedText) -> db.onto.Location:
if tagged_text.contains_text("here"):
return self.detect_current_finger_location()
elif tagged_text.contains_text("down"):
return self.detect_current_robot_handle_location()
return self.detect_location_from_text(tagged_text)
def match(self, tagged_text: TaggedText) -> None:
od = ObjectDetector()
ld = LocationDetector()
self.location = ld.detect_location(tagged_text)
# TODO: temporary solution: detect the "put subject" in 3 words after the "put" verb. Should be improved with better grammar parsing.
put_index = tagged_text.indices_of("put")[0]
tagged_text_cut = tagged_text.cut(put_index + 1, put_index + 4)
self.object_to_put = od.detect_object(tagged_text_cut)
def match(self, tagged_text: TaggedText, language='en') -> None:
od = ObjectDetector(language=language)
# TODO: temporary solution: detect the "put subject" in 3 words after the "put" verb. Should be improved with better grammar parsing.
try:
put_index = tagged_text.indices_of(
self.templ_det[language]['tidy'])[0]
tagged_text_cut = tagged_text.cut(put_index + 1, put_index + 4)
self.object_to_put = od.detect_object(tagged_text_cut)
except:
self.logger.debug(f"Put index not detected.")
def flatten(self) -> TaggedText:
"""
Return the tagged text resulting from left-to-right pass through the tree.
"""
tagged_text = TaggedText()
for subnode in self.subnodes:
if type(subnode) is TaggedToken:
tagged_text.add_tagged_token(token=subnode.token, tag=subnode.tag)
else:
sub_tagged_text = subnode.flatten()
tagged_text += sub_tagged_text
return tagged_text
def detect_location(self, tagged_text: TaggedText):
"""
Tries to extract the information about the location in the text.
Parameters
----------
tagged_text a text in which the location should be detected
Returns
-------
the location object or None if no location is detected
"""
tagged_text.lang = self.lang
loc = self.detect_absolute_location(tagged_text)
if not loc:
self.logger.debug(
f"Absolute Location not detected for \"{tagged_text.get_text()}\": {loc}. Trying relative location"
)
loc = self.detect_relative_location(tagged_text)
if loc:
self.logger.debug(
f"Location detected for \"{tagged_text.get_text()}\": {loc}")
else:
self.logger.debug(
f"Location not detected for \"{tagged_text.get_text()}\": {loc}"
)
return loc
def match(self, tagged_text: TaggedText) -> None:
match = tagged_text.match_regex(r"called (.*)")
if match:
self.task_name = match.group(1)
else:
uim = UserInputManager()
self.task_name = uim.ask_for_name("tower")
def match(self, tagged_text : TaggedText) -> None:
# match text until "with" or "corners" is encountered
match = tagged_text.match_regex(r"called (.*?)(?= with| corners)")
if match:
self.area_name = match.group(1)
else:
uim = UserInputManager()
self.area_name = uim.ask_for_name("area")
# match eight digits for area corners
regex_pos_list = [("corners", "NN")] + ([(r".*", "CD")] * 8)
matches = tagged_text.match_regex_pos_list(regex_pos_list)
for i in range(1, len(matches), 2):
coord_x = nlp.get_int(matches[i].string)
coord_y = nlp.get_int(matches[i+1].string)
corner = db.onto.Location(x=coord_x, y=coord_y, z=0)
setattr(self, f"corner_{i // 2}", corner)
def detect_geometry(self, tagged_text: TaggedText):
go = None
if tagged_text.contains_text(self.templ_det[self.lang]['point']):
go = db.onto.GeometricObject("point")
if go:
self.logger.debug(
f"Geometry detected for \"{tagged_text.get_text()}\": {go}")
return go
def detect_area(self,
tagged_text: TaggedText) -> db.onto.ObjectPlaceholder:
area_list = self.db_api.get_class_objects(db.onto.Area)
for area in area_list:
if tagged_text.contains_text(area.name):
self.logger.debug(
f"Area detected for \"{tagged_text.get_text()}\": {area}")
return area
return None
def detect_color(self, text: TaggedText):
"""
A simple method for detecting a color in text.
Yes, there is some space for improvement.
Parameters
----------
text an input text
"""
for color in self.colors:
if color in text.get_text():
return color
return None
def detect_color(self, text: TaggedText):
"""
A simple method for detecting a color in text.
Yes, there is some space for improvement.
Parameters
----------
text an input text
"""
for color in self.colors:
try:
color_lang = self.templ_det[self.lang][color]
if color_lang in text.get_text():
return color
except:
pass
return None