def object_parse(text):
'''
This function uses regular expressions to determine whether the input is
an object detection command and uses regular expressions to parse the necessary
data from the inputed text.
@param text: The sentence to check if it is an object detection command.
***MUST BE IN LOWERCASE***
@return: A string depecting what the object is to be picked up. If the o
object contains a descriptor or is two words, the program will return
the whole thing. If the input text is not a locomation command, the function
returns an empty string.
'''
item = ""
itemExp = r"""
RB: {(<NN>)+}
"""
if isObjCommand(text):
locItem = nlp_util.match_regex_and_keywords(text,
itemExp,
custom_tags=custom)
firstItem = locItem[0]
nounsList = firstItem[0]
for noun in nounsList:
item = item + noun[0] + " "
item = item.strip()
with open("util/coco.txt") as f:
for line in f:
if item in line:
return item
return None
def preprocess(text):
"""
Returns the same text, with all numbers converted from English words to
decimal form.
Ex. "move five feet forward" returns "move 5 feet forward"
@param text: the original text (must be in lowercase)
"""
text = text.translate(str.maketrans('', '', string.punctuation))
quant = parser.parse(text)
for q in quant:
words = str(q).split(' ')
number_word = words[0]
number = int(q.value)
text = text.replace(number_word, str(number))
lst = text.split(' ', 1)
text = text if len(lst) <= 1 else lst[1]
print(text)
r_expr2 = r"""
DirectionFirst: {(((<TO|IN>)<DT>)?<RB|VBD|JJ|VBP|NN|VBN><CD><NNS|NN>?)}
NumberFirst: {(<CD><NNS|NN>?((<TO|IN>)<DT>)?<RB|VBD|JJ|VBP|NN|VBN>)}
"""
target_verbs = [
"move", "spin", "rotate", "turn", "go", "drive", "stop", "travel"
]
target_words = [
"degrees", "left", "right", "forward", "backward", "clockwise",
"counterclockwise"
]
locPhrase, keywords = nlp_util.match_regex_and_keywords(
text, r_expr2, target_words)
return locPhrase, keywords
def faceRecog(text):
"""
This function takes a text input and determines which facial recognition
command it is. It also appends the name of the command to a new file. If it
is a make friends command, it will append the name of the new friend to a
file.
@param text: The sentence to check
@return: A new file with the command name listed.
"""
greetings_keywords = {
"wave ", "hello ", "hi ", "greetings ", "what's up ", "Wave ",
"Hello ", "Hi ", "Greetings ", "What's up "
}
if isFaceRecognition(text):
deleteFiles()
if "attendance " in text:
live_streaming.append_to_file("attendance.txt", "attendance")
print("created new attendance file")
for greeting in greetings_keywords:
if greeting in text:
live_streaming.append_to_file("greeting.txt", "greeting")
print("created new greetings file")
if ("call me ") in text or "Call me " in text:
name = ""
nameE = r"""
nameE: {(<NNP>)+}
"""
namePhrase = nlp_util.match_regex_and_keywords(text, nameE)
nameList = namePhrase[0][0]
for noun in nameList:
name = name + noun[0] + " "
live_streaming.append_to_file("friends.txt", name)
print("created new attendance file with " + name)
def pathPlanning(text):
'''
This function uses regular expressions to determine whether the input is
a locomotion command and uses regular expressions to parse the necessary
data from the inputed text.
@param text: The sentence to check if it is a locomotion command and
parse the direction and distance from it. ***MUST BE IN LOWERCASE***
@return: A triple. The first element is the body part to move. Second
element is the direction (i.e. 90 if right, 0 if forward, -90 if left, or
180 of backwards). The Third element is the
distance. If the input text is not a locomation command, the function returns
("", -500, -500) by default.
'''
# return variables
direction = -500
moveAmmount = -500
itemMove = ""
target_directions = [
"forward", "backward", "left", "right", "up", "down", "forwards",
"backwards"
]
target_movements = [
"strong arm", "precision arm", "body", "C1C0", "head", "cico", "c1c0",
"kiko", "strongarm", "precisionarm", "strong-arm", "precision-arm"
]
# if we find a path related phrase
if isLocCommand(text):
ammountE = r"""
RB: {<CD>}
"""
# grabs the number of steps from the phrase
movePhrase, keyword = nlp_util.match_regex_and_keywords(text, ammountE)
firstItem = movePhrase[0]
temp = firstItem[0]
moveAmmount = temp[0]
for item in target_movements:
if item in text:
itemMove = item
# based on the direction, returns the corresponding degrees
if "left" in text:
direction = -90
if "right" in text:
direction = 90
if "forward" in text or "up" in text:
direction = 0
if "backward" in text or "down" in text:
direction = 180
return (itemMove, direction, moveAmmount)
def isObjCommand(text):
'''
Determines whether a string is an object detection command or not based on the
sentence structure
@param text: The sentence to check (must be in lowercase) Some examples of phrases
include "grab the watter bottle" or "pick up the pen"
@return: A boolean. True indicates that the input is an object detection command
'''
r_expr = r"""
VP: {<VB.*>(<RP>)?(<DT>|<PRP.*>)?(<NN>)+}
"""
target_verbs = ["grab", "get", "take", "pick"]
verbPhrase, keywords = nlp_util.match_regex_and_keywords(
text, r_expr, keywords=target_verbs, custom_tags=custom)
if len(verbPhrase) > 0 and verbPhrase[0].label() != 'S':
return True
else:
return False
