def speak(text):
engineio = pyttsx3.init()
voices = engineio.getProperty('voices')
engineio.setProperty('rate',
130) # Aquí puedes seleccionar la velocidad de la voz
engineio.setProperty('voice', voices[0].id)
engineio.say(text)
engineio.runAndWait()
def identifyTrafficSign(image):
'''
In this function we select some ROI in which we expect to have the sign parts. If the ROI has more active pixels than threshold we mark it as 1, else 0
After path through all four regions, we compare the tuple of ones and zeros with keys in dictionary SIGNS_LOOKUP
'''
# define the dictionary of signs segments so we can identify
# each signs on the image
SIGNS_LOOKUP = {
(1, 0, 0, 1): 'Turn Right', # turnRight
(0, 0, 1, 1): 'Turn Left', # turnLeft
(0, 1, 0, 1): 'Move Straight', # moveStraight
(1, 0, 1, 1): 'Turn Back', # turnBack
}
THRESHOLD = 150
image = cv2.bitwise_not(image)
# (roiH, roiW) = roi.shape
#subHeight = thresh.shape[0]/10
#subWidth = thresh.shape[1]/10
(subHeight, subWidth) = np.divide(image.shape, 10)
subHeight = int(subHeight)
subWidth = int(subWidth)
# mark the ROIs borders on the image
cv2.rectangle(image, (subWidth, 4*subHeight), (3*subWidth, 9*subHeight), (0,255,0),2) # left block
cv2.rectangle(image, (4*subWidth, 4*subHeight), (6*subWidth, 9*subHeight), (0,255,0),2) # center block
cv2.rectangle(image, (7*subWidth, 4*subHeight), (9*subWidth, 9*subHeight), (0,255,0),2) # right block
cv2.rectangle(image, (3*subWidth, 2*subHeight), (7*subWidth, 4*subHeight), (0,255,0),2) # top block
# substract 4 ROI of the sign thresh image
leftBlock = image[4*subHeight:9*subHeight, subWidth:3*subWidth]
centerBlock = image[4*subHeight:9*subHeight, 4*subWidth:6*subWidth]
rightBlock = image[4*subHeight:9*subHeight, 7*subWidth:9*subWidth]
topBlock = image[2*subHeight:4*subHeight, 3*subWidth:7*subWidth]
# we now track the fraction of each ROI
leftFraction = np.sum(leftBlock)/(leftBlock.shape[0]*leftBlock.shape[1])
centerFraction = np.sum(centerBlock)/(centerBlock.shape[0]*centerBlock.shape[1])
rightFraction = np.sum(rightBlock)/(rightBlock.shape[0]*rightBlock.shape[1])
topFraction = np.sum(topBlock)/(topBlock.shape[0]*topBlock.shape[1])
segments = (leftFraction, centerFraction, rightFraction, topFraction)
segments = tuple(1 if segment > THRESHOLD else 0 for segment in segments)
cv2.imshow("Warped", image)
if segments in SIGNS_LOOKUP:
k=SIGNS_LOOKUP[segments]
print(k)
engineio.say(k)
engineio.runAndWait()
#print(p+" "+k)
return SIGNS_LOOKUP[segments]
else:
return None
def speak():
"SPEAK 是存放输入字符串的列表,定义正全局变量"
engineio = pyttsx3.init() # 初始化
voices = engineio.getProperty('voices')
engineio.setProperty('rate', 130) # 这里是管控语音速度的
engineio.setProperty('voice', voices[0].id)
if len(SPEAK):
engineio.say(SPEAK[len(SPEAK) - 1]) # 获取最新输入的值
engineio.runAndWait()
data = SPEAK[0]
if data != SPEAK[len(SPEAK) - 1]: # 比较两个值要是不同就证明有新的值进入了,将原先的给删除掉
del SPEAK[0]
else:
engineio.say("请输入你要翻译的英文或者汉字")
engineio.runAndWait()
sys.exit()
to_confirm = preferences
return 'ask confirmation', preferences
#default new state if we did not recognise the input / old state combination
return 'ask to repeat', preferences
print('Done!\n')
while True:
#give some output to the user
output = generateOutput(currentstate)
#text to speech output is available
if textToSpeech == True:
engineio = pyttsx3.init()
engineio.say(output)
engineio.runAndWait()
#prints info for debugging purposes
if verbose:
print(currentstate)
print(preferences)
print(output)
#presents user with info about remaining number of utterances
if maxUtterances:
if utterancecount != utterancemax:
print('[', utterancemax - utterancecount, 'sentences left ]')
#end program if we're done
def speak(text):
engineio.say(text)
engineio.runAndWait()
def speak(text):
engineio.say(text) # fala a string setada
engineio.runAndWait() # executa e espera
def GLaDOS(audio):
"""speaks audio passed as argument"""
print(audio)
for line in audio.splitlines():
engineio.say(audio)
engineio.runAndWait()
def speak(text): #Function to speak text
engineio.say(text)
engineio.runAndWait()
cv2.putText(
img,
str(id),
(x+5,y-5),
font,
1,
(255,255,255),
2
)
cv2.imshow('camera',img)
k = cv2.waitKey(10) & 0xff
if k == 27:
break
engineio.say('Thank you {0}'.format(id))
engineio.runAndWait()
print("\n Thank you for the test.")
cam.release()
cv2.destroyAllWindows()
import pyttsx3
import engineio
import speech_recognition as sr
r = sr.Recognizer()
engineio = pyttsx3.init()
engineio.say('say something.')
engineio.runAndWait()
with sr.Microphone(device_index=6, sample_rate=16000,
chunk_size=1024) as source:
audio = r.listen(source)
try:
print("You said " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Could not understand audio")
except sr.RequestError as e:
print("Could not request results; {0}".format(e))
def speak(audio):
engineio.say(audio)
engineio.runAndWait()
import pyttsx3
import engineio
import time
engineio = pyttsx3.init()
voices = engineio.getProperty('voices')
print(voices)
engineio.setProperty('rate', 100)
engineio.setProperty('voice', voices[1].id)
file = open("HandsOn_ModuleB.txt", "rt")
for line in file:
for word in line.split():
engineio.say(word)
engineio.runAndWait()