def play(song):
player = MPyg321Player()
player.play_song(song)
print('enter sleep')
sleep(10)
player.stop()
print('stop player')
def MusicPlay(RareInput):
MusicName, text = RareInput.split("()")
print(MusicName, text)
player = MPyg321Player() # instanciate the player
player.play_song(MusicName + ".mp3") # play a song
sleep(FindHowMuchToPlay(MusicName, text))
player.quit()
def main():
text = story.text
full_replace = story.full_replace
for x in range(len(full_replace)):
full_replace[x] = full_replace[x].strip()
unique_replace = list(OrderedDict.fromkeys(full_replace))
print(
"Welcome Story Class!! Below I will ask a series of questions to help make Tim's story. Shout out answers "
"as you see fit!")
for term in unique_replace:
temp = input(term + ": ")
if temp == "break":
break
for item in full_replace:
if item is term:
full_replace[full_replace.index(item)] = temp
final_text = ""
for count in range(len(text)):
final_text += text[count]
if count <= 123:
final_text += full_replace[count]
count += 1
wrapper = textwrap.TextWrapper(width=140)
word_list = wrapper.fill(text=final_text)
to_print = open("one_run.txt", "w+")
to_print.write(word_list)
print(word_list)
language = "en"
myobj = gTTS(text=final_text, lang=language, slow=False)
myobj.save("one_audio.mp3")
player = MPyg321Player()
player.play_song("one_audio.mp3")
def browse():
global player
file = filedialog.askopenfilename(initialdir='/home/jpolak/Muzyka')
player = MPyg321Player()
player.play_song(file)
global napis
napis = Label(okno)
napis.config(text=file.strip('/home/jpolak/Muzyka'))
napis.grid(column=0, row=1)
import wit
import pyaudio
import pvporcupine
from gtts import gTTS
from logmmse import logmmse_from_file
from mpyg321.mpyg321 import MPyg321Player
from responder import Responder
KEYWORDS = ["jarvis", "bumblebee"]
rp = Responder()
pa = pyaudio.PyAudio()
pl = MPyg321Player()
ai = wit.Wit(os.getenv("WITAI_TOKEN"))
porcupine = pvporcupine.create(keywords=KEYWORDS)
sample_rate = porcupine.sample_rate
frames_per_buffer = porcupine.frame_length
DURATION = 4.5
audio_stream = pa.open(
rate=sample_rate,
channels=1,
format=pyaudio.paInt16,
input=True,
frames_per_buffer=frames_per_buffer,
)
def main():
"""Do the magic"""
player = MPyg321Player()
do_some_play_pause(player)
do_some_jumps(player)
player.quit()
import signal
import speech_recognition as sr
import os
import re
import asyncio
from gtts import gTTS
from io import BytesIO
from pygame import mixer as Player
import pygame
# Music Player Setup
from mpyg321.mpyg321 import MPyg321Player
from time import sleep
musicPlayer = MPyg321Player()
#___________________
from datetime import datetime
from wit import Wit
witClient = Wit("JTUTA7EED6JP2VNJPJJOQKT3P7UPQ2HK")
import json
import random
import pyowm
owm = pyowm.OWM(
'dcac096e8c94a58b502991795e61f6d4') # You MUST provide a valid API key
import requests
import pyttsx3
from gtts import gTTS
from mpyg321.mpyg321 import MPyg321Player
import os
from time import sleep
engine = pyttsx3.init()
player = MPyg321Player()
connected = False
response = requests.get("https://google.com")
debug_option = True
def debug(text):
if (debug_option):
print(str(text))
if (response.status_code == 200):
connected = True
debug("Connection established")
def disconnectedVoice(text):
engine.say(text)
engine.runAndWait()
def connectedVoice(text):
tts = gTTS(text=text, lang='en')
def play(self, filepath):
player = MPyg321Player()
player.play_song(filepath)
# -*- coding: utf-8 -*-
import random
from mpyg321.mpyg321 import MPyg321Player
from pkg_resources import resource_listdir, resource_filename
PLAYER = MPyg321Player()
PACKAGE = 'vendingmachine.resources.sounds'
# TODO: get rid of this / switch to enum:
BUTTON_PRESS = 'button_press'
COIN_INSERT = 'coin_insert'
COIN_REJECT = 'coin_reject'
MUSIC = "music"
NOISE = "noise"
def get_random_mp3_file(subpackage):
""" Example: get_random_mp3_file(COIN_REJECT) """
mp3s = []
subpackage = "{}.{}".format(PACKAGE, subpackage)
for n in resource_listdir(subpackage, ''):
filename = resource_filename(subpackage, n)
if filename.endswith('.mp3'):
mp3s.append(filename)
return random.choice(mp3s)
def play_random(subpackage):
PLAYER.play_song(get_random_mp3_file(subpackage))
def face_recon():
# Initialize some variables
face_locations = []
face_names = []
data = {}
xl_encoding = []
process_this_frame = True
global known_face_encodings, known_face_names
player = MPyg321Player()
already_recognized_face_names = []
try:
face_records = pd.read_excel('face_codes.xlsx')
for cols in face_records:
xl_encoding.append(np.array(face_records[cols]))
xl_name = list(face_records.columns)
known_face_encodings = xl_encoding
known_face_names = xl_name
print("4", known_face_encodings)
except:
pass
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(
known_face_encodings, face_encoding)
name = "Unknown"
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
if name not in already_recognized_face_names:
speak_name = gTTS(text="Hello " + name +
" you are handsome",
lang='en',
slow=False)
speak_name.save('name.mp3')
player.play_song("name.mp3")
print(face_names, name)
already_recognized_face_names.append(name)
else:
new_embedding = face_recognition.face_encodings(frame)[0]
speak_name = gTTS(text="Hello there, what's your name?",
lang='en',
slow=False)
speak_name.save('name.mp3')
player.play_song("name.mp3")
print("Enter : ")
time.sleep(4)
name = hear()
known_face_encodings.append(new_embedding)
known_face_names.append(name)
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations,
face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0,
(255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
for keys in known_face_names:
for values in known_face_encodings:
data[keys] = values
known_face_encodings.remove(values)
break
print(data)
df_k = pd.DataFrame(data)
print("df", df_k)
df_k.to_excel('face_codes.xlsx', index=False)
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
