As you can see below I have a program that uses pyqtgraph to display a spectrum of two signals on some noise using numpy. It may well be I am pushing the limits here. I am using a sample rate of 300000 to put some pressure on things. None-the-less I saw no improvements in splitting up this app using multiprocess or threading from putting the code lines in the signal() function from having it all under the update() function. I also tried pyfftw, which showed no improvement when substituting that in for np.fft. I show 1 core always at 100% so I suspect multiprocess (or threading) may not really be working the way I expect it either. Depending on your computer at aa rate 300000 it updates and pauses and updates and pauses. I would like to hit something like 2400000 smoothly without the pauses between updates. Anyone know how I can speed this up please?
# -*- coding: utf-8 -*-
from pyqtgraph.Qt import QtGui, QtCore
import numpy as np
from threading import Thread
from queue import Queue
from numpy import arange, sin, cos, pi
from scipy.fftpack import fft, rfft
import pyqtgraph as pg
import sys
import multiprocessing
class Plot2D():
def __init__(self):
self.traces = dict()
#QtGui.QApplication.setGraphicsSystem('raster')
self.app = QtGui.QApplication([])
#mw = QtGui.QMainWindow()
#mw.resize(800,800)
self.win = pg.GraphicsWindow(title="Basic plotting examples")
self.win.resize(1000,600)
self.win.setWindowTitle('pyqtgraph example: Plotting')
# Enable antialiasing for prettier plots
pg.setConfigOptions(antialias=True)
self.canvas = self.win.addPlot(title="Pytelemetry")
self.canvas.setYRange(-10, 100, padding=0)
def start(self):
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
def trace(self,name,dataset_x,dataset_y):
if name in self.traces:
self.traces[name].setData(dataset_x,dataset_y)
else:
self.traces[name] = self.canvas.plot(pen='y')
## Start Qt event loop unless running in interactive mode or using pyside.
if __name__ == '__main__':
p = Plot2D()
i = 0
def signal():
rate = 300000 # sampling rate
t = np.arange(0, 10, 1/rate)
sig = np.sin(2000*np.pi*4*t) + np.sin(2000*np.pi*7*t) + np.random.randn(len(t))*0.02 #4k + 7k tone + noise
return sig
def update():
rate = 300000 # sampling rate
z = 20*np.log10(np.abs(np.fft.rfft(signal()))) #rfft trims imag and leaves real values
f = np.linspace(0, rate/2, len(z))
p.trace("Amplitude", f, z)
timer = QtCore.QTimer()
timer.timeout.connect(lambda: update())
timer.start(10)
p.start()
t1 = multiprocessing.Process(target=signal)
t1.start
Related
I am quite new to python and tkinter ,I am working on a project where i give demand to slave device through rotary encoder value and reading feedback value from slave devices through communication.
I face issue when i use root.after () function .
Any suggestion regarding proper use of root.after () function so that my interrupt routine not affected welcome heartly.
Thank you.
Here is my code.
import pigpio
import tkinter as tk
from PIL import ImageTk,Image
import time
from RPi import GPIO
import serial
i="*00T%"
j="*01T%"
s=serial.Serial(port='/dev/ttyS0',
baudrate=9600,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1)
class decoder: # class for decoding rotary encoder
def __init__(self, pi, gpioA, gpioB, callback):
----------
---------------
----------------
if __name__ == "__main__":
import RPi.GPIO as GPIO
import time
import pigpio
import tkinter as tk
import board
import busio
rpm=0
tor=0
pow=0
pi = pigpio.pi()
st=1
pos=0
def callback(way): # interrupt event sense on pin no 17,18
global pos
global st
if st ==1:
pos += way
if pos >= 9999:
pos=9999
if pos <= 0:
pos=0
var.set(pos)
print("pos={}".format(pos))
def rpm_update():
global rpm
s.write(str.encode(i))
print(i)
time.sleep(0.2)
if s.inWaiting():
rpm=s.readline(s.inWaiting())
rpm=rpm.decode('utf-8',errors='ignore')
rpm=rpm[5:-1]
print(rpm)
var2.set(rpm)
def tor_update():
global tor
s.write(str.encode(j))
print(j)
time.sleep(0.2)
if s.inWaiting():
tor=s.readline(s.inWaiting())
tor=tor.decode('utf-8',errors='ignore')
tor=tor[4:-1]
print(tor)
var3.set(tor)
def pow_update():
global rpm,tor,pow
try:
rpm=float(rpm)
tor=float(tor)
except ValueError:
pass
pow=int(rpm*tor/5252)
var4.set(pow)
def update():
rpm_update()
time.sleep(0.5)
tor_update()
time.sleep(0.5)
pow_update()
root.after(1000,update)
path="/home/pi/logo.png"
root=tk.Tk()
img=ImageTk.PhotoImage(Image.open(path))
panel=tk.Label(root,image=img)
panel.pack()
panel.place(x=195,y=10,height=50,width=80)
root.title("Dynalec")
root.geometry("500x600")
{
body of tkinter screen
}
decoder=decoder(pi, 17, 18, callback)
root.after(1000,update)
root.mainloop()
everything is working fine before root.after() function " root.after(1000,update)
but i need it because i have to read slave values in every sec and update in gui screen.
Adding as a answer because long for comments, will remove soon.
Try this out:
def update():
root.after(500,rpm_update)
root.after(500,tor_update)
root.after(500,pow_update)
root.after(1000,update)
Do let me know.
I was able to create a ScatterPlotItem in pyqtgraph without a hitch by promoting a Graphics View widget to a PlotWidget in Qt Designer. I plotted some random data on it and now I want to access the individual points I click on. The docs say that one can connect the sigClicked(self, points) signal, which, in theory, should return the points under the cursor. But that does not seem to be the case, because when I click on a point I get the same object regardless of which point I clicked. I suspect that this signal returns the entire ScatterPlotItem and not any specific point.
Here is my code so far:
import sys, time
from timeit import default_timer as timer
from PyQt5 import QtGui
from PyQt5.QtCore import pyqtSlot, Qt, QPoint, QUrl, QEvent
from PyQt5.QtWidgets import *
from PyQt5 import QtMultimedia
from PyQt5.uic import loadUi
import pyqtgraph as pg
import numpy as np
class ScatterExample(QMainWindow):
def __init__(self):
# Main Loop
super(ScatterExample, self).__init__()
loadUi('<path/to/ui file>.ui', self)
self.setWindowTitle('ScatterExample')
self.scatter = pg.ScatterPlotItem(pxMode=False, pen=pg.mkPen(width=1, color='g'), symbol='t', size=1)
self.scatter.sigClicked.connect(self.onPointsClicked)
self.Scatter_Plot_View.addItem(self.scatter) # Scatter_Plot_View is the Graphics View I promoted to PlotWidget
n = 5
print('Number of points: ' + str(n))
data = np.random.normal(size=(2, n))
pos = [{'pos': data[:, i]} for i in range(n)]
now = pg.ptime.time()
self.scatter.setData(pos)
print(self.scatter.data)
def onPointsClicked(self, points):
print('Ain\'t getting individual points ', points)
points.setPen('b', width=2) # this turns EVERY point blue, not just the one clicked.
The above print statement prints:
Ain't getting individual points <pyqtgraph.graphicsItems.ScatterPlotItem.ScatterPlotItem object at 0x000001C36577F948>
How can I get the points I click on and their corresponding attributes, such as x and y coordinates?
As eyllansec was kind enough to suggest, I changed my def onPointsClicked(self, points): to def onPointsClicked(self, obj, points): and now pyqtgraph works a expected.
For years, I've been struggling to get efficient live plotting in matplotlib, and to this day I remain unsatisfied.
I want a redraw_figure function that updates the figure "live" (as the code runs), and will display the latest plots if I stop at a breakpoint.
Here is some demo code:
import time
from matplotlib import pyplot as plt
import numpy as np
def live_update_demo():
plt.subplot(2, 1, 1)
h1 = plt.imshow(np.random.randn(30, 30))
redraw_figure()
plt.subplot(2, 1, 2)
h2, = plt.plot(np.random.randn(50))
redraw_figure()
t_start = time.time()
for i in xrange(1000):
h1.set_data(np.random.randn(30, 30))
redraw_figure()
h2.set_ydata(np.random.randn(50))
redraw_figure()
print 'Mean Frame Rate: %.3gFPS' % ((i+1) / (time.time() - t_start))
def redraw_figure():
plt.draw()
plt.pause(0.00001)
live_update_demo()
Plots should update live when the code is run, and we should see the latest data when stopping at any breakpoint after redraw_figure(). The question is how to best implement redraw_figure()
In the implementation above (plt.draw(); plt.pause(0.00001)), it works, but is very slow (~3.7FPS)
I can implement it as:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
And it runs faster (~11FPS), but plots are not up-to date when you stop at breakpoints (eg if I put a breakpoint on the t_start = ... line, the second plot does not appear).
Strangely enough, what does actually work is calling the show twice:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
plt.show(block=False)
Which gives ~11FPS and does keep plots up-to-data if your break on any line.
Now I've heard it said that the "block" keyword is deprecated. And calling the same function twice seems like a weird, probably-non-portable hack anyway.
So what can I put in this function that will plot at a reasonable frame rate, isn't a giant kludge, and preferably will work across backends and systems?
Some notes:
I'm on OSX, and using TkAgg backend, but solutions on any backend/system are welcome
Interactive mode "On" will not work, because it does not update live. It just updates when in the Python console when the interpreter waits for user input.
A blog suggested the implementation:
def redraw_figure():
fig = plt.gcf()
fig.canvas.draw()
fig.canvas.flush_events()
But at least on my system, that does not redraw the plots at all.
So, if anybody has an answer, you would directly make me and thousands of others very happy. Their happiness would probably trickle through to their friends and relatives, and their friends and relatives, and so on, so that you could potentially improve the lives of billions.
Conclusions
ImportanceOfBeingErnest shows how you can use blit for faster plotting, but it's not as simple as putting something different in the redraw_figure function (you need to keep track of what things to redraw).
First of all, the code that is posted in the question runs with 7 fps on my machine, with QT4Agg as backend.
Now, as has been suggested in many posts, like here or here, using blit might be an option. Although this article mentions that blit causes strong memory leakage, I could not observe that.
I have modified your code a bit and compared the frame rate with and without the use of blit. The code below gives
28 fps when run without blit
175 fps with blit
Code:
import time
from matplotlib import pyplot as plt
import numpy as np
def live_update_demo(blit = False):
x = np.linspace(0,50., num=100)
X,Y = np.meshgrid(x,x)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
img = ax1.imshow(X, vmin=-1, vmax=1, interpolation="None", cmap="RdBu")
line, = ax2.plot([], lw=3)
text = ax2.text(0.8,0.5, "")
ax2.set_xlim(x.min(), x.max())
ax2.set_ylim([-1.1, 1.1])
fig.canvas.draw() # note that the first draw comes before setting data
if blit:
# cache the background
axbackground = fig.canvas.copy_from_bbox(ax1.bbox)
ax2background = fig.canvas.copy_from_bbox(ax2.bbox)
plt.show(block=False)
t_start = time.time()
k=0.
for i in np.arange(1000):
img.set_data(np.sin(X/3.+k)*np.cos(Y/3.+k))
line.set_data(x, np.sin(x/3.+k))
tx = 'Mean Frame Rate:\n {fps:.3f}FPS'.format(fps= ((i+1) / (time.time() - t_start)) )
text.set_text(tx)
#print tx
k+=0.11
if blit:
# restore background
fig.canvas.restore_region(axbackground)
fig.canvas.restore_region(ax2background)
# redraw just the points
ax1.draw_artist(img)
ax2.draw_artist(line)
ax2.draw_artist(text)
# fill in the axes rectangle
fig.canvas.blit(ax1.bbox)
fig.canvas.blit(ax2.bbox)
# in this post http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
# it is mentionned that blit causes strong memory leakage.
# however, I did not observe that.
else:
# redraw everything
fig.canvas.draw()
fig.canvas.flush_events()
#alternatively you could use
#plt.pause(0.000000000001)
# however plt.pause calls canvas.draw(), as can be read here:
#http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
live_update_demo(True) # 175 fps
#live_update_demo(False) # 28 fps
Update:
For faster plotting, one may consider using pyqtgraph.
As the pyqtgraph documentation puts it: "For plotting, pyqtgraph is not nearly as complete/mature as matplotlib, but runs much faster."
I ported the above example to pyqtgraph. And although it looks kind of ugly, it runs with 250 fps on my machine.
Summing that up,
matplotlib (without blitting): 28 fps
matplotlib (with blitting): 175 fps
pyqtgraph : 250 fps
pyqtgraph code:
import sys
import time
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
import pyqtgraph as pg
class App(QtGui.QMainWindow):
def __init__(self, parent=None):
super(App, self).__init__(parent)
#### Create Gui Elements ###########
self.mainbox = QtGui.QWidget()
self.setCentralWidget(self.mainbox)
self.mainbox.setLayout(QtGui.QVBoxLayout())
self.canvas = pg.GraphicsLayoutWidget()
self.mainbox.layout().addWidget(self.canvas)
self.label = QtGui.QLabel()
self.mainbox.layout().addWidget(self.label)
self.view = self.canvas.addViewBox()
self.view.setAspectLocked(True)
self.view.setRange(QtCore.QRectF(0,0, 100, 100))
# image plot
self.img = pg.ImageItem(border='w')
self.view.addItem(self.img)
self.canvas.nextRow()
# line plot
self.otherplot = self.canvas.addPlot()
self.h2 = self.otherplot.plot(pen='y')
#### Set Data #####################
self.x = np.linspace(0,50., num=100)
self.X,self.Y = np.meshgrid(self.x,self.x)
self.counter = 0
self.fps = 0.
self.lastupdate = time.time()
#### Start #####################
self._update()
def _update(self):
self.data = np.sin(self.X/3.+self.counter/9.)*np.cos(self.Y/3.+self.counter/9.)
self.ydata = np.sin(self.x/3.+ self.counter/9.)
self.img.setImage(self.data)
self.h2.setData(self.ydata)
now = time.time()
dt = (now-self.lastupdate)
if dt <= 0:
dt = 0.000000000001
fps2 = 1.0 / dt
self.lastupdate = now
self.fps = self.fps * 0.9 + fps2 * 0.1
tx = 'Mean Frame Rate: {fps:.3f} FPS'.format(fps=self.fps )
self.label.setText(tx)
QtCore.QTimer.singleShot(1, self._update)
self.counter += 1
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
thisapp = App()
thisapp.show()
sys.exit(app.exec_())
Here's one way to do live plotting: get the plot as an image array then draw the image to a multithreaded screen.
Example using a pyformulas screen (~30 FPS):
import pyformulas as pf
import matplotlib.pyplot as plt
import numpy as np
import time
fig = plt.figure()
screen = pf.screen(title='Plot')
start = time.time()
for i in range(10000):
t = time.time() - start
x = np.linspace(t-3, t, 100)
y = np.sin(2*np.pi*x) + np.sin(3*np.pi*x)
plt.xlim(t-3,t)
plt.ylim(-3,3)
plt.plot(x, y, c='black')
# If we haven't already shown or saved the plot, then we need to draw the figure first...
fig.canvas.draw()
image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
screen.update(image)
#screen.close()
Disclaimer: I'm the maintainer of pyformulas
Hello I'm coming with up an error when I'm using popen with my facial recognition software, in Python version 3.
What I'm aiming to do is for the code to take a photo of the person's face and if the person is in the 'known' folder of people then it turns on the green LED, if the person doesn't match any of the faces in the 'known' folder then it turns on the red LED. This is the code I'm using.
import os
import RPi.GPIO as GPIO
import time
import sys
import subprocess
os.system('raspistill -o image.jpg -w 1280 -h 1024')
x = os.system('cp image.jpg /home/pi/face_recognition/face_recognition/unknown/unknown.jpg')
print(x)
process = subprocess.Popen('python3 cli.py known unknown | cut -d "," -f2', shell=True)
process.wait()
output = process.check_output()
print(output)
if output == b'unknown_person\n':
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
GPIO.setup(18,GPIO.OUT)
print("Red LED on")
GPIO.output(18,GPIO.HIGH)
time.sleep(3)
print("Red LED off")
GPIO.output(18,GPIO.LOW)
else:
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
GPIO.setup(17,GPIO.OUT)
print("Green LED on")
GPIO.output(17,GPIO.HIGH)
time.sleep(3)
print("Green LED off")
GPIO.output(17,GPIO.LOW)
The command that we took from the command prompt runs the following code:
# -*- coding: utf-8 -*-
from __future__ import print_function
import click
import os
import re
import scipy.misc
import warnings
import face_recognition.api as face_recognition
import multiprocessing
import itertools
import sys
def scan_known_people(known_people_folder):
known_names = []
known_face_encodings = []
for file in image_files_in_folder(known_people_folder):
basename = os.path.splitext(os.path.basename(file))[0]
img = face_recognition.load_image_file(file)
encodings = face_recognition.face_encodings(img)
if len(encodings) > 1:
click.echo("WARNING: More than one face found in {}. Only considering the first face.".format(file))
if len(encodings) == 0:
click.echo("WARNING: No faces found in {}. Ignoring file.".format(file))
else:
known_names.append(basename)
known_face_encodings.append(encodings[0])
return known_names, known_face_encodings
def print_result(filename, name, distance, show_distance=False):
if show_distance:
print("{},{},{}".format(filename, name, distance))
else:
print("{},{}".format(filename, name))
def test_image(image_to_check, known_names, known_face_encodings, tolerance=0.6, show_distance=False):
unknown_image = face_recognition.load_image_file(image_to_check)
# Scale down image if it's giant so things run a little faster
if unknown_image.shape[1] > 1600:
scale_factor = 1600.0 / unknown_image.shape[1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
unknown_image = scipy.misc.imresize(unknown_image, scale_factor)
unknown_encodings = face_recognition.face_encodings(unknown_image)
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings, unknown_encoding)
result = list(distances <= tolerance)
if True in result:
[print_result(image_to_check, name, distance, show_distance) for is_match, name, distance in zip(result, known_names, distances) if is_match]
else:
print_result(image_to_check, "unknown_person", None, show_distance)
def image_files_in_folder(folder):
return [os.path.join(folder, f) for f in os.listdir(folder) if re.match(r'.*\.(jpg|jpeg|png)', f, flags=re.I)]
def process_images_in_process_pool(images_to_check, known_names, known_face_encodings, number_of_cpus, tolerance, show_distance):
if number_of_cpus == -1:
processes = None
else:
processes = number_of_cpus
# macOS will crash due to a bug in libdispatch if you don't use 'forkserver'
context = multiprocessing
if "forkserver" in multiprocessing.get_all_start_methods():
context = multiprocessing.get_context("forkserver")
pool = context.Pool(processes=processes)
function_parameters = zip(
images_to_check,
itertools.repeat(known_names),
itertools.repeat(known_face_encodings),
itertools.repeat(tolerance),
itertools.repeat(show_distance)
)
pool.starmap(test_image, function_parameters)
#click.command()
#click.argument('known_people_folder')
#click.argument('image_to_check')
#click.option('--cpus', default=1, help='number of CPU cores to use in parallel (can speed up processing lots of images). -1 means "use all in system"')
#click.option('--tolerance', default=0.6, help='Tolerance for face comparisons. Default is 0.6. Lower this if you get multiple matches for the same person.')
#click.option('--show-distance', default=False, type=bool, help='Output face distance. Useful for tweaking tolerance setting.')
def main(known_people_folder, image_to_check, cpus, tolerance, show_distance):
known_names, known_face_encodings = scan_known_people(known_people_folder)
# Multi-core processing only supported on Python 3.4 or greater
if (sys.version_info < (3, 4)) and cpus != 1:
click.echo("WARNING: Multi-processing support requires Python 3.4 or greater. Falling back to single-threaded processing!")
cpus = 1
if os.path.isdir(image_to_check):
if cpus == 1:
[test_image(image_file, known_names, known_face_encodings, tolerance, show_distance) for image_file in image_files_in_folder(image_to_check)]
else:
process_images_in_process_pool(image_files_in_folder(image_to_check), known_names, known_face_encodings, cpus, tolerance, show_distance)
else:
test_image(image_to_check, known_names, known_face_encodings, tolerance, show_distance)
if __name__ == "__main__":
main()
enter code here
The error that comes up from this is as follows.
Traceback (most recent call last):
File "/home/pi/DataFace code.py", line 13, in <module>
output = process.check_output()
AttributeError: 'Popen' object has no attribute 'check_output'
If I could as quick of a response as quick as possible that would be greatly appreciated ^_^
My goal for this simple program is to be able to show a progress bar of time the mic has been recording(range from 0 - 30 seconds). I am using the Tkinter library, Python 3.6, and PyAudio to record.
I finished writing the code for the individual parts(recording + updating progress bar) but I don't know how to change the code so that they happen at the same time(not one after the other). Also, I've realized that while I am recording, the GUI freezes(I can't click or resize buttons), I am also wondering about how to fix this issue. After doing some more research, I have a feeling that the answer lies with multi-threading or root.update() but I am not sure.
I am looking for a code example of recording audio while I simultaneously update the progress bar.
Here is an example:
from tkinter import *
from tkinter import ttk
import tkinter as tk
import pyaudio
import numpy as np
np.random.seed(123) # for reproducibility
import matplotlib
matplotlib.use('Agg') # No pictures displayed
class Test_GUI(tk.Tk):
def __init__(self, *args, **kwargs):
tk.Tk.__init__(self, *args, **kwargs)
root = self
label_title = Label(root, text="Progress Bar Recording Sample")
label_title.pack(fil="x")
topFrame = Frame(root)
topFrame.pack(side=TOP)
self.recordButton = Button(topFrame, text="Record", command=self.start)
self.recordButton.pack(side=LEFT)
self.progress = ttk.Progressbar(topFrame, orient="horizontal",
length=200, mode="determinate")
self.min = 0
self.maxMin = 0
self.progress.pack(side=RIGHT)
# recording attributes.
self.CHUNK = 1024
self.FORMAT = pyaudio.paInt16
self.CHANNELS = 2
self.RATE = 44100
self.RECORD_SECONDS = 15
self.WAVE_OUTPUT_FILENAME = "/Users/Sree/Desktop/PythonPrograms/audio_output_test.wav"
def start(self):
print("here")
self.progress["value"] = 0
self.maxMin = 30
self.progress["maximum"] = 30
'''
I need the following 2 lines to happen at the same time
the progress bar needs to update from 1-30 seconds(which is the time I am recording)
'''
self.record()
self.update_progress() #The progress bar is from 0 sec to 30 sec indicating what percentage of the time the mic has been recording for.
def update_progress(self):
self.min += 0.1
self.progress["value"] = self.min
if self.min < self.maxMin:
# update after 100 ms
self.after(100, self.update_progress)
def record(self):
p = pyaudio.PyAudio()
stream = p.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
frames_per_buffer=self.CHUNK)
print("* recording")
frames = []
for i in range(0, int(self.RATE / self.CHUNK * self.RECORD_SECONDS)):
data = stream.read(self.CHUNK)
frames.append(data)
print("* done recording")
stream.stop_stream()
stream.close()
p.terminate()
wf = wave.open(self.WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(self.CHANNELS)
wf.setsampwidth(p.get_sample_size(self.FORMAT))
wf.setframerate(self.RATE)
wf.writeframes(b''.join(frames))
wf.close()
app = Test_GUI()
app.mainloop()
To prevent the GUI from freezing while recording, you need to launch the recording in a separate thread. To do so, you can use the threading module for instance and replace self.record() in your start method by:
record_thread = threading.Thread(target=self.record, daemon=True)
record_thread.start()