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• Python opencv ffmpeg threading exit functions

  29 novembre 2020, par scacchi

  i'm trying to finish the audio/video recording loop by pressing a key (or by other event), if i use the simple time.sleep() in the main loop work perfectly, after 5 second stop video and create file, if i use the function keyboard_pressed() doesnt execute stop_AVrecording() correctly and file_manager().
What's im doing wrong ?
Thanks in advance

  


  from __future__ import print_function, division
import numpy as np
import cv2
import pyaudio
import wave
import threading
import time
import subprocess
import os
import keyboard

class VideoRecorder:
    "Video class based on openCV"
    def __init__(self, name="temp_video.avi", fourcc="MJPG", sizex=640, sizey=480, fps=30):
        self.open = True
        self.fps = fps                  # fps should be the minimum constant rate at which the camera can
        self.fourcc = fourcc            # capture images (with no decrease in speed over time; testing is required)
        self.frameSize = (sizex, sizey) # video formats and sizes also depend and vary according to the camera used
        self.video_filename = name
        self.video_cap = cv2.VideoCapture(1, cv2.CAP_DSHOW)
        self.video_writer = cv2.VideoWriter_fourcc(*self.fourcc)
        self.video_out = cv2.VideoWriter(self.video_filename, self.video_writer, self.fps, self.frameSize)
        self.frame_counts = 1
        self.start_time = time.time()

    def record(self):
        "Video starts being recorded"
        counter = 1
        timer_start = time.time()
        timer_current = 0
        while self.open:
            ret, video_frame = self.video_cap.read()
            if ret:
                self.video_out.write(video_frame)
                # print(str(counter) + " " + str(self.frame_counts) + " frames written " + str(timer_current))
                self.frame_counts += 1
                counter += 1
                timer_current = time.time() - timer_start
                #time.sleep(1/self.fps)
                # gray = cv2.cvtColor(video_frame, cv2.COLOR_BGR2GRAY)
                cv2.imshow('video_frame', video_frame)
                cv2.waitKey(1)
            else:
                break

    def stop(self):
        "Finishes the video recording therefore the thread too"
        if self.open:
            self.open=False
            self.video_out.release()
            self.video_cap.release()
            cv2.destroyAllWindows()

    def start(self):
        "Launches the video recording function using a thread"
        video_thread = threading.Thread(target=self.record)
        video_thread.start()

class AudioRecorder():
    "Audio class based on pyAudio and Wave"
    def __init__(self, filename="temp_audio.wav", rate=44100, fpb=1024, channels=2):
        self.open = True
        self.rate = rate
        self.frames_per_buffer = fpb
        self.channels = channels
        self.format = pyaudio.paInt16
        self.audio_filename = filename
        self.audio = pyaudio.PyAudio()
        self.stream = self.audio.open(format=self.format,
                                      channels=self.channels,
                                      rate=self.rate,
                                      input=True,
                                      frames_per_buffer = self.frames_per_buffer)
        self.audio_frames = []

    def record(self):
        "Audio starts being recorded"
        self.stream.start_stream()
        while self.open:
            data = self.stream.read(self.frames_per_buffer)
            self.audio_frames.append(data)
            if not self.open:
                break

    def stop(self):
        "Finishes the audio recording therefore the thread too"
        if self.open:
            self.open = False
            self.stream.stop_stream()
            self.stream.close()
            self.audio.terminate()
            waveFile = wave.open(self.audio_filename, 'wb')
            waveFile.setnchannels(self.channels)
            waveFile.setsampwidth(self.audio.get_sample_size(self.format))
            waveFile.setframerate(self.rate)
            waveFile.writeframes(b''.join(self.audio_frames))
            waveFile.close()

    def start(self):
        "Launches the audio recording function using a thread"
        audio_thread = threading.Thread(target=self.record)
        audio_thread.start()

def start_AVrecording(filename="test"):
    global video_thread
    global audio_thread
    video_thread = VideoRecorder()
    audio_thread = AudioRecorder()
    audio_thread.start()
    video_thread.start()
    return filename


def start_video_recording(filename="test"):
    global video_thread
    video_thread = VideoRecorder()
    video_thread.start()
    return filename

def start_audio_recording(filename="test"):
    global audio_thread
    audio_thread = AudioRecorder()
    audio_thread.start()
    return filename

def stop_AVrecording(filename="test"):
    audio_thread.stop()
    frame_counts = video_thread.frame_counts
    elapsed_time = time.time() - video_thread.start_time
    recorded_fps = frame_counts / elapsed_time
    print("total frames " + str(frame_counts))
    print("elapsed time " + str(elapsed_time))
    print("recorded fps " + str(recorded_fps))
    video_thread.stop()

    # Makes sure the threads have finished
    while threading.active_count() > 1:
        time.sleep(1)

    # Merging audio and video signal
    if abs(recorded_fps - 6) >= 0.01:    # If the fps rate was higher/lower than expected, re-encode it to the expected
        print("Re-encoding")
        cmd = "ffmpeg -r " + str(recorded_fps) + " -i temp_video.avi -pix_fmt yuv420p -r 6 temp_video2.avi"
        subprocess.call(cmd, shell=True)
        print("Muxing")
        cmd = "ffmpeg -y -ac 2 -channel_layout stereo -i temp_audio.wav -i temp_video2.avi -pix_fmt yuv420p " + filename + ".avi"
        subprocess.call(cmd, shell=True)
    else:
        print("Normal recording\nMuxing")
        cmd = "ffmpeg -y -ac 2 -channel_layout stereo -i temp_audio.wav -i temp_video.avi -pix_fmt yuv420p " + filename + ".avi"
        subprocess.call(cmd, shell=True)
        print("..")

def file_manager(filename="test"):
    "Required and wanted processing of final files"
    local_path = os.getcwd()
    if os.path.exists(str(local_path) + "/temp_audio.wav"):
        os.remove(str(local_path) + "/temp_audio.wav")
    if os.path.exists(str(local_path) + "/temp_video.avi"):
        os.remove(str(local_path) + "/temp_video.avi")
    if os.path.exists(str(local_path) + "/temp_video2.avi"):
        os.remove(str(local_path) + "/temp_video2.avi")
    # if os.path.exists(str(local_path) + "/" + filename + ".avi"):
    #     os.remove(str(local_path) + "/" + filename + ".avi")

def keyboard_pressed():
    while True:
        if keyboard.is_pressed('q'):  # if key 'q' is pressed
            print('------------You Pressed Q Key!--------------')
            #time.sleep(5)
            break


if __name__ == '__main__':
    start_AVrecording()
    #time.sleep(5)
    keyboard_pressed()
    print('-------------Time Sleep-------------------')
    time.sleep(5)
    print('-------------Stop AVrecording-------------')
    stop_AVrecording()
    print('-------------File Manager-----------------')
    file_manager()
    print('-----------------End----------------------')


  


  '''

  


• Today we celebrate Data Privacy Day 2019

  28 janvier 2019, par Jake Thornton — Privacy
  Today we celebrate Data Privacy Day 2019 !!!

  What is Data Privacy Day ?

  Wikipedia tells us that : The purpose of Data Privacy Day is to raise awareness and promote privacy and data protection best practices.

  Our personal data is our online identity. When you think what personal data means – our phone records, credit card transactions, GPS position, IP addresses, browsing history and so much more. All so valuable and personal to us as human beings.

  That’s why we cannot take our personal data online for granted. We have a right to know which websites collect our data and how it’s then used, something that’s often not visible or easily recognisable when browsing.

  What Data Privacy Day means to Matomo

  Every year the team at Matomo uses this day as a chance to reflect on how far the Matomo (formerly Piwik) project has come. But then also reflect how far we still have to go in spreading the message that our data and personal information online matters.

  2018 saw the introduction of the EU General Data Protection Regulation (GDPR) to protect people’s data online. As a team, Matomo was at the forefront of this development in the analytics space and have since built a GDPR Manager to ensure our users can be fully compliant with the GDPR.

  With every new release of Matomo, we are ensuring that security continues to be at the highest standard and we will continue to be committed to our bug bounty program. Our most recent release of Matomo 3.8.0 alone added a Two Factor Authentication (2FA) feature and a password brute force prevention.

  What next for Matomo and data privacy ?

  As always, security is a top priority for every new release of Matomo and continues to only get better and better. We have a duty to spread our message further that the protection of personal data matters and today is a vital reminder of that. We are, and forever will be, the #1 open-source (and free to use) web analytics platform in the world that fully respects user privacy and gives our users 100% data ownership.

  In 2018 we changed our name, we updated our logo and website, and advanced our platform to compete with the most powerful web analytics tools in the world, all so we can spread our message further and continue our mission.

  Come with us on this exciting journey. Now is the time to take back control of your data and let’s continue creating a safer web for everyone.

  Please help us spread this message.

• What is PII ? Your introduction to personally identifiable information

  15 janvier 2020, par Joselyn Khor — Analytics Tips, Privacy, Security
  Most websites you visit collect information about you via tools like Google Analytics and Matomo – sometimes collecting personally identifiable information (PII).

  When it comes to PII, people are becoming more concerned about data privacy. Identifiable information can be used for illegal purposes like identity theft and fraud. 

  So how can you protect yourself as an innocent internet browser ? In the case of website owners – how do you protect users and your company from falling prey to privacy breaches ?

  

  As one of the most trusted analytics companies, we feel our readers would benefit from being as informed as possible about data privacy issues and PII. Learn what it means, and what you can do to keep yours or others’ information safe.

  Table of Contents

  What does PII stand for ?

  PII acronym

  PII is an acronym for personally identifiable information.

  PII definition

  Personally identifiable information (PII) is a term used predominantly in the United States.

  The appendix of OMB M-10-23 (Guidance for Agency Use of Third-Party Website and Applications) gives this definition for PII :

  “The term ‘personally identifiable information’ refers to information which can be used to distinguish or trace an individual’s identity, such as their name, social security number, biometric records, etc. alone, or when combined with other personal or identifying information which is linked or linkable to a specific individual, such as date and place of birth, mother’s maiden name, etc.”

  What can be considered personally identifiable information (PII) ? Some PII examples :

  • Full name/usernames
  • Home address/mailing address
  • Email address
  • Credit card numbers
  • Date of birth
  • Phone numbers
  • Login details

  • Precise locations
  • Account numbers
  • Passwords
  • Security codes (including biometric records)
  • Personal identification numbers
  • Driver license number
  • Get a more comprehensive list here

  What’s non-PII ?

  Anonymous information, or information that can’t be traced back to an individual, can be considered non-PII.

  Who is affected by the exploitation of PII ?

  Anyone can be affected by the exploitation of personal data, where you have identity theft, account fraud and account takeovers. When websites resort to illegally selling or sharing your data and compromising your privacy, the fear is falling victim to such fraudulent activity. 

  PII can also be an issue when employees have access to the database and the data is not encrypted. For example, anyone working in a bank can access your accounts ; anyone working at Facebook may be able to read your messages. This shows how privacy breaches can easily happen when employees have access to PII.

  Website owner’s responsibility for data privacy (PII and analytics)

  To respect your website visitor’s privacy, best practice is to avoid collecting PII whenever possible. If you work in an industry which requires people to disclose personal information (e.g. healthcare, security industries, public sector), then you must ensure this data is collected and handled securely. 

  

  The US National Institute of Standards and Technology states : “The likelihood of harm caused by a breach involving PII is greatly reduced if an organisation minimises the amount of PII it uses, collects, and stores. For example, an organisation should only request PII in a new form if the PII is absolutely necessary.” 

  How you’re held accountable remains up to the privacy laws of the country you’re doing business in. Make sure you are fully aware of the privacy and data protection laws that relate specifically to you. 

  To reduce the risk of privacy breaches, try collecting as little PII as you can ; purging it as soon as you can ; and making sure your IT security is updated and protected against security threats. 

  If you’re using data collection tools like web analytics, data may be tracked through features like User ID, custom variables, and custom dimensions. Sometimes they are also harder to identify when they are present, for example, in page URLs, page titles, or referrers URLs. So make sure you’re optimising your web analytics tools’ settings to ensure you’re asking your users for consent and respecting users’ privacy.

  If you’re using a GDPR compliant tool like Matomo, learn how you can stop processing such personal data

  PII, GDPR and businesses in the US/EU

  Because PII is broad, you may run into confusion when considering PII and GDPR (which applies in the EU). The General Data Protection Regulation (GDPR) provides more safeguards for user privacy.

  GDPR grants people in the EU more rights concerning their “personal data” (more on PII vs personal data below). In the EU the GDPR restricts the collection and processing of personal data. The repercussions are severe penalties and fines for privacy infringements. Businesses are required to handle this personal data carefully. You can be fined up to 4% of their yearly revenue for data breaches or non-compliance. 

  

  Although there isn’t an overarching data protection law in the US, there are hundreds of laws on both the federal and state levels to protect the personal data of US residents. US Congress has also enacted industry-specific statutes related to data privacy, and the state of California passed the California Consumer Privacy Act. 

  To be on the safe side, if you are using analytics, follow matters relating to “personal data” in the GDPR. It’s all-encompassing when it comes to protecting user privacy. GDPR rules still apply whenever an EU citizen visits any non EU site (that processes personal data).

  Personally identifiable information (PII) vs personal data

  PII and “personal data” aren’t used interchangeably. All personal data can be PII, but not all PII can be defined as personal data.

  The definition of “personal data” according to the GDPR :

  

  This means “personal data” encompasses a greater number of identifiers which include the online sphere. Examples include : IP addresses and URL names. As well as seemingly “innocent” data like height, job position, company etc. 

  What’s considered personal data depends on the context. If a piece of information can be combined with others to establish someone’s identity then that can be considered personal data. 

  Under GDPR, when processing personal data, you need explicit consent. You need to ensure you’re compliant according to GDPR definitions of “personal data” not just what’s considered “PII”.

  How Matomo deals with PII and personal data

  Although Matomo Analytics is a web analytics software that tracks user activity on your website, we take privacy and PII very seriously – on both our Cloud and On-Premise offerings. 

  If you’re using Matomo and would like to know how you can be fully GDPR compliant and protect user privacy, read more :
  

  • Learn how to not process any personally identifiable information – Anonymise IP addresses, user IDs, and order IDs
  • Matomo protects user privacy by talking the talk and walking the walk
  • Stay ahead of the GDPR with a privacy-respecting analytics platform
  • 11 ways Matomo helps you protect your visitor’s privacy

  Disclaimer

  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to issues you may encounter when dealing with PII. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns. 

  Additional resources :

  • https://www.consumeraffairs.com/finance/identity-theft-statistics.html#identity-theft-trends-in-2019
  • https://ec.europa.eu/info/law/law-topic/data-protection_en
  • https://iclg.com/practice-areas/data-protection-laws-and-regulations/usa
  • https://www.csoonline.com/article/3215864/how-to-protect-personally-identifiable-information-pii-under-gdpr.html
  • https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/key-definitions/what-is-personal-data/