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• Open CV Codec FFMPEG Error fallback to use tag 0x7634706d/'mp4v'

  22 mai 2019, par Cohen

  Doing a filter recording and all is fine. The code is running, but at the end the video is not saved as MP4. I have this error :

  OpenCV: FFMPEG: tag 0x44495658/'XVID' is not supported with codec id 12 and format 'mp4 / MP4 (MPEG-4 Part 14)'
  
  OpenCV: FFMPEG: fallback to use tag 0x7634706d/'mp4v'

  Using a MAC and the code is running correctly, but is not saving. I tried to find more details about this error, but wasn’t so fortunate. I use as editor Sublime. The code run on Atom tough but is giving this error :

  OpenCV: FFMPEG: tag 0x44495658/'XVID' is not supported with codec id 12 and format 'mp4 / MP4 (MPEG-4 Part 14)'
  
  OpenCV: FFMPEG: fallback to use tag 0x7634706d/'mp4v'
  
  2018-05-28 15:04:25.274 Python[17483:2224774] AVF: AVAssetWriter status: Cannot create file

  ....

  import numpy as np
  
  import cv2
  
  import random
  
  from utils import CFEVideoConf, image_resize
  
  import glob
  
  import math
  
  
  
  
  
  cap = cv2.VideoCapture(0)
  
  
  
  frames_per_seconds = 24
  
  save_path='saved-media/filter.mp4'
  
  config = CFEVideoConf(cap, filepath=save_path, res='360p')
  
  out = cv2.VideoWriter(save_path, config.video_type, frames_per_seconds, config.dims)
  
  
  
  
  
  def verify_alpha_channel(frame):
  
      try:
  
          frame.shape[3] # looking for the alpha channel
  
      except IndexError:
  
          frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA)
  
      return frame
  
  
  
  
  
  def apply_hue_saturation(frame, alpha, beta):
  
      hsv_image = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
  
      h, s, v = cv2.split(hsv_image)
  
      s.fill(199)
  
      v.fill(255)
  
      hsv_image = cv2.merge([h, s, v])
  
  
  
      out = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
  
      frame = verify_alpha_channel(frame)
  
      out = verify_alpha_channel(out)
  
      cv2.addWeighted(out, 0.25, frame, 1.0, .23, frame)
  
      return frame
  
  
  
  
  
  def apply_color_overlay(frame, intensity=0.5, blue=0, green=0, red=0):
  
      frame = verify_alpha_channel(frame)
  
      frame_h, frame_w, frame_c = frame.shape
  
      sepia_bgra = (blue, green, red, 1)
  
      overlay = np.full((frame_h, frame_w, 4), sepia_bgra, dtype='uint8')
  
      cv2.addWeighted(overlay, intensity, frame, 1.0, 0, frame)
  
      return frame
  
  
  
  
  
  def apply_sepia(frame, intensity=0.5):
  
      frame = verify_alpha_channel(frame)
  
      frame_h, frame_w, frame_c = frame.shape
  
      sepia_bgra = (20, 66, 112, 1)
  
      overlay = np.full((frame_h, frame_w, 4), sepia_bgra, dtype='uint8')
  
      cv2.addWeighted(overlay, intensity, frame, 1.0, 0, frame)
  
      return frame
  
  
  
  
  
  def alpha_blend(frame_1, frame_2, mask):
  
      alpha = mask/255.0 
  
      blended = cv2.convertScaleAbs(frame_1*(1-alpha) + frame_2*alpha)
  
      return blended
  
  
  
  
  
  def apply_circle_focus_blur(frame, intensity=0.2):
  
      frame = verify_alpha_channel(frame)
  
      frame_h, frame_w, frame_c = frame.shape
  
      y = int(frame_h/2)
  
      x = int(frame_w/2)
  
  
  
      mask = np.zeros((frame_h, frame_w, 4), dtype='uint8')
  
      cv2.circle(mask, (x, y), int(y/2), (255,255,255), -1, cv2.LINE_AA)
  
      mask = cv2.GaussianBlur(mask, (21,21),11 )
  
  
  
      blured = cv2.GaussianBlur(frame, (21,21), 11)
  
      blended = alpha_blend(frame, blured, 255-mask)
  
      frame = cv2.cvtColor(blended, cv2.COLOR_BGRA2BGR)
  
      return frame
  
  
  
  
  
  def portrait_mode(frame):
  
      cv2.imshow('frame', frame)
  
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  
      _, mask = cv2.threshold(gray, 120,255,cv2.THRESH_BINARY)
  
  
  
      mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGRA)
  
      blured = cv2.GaussianBlur(frame, (21,21), 11)
  
      blended = alpha_blend(frame, blured, mask)
  
      frame = cv2.cvtColor(blended, cv2.COLOR_BGRA2BGR)
  
      return frame
  
  
  
  
  
  def apply_invert(frame):
  
      return cv2.bitwise_not(frame)
  
  
  
  while(True):
  
      # Capture frame-by-frame
  
      ret, frame = cap.read()
  
      frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA) 
  
      #cv2.imshow('frame',frame)
  
  
  
  
  
      hue_sat = apply_hue_saturation(frame.copy(), alpha=3, beta=3)
  
      cv2.imshow('hue_sat', hue_sat)
  
  
  
      sepia = apply_sepia(frame.copy(), intensity=.8)
  
      cv2.imshow('sepia',sepia)
  
  
  
      color_overlay = apply_color_overlay(frame.copy(), intensity=.8, red=123, green=231)
  
      cv2.imshow('color_overlay',color_overlay)
  
  
  
      invert = apply_invert(frame.copy())
  
      cv2.imshow('invert', invert)
  
  
  
      blur_mask = apply_circle_focus_blur(frame.copy())
  
      cv2.imshow('blur_mask', blur_mask)
  
  
  
      portrait = portrait_mode(frame.copy())
  
      cv2.imshow('portrait',portrait)
  
  
  
      if cv2.waitKey(20) & 0xFF == ord('q'):
  
          break
  
  
  
  # When everything done, release the capture
  
  cap.release()
  
  cv2.destroyAllWindows()

• Extremely slow ffmpeg/sws_scale() - only on heavy duty

  28 septembre 2020, par user2328447

  I am writing a video player using ffmpeg (Windows only, Visual Studio 2015, 64 bit compile).
With common videos (up to 4K @ 30FPS), it works pretty good. But with my maximum target - 4K @ 60FPS, it fails. Decoding still is fast enough, but when it comes to YUV/BGRA conversion it is simply not fast enough, even though it's done in 16 threads (one thread per frame on a 16/32 core machine).

  



  So as a first countermeasure I skipped the conversion of some frames and got a stable frame rate of 40 that way. Comparing the two versions in Concurrency Visualizer, I found a strange issue I don't know the reason of.

  



  .

  



  Here's an image of the frameskip version :

You see that the conversion is pretty quick (average roughly 35ms)
Thus, as multiple threads are used, it also should be quick enough for 60FPS, but it isn't !

  



  .

  



  The image of the non-frameskip version shows why :

The conversion of a single frame has become ten times slower than before (average roughly 350ms). Now a heavy workload on many cores would of course cause a minor slowdown per core due to reduced turbo - let's say 10 or 20%. But never an extreme slowdown of 1000%.

  



  .

  



  Interesting detail is, that the stack trace of the non-frameskip version shows some system activity I don't really understand - beginning with ntoskrnl.exe!KiPageFault+0x373. There are no exceptions, other error messages or such - it just becomes extremely slow.

  



  Edit : A colleague just told me that this looks like a memory problem with paged-out memory at first glance - but my memory utilization is low (below 1GB, and more than 20GB free)

  



  Can anyone tell me what could be causing this ?

  


• Developers and vendors : Want a Matomo Hoodie ? Add a tag to the Matomo Open Source Tag Manager and this could be yours !

  7 juin 2018, par Matomo Core Team — Community, Development

  The Free Open Source Tag Manager is now available as a public beta on the Matomo Marketplace. Don’t know what a Tag Manager is ? Learn more here. In Short : It lets you easily manage all your third party JavaScript and HTML snippets (analytics, ads, social media, remarketing, affiliates, etc) through a single interface.

  Over the last few months we have worked on building the core for the Matomo Tag Manager which comes with a great set of features and a large set of pre-configured triggers and variables. However, we currently lack tags.

  This is where we need your help ! Together we can build a complete and industry leading open source tag manager.

  Tag examples include Google AdWords Conversion Tracking, Facebook Buttons, Facebook Pixels, Twitter Universal Website Tags, LinkedIn Insights.

  Are you a developer who is familiar with JavaScript and keen on adding a tag ? Or are you a vendor ? Don’t be shy, we appreciate any tags, even analytics related :) We have documented how to develop a new tag here, which is quite easy and straightforward. You may also need to understand a tiny bit of PHP but you’ll likely be fine even if you don’t (here is an example PHP file and the related JS file).

  As we want to ship the Matomo Tag Manager with as many tags as possible out of the box, we appreciate any new tag additions as a pull request on https://github.com/matomo-org/tag-manager.

  We will send out “Matomo Contributor” stickers that cannot be purchased anywhere for every contributor who contributes a tag within the next 3 months. As for the top 3 contributors… you’ll receive a Matomo hoodie ! Simply send us an email at hello@matomo.org after your tag has been merged. If needed, a draw will decide who gets the hoodies.

  FYI : The Matomo Tag Manager is already prepared to be handled in different contexts and we may possibly generate containers for Android and iOS. If you are keen on building the official Matomo SDKs for any of these mobile platforms, please get in touch.