Recherche avancée

Sur d’autres sites (6694)

• OpenCV output file empty

  9 mai 2023, par Marco Bob

  I want to display 12 videos in a format of 4:3 in one video. The below code runs without errors but the output file is always empty. I have tried following encoder formats :

  


  fourcc = cv2.VideoWriter_fourcc(*'mp4v')
fourcc = cv2.VideoWriter_fourcc(*'avc1')


  


  Note : the duration of the videos is not exactly identical for all.

  


  I am on Windows10 with Pycharm, Python 3.9, opencv-python=4.6.066, ffmpeg is installed with PATH variable set, 264 library seems to load fine.

  


  import cv2
import numpy as np
import math
from tqdm import tqdm

# Set the dimensions of the merged video
video_width = 1920
video_height = 1080

# Set the dimensions of each video
sub_video_width = 480
sub_video_height = 360

# Set the margin between each video
margin = 20

# Create a black background frame
bg_frame = np.zeros((video_height, video_width, 3), dtype=np.uint8)

file_name_list = ['CAM010000000_compressed.mp4', 'CAM020000000_compressed.mp4', 'CAM030000000_compressed.mp4', 'CAM040000000_compressed.mp4', 'CAM050000000_compressed.mp4', 'CAM060000000_compressed.mp4', 'CAM070000000_compressed.mp4', 'CAM080000000_compressed.mp4', 'CAM090000000_compressed.mp4', 'CAM100000000_compressed.mp4', 'CAM110000000_compressed.mp4', 'CAM120000000_compressed.mp4']

# Calculate the number of rows and columns
num_videos = len(file_name_list)
num_rows = 3
num_cols = 4
num_total = num_rows * num_cols

# Determine the size of each sub-clip
sub_video_x = sub_video_width + margin
sub_video_y = sub_video_height + margin

# Calculate the total video duration
total_duration = 0
for filename in file_name_list:
    cap = cv2.VideoCapture(filename)
    total_duration += cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)
    cap.release()

# Initialize the final video
#fourcc = cv2.VideoWriter_fourcc(*'mp4v')
#out = cv2.VideoWriter('merged_video.mp4', fourcc, 30, (video_width, video_height))

fourcc = cv2.VideoWriter_fourcc(*'avc1')
out = cv2.VideoWriter('merged_video.mp4', fourcc, 30, (video_width, video_height))

# Iterate over each row and column
for i in tqdm(range(num_rows), desc='Processing rows'):
    for j in tqdm(range(num_cols), desc='Processing columns'):
        # Calculate the index of the video to be inserted
        video_index = i * num_cols + j

        # If there is no video at this index, skip to the next one
        if video_index >= num_videos:
            continue

        # Load the sub-clip and resize it
        cap = cv2.VideoCapture(file_name_list[video_index])
        frames = []
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            frames.append(frame)
        cap.release()
        clip = cv2.resize(frames[0], (sub_video_width, sub_video_height))
        for frame in frames[1:]:
            frame = cv2.resize(frame, (sub_video_width, sub_video_height))
            clip = np.concatenate((clip, frame), axis=1)

        # Calculate the position of the sub-clip
        x = sub_video_x * j + margin
        y = sub_video_y * i + margin

        # Add the filename to the sub-clip
        font = cv2.FONT_HERSHEY_SIMPLEX
        text = file_name_list[video_index]
        textsize = cv2.getTextSize(text, font, 1, 2)[0]
        text_x = x + sub_video_width - textsize[0] - margin
        text_y = y + sub_video_height - textsize[1] - margin
        cv2.putText(clip, text, (text_x, text_y), font, 1, (255, 255, 255), 2, cv2.LINE_AA)

        # Add the sub-clip to the final video
        out.write(clip) # write the sub-clip to the output video

# Release the final video
out.release()


  


• How to use Behavioural Analytics to Improve Website Performance

  20 septembre 2021, par Ben Erskine — Analytics Tips, Plugins, Heatmap
  

  User behavioural analytics (UBA) give your business unique insights into your customers. 

  Where traditional website metrics track what actions are completed or how many visitors you have, user behaviour shows the driving factors behind those actions. UBA tools such as website heatmap software provide an easy-to-read visualisation of this data. 

  Ultimately, user behaviour analysis improves website performance and conversions by boosting customer engagement, optimising positive customer experiences, and focusing on the most important part of your sales : the people who are actually buying from you. 

  What is user behaviour analytics ?

  User behaviour analytics (UBA) is data that shows how customers and website visitors interact with your brand online. 

  UBA is tracked using tools such as heatmaps, session recordings and data visualisation software. 

  Where traditional web analytics track metrics such as page views and bounce rates, behavioural analytics provide an even more in-depth picture of your website or funnel success. 

  For example, UBA tracks actions like 

  • How far users are scrolling down the page 
  • Which CTA’s and copy they are focusing on (or not focusing on) 
  • Which design elements, links or buttons they are interacting with 
  • What is happening in between each action

  Tracking user behaviour metrics help keep visitors on your website longer because they analyse where customers may be confused or unclear so you can fix it. 

  What’s the difference between data and behavioural analytics ?

  There are a few key differences between data and behavioural analytics. While data analytics are beneficial to improving website performance, using UBA creates a more customer-centric approach to funnel building. 

  The biggest difference between data and behavioural analytics ? Metric data shows which actions are happening. Behavioural analytics show you WHY they are happening. 

  For example, data can show you that a customer bounced or clicked away. Behaviour analytics show you that a page took a long time to load, they tried to click a link several times and then maybe got frustrated and clicked away. 

  Key differences between data analytics and behavioural analytics : 

  • What is happening versus what is driving it 
  • Track an action (e.g. click-through) versus tracking inaction (e.g. hover without clicking) 
  • Measuring completion of an action versus the flow of actions to complete action 
  • Source of traffic versus individual actions 
  • What happens when someone takes an action versus what happens in between taking action 

  Matomo heatmaps offer both website analytics and user behaviour for a comprehensive analysis.

  Why do behavioural analytics help improve website performance ?

  User behaviour is important because it doesn’t matter how many website visitors you have if they don’t convert. 

  If you have a lot of traffic on mobile devices, but a low CTR, heatmaps show you what is causing the low conversions. Perhaps there is a button that isn’t optimised for mobile scrolling, or a pop up that covers important copy. 

  Analysing the driving factors behind each decision means that you can increase sign-ups and conversions without losing money on website traffic that never actually buys. 

  

  How do heatmap tools show website user behaviour analytics ? 

  Heatmap tools provide a visual representation of user behaviour. 

  There are several key ways that heatmap tracking can improve website performance and therefore your overall conversions.

  Firstly, heatmaps show where to optimise website structure. It uses real visitor experiences to indicate whether customers have to scroll to reach important content, whether important messages are being missed, and whether CTAs are clear. 

  Secondly, heatmaps provide always-on UX and useability testing for your website, identifying user frustrations and optimising their experience over time.

  They also show valuable user experience insights for A/B versions of a landing page. Not only will you see the raw conversion data, but you will also understand why one page converts more than another.

  Ultimately, heatmaps increase ROI on marketing by optimising the traffic that you are sending to your website.

  

  5 ways heatmaps and user behaviour analytics improve website performance and conversions

  #1. Improve customer experience

  One of the most important uses for UBA is to improve your customer experience. 

  Imagine you had a physical store. If there was something blocking customers from getting to the counter you could easily see and fix the problem. 

  It is just as important for an online store to find and fix these “roadblocks”. 

  Not only does it reduce friction in the sales funnel and make it easy for customers to buy from you, it improves their overall experience. And when 86% of buyers are willing to pay more for a great customer experience, UBA should be one of your number one priorities for growing your bottom line. 

  #2. Improve customer engagement

  Customer engagement is any interaction between a customer/product user and your business. 

  User behaviour analytics increase engagement at each customer journey touch point. 

  Using data from heatmaps will improve customer engagement because it gives you insights into how you can make your website more user friendly. This reduces friction and increases customer loyalty by making sure customers :

  • See important content 
  • Are not distracted by unnecessary elements 
  • Can easily access information or pages no matter what device they are using 
  • Are clicking on important page elements that take them further through the customer journey 

  For example, say a customer is on a sales page. A heatmap might show that pop ups or design elements like links to another page are pulling their attention away from the primary focus (i.e. the sales copy). 

  #3. Focus on customer-centric approach 

  A customer-centric approach means putting your customers at the centre of everything that you do. There is a lot of competition for your customers’ hard earned dollars, so you need to stand out. A good product or service is not enough on its own anymore. 

  User behaviour analytics are at the heart of customer-centric strategies. Instead of guessing how customers interact with your online presence, tools like heatmaps give insight into exactly what customers need. 

  This matched with an effective customer feedback strategy gives a holistic and effective approach to improving your customer experiences. 
  

  #4. Capture customer data across multiple channels

  Most customers won’t convert on their very first visit to a website. They might interact with your business across many channels and research your product multiple times before purchasing. 

  Multi Channel Conversion Attribution, also known as Cross Channel Attribution, lets you assign a value to each visit prior to a conversion or prior to a sale. By applying different attribution models, you get a better view on which channels actually lead to a conversion.

  User behaviour analytics like the multi channel conversion attribution that Matomo offers can show you exactly where you should focus your money to acquire new customers. 

  #5. Track and measure business objectives

  User behaviour analytics like heatmaps can show you whether you are actually hitting your targets. 

  Setting goals helps track your website performance against business objectives. 

  These include objectives such as lead generation, online sales and increased brand exposure. Matomo has a specific function for tracking goals and measuring analytics.

  Using a combination of UBA and data metrics will produce the most effective conversions. 

  For example, a customer reaching the payment confirmation page is a common objective to measure conversions. However, it is only tracked if they actually complete the action. Measuring on-page customer activity with heatmaps shows why they do or do not convert so you can fix issues. 

  Final thoughts on user behaviour analytics 

  User behavioural analytics (UBA) provide a unique and in-depth insight into your customers and their needs. Unlike traditional data metrics that track completed actions, UBA like heatmaps show you what happens in between each action and help fix any critical issues. 

  Heatmaps are your secret weapon to improving website performance while staying customer-centric ! 

  Want to know how heatmap analytics increase conversions and improve customer experience without spending more on traffic or marketing ? Check out some of the other in depth guides below. 

  The Ultimate Guide to Heatmap Software

  10 Proven Ways Heatmap Software Improves Website Conversions

  Heatmap Video

  Session Recording Video

• ffmpeg segmentation fails with floating point exception

  19 août 2020, par painfulenglish

  I'm trying to segment a video using the following command :

  


  ffmpeg -i /home/user/videos/0001.MP4 -codec copy -map 0 -f segment -segment_frames 66 /tmp/boos/0001/0001_%03d.MP4


  


  After working for a few seconds, the output being

  


  frame=  251 fps=0.0 q=-1.0 size=N/A time=00:00:10.02 bitrate=N/A speed=19.6x    


  


  the process fails with a floating point exception. The full output is given below. I have a suspicion that the problem may be specific to GoPro videos due to the following observations :

  


  

  1. The same video is segmented successfully if I first re-encode it using ffmpeg.

  


  2. Other videos (non-GoPro) do not cause the problem.

  


  


  I would appreciate any suggestions on how to track down or even solve the problem.

  


  Full output :

  


  ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers&#xA;  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)&#xA;  configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared&#xA;  libavutil      55. 78.100 / 55. 78.100&#xA;  libavcodec     57.107.100 / 57.107.100&#xA;  libavformat    57. 83.100 / 57. 83.100&#xA;  libavdevice    57. 10.100 / 57. 10.100&#xA;  libavfilter     6.107.100 /  6.107.100&#xA;  libavresample   3.  7.  0 /  3.  7.  0&#xA;  libswscale      4.  8.100 /  4.  8.100&#xA;  libswresample   2.  9.100 /  2.  9.100&#xA;  libpostproc    54.  7.100 / 54.  7.100&#xA;Input #0, mov,mp4,m4a,3gp,3g2,mj2, from &amp;apos;/home/broadbelt/videos/0001.MP4&amp;apos;:&#xA;  Metadata:&#xA;    major_brand     : mp41&#xA;    minor_version   : 538120216&#xA;    compatible_brands: mp41&#xA;    creation_time   : 2019-11-18T13:55:42.000000Z&#xA;    firmware        : HD7.01.01.80.00&#xA;  Duration: 00:17:38.22, start: 0.000000, bitrate: 30245 kb/s&#xA;    Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuvj420p(pc, bt709), 1920x1080 [SAR 1:1 DAR 16:9], 30000 kb/s, 25 fps, 25 tbr, 90k tbn, 50 tbc (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro AVC  &#xA;      encoder         : GoPro AVC encoder&#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 189 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro AAC  &#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:2(eng): Data: none (tmcd / 0x64636D74) (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro TCD  &#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:3(eng): Data: none (gpmd / 0x646D7067), 37 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro MET  &#xA;    Stream #0:4(eng): Data: none (fdsc / 0x63736466), 9 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro SOS  &#xA;<span style="background-color:#2E3436"><font color="#FF87FF">[segment @ 0x562a95062f40] </font></span>Opening &amp;apos;/tmp/boost/0001/0001_000.MP4&amp;apos; for writing&#xA;<span style="background-color:#2E3436"><font color="#FF87FF">[mp4 @ 0x562a9506a600] </font></span><span style="background-color:#2E3436"><font color="#FF0000">Timecode frame rate must be specified</font></span>&#xA;Output #0, segment, to &amp;apos;/tmp/boost/0001/0001_%03d.MP4&amp;apos;:&#xA;  Metadata:&#xA;    major_brand     : mp41&#xA;    minor_version   : 538120216&#xA;    compatible_brands: mp41&#xA;    firmware        : HD7.01.01.80.00&#xA;    encoder         : Lavf57.83.100&#xA;    Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuvj420p(pc, bt709), 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 30000 kb/s, 25 fps, 25 tbr, 12800 tbn, 25 tbc (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro AVC  &#xA;      encoder         : GoPro AVC encoder&#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 189 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro AAC  &#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:2(eng): Data: none (tmcd / 0x64636D74) (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro TCD  &#xA;      timecode        : 13:55:42:21&#xA;    Stream #0:3(eng): Data: none (gpmd / 0x646D7067), 37 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro MET  &#xA;    Stream #0:4(eng): Data: none (fdsc / 0x63736466), 9 kb/s (default)&#xA;    Metadata:&#xA;      creation_time   : 2019-11-18T13:55:42.000000Z&#xA;      handler_name    : GoPro SOS  &#xA;Stream mapping:&#xA;  Stream #0:0 -&amp;gt; #0:0 (copy)&#xA;  Stream #0:1 -&amp;gt; #0:1 (copy)&#xA;  Stream #0:2 -&amp;gt; #0:2 (copy)&#xA;  Stream #0:3 -&amp;gt; #0:3 (copy)&#xA;  Stream #0:4 -&amp;gt; #0:4 (copy)&#xA;Press [q] to stop, [?] for help&#xA;**Floating point exception (core dumped)me=00:00:01.00 bitrate=N/A speed=0.143x**   &#xA;