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• Revision 36454 : uniformiser les inputs du formulaire de login

  19 mars 2010, par brunobergot@… — Log

  uniformiser les inputs du formulaire de login

• why it is taking a lot of time to convert image to video in ffmpeg ?

  20 avril 2021, par al pacino

  Below is a snippet of code i am using to convert images into videos but It is taking around 3 minutes to convert image to 5 second video.
If I run the command on Image present on a directory then it converts images to videos very swiftly. but i need to use try except to catch ConnectionError..
can anyone tell what I am doing wrong ?

  


  video = []
video_list = []
for video in videos:
    try:
        r = requests.get(video, stream=True)
        print(r.status_code)
        if not r.status_code == 200:
            message = "broken url process could not be completed"
            return message
        if r.status_code == 200:
            if video.endswith(".jpg") or video.endswith(".png") or video.endswith(".jpeg"):
                filename = str(uuid.uuid4())
                url2 = download_path + filename +".webm"
                #cmd = "ffmpeg -loop 1 -i '{}' -c:v libx264 -t 5 -pix_fmt yuv420p -vf scale={}:{} ".format(video, width, height)+ url2
                cmd = "ffmpeg -loop 1 -i '{}' -t 5 -vf scale={}:{} ".format(video, width, height) + url2
                os.system(cmd)
                path_remover(video)
                video_list.append(url2)
            else:
                video_list.append(video)
    except ConnectionError:
        for i in video_list:
            path_remover(i)
        message = "broken url process could not be completed"
        return message


  


  full log after running the command with online url :

  


  ffmpeg -loop 1 -i 'https://cdn.pixabay.com/photo/2020/10/23/12/03/arch-5678549__340.jpg' -t 5 -vf scale=1280:780 output.mp4 


ffmpeg version n4.3.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)
  configuration: --prefix= --prefix=/usr --disable-debug --disable-doc --disable-static --enable-cuda --enable-cuda-sdk --enable-cuvid --enable-libdrm --enable-ffplay --enable-gnutls --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfontconfig --enable-libfreetype --enable-libmp3lame --enable-libnpp --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopus --enable-libpulse --enable-sdl2 --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libv4l2 --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxvid --enable-nonfree --enable-nvenc --enable-omx --enable-openal --enable-opencl --enable-runtime-cpudetect --enable-shared --enable-vaapi --enable-vdpau --enable-version3 --enable-xlib
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
Input #0, image2, from 'https://cdn.pixabay.com/photo/2021/04/17/18/26/woman-6186493__340.jpg':
  Duration: 00:00:00.04, start: 0.000000, bitrate: 5538 kb/s
    Stream #0:0: Video: mjpeg (Progressive), yuvj420p(pc, bt470bg/unknown/unknown), 511x340 [SAR 1:1 DAR 511:340], 25 fps, 25 tbr, 25 tbn, 25 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (mjpeg (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[swscaler @ 0x5580ac8e7c40] deprecated pixel format used, make sure you did set range correctly
[libx264 @ 0x5580ac186b40] using SAR=1034/1129
[libx264 @ 0x5580ac186b40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512
[libx264 @ 0x5580ac186b40] profile High, level 3.2
[libx264 @ 0x5580ac186b40] 264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'output.mp4':
  Metadata:
    encoder         : Lavf58.45.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuvj420p(pc), 1280x780 [SAR 19929:21760 DAR 511:340], q=-1--1, 25 fps, 12800 tbn, 25 tbc
    Metadata:
      encoder         : Lavc58.91.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=  100 fps=0.0 q=28.0 size=       0kB time=00:00:01.64 bitrate=   0.2kbits/frame=  125 fps=0.0 q=-1.0 Lsize=      90kB time=00:00:04.88 bitrate= 151.7kbits/s speed=6.79x    
video:88kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.618829%
[libx264 @ 0x5580ac186b40] frame I:1     Avg QP:16.31  size: 75247
[libx264 @ 0x5580ac186b40] frame P:31    Avg QP:16.13  size:   314
[libx264 @ 0x5580ac186b40] frame B:93    Avg QP:31.33  size:    49
[libx264 @ 0x5580ac186b40] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
[libx264 @ 0x5580ac186b40] mb I  I16..4: 10.6% 77.6% 11.8%
[libx264 @ 0x5580ac186b40] mb P  I16..4:  0.0%  0.0%  0.0%  P16..4:  2.5%  0.2%  0.2%  0.0%  0.0%    skip:97.2%
[libx264 @ 0x5580ac186b40] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.4%  0.0%  0.0%  direct: 0.0%  skip:99.6%  L0: 8.3% L1:91.7% BI: 0.0%
[libx264 @ 0x5580ac186b40] 8x8 transform intra:77.5% inter:98.7%
[libx264 @ 0x5580ac186b40] coded y,uvDC,uvAC intra: 88.4% 95.3% 79.5% inter: 0.1% 0.5% 0.0%
[libx264 @ 0x5580ac186b40] i16 v,h,dc,p:  2%  3%  0% 95%
[libx264 @ 0x5580ac186b40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 18%  7%  6% 11% 10% 10%  8%  8%
[libx264 @ 0x5580ac186b40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 18%  7%  5% 16% 11%  9%  6%  4%
[libx264 @ 0x5580ac186b40] i8c dc,h,v,p: 33% 21% 22% 24%
[libx264 @ 0x5580ac186b40] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x5580ac186b40] ref P L0: 85.7%  0.1% 13.2%  1.0%
[libx264 @ 0x5580ac186b40] ref B L0: 53.6% 46.4%
[libx264 @ 0x5580ac186b40] ref B L1: 92.8%  7.2%
[libx264 @ 0x5580ac186b40] kb/s:143.20
aman@aman:~/Desktop/a$ ffmpeg -loop 1 -i 'https://cdn.pixabay.com/photo/2021/04/17/18/26/woman-6186493__340.jpg' -t 5 -vf scale=1280:780 output.webm
ffmpeg version n4.3.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)
  configuration: --prefix= --prefix=/usr --disable-debug --disable-doc --disable-static --enable-cuda --enable-cuda-sdk --enable-cuvid --enable-libdrm --enable-ffplay --enable-gnutls --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfontconfig --enable-libfreetype --enable-libmp3lame --enable-libnpp --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopus --enable-libpulse --enable-sdl2 --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libv4l2 --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxvid --enable-nonfree --enable-nvenc --enable-omx --enable-openal --enable-opencl --enable-runtime-cpudetect --enable-shared --enable-vaapi --enable-vdpau --enable-version3 --enable-xlib
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
Input #0, image2, from 'https://cdn.pixabay.com/photo/2021/04/17/18/26/woman-6186493__340.jpg':
  Duration: 00:00:00.04, start: 0.000000, bitrate: 5538 kb/s
    Stream #0:0: Video: mjpeg (Progressive), yuvj420p(pc, bt470bg/unknown/unknown), 511x340 [SAR 1:1 DAR 511:340], 25 fps, 25 tbr, 25 tbn, 25 tbc
File 'output.webm' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (mjpeg (native) -> vp9 (libvpx-vp9))
Press [q] to stop, [?] for help
[swscaler @ 0x5630553879c0] deprecated pixel format used, make sure you did set range correctly
[libvpx-vp9 @ 0x563054cd2500] v1.7.0
[libvpx-vp9 @ 0x563054cd2500] Neither bitrate nor constrained quality specified, using default CRF of 32
Output #0, webm, to 'output.webm':
  Metadata:
    encoder         : Lavf58.45.100
    Stream #0:0: Video: vp9 (libvpx-vp9), yuv420p, 1280x780 [SAR 19929:21760 DAR 511:340], q=-1--1, 25 fps, 1k tbn, 25 tbc
    Metadata:
      encoder         : Lavc58.91.100 libvpx-vp9
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=   57 fps=0.0 q=0.0 size=       1kB time=00:00:01.28 bitrate=   3.3kbits/sframe=  103 fps=102 q=0.0 size=       1kB time=00:00:03.12 bitrate=   1.3kbits/sframe=  125 fps= 85 q=0.0 Lsize=      51kB time=00:00:04.96 bitrate=  84.0kbits/s speed=3.37x    
video:50kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.571023%


  


• The Ultimate Guide to HeatMap Software

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

  One of the most effective ways to improve the user experience on your website is to use heatmap software. As well as in-depth insight on how to improve your website and funnels, user behaviour analytics complement traditional web metrics with insights from your customers’ point of view. 

  Heatmap software shows actual user behaviour. That means that you have a visual representation of why a customer might not be converting instead of guessing. 

  By tracking clicks, mouse movement, and page scrolling as well as analysing above the fold content engagement and overall session recordings, heatmap software helps improve user experience and therefore customer retention and conversions.  

  

  What is heatmap software ?

  Heatmap software is a data visualisation tool that uses colour to show what actions a user is taking on a website. 

  If there is a design element on a page that many users engage with, it will show as red/hot. For elements that are less engaging, it will show on the analysis as blue/cold. 

   

  Heatmap software like Matomo helps businesses to improve user experience and increase conversions by tracking elements such as :

  • Flow through sales funnels 
  • Individual session recordings for user experience 
  • A/B page comparisons 
  • Form analytics for sign ups and conversions 
  • Media interaction such as instruction or sales videos
  • Clicks on important links and information 

  Using data visualisation software like a heatmap provides more in-depth data when combined with standard website metrics. 

  What is heatmap software used for ?

  Heatmap software tracks website user behaviour to improve website performance and increase conversions. 

  Heatmaps can show you a detailed analysis of : 

  • Where visitors are clicking (or not clicking) 
  • Where visitors are hovering with their mouse
  • How far users are scrolling or stopping 
  • Where the focus is above the fold 
  • What roadblocks or frictions customers are facing in the sales funnel

  Analysing activity on your website and across channels from your customers point of view is critical in developing a customer-centric business model. 

  This is because heatmaps not only show you what customers are doing but why they are doing it. 

  Heatmap software is ideal for businesses updating and redesigning websites. It also helps to answer important growth questions such as “how can we improve our user experience ?” and “why is our sales funnel not converting better ?”. 

  The benefits of using data visualisation like heatmaps for your website

  Heatmaps are critical for improving websites because they drastically improve customer experience. 

  Customer experience is one of the most important factors in modern business success. A Walker study found that customer experience is one of the biggest differentiators between brands, overtaking other factors such as price. 

  Where straightforward website metrics show customers left a page without action, data visualisation and session recordings show what happens in between them arriving and leaving. This gives web developers and marketers invaluable insights to improve website design and ultimately increase conversions. 

  How heatmap software improves your website and conversions

  There are a few key ways that heatmap software boosts website performance and conversions. All of them focus on both creating a seamless buyer journey and using data to improve results over time. 

  How heatmap software improves conversions ; 

  • By improving UX and usability– 70% of online businesses fail due to bad usability. Heatmaps identify user frustrations and optimise accordingly 
  • By improving content structure – Heatmaps take the guesswork out of design layout and content structure by showing real visitor experiences on your website 
  • By comparing A/B landing pages – Using heatmaps on alternate landing pages can show you why conversions are working or not working based on user activity on the page
  • By optimising across devices – See how your visitors are interacting with your content to learn how well optimised your website is for various devices and remove roadblocks 

  Heatmap analytics you need to improve website user experience

  Click heatmap

  Click heatmaps are useful for two key reasons.

  Firstly, it shows where website users are clicking. 

  Heatmaps that show clicks give you a visual representation of whether copy and CTA links are clear from the customers’ point of view. It can also show whether a customer is clicking on a design feature that doesn’t link anywhere. 

  Secondly, it shows where website users are not clicking. This is just as important when developing funnels and improving user experiences.

  For example, you may have a CTA button for a free trial or purchase. A click heatmap analysis would show if this isn’t clicked on mobile devices and informs developers that it needs to be more mobile-friendly.

  

  Mouse move or hover heatmap

  Like a click heatmap, a mouse hover heatmap shows how you can improve the overall user experience.

  For example, hover heatmaps identify where your visitors engage on a particular webpage. Ideally, of course, you want them to engage with CTAs. Analysing their mouse movements or where they are hovering for more information gives you an indication of any page elements that are distracting them or not working.

  

  Scroll heatmap

  A scroll heatmap uses colours to visualise how far down in a page your visitors scroll. For most web pages, the top will have the most impressions and will naturally get less views (i.e. get “colder” on the heatmap) further down the page. 

  This lets you find out if there is important content positioned too far down the page or if the page is designed to encourage users to keep scrolling.

  No matter how good your product or service is, it won’t convert if potential customers aren’t engaged and scrolling far enough to see it.

  

  Above the fold analysis 

  Above the fold is the content that a visitor sees without scrolling. 

  In a heatmap, the “Average Above the Fold” line will show you how much content your visitors see on average when they open your page. It also shows whether the page design is engaging, whether it encourages visitors to keep scrolling, and whether important information is too far down the page and therefore being missed. 

  Above the fold analysis is arguably the most important as this is the section that the highest number of traffic will see. Using this information ensures that the right content for conversion is seen by the highest number of visitors. 

  

  Session recording

  Session Recording lets you record a real visitor session, so you can see clicks, mouse movements, scrolls, window resizes, page changes, and form interactions all in one. 

  They allow you to understand the experience from the point of view of your visitor and then optimise your website to maximise your success.

  Heatmap software like Matomo takes this one step further and allows you to gather session recordings for individual segments. By analysing sessions based on segments, you can further personalise and optimise based on customer history and patterns.

  

  Final thoughts on heatmap software 

  Heatmap software improves your user experience by easily spotting critical issues that you can then address. 

  As well as that, heatmap analytics like clicks, mouse movement, scroll, above the fold analysis and session recordings increase your marketing ROI by making the most of your existing traffic. 

  It’s a win-win ! 

  Now that you know what heatmap software is, the benefits of using heatmaps on your website and how it can improve your user experience, check out this user guide on heatmap analytics.