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• (osx) ffmpeg combining mp3 and png to mp4 resulting in mp4 with no audio

  18 juin 2016, par Ian H

  I’m writing a python script that uses unix commands to do some file conversions/renderings. I’m trying to join some mp3 files with png files to get mp4s that are the picture with the mp3 playing over them. However, I’ve tried this with lots of different codecs and settings, and the output mp4 video never seems to have audio in it. I’ve looked at any answer to any question even related to ffmpeg and haven’t found a solution.

  Some commands I’m trying to get working currently :

  ffmpeg -loop 1 -i slide_shot%d.png -i %s -c:v libx264 -pix_fmt yuv420p 
  
  -s 720x540 -t %.3f -c:a aac -b:a 192k -shortest out%d.mp4" 
  
  % (i, aud, slideTime, i)
  
  
  
  ffmpeg -loop 1 -i slide_shot%d.png -i %s -shortest -t %.3f -write_xing 
  
  0 -c:v libx264 -c:a libmp3lame -pix_fmt yuv420p -tune stillimage out%d.mp4" 
  
  % (i, aud, slideTime, i)
  
  
  
  ffmpeg -loop 1 -i slide_shot%d.png -i %s -shortest -t %.3f -write_xing 
  
  0 -c:v libx264 -c:a copy -pix_fmt yuv420p -tune stillimage out%d.mp4" 
  
  % (i, aud, slideTime, i)

  I’m currently using the third one. However, none of them are giving me any audio. For reference, i is a loop iterator for naming consistency, aud is the audio filepath, and slideTime is the number of seconds the video should take.

  Using this command, I’m currently getting this output in the Terminal :

  ffmpeg version 3.0.2 Copyright (c) 2000-2016 the FFmpeg developers
  
  built with Apple LLVM version 7.0.2 (clang-700.1.81)
  
  configuration: --prefix=/usr/local/Cellar/ffmpeg/3.0.2 --enable-shared    
  
  --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-
  
  tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --
  
  enable-opencl --enable-libx264 --enable-libmp3lame --enable-libxvid --
  
  enable-vda
  
  libavutil      55. 17.103 / 55. 17.103
  
  libavcodec     57. 24.102 / 57. 24.102
  
  libavformat    57. 25.100 / 57. 25.100
  
  libavdevice    57.  0.101 / 57.  0.101
  
  libavfilter     6. 31.100 /  6. 31.100
  
  libavresample   3.  0.  0 /  3.  0.  0
  
  libswscale      4.  0.100 /  4.  0.100
  
  libswresample   2.  0.101 /  2.  0.101
  
  libpostproc    54.  0.100 / 54.  0.100
  
  Input #0, png_pipe, from 'slide_shot16.png':
  
  Duration: N/A, bitrate: N/A
  
  Stream #0:0: Video: png, rgba(pc), 720x540, 25 fps, 25 tbr, 25 tbn, 25     
  
  tbc
  
  [mp3 @ 0x7fe4f1817e00] Skipping 0 bytes of junk at 0.
  
  [mp3 @ 0x7fe4f1817e00] Estimating duration from bitrate, this may be inaccurate
  
  Input #1, mp3, from 'pres_projects/Cytokine sepsis 13/data/a24x43.mp3':
  
  Duration: 00:02:04.11, start: 0.000000, bitrate: 23 kb/s
  
  Stream #1:0: Audio: mp3, 22050 Hz, mono, s16p, 24 kb/s
  
  [libx264 @ 0x7fe4f1808000] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
  
  [libx264 @ 0x7fe4f1808000] profile High, level 3.0
  
  [libx264 @ 0x7fe4f1808000] 264 - core 148 r2668 fd2c324 - H.264/MPEG-4 AVC codec - Copyleft 2003-2016 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:-3:-3 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=2.00:0.70 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-4 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=1:1.20
  
  Output #0, mp4, to 'out16.mp4':
  
  Metadata:
  
  encoder         : Lavf57.25.100
  
  Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 720x540, q=-1--1, 25 fps, 12800 tbn, 25 tbc
  
  Metadata:
  
    encoder         : Lavc57.24.102 libx264
  
  Side data:
  
    unknown side data type 10 (24 bytes)
  
  Stream #0:1: Audio: mp3 (i[0][0][0] / 0x0069), 22050 Hz, mono, 24 kb/s
  
  Stream mapping:
  
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
  
  Stream #1:0 -> #0:1 (copy)
  
  Press [q] to stop, [?] for help
  
  frame=  132 fps=0.0 q=28.0 size=40kB time=00:00:02.96 bitrate=111.8kbits/
  
  frame=  272 fps=271 q=28.0 size=      61kB time=00:00:08.56 bitrate=  58.2kbits/
  
  frame=  404 fps=269 q=28.0 size=     113kB time=00:00:13.84 bitrate=  66.6kbits/
  
  frame=  537 fps=268 q=28.0 size=     132kB time=00:00:19.16 bitrate=  56.2kbits/
  
  frame=  672 fps=268 q=28.0 size=     184kB time=00:00:24.56 bitrate=  61.3kbits/
  
  frame=  808 fps=268 q=28.0 size=     236kB time=00:00:30.00 bitrate=  64.5kbits/
  
  frame=  943 fps=268 q=28.0 size=     255kB time=00:00:35.40 bitrate=  59.1kbits/
  
  frame= 1087 fps=271 q=28.0 size=     309kB time=00:00:41.16 bitrate=  61.5kbits/
  
  frame= 1219 fps=270 q=28.0 size=     328kB time=00:00:46.44 bitrate=  57.8kbits/
  
  frame= 1355 fps=270 q=28.0 size=     380kB time=00:00:51.88 bitrate=  60.0kbits/frame= 1494 fps=271 q=28.0 size=     400kB time=00:00:57.44 bitrate=  57.1kbits/
  
  frame= 1632 fps=271 q=28.0 size=     453kB time=00:01:02.96 bitrate=  58.9kbits/
  
  frame= 1767 fps=271 q=28.0 size=     472kB time=00:01:08.36 bitrate=  56.6kbits/
  
  frame= 1893 fps=269 q=28.0 size=     523kB time=00:01:13.40 bitrate=  58.4kbits/
  
  frame= 2020 fps=268 q=28.0 size=     541kB time=00:01:18.48 bitrate=  56.5kbits/
  
  frame= 2147 fps=267 q=28.0 size=     592kB time=00:01:23.56 bitrate=  58.1kbits/
  
  frame= 2275 fps=267 q=28.0 size=     611kB time=00:01:28.68 bitrate=  56.4kbits/
  
  frame= 2401 fps=266 q=28.0 size=     661kB time=00:01:33.72 bitrate=  57.8kbits/
  
  frame= 2528 fps=265 q=28.0 size=     680kB time=00:01:38.80 bitrate=  56.4kbits/
  
  frame= 2654 fps=264 q=28.0 size=     731kB time=00:01:43.84 bitrate=  57.6kbits/
  
  frame= 2781 fps=264 q=28.0 size=     749kB time=00:01:48.92 bitrate=  56.3kbits/
  
  frame= 2906 fps=263 q=28.0 size=     799kB time=00:01:53.92 bitrate=  57.5kbits/
  
  frame= 3033 fps=263 q=28.0 size=     818kB time=00:01:59.00 bitrate=  56.3kbits/
  
  frame= 3102 fps=261 q=-1.0 Lsize=     983kB time=00:02:04.08 bitrate=  64.9kbits/s speed=10.5x    
  
  video:505kB audio:364kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 13.169518%
  
  [libx264 @ 0x7fe4f1808000] frame I:13    Avg QP:14.07  size: 33159
  
  [libx264 @ 0x7fe4f1808000] frame P:782   Avg QP: 6.24  size:    36
  
  [libx264 @ 0x7fe4f1808000] frame B:2307  Avg QP: 9.67  size:    25
  
  [libx264 @ 0x7fe4f1808000] consecutive B-frames:  0.8%  0.0%  0.0% 9 9.2%
  
  [libx264 @ 0x7fe4f1808000] mb I  I16..4: 44.1% 26.2% 29.6%
  
  [libx264 @ 0x7fe4f1808000] mb P  I16..4:  0.0%  0.0%  0.0%  P16..4:  0.0%  0.0%  0.0%  0.0%  0.0%    skip:100.0%
  
  [libx264 @ 0x7fe4f1808000] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.1%  0.0%  0.0%  direct: 0.0%  skip:99.9%  L0:40.4% L1:59.6% BI: 0.0%
  
  [libx264 @ 0x7fe4f1808000] 8x8 transform intra:26.1% inter:77.7%
  
  [libx264 @ 0x7fe4f1808000] coded y,uvDC,uvAC intra: 23.8% 9.6% 8.1% inter: 0.0% 0.0% 0.0%
  
  [libx264 @ 0x7fe4f1808000] i16 v,h,dc,p: 60% 33%  7%  0%
  
  [libx264 @ 0x7fe4f1808000] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 57% 12% 29%  0%  0%  0%  0%  0%  2%
  
  [libx264 @ 0x7fe4f1808000] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 36% 29% 14%  2%  3%  4%  4%  3%  4%
  
  [libx264 @ 0x7fe4f1808000] i8c dc,h,v,p: 74% 21%  5%  0%
  
  [libx264 @ 0x7fe4f1808000] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 0x7fe4f1808000] ref P L0: 95.4%  1.1%  3.5%
  
  [libx264 @ 0x7fe4f1808000] ref B L0:  8.5% 90.2%  1.3%
  
  [libx264 @ 0x7fe4f1808000] kb/s:33.31

  Has anyone ran into a similar problem, and if so, how did you go about fixing it ? Thanks in advance for looking at my question.

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

• FFMPEG MP4 conversion takes so long its not practical

  30 avril 2018, par Chrisco420365

  I want to start out by saying that I’m not just stating the fact that FFMPEG to MP4 conversion is so slow, but I’m hoping someone here can help me with this as I’ve searched around and haven’t really found out what to do in order to fix my problem.

  So I found a script that seems to do the job for me, it inputs several video file formats and will in turn convert to MP4 which I will later allow the web user to watch online.

  Two main things are done in this script by FFMPEG, a still image is captured in .jpg format and the video is converted to MP4. After some tweaking the script seems to work but at first I thought that it wasn’t working, that it was simply halting my server.

  Let me back up for a minute... I am using FFMPEG on my development server, which is really just my crappy laptop with XAMPP installed on Windows 10 and only 2GB of RAM. Once I have the site working perfectly I will move from my crappy laptop development environment to probably a entry level dedicated server hosting plan from Godaddy or other, since at first I expect the traffic to my website to be very low.

  The problem I am having is I am testing out the script that I will show you, and even with a 10MB video, it takes over 2 minutes to finish. Meanwhile the upload progress bar shows 100% since the upload is in fact complete, but no message for the user to know that something is going on behind the scenes. Obviously that I can figure out how to fix myself, maybe even just put a message letting them know that it will be a few minutes. When I tried a video that is 120MB, it took over 5 minutes which means I had to not only modify my php.ini file to allow for such script execution times, but it also makes it so that I can do nothing on the website while this is happening.

  Not only can I not even so much as scroll the page up or down, but if I try to open another tab and load my website it just sits there with a blank screen as if its trying to access my site. Obviously it’s because FFMPEG is using up all system resources during its conversion of the video file. If I open file explorer and click once on the video file that is being created, and continue clicking once on it I’ll see the file size of this file slowly get larger and larger, which is obvious since the file is being filled. This problem of course is with no users on it other than myself since its in its development stage, so I wonder what it will be like on a dedicated server with users online. Will the other users not be able to do anything for however many minutes until whoever is uploading a video has their video finished ?

  Should it be necessary for me to increase the max execution time in the php.ini file to more than 5 minutes for a 120MB file ? What will happen if a user tries to upload a file larger than 120MB ? Should I cut them off at 500MB perhaps ?

  I love the fact that my users will be able to upload videos and I can get thumbnails and even convert to MP4 to display using HTML5 but not thrilled if noone, including the user uploading the video, can use the site as the system resources are pegged. The last time I uploaded a video on YouTube I think I remember a message saying that it would take several minutes to finish but I don’t remember the website just completely stopping for several minutes. Perhaps this is because I’m running on my insignificant laptop ?

  While searching for answers to this I did come across some people complaining about it being slow but didn’t find any solutions and in fact don’t think I saw people saying it completely locked up the website until finished. As I said, I’d hate for others not to be able to get to my website or be kicked off simply because someone is uploading a video.

  Perhaps this is a common issue that can be resolved with a powerful enough dedicated server once I move to production ? I would greatly appreciate any and all suggestions on how to resolve this so the user may at least continue using other areas of our website, while the conversion is taking place. I can send them an alert once the conversion is finished. If there are any suggestions as to a minimum dedicated server specs that would help alleviate this from happening, I am all ears ! :) Thanks !

  Here is the script that I’m currently using :

  <?php
  
  include_once($_SERVER['DOCUMENT_ROOT'].'/includes/dbc.php');
  
  // size input prevents buffer overrun exploits.
  
     function sizeinput($input, $len){
  
          (int)$len;
  
       (string)$input;
  
       $n = substr($input, 0,$len);
  
       $ret = trim($n);
  
       $out = htmlentities($ret, ENT_QUOTES);
  
       return $out;
  
  }
  
  
  
   //Check the file is of correct format.  
  
   function checkfile($input){
  
      $ext = array('mpg', 'wma', 'mov', 'flv', 'mp4', 'avi', 'qt', 'wmv', 'rm');
  
      $extfile = substr($input['name'],-4); 
  
      $extfile = explode('.',$extfile);
  
      $good = array();
  
      $extfile = $extfile[1];
  
      if(in_array($extfile, $ext)){
  
            $good['safe'] = true;
  
           $good['ext'] = $extfile;
  
      }else{
  
            $good['safe'] = false;
  
     }
  
       return $good;
  
   }
  
  
  
   $user_id = $_SESSION['user_id'];
  
   // if the form was submitted process request if there is a file for uploading
  
   if($_POST && array_key_exists("vid_file", $_FILES)){
  
                             //$uploaddir is for videos before conversion
  
                            $uploaddir = 'temp/';
  
                             //$live_dir is for videos after converted to flv
  
          $live_dir = 'library/';
  
                              //$live_img is for the first frame thumbs.
  
          $live_img = 'thumbs/';      
  
                             $seed = time();       
  
          $upload = $seed;
  
          $uploadfile = 'temp/'.$upload.'.mp4';        
  
          $vid_title = sizeinput($_POST['vidTitle'], 50);
  
          $vid_title = sanitizeString($vid_title);
  
          $vid_desc = sizeinput($_POST['vidDesc'], 2000);
  
          $vid_desc = sanitizeString($vid_desc);
  
          $vid_cat = (int)$_POST['vidCat'];
  
          $safe_file = checkfile($_FILES['vid_file']);
  
          if($safe_file['safe'] == 1){
  
              if (move_uploaded_file($_FILES['vid_file']['tmp_name'], 'temp/'.$upload.'.mp4')) {
  
                     echo "File was successfully uploaded.<br />";
  
                      //$base = basename($uploadfile, $safe_file['ext']);
  
                      $new_file = $seed.'.mp4';
  
                      $new_image = $seed.'.jpg';
  
                      $new_image_path = "thumbs/".$seed.'.jpg';
  
                      $new_flv = "library/".$new_file;
  
                      //exec('ffmpeg -i '.$uploadfile.' -an -ss 00:00:01-r 1 -vframes 1 -f mjpeg -y '.$new_image_path);
  
                      exec('ffmpeg  -i '.$uploadfile.' -f mjpeg -vframes 1 -s 300x300 -an '.$new_image_path.'');
  
                      //ececute ffmpeg generate flv
  
                        exec('ffmpeg -i '.$uploadfile.' -f mp4 '.$new_flv);
  
                         //execute ffmpeg and create thumb
  
  
  
  
  
              echo 'Thank You For Your Video!<br />';
  
                         //create query to store video
  
  
  
          $sql = "INSERT INTO videos (`user_id`, `title`,`desc`, `file`, `thumb`) VALUES('".$user_id."','".$vid_title."','".$vid_desc."','".$new_file."','".$new_image."')";
  
  
  
  
  
                      echo '<img src="http://stackoverflow.com/feeds/tag/&#039;.$new_image_path.&#039;" style='max-width: 300px; max-height: 300px' /><br />
  
                            <h3>'.$vid_title.'</h3>';
  
                      mysqli_query($link, $sql) or die(mysqli_error($mysql));
  
               } else {
  
                      echo "Possible file upload attack!\n";
  
                      print_r($_FILES);
  
               }
  
  
  
          }else{
  
  
  
               echo 'Invalid File Type Please Try Again. You file must be of type 
  
               .mpg, .wma, .mov, .flv, .mp4, .avi, .qt, .wmv, .rm';
  
  
  
          }
  
   }
  
   ?>