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• How to merge special ts files to mp4 ? [closed]

  21 février 2024, par mikezang

  I have some ts files and I want to merge them to mp4, I used command as below, some of ts can be merged without any problems.

  


  ffmpeg -i 'concat:01.ts|02.ts|03.ts|' -c copy -bsf:a aac_adtstoasc 01.mp4


  


  Then I found for some ts files, I got error as below when I used the same command above, what can I do to merge these ts files ?

  


  ffmpeg version 2023-11-13-git-67a2571a55-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --pkg-config=pkgconf --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-dxva2 --enable-d3d11va --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 32.100 / 58. 32.100
  libavcodec     60. 33.100 / 60. 33.100
  libavformat    60. 17.100 / 60. 17.100
  libavdevice    60.  4.100 / 60.  4.100
  libavfilter     9. 13.100 /  9. 13.100
  libswscale      7.  6.100 /  7.  6.100
  libswresample   4. 13.100 /  4. 13.100
  libpostproc    57.  4.100 / 57.  4.100
[png @ 0000028b191c1cc0] inflate returned error -3
Input #0, png_pipe, from 'concat:001.ts|002.ts|003.ts':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: png, pal8(pc, gbr/unknown/unknown), 800x800, 25 fps, 25 tbr, 25 tbn
Output #0, mp4, to '01.mp4':
  Metadata:
    encoder         : Lavf60.17.100
  Stream #0:0: Video: png (mp4v / 0x7634706D), pal8(pc, gbr/unknown/unknown), 800x800, q=2-31, 25 fps, 25 tbr, 12800 tbn
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[out#0/mp4 @ 0000028b19260cc0] video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
[out#0/mp4 @ 0000028b19260cc0] Output file is empty, nothing was encoded


  


  The successful ts file by ffprobe :

  


  Input #0, mpegts, from '001.ts':
  Duration: 00:00:12.04, start: 0.038000, bitrate: 942 kb/s
  Program 1
  Stream #0:0[0x100]: Video: h264 (High) ([27][0][0][0] / 0x001B), yuv420p(tv, bt709, progressive), 1920x1080 [SAR 1:1 DAR 16:9], 25 fps, 25 tbr, 90k tbn
  Stream #0:1[0x101]: Audio: aac (LC) ([15][0][0][0] / 0x000F), 48000 Hz, stereo, fltp, 97 kb/s


  


  The failed ts file by ffprobe :

  


  [png @ 000001e9016fe940] inflate returned error -3
Input #0, png_pipe, from '001.ts':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: png, pal8(pc, gbr/unknown/unknown), 800x800, 25 fps, 25 tbr, 25 tbn


  


• Custom Segmentation Guide : How it Works & Segments to Test

  13 novembre 2023, par Erin — Analytics Tips, Uncategorized

  Struggling to get the insights you’re looking for with premade reports and audience segments in your analytics ?

  Custom segmentation can help you better understand your customers, app users or website visitors, but only if you know what you’re doing.

  You can derive false insights with the wrong segments, leading your marketing campaigns or product development in the wrong direction.

  In this article, we’ll break down what custom segmentation is, useful custom segments to consider, how new privacy laws affect segmentation options and how to create these segments in an analytics platform.

  What is custom segmentation ?

  Custom segmentation is when you divide your audience (customers, users, website visitors) into bespoke segments of your own design, not premade segments designed by the analytics or marketing platform provider.

  To do this, you single out “custom segment input” — data points you will use to pinpoint certain users. For example, it could be everyone who has visited a certain page on your site.

  

  Segmentation isn’t just useful for targeting marketing campaigns and also for analysing your customer data. Creating segments is a great way to dive deeper into your data beyond surface-level insights.

  You can explore how various factors impact engagement, conversion rates, and customer lifetime value. These insights can help guide your higher-level strategy, not just campaigns.

  How custom segments can help your business

  As the global business world clamours to become more “data-driven,” even smaller companies collect all sorts of data on visitors, users, and customers.

  However, inexperienced organisations often become “data hoarders” without meaningful insights. They have in-house servers full of data or gigabytes stored by Google Analytics and other third-party providers.

  

  One way to leverage this data is with standard customer segmentation models. This can help you get insights into your most valuable customer groups and other standard segments.

  Custom segments, in turn, can help you dive deeper. They help you unlock insights into the “why” of certain behaviours. They can help you segment customers and your audience to figure out :

  • Why and how someone became a loyal customer
  • How high-order-value customers interact with your site before purchases
  • Which behaviours indicate audience members are likely to convert
  • Which traffic sources drive the most valuable customers

  This specific insight’s power led Gartner to predict that 70% of companies will shift focus from “big data” to “small and wide” by 2025. The lateral detail is what helps inform your marketing strategy. 

  You don’t need the same volume of data if you’re analysing and segmenting it effectively.

  Custom segment inputs : 6 data points you can use to create valuable custom segments 

  To help you get started, here are six useful data points you can use as a basis to create segments — AKA customer segment inputs :

  

  Visits to certain pages

  A basic data point that’s great for custom segments is visits to certain pages. Create segments for popular middle-of-funnel pages and compare their engagement and conversion rates. 

  For example, if a user visits a case study page, you can compare their likelihood to convert vs. other visitors.

  This is a type of behavioural segmentation, but it is the easiest custom segment to set up in terms of analysis and marketing efforts.

  Visitors who perform certain actions

  The other important type of behavioural segment is visitors or users who take certain actions. Think of things like downloading a file, clicking a link, playing a video or scrolling a certain amount.

  For instance, you can create a segment of all visitors who have downloaded a white paper. This can help you explore, for example, what drives someone to download a white paper. You can look at the typical user journey and make it easier for them to access the white paper — especially if your sales reps indicate many inbound leads mention it as a key driver of their interest.

  User devices

  Device-based segmentation lets you compare engagement and conversion rates on mobile, desktop and tablets. You can also get insights into their usage patterns and potential issues with certain mobile elements.

  

  This is one aspect of technographic segmentation, where you segment based on users’ hardware or software. You can also create segments based on browser software or even specific versions.

  Loyal or high-value customers

  The best way to get more loyal or high-value customers is to explore their journey in more detail. These types of segments can help you better understand your ideal customers and how they act on your site.

  You can then use this insight to alter your campaigns or how you communicate with your target audience.

  For example, you might notice that high-value customers tend to come from a certain source. You can then focus your marketing efforts on this source to reach more of your ideal customers.

  Visitor or customer source

  You need to track the results if you’re investing in marketing (like an influencer campaign or a sponsored post) outside platforms with their own analytics.

  

  Before you can create a reliable segment, you need to make sure that you use campaign tracking parameters to reliably track the source. You can use our free campaign tracking URL builder for that.

  Demographic segments — location (country, state) and more

  Web analytics tools, such as Matomo, use visitors’ IP addresses to pinpoint their location more accurately by cross-referencing with a database of known and estimated IP locations. In addition, these tools can detect a visitor’s location through the language settings in their browser. 

  This can help create segments based on location or language. By exploring these trends, you can identify patterns in behaviour, tailor your content to specific audiences, and adapt your overall strategy to better meet the preferences and needs of your diverse visitor base.

  How new privacy laws affect segmentation options

  Over the past few years, new legislation regarding privacy and customer data has been passed globally. The most notable privacy laws are the GDPR in the EU, the CCPA in California and the VCDPA in Virginia.

  

  For most companies, it can save a lot of work and future headaches to choose a GDPR-compliant web analytics solution not only streamlines operations, saving considerable effort and preventing future headaches, but also ensures peace of mind by guaranteeing the collection of compliant and accurate data. This approach allows companies to maintain compliance with privacy regulations while remaining firmly committed to a data-driven strategy.

  Create your very own custom segments in Matomo (while ensuring compliance and data accuracy)

  Crafting precise marketing messages and optimising ROI is crucial, but it becomes challenging without the right tools, especially when it comes to maintaining accurate data.

  That’s where Matomo comes in. Our privacy-friendly web analytics platform is GDPR-compliant and ensures accurate data, empowering you to effortlessly create and analyse precise custom segments.

  If you want to improve your marketing campaigns while remaining GDPR-compliant, start your 21-day free trial of Matomo. No credit card required.

• Converting `webm` to `mov` format using ffmpeg loses transparency - how can I keep it ?

  10 mars 2023, par M.A Shahbazi

  I am trying to convert a webm video to mov format without losing quality and transparency using the following command :

  


  ffmpeg -c:v libvpx-vp9 -i input.webm ouput.mov


  


  The video gets converted, but it loses transparency and the background of video is black.

  


  I also tried the following command :

  


  ffmpeg -c:v png -i input.webm output.mov


  


  But it gives me the following error output :

  


    libavutil      58.  3.100 / 58.  3.100
  libavcodec     60.  4.100 / 60.  4.100
  libavformat    60.  4.100 / 60.  4.100
  libavdevice    60.  2.100 / 60.  2.100
  libavfilter     9.  4.100 /  9.  4.100
  libswscale      7.  2.100 /  7.  2.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100
[png @ 0000028041ca7000] Invalid PNG signature 0x824983420031F031.
[matroska,webm @ 0000028041c91240] Could not find codec parameters for stream 0 (Video: png, none(pc, progressive), 800x800): unspecified pixel format
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, matroska,webm, from 'hero.webm':
  Metadata:
    COMPATIBLE_BRANDS: qt
    creation_time   : 2023-02-23T10:43:46.703800Z
    MAJOR_BRAND     : qt
    MINOR_VERSION   : 537199360
    ENCODER         : Lavf59.35.100
  Duration: 00:00:09.00, start: 0.000000, bitrate: 1378 kb/s
  Stream #0:0: Video: png, none(pc, progressive), 800x800, SAR 1:1 DAR 1:1, 24 fps, 24 tbr, 1k tbn
    Metadata:
      alpha_mode      : 1
      ENCODER         : Lavc59.56.100 libvpx-vp9
      DURATION        : 00:00:09.000000000
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[png @ 0000028041ca7680] Invalid PNG signature 0x824983420031F031.
Error while decoding stream #0:0: Invalid data found when processing input
Cannot determine format of input stream 0:0 after EOF
Error marking filters as finished
Conversion failed!


  


  How can I maintain transparency when converting from webm to mov format ?

  


  Here's the video info in case it can be helpful :

  


  {
    "streams": [
        {
            "index": 0,
            "codec_name": "vp9",
            "codec_long_name": "Google VP9",
            "profile": "Profile 0",
            "codec_type": "video",
            "codec_tag_string": "[0][0][0][0]",
            "codec_tag": "0x0000",
            "width": 800,
            "height": 800,
            "coded_width": 800,
            "coded_height": 800,
            "closed_captions": 0,
            "film_grain": 0,
            "has_b_frames": 0,
            "sample_aspect_ratio": "1:1",
            "display_aspect_ratio": "1:1",
            "pix_fmt": "yuv420p",
            "level": -99,
            "color_range": "tv",
            "field_order": "progressive",
            "refs": 1,
            "r_frame_rate": "24/1",
            "avg_frame_rate": "24/1",
            "time_base": "1/1000",
            "start_pts": 0,
            "start_time": "0.000000",
            "disposition": {
                "default": 0,
                "dub": 0,
                "original": 0,
                "comment": 0,
                "lyrics": 0,
                "karaoke": 0,
                "forced": 0,
                "hearing_impaired": 0,
                "visual_impaired": 0,
                "clean_effects": 0,
                "attached_pic": 0,
                "timed_thumbnails": 0,
                "captions": 0,
                "descriptions": 0,
                "metadata": 0,
                "dependent": 0,
                "still_image": 0
            },
            "tags": {
                "alpha_mode": "1",
                "ENCODER": "Lavc59.56.100 libvpx-vp9",
                "DURATION": "00:00:09.000000000"
            }
        }
    ],
    "format": {
        "filename": ".\\hero.webm",
        "nb_streams": 1,
        "nb_programs": 0,
        "format_name": "matroska,webm",
        "format_long_name": "Matroska / WebM",
        "start_time": "0.000000",
        "duration": "9.000000",
        "size": "1550466",
        "bit_rate": "1378192",
        "probe_score": 100,
        "tags": {
            "COMPATIBLE_BRANDS": "qt  ",
            "creation_time": "2023-02-23T10:43:46.703800Z",
            "MAJOR_BRAND": "qt  ",
            "MINOR_VERSION": "537199360",
            "ENCODER": "Lavf59.35.100"
        }
    }
}