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• Calculate VMAF score while encoding a video with FFmpeg

  31 octobre 2024, par user8071576

  I have an ffmpeg version built with VMAF library. I can use it to calculate the VMAF scores of a distorted video against a reference video using commands like this :

  


  ffmpeg -i distorted.mp4 -i original.mp4 -filter_complex "[0:v]scale=640:480:flags=bicubic[main];[main][1:v]libvmaf=model_path=model/vmaf_v0.6.1.json:log_path=log.json" -f null -


  


  Now, I remember there was a way to get VMAF scores while performing regular ffmpeg encoding. How can I do that at the same time ?

  


  I want to encode a video like this, while also calulate the VMAF of the output file :

  


  ffmpeg -i original.mp4 -crf 27 -s 640x480 out.mp4


  


• Python : Extracting device and lens information from video metadata

  14 mai 2023, par cat_got_my_tongue

  I am interested in extracting device and lens information from videos. Specifically, make and model of the device and the focal length. I was able to do this successfully for still images using the exifread module and extract a whole bunch of very useful information :

  


  image type      : MPO
Image ImageDescription: Shot with DxO ONE
Image Make: DxO
Image Model: DxO ONE
Image Orientation: Horizontal (normal)
Image XResolution: 300
Image YResolution: 300
Image ResolutionUnit: Pixels/Inch
Image Software: V3.0.0 (2b448a1aee) APP:1.0
Image DateTime: 2022:04:05 14:53:45
Image YCbCrCoefficients: [299/1000, 587/1000, 57/500]
Image YCbCrPositioning: Centered
Image ExifOffset: 158
Thumbnail Compression: JPEG (old-style)
Thumbnail XResolution: 300
Thumbnail YResolution: 300
Thumbnail ResolutionUnit: Pixels/Inch
Thumbnail JPEGInterchangeFormat: 7156
Thumbnail JPEGInterchangeFormatLength: 24886
EXIF ExposureTime: 1/3
EXIF FNumber: 8
EXIF ExposureProgram: Aperture Priority
EXIF ISOSpeedRatings: 100
EXIF SensitivityType: ISO Speed
EXIF ISOSpeed: 100
EXIF ExifVersion: 0221
EXIF DateTimeOriginal: 2022:04:05 14:53:45
EXIF DateTimeDigitized: 2022:04:05 14:53:45
EXIF ComponentsConfiguration: CrCbY
EXIF CompressedBitsPerPixel: 3249571/608175
EXIF ExposureBiasValue: 0
EXIF MaxApertureValue: 212/125
EXIF SubjectDistance: 39/125
EXIF MeteringMode: MultiSpot
EXIF LightSource: Unknown
EXIF Flash: Flash did not fire
EXIF FocalLength: 1187/100
EXIF SubjectArea: [2703, 1802, 675, 450]
EXIF MakerNote: [68, 88, 79, 32, 79, 78, 69, 0, 12, 0, 0, 0, 21, 0, 3, 0, 5, 0, 2, 0, ... ]
EXIF SubSecTime: 046
EXIF SubSecTimeOriginal: 046
EXIF SubSecTimeDigitized: 046
EXIF FlashPixVersion: 0100
EXIF ColorSpace: sRGB
EXIF ExifImageWidth: 5406
EXIF ExifImageLength: 3604
Interoperability InteroperabilityIndex: R98
Interoperability InteroperabilityVersion: [48, 49, 48, 48]
EXIF InteroperabilityOffset: 596
EXIF FileSource: Digital Camera
EXIF ExposureMode: Auto Exposure
EXIF WhiteBalance: Auto
EXIF DigitalZoomRatio: 1
EXIF FocalLengthIn35mmFilm: 32
EXIF SceneCaptureType: Standard
EXIF ImageUniqueID: C01A1709306530020220405185345046
EXIF BodySerialNumber: C01A1709306530


  


  Unfortunately, I have been unable to extract this kind of info from videos so far.

  


  This is what I have tried so far, with the ffmpeg module :

  


  import ffmpeg
from pprint import pprint

test_video = "my_video.mp4"
pprint(ffmpeg.probe(test_video)["streams"])


  


  And the output I get contains a lot of info but nothing related to the device or lens, which is what I am looking for :

  


  [{'avg_frame_rate': '30/1',
  'bit_rate': '1736871',
  'bits_per_raw_sample': '8',
  'chroma_location': 'left',
  'codec_long_name': 'H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10',
  'codec_name': 'h264',
  'codec_tag': '0x31637661',
  'codec_tag_string': 'avc1',
  'codec_time_base': '1/60',
  'codec_type': 'video',
  'coded_height': 1088,
  'coded_width': 1920,
  'display_aspect_ratio': '16:9',
  'disposition': {'attached_pic': 0,
                  'clean_effects': 0,
                  'comment': 0,
                  'default': 1,
                  'dub': 0,
                  'forced': 0,
                  'hearing_impaired': 0,
                  'karaoke': 0,
                  'lyrics': 0,
                  'original': 0,
                  'timed_thumbnails': 0,
                  'visual_impaired': 0},
  'duration': '20.800000',
  'duration_ts': 624000,
  'has_b_frames': 0,
  'height': 1080,
  'index': 0,
  'is_avc': 'true',
  'level': 40,
  'nal_length_size': '4',
  'nb_frames': '624',
  'pix_fmt': 'yuv420p',
  'profile': 'Constrained Baseline',
  'r_frame_rate': '30/1',
  'refs': 1,
  'sample_aspect_ratio': '1:1',
  'start_pts': 0,
  'start_time': '0.000000',
  'tags': {'creation_time': '2021-05-08T13:23:20.000000Z',
           'encoder': 'AVC Coding',
           'handler_name': 'VideoHandler',
           'language': 'und'},
  'time_base': '1/30000',
  'width': 1920},
 {'avg_frame_rate': '0/0',
  'bit_rate': '79858',
  'bits_per_sample': 0,
  'channel_layout': 'stereo',
  'channels': 2,
  'codec_long_name': 'AAC (Advanced Audio Coding)',
  'codec_name': 'aac',
  'codec_tag': '0x6134706d',
  'codec_tag_string': 'mp4a',
  'codec_time_base': '1/48000',
  'codec_type': 'audio',
  'disposition': {'attached_pic': 0,
                  'clean_effects': 0,
                  'comment': 0,
                  'default': 1,
                  'dub': 0,
                  'forced': 0,
                  'hearing_impaired': 0,
                  'karaoke': 0,
                  'lyrics': 0,
                  'original': 0,
                  'timed_thumbnails': 0,
                  'visual_impaired': 0},
  'duration': '20.864000',
  'duration_ts': 1001472,
  'index': 1,
  'max_bit_rate': '128000',
  'nb_frames': '978',
  'profile': 'LC',
  'r_frame_rate': '0/0',
  'sample_fmt': 'fltp',
  'sample_rate': '48000',
  'start_pts': 0,
  'start_time': '0.000000',
  'tags': {'creation_time': '2021-05-08T13:23:20.000000Z',
           'handler_name': 'SoundHandler',
           'language': 'und'},
  'time_base': '1/48000'}]


  


  Are these pieces of info available for videos ? Should I be using a different package ?

  


  Thanks.

  


  Edit :

  


  pprint(ffmpeg.probe(test_video)["format"]) gives

  


  {'bit_rate': '1815244',
 'duration': '20.864000',
 'filename': 'my_video.mp4',
 'format_long_name': 'QuickTime / MOV',
 'format_name': 'mov,mp4,m4a,3gp,3g2,mj2',
 'nb_programs': 0,
 'nb_streams': 2,
 'probe_score': 100,
 'size': '4734158',
 'start_time': '0.000000',
 'tags': {'artist': 'Microsoft Game DVR',
          'compatible_brands': 'mp41isom',
          'creation_time': '2021-05-08T12:12:33.000000Z',
          'major_brand': 'mp42',
          'minor_version': '0',
          'title': 'Snipping Tool'}}


  


• Announcing our latest open source project : DeviceDetector

  30 juillet 2014, par Stefan Giehl — Community, Development, Meta, DeviceDetector

  This blog post is an announcement for our latest open source project release : DeviceDetector ! The Universal Device Detection library will parse any User Agent and detect the browser, operating system, device used (desktop, tablet, mobile, tv, cars, console, etc.), brand and model.

  Read on to learn more about this exciting release.

  Why did we create DeviceDetector ?

  Our previous library UserAgentParser only had the possibility to detect operating systems and browsers. But as more and more traffic is coming from mobile devices like smartphones and tablets it is getting more and more important to know which devices are used by the websites visitors.

  To ensure that the device detection within Piwik will gain the required attention, so it will be as accurate as possible, we decided to move that part of Piwik into a separate project, that we will maintain separately. As an own project we hope the DeviceDetector will gain a better visibility as well as a better support by and for the community !

  DeviceDetector is hosted on GitHub at piwik/device-detector. It is also available as composer package through Packagist.

  How DeviceDetector works

  Every client requesting data from a webserver identifies itself by sending a so-called User-Agent within the request to the server. Those User Agents might contain several information such as :

  • client name and version (clients can be browsers or other software like feed readers, media players, apps,…)
  • operating system name and version
  • device identifier, which can be used to detect the brand and model.

  For Example :

  Mozilla/5.0 (Linux; Android 4.4.2; Nexus 5 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.99 Mobile Safari/537.36

  This User Agent contains following information :

  Operating system is Android 4.4.2, client uses the browser Chrome Mobile 32.0.1700.99 and the device is a Google Nexus 5 smartphone.

  What DeviceDetector currently detects

  DeviceDetector is able to detect bots, like search engines, feed fetchers, site monitors and so on, five different client types, including around 100 browsers, 15 feed readers, some media players, personal information managers (like mail clients) and mobile apps using the AFNetworking framework, around 80 operating systems and nine different device types (smartphones, tablets, feature phones, consoles, tvs, car browsers, cameras, smart displays and desktop devices) from over 180 brands.

  Note : Piwik itself currently does not use the full feature set of DeviceDetector. Client detection is currently not implemented in Piwik (only detected browsers are reported, other clients are marked as Unknown). Client detection will be implemented into Piwik in the future, follow #5413 to stay updated.

  Performance of DeviceDetector

  Our detections are currently handled by an enormous number of regexes, that are defined in several .YML Files. As parsing these .YML files is a bit slow, DeviceDetector is able to cache the parsed .YML Files. By default DeviceDetector uses a static cache, which means that everything is cached in static variables. As that only improves speed for many detections within one process, there are also adapters to cache in files or memcache for speeding up detections across requests.

  How can users help contribute to DeviceDetector ?

  Submit your devices that are not detected yet

  If you own a device, that is currently not correctly detected by the DeviceDetector, please create a issue on GitHub
  In order to check if your device is detected correctly by the DeviceDetector go to your Piwik server, click on ‘Settings’ link, then click on ‘Device Detection’ under the Diagnostic menu. If the data does not match, please copy the displayed User Agent and use that and your device data to create a ticket.

  Submit a list of your User Agents

  In order to create new detections or improve the existing ones, it is necessary for us to have lists of User Agents. If you have a website used by mostly non desktop devices it would be useful if you send a list of the User Agents that visited your website. To do so you need access to your access logs. The following command will extract the User Agents :

  zcat ~/path/to/access/logs* | awk -F'"' '{print $6}' | sort | uniq -c | sort -rn | head -n20000 > /home/piwik/top-user-agents.txt

  If you want to help us with those data, please get in touch at devicedetector@piwik.org

  Submit improvements on GitHub

  As DeviceDetector is free/libre library, we invite you to help us improving the detections as well as the code. Please feel free to create tickets and pull requests on Github.

  What’s the next big thing for DeviceDetector ?

  Please check out the list of issues in device-detector issue tracker.

  We hope the community will answer our call for help. Together, we can build DeviceDetector as the most powerful device detection library !

  Happy Device Detection,