Recherche avancée

Sur d’autres sites (3241)

• avfilter/buffersrc : postpone removal of sws_param

  19 avril 2021, par James Almer

  avfilter/buffersrc : postpone removal of sws_param
  It was depreacted less than two years ago
  Signed-off-by : James Almer <jamrial@gmail.com>
  

  • [DH] libavfilter/version.h

• Developing MobyCAIRO

  26 mai 2021, par Multimedia Mike — General

  I recently published a tool called MobyCAIRO. The ‘CAIRO’ part stands for Computer-Assisted Image ROtation, while the ‘Moby’ prefix refers to its role in helping process artifact image scans to submit to the MobyGames database. The tool is meant to provide an accelerated workflow for rotating and cropping image scans. It works on both Windows and Linux. Hopefully, it can solve similar workflow problems for other people.

  As of this writing, MobyCAIRO has not been tested on Mac OS X yet– I expect some issues there that should be easily solvable if someone cares to test it.

  The rest of this post describes my motivations and how I arrived at the solution.

  Background
  I have scanned well in excess of 2100 images for MobyGames and other purposes in the past 16 years or so. The workflow looks like this :

  
  

  

  Image workflow

  
  

  It should be noted that my original workflow featured me manually rotating the artifact on the scanner bed in order to ensure straightness, because I guess I thought that rotate functions in image editing programs constituted dark, unholy magic or something. So my workflow used to be even more arduous :

  
  

  

  I can’t believe I had the patience to do this for hundreds of scans

  
  

  Sometime last year, I was sitting down to perform some more scanning and found myself dreading the oncoming tedium of straightening and cropping the images. This prompted a pivotal question :

  
  Why can’t a computer do this for me ?
  

  After all, I have always been a huge proponent of making computers handle the most tedious, repetitive, mind-numbing, and error-prone tasks. So I did some web searching to find if there were any solutions that dealt with this. I also consulted with some like-minded folks who have to cope with the same tedious workflow.

  I came up empty-handed. So I endeavored to develop my own solution.

  Problem Statement and Prior Work
  
  I want to develop a workflow that can automatically rotate an image so that it is straight, and also find the most likely crop rectangle, uniformly whitening the area outside of the crop area (in the case of circles).

  As mentioned, I checked to see if any other programs can handle this, starting with my usual workhorse, Photoshop Elements. But I can’t expect the trimmed down version to do everything. I tried to find out if its big brother could handle the task, but couldn’t find a definitive answer on that. Nor could I find any other tools that seem to take an interest in optimizing this particular workflow.

  When I brought this up to some peers, I received some suggestions, including an idea that the venerable GIMP had a feature like this, but I could not find any evidence. Further, I would get responses of “Program XYZ can do image rotation and cropping.” I had to tamp down on the snark to avoid saying “Wow ! An image editor that can perform rotation AND cropping ? What a game-changer !” Rotation and cropping features are table stakes for any halfway competent image editor for the last 25 or so years at least. I am hoping to find or create a program which can lend a bit of programmatic assistance to the task.

  Why can’t other programs handle this ? The answer seems fairly obvious : Image editing tools are general tools and I want a highly customized workflow. It’s not reasonable to expect a turnkey solution to do this.

  Brainstorming An Approach
  I started with the happiest of happy cases— A disc that needed archiving (a marketing/press assets CD-ROM from a video game company, contents described here) which appeared to have some pretty clear straight lines :

  
  
  

  My idea was to try to find straight lines in the image and then rotate the image so that the image is parallel to the horizontal based on the longest single straight line detected.

  I just needed to figure out how to find a straight line inside of an image. Fortunately, I quickly learned that this is very much a solved problem thanks to something called the Hough transform. As a bonus, I read that this is also the tool I would want to use for finding circles, when I got to that part. The nice thing about knowing the formal algorithm to use is being able to find efficient, optimized libraries which already implement it.

  Early Prototype
  A little searching for how to perform a Hough transform in Python led me first to scikit. I was able to rapidly produce a prototype that did some basic image processing. However, running the Hough transform directly on the image and rotating according to the longest line segment discovered turned out not to yield expected results.

  
  
  

  It also took a very long time to chew on the 3300×3300 raw image– certainly longer than I care to wait for an accelerated workflow concept. The key, however, is that you are apparently not supposed to run the Hough transform on a raw image– you need to compute the edges first, and then attempt to determine which edges are ‘straight’. The recommended algorithm for this step is the Canny edge detector. After applying this, I get the expected rotation :

  
  
  

  The algorithm also completes in a few seconds. So this is a good early result and I was feeling pretty confident. But, again– happiest of happy cases. I should also mention at this point that I had originally envisioned a tool that I would simply run against a scanned image and it would automatically/magically make the image straight, followed by a perfect crop.

  Along came my MobyGames comrade Foxhack to disabuse me of the hope of ever developing a fully automated tool. Just try and find a usefully long straight line in this :

  
  

  Darn it, Foxhack…
  

  There are straight edges, to be sure. But my initial brainstorm of rotating according to the longest straight edge looks infeasible. Further, it’s at this point that we start brainstorming that perhaps we could match on ratings badges such as the standard ESRB badges omnipresent on U.S. video games. This gets into feature detection and complicates things.

  This Needs To Be Interactive
  At this point in the effort, I came to terms with the fact that the solution will need to have some element of interactivity. I will also need to get out of my safe Linux haven and figure out how to develop this on a Windows desktop, something I am not experienced with.

  I initially dreamed up an impressive beast of a program written in C++ that leverages Windows desktop GUI frameworks, OpenGL for display and real-time rotation, GPU acceleration for image analysis and processing tricks, and some novel input concepts. I thought GPU acceleration would be crucial since I have a fairly good GPU on my main Windows desktop and I hear that these things are pretty good at image processing.

  I created a list of prototyping tasks on a Trello board and made a decent amount of headway on prototyping all the various pieces that I would need to tie together in order to make this a reality. But it was ultimately slowgoing when you can only grab an hour or 2 here and there to try to get anything done.

  Settling On A Solution
  Recently, I was determined to get a set of old shareware discs archived. I ripped the data a year ago but I was blocked on the scanning task because I knew that would also involve tedious straightening and cropping. So I finally got all the scans done, which was reasonably quick. But I was determined to not manually post-process them.

  This was fairly recent, but I can’t quite recall how I managed to come across the OpenCV library and its Python bindings. OpenCV is an amazing library that provides a significant toolbox for performing image processing tasks. Not only that, it provides “just enough” UI primitives to be able to quickly create a basic GUI for your program, including image display via multiple windows, buttons, and keyboard/mouse input. Furthermore, OpenCV seems to be plenty fast enough to do everything I need in real time, just with (accelerated where appropriate) CPU processing.

  So I went to work porting the ideas from the simple standalone Python/scikit tool. I thought of a refinement to the straight line detector– instead of just finding the longest straight edge, it creates a histogram of 360 rotation angles, and builds a list of lines corresponding to each angle. Then it sorts the angles by cumulative line length and allows the user to iterate through this list, which will hopefully provide the most likely straightened angle up front. Further, the tool allows making fine adjustments by 1/10 of an angle via the keyboard, not the mouse. It does all this while highlighting in red the straight line segments that are parallel to the horizontal axis, per the current candidate angle.

  
  
  

  The tool draws a light-colored grid over the frame to aid the user in visually verifying the straightness of the image. Further, the program has a mode that allows the user to see the algorithm’s detected edges :

  
  
  

  For the cropping phase, the program uses the Hough circle transform in a similar manner, finding the most likely circles (if the image to be processed is supposed to be a circle) and allowing the user to cycle among them while making precise adjustments via the keyboard, again, rather than the mouse.

  
  
  

  Running the Hough circle transform is a significantly more intensive operation than the line transform. When I ran it on a full 3300×3300 image, it ran for a long time. I didn’t let it run longer than a minute before forcibly ending the program. Is this approach unworkable ? Not quite– It turns out that the transform is just as effective when shrinking the image to 400×400, and completes in under 2 seconds on my Core i5 CPU.

  For rectangular cropping, I just settled on using OpenCV’s built-in region-of-interest (ROI) facility. I tried to intelligently find the best candidate rectangle and allow fine adjustments via the keyboard, but I wasn’t having much success, so I took a path of lesser resistance.

  Packaging and Residual Weirdness
  I realized that this tool would be more useful to a broader Windows-using base of digital preservationists if they didn’t have to install Python, establish a virtual environment, and install the prerequisite dependencies. Thus, I made the effort to figure out how to wrap the entire thing up into a monolithic Windows EXE binary. It is available from the project’s Github release page (another thing I figured out for the sake of this project !).

  The binary is pretty heavy, weighing in at a bit over 50 megabytes. You might advise using compression– it IS compressed ! Before I figured out the --onefile command for pyinstaller.exe, the generated dist/ subdirectory was 150 MB. Among other things, there’s a 30 MB FORTRAN BLAS library packaged in !

  Conclusion and Future Directions
  Once I got it all working with a simple tkinter UI up front in order to select between circle and rectangle crop modes, I unleashed the tool on 60 or so scans in bulk, using the Windows forfiles command (another learning experience). I didn’t put a clock on the effort, but it felt faster. Of course, I was livid with proudness the whole time because I was using my own tool. I just wish I had thought of it sooner. But, really, with 2100+ scans under my belt, I’m just getting started– I literally have thousands more artifacts to scan for preservation.

  The tool isn’t perfect, of course. Just tonight, I threw another scan at MobyCAIRO. Just go ahead and try to find straight lines in this specimen :

  
  
  

  I eventually had to use the text left and right of center to line up against the grid with the manual keyboard adjustments. Still, I’m impressed by how these computer vision algorithms can see patterns I can’t, highlighting lines I never would have guessed at.

  I’m eager to play with OpenCV some more, particularly the video processing functions, perhaps even some GPU-accelerated versions.

  The post Developing MobyCAIRO first appeared on Breaking Eggs And Making Omelettes.

• Use data to develop impactful video content

  28 septembre 2021, par Ben Erskine — Analytics Tips, Plugins

  Creating impactful video content is at the heart of what you do. How you really engage with your audience, change behaviours and influence customers to complete your digital goals. But how do you create truly impactful marketing content ? By testing, trialling, analysing and ultimately tweaking and reacting to data-informed insights that gear your content to your audience (rather than simply producing great content and shooting arrows in the dark).

  Whether you want to know how many plays your video has, finish rates, how your video is consumed over time, how video was consumed on specific days or even which locations users are viewing your video content. Media Analytics will gather all of your video data in one place and provide answers to all of these questions (and much more).

  

  What is impactful video content ?

  Impactful video content grabs your audience’s attention, keeps their attention and promotes them to take measurable action. Be that time spent on your website, goal completion or brand engagement (including following, commenting or sharing on social). Maybe you’ve developed video content, had some really great results, but not consistently, nor every time and it can be difficult to identify what exactly it is that engages and entices each and every time. And we all want to find where that lovely sweet spot is for your audience.

  Embedded video on your website can be a marketing piece that talks about the benefits of your product. Or can be educational or informative that support the brand and overall impression of the brand. And at the very best entertaining at the same time. 

  84% of people say that they’ve been convinced to buy a product or service by watching a brand’s video. Building trust, knowledge and engagement are simply quicker with video. Viewers interact more, and are engaged longer with video, they are more likely to take in the message and trust what they are seeing through educational, informative or even entertaining video marketing content than solely through reading content on a website. And even better they take action, complete goals on your website and engage with your brand (potentially long term).

  It is not only necessary to have embedded video content on your website, it needs to deliver all the elements of a well functioning website, creating the very best user experience is essential to keeping your viewers engaged. This includes ensuring the video is quick to load, on-brand, expected (in format and tone) and easy to use and/or find. Ensuring that your video content is all of these things can mean that your website users will stick around longer on your website, spend more time exploring (and reading) your website and ultimately complete more of your goals. With a great user experience, your users, in turn, are more likely to come back again to your website and trust your brand. 

  All great reasons to create impactful video content that supports your website and brand ! And to analyse data around this behaviour to repeat (or better) the video content that really hits the mark.

  

  Let’s talk stats

  In terms of video marketing, there are stats to support that viewers retain 95% of a message when they view it in a video format. The psychology behind this should be fairly obvious. It is easier (and quicker) for humans to consume video and watch someone explain something than it is to read and take action. Simply look at the rise of YouTube for explanatory and instructional video content !

  And how about the 87% of marketers that report a positive ROI on using video in their marketing ? This number has steadily increased since 2015 and matches the increase in video views over the years. This should be enough to demonstrate that video marketing is the way forward, however it needs to be the right type of video to create impact and engagement.

  Do you need more reasons to consider honing and refining your video content for your audience ? And riding this wave of impactful video marketing success ?

  

  But, how do we do that ?

  So, how do you make content that consistently converts your audience to engaged customers ? The answer is in the numbers. The data. Collecting data on each and every piece of media that is produced and put out into the world. Measuring everything, from where it is viewed, how it is viewed, how much of it is viewed and what is your viewer’s action after the fact.

  While Vimeo and YouTube have their own video analytics they are each to their own, meaning a lot more work for you to combine and analyse your data before forming insights that are useful. 

  Your data is collected by external parties, and is owned and used by these platforms, for their own means. Using Web Analytics from Matomo to collect and collate media data can mean your robust data insights are all in one place. And you own the data, keeping your data private, clean and easy to digest. 

  Once your data is across a single platform, your time can be spent on analysing the data (rather than collating) and discovering those super valuable insights. Additionally, these insights can be collated and reported, in one place, and used to inform future digital and video marketing planning. Working with the data and alongside creative teams to produce video that talks to your audience in an impactful way.

  The more data that is collected the deeper the insights. Saving time and money across a single platform and with data-backed insights to inform decisions that can influence the time (and money) spent producing video content that truly hits the mark with your audience. No more wasted investment and firing into the dark without knowledge. 

  Interrogating the ideal length of your video media means it is more likely to be viewed to the end. Or understanding the play rate on your website of any video. How often is the video played ? And which is played more often ? Constant tweaking and updating of your video content planning can be informed by data-driven human-centric insights. By consistently tracking your media, analysing and forming insights you can build upon past work, and create a fuller picture of who your audience is and how they will engage with future video content. Understanding your media over time can lead to informed decisions that can impact the video content and the level of investment to deliver ROI that means something.

  

  Wrap Up

  Media Analytics puts you at the heart of video engagement. No more guessing at what your audience wants to see, how long or when. Make every piece of video content have the impact you want (and need) to drive engagement, goal completion and customer conversion. Create a user experience that keeps your users on your website for longer. Delivering on all of those delicious digital marketing goals and speaking the language of key stakeholders throughout the business. Back your digital marketing, with truly impactful content, and above all else deliver to your audience content that keeps them engaged and coming back for more.

  Don’t just take our word for it ! Take a look at what Matomo can offer you with streamlined and insightful Media Analytics, all in one place. And go forth and create impactful content, that matters.

  Next steps :

  Check out our detailed user guide to Media Analytics

  Or, if you have questions, see our helpful Video & Audio Analytics FAQ’s