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• Use FFMPEG to stream images from one client to another through IIS (or other) server

  20 avril 2012, par eselk

  I'm new to FFMPEG and maybe I should post this in their forums, but you guys here seem to know everything, so here goes. I have a client app that takes screen shots and saves them as images (256 color bitmaps currently, can change if needed), it does this at a rate of about 4 fps. I currently use my own socket code written in C# to push these to my socket server (also C#) running on a Windows 2008 server. That server then sends these images out to several clients that display them as they are received and also buffers them to allow for rewind, pause, etc, like a DVR. My current format requires approx 100KB per frame, and thus only works for a very small number of clients.

  I started looking at FFMPEG and the compression with MPEG1 and especially MPEG4 is amazing, and so is the quality. What I'm looking for is a basic guide, tutorial, or steps, to produce something similar to my current design, but using FFMPEG and actual video streaming. Ideally the player side could be something like Flash or anything that is easy to embed in a .NET WinForm (or a browser control I can host in the WinForm), and it would need to support buffering still so they can pause and rewind (about 5 or 10 mins, which seems like a lot, but remember this is only 4 fps and 256 color, about 1 or 2 MB per min in my testing).

  I see that FFMPEG, the command-line utility, and I assume the API, even has options for posting to a server via UDP or TCP, so maybe I'll use that instead of my own socket code. Ideally my app would feed images to FFMPEG library at a rate of 4fps as they come from the screen-shot unit, and it would send these up to my IIS server (or another server ?) which would then server them to client(s) that could use them similar to a YouTube video.

• Ffprobe with print json doesn't print anything

  10 septembre 2012, par Richard Knop

  I am trying to get information about a movie (resolution, frame rate, bit rate, codecs, duration etc) in a human readable way. I found this commnad :

  ffprobe -v quiet -print_format json -show_format -show_streams somefile.asf

  In this Stack Overflow question : Get ffmpeg information in friendly way

  But it doesn't work for me. When I try it in a terminal, the output is empty :

  richard@richard-desktop:~/projects/hello-python$ ffprobe -v quiet -print_format json -show_format -show_streams tests/test_1.mpg 
  
  richard@richard-desktop:~/projects/hello-python$ 

• Method For Crawling Google

  28 mai 2011, par Multimedia Mike — Big Data

  I wanted to crawl Google in order to harvest a large corpus of certain types of data as yielded by a certain search term (we’ll call it “term” for this exercise). Google doesn’t appear to offer any API to automatically harvest their search results (why would they ?). So I sat down and thought about how to do it. This is the solution I came up with.

  
  
  

  FAQ
  Q : Is this legal / ethical / compliant with Google’s terms of service ?
  A : Does it look like I care ? Moving right along…

  Manual Crawling Process
  For this exercise, I essentially automated the task that would be performed by a human. It goes something like this :

  1. Search for “term”
  2. On the first page of results, download each of the 10 results returned
  3. Click on the next page of results
  4. Go to step 2, until Google doesn’t return anymore pages of search results

  Google returns up to 1000 results for a given search term. Fetching them 10 at a time is less than efficient. Fortunately, the search URL can easily be tweaked to return up to 100 results per page.

  Expanding Reach
  Problem : 1000 results for the “term” search isn’t that many. I need a way to expand the search. I’m not aiming for relevancy ; I’m just searching for random examples of some data that occurs around the internet.

  My solution for this is to refine the search using the “site” wildcard. For example, you can ask Google to search for “term” at all Canadian domains using “site :.ca”. So, the manual process now involves harvesting up to 1000 results for every single internet top level domain (TLD). But many TLDs can be more granular than that. For example, there are 50 sub-domains under .us, one for each state (e.g., .ca.us, .ny.us). Those all need to be searched independently. Same for all the sub-domains under TLDs which don’t allow domains under the main TLD, such as .uk (search under .co.uk, .ac.uk, etc.).

  Another extension is to combine “term” searches with other terms that are likely to have a rich correlation with “term”. For example, if “term” is relevant to various scientific fields, search for “term” in conjunction with various scientific disciplines.

  Algorithmically
  My solution is to create an SQLite database that contains a table of search seeds. Each seed is essentially a “site :” string combined with a starting index.

  Each TLD and sub-TLD is inserted as a searchseed record with a starting index of 0.

  A script performs the following crawling algorithm :

  • Fetch the next record from the searchseed table which has not been crawled
  • Fetch search result page from Google
  • Scrape URLs from page and insert each into URL table
  • Mark the searchseed record as having been crawled
  • If the results page indicates there are more results for this search, insert a new searchseed for the same seed but with a starting index 100 higher

  Digging Into Sites
  Sometimes, Google notes that certain sites are particularly rich sources of “term” and offers to let you search that site for “term”. This basically links to another search for ‘term site:somesite”. That site gets its own search seed and the program might harvest up to 1000 URLs from that site alone.

  Harvesting the Data
  Armed with a database of URLs, employ the following algorithm :

  • Fetch a random URL from the database which has yet to be downloaded
  • Try to download it
  • For goodness sake, have a mechanism in place to detect whether the download process has stalled and automatically kill it after a certain period of time
  • Store the data and update the database, noting where the information was stored and that it is already downloaded

  This step is easy to parallelize by simply executing multiple copies of the script. It is useful to update the URL table to indicate that one process is already trying to download a URL so multiple processes don’t duplicate work.

  Acting Human
  A few factors here :

  • Google allegedly doesn’t like automated programs crawling its search results. Thus, at the very least, don’t let your script advertise itself as an automated program. At a basic level, this means forging the User-Agent : HTTP header. By default, Python’s urllib2 will identify itself as a programming language. Change this to a well-known browser string.
  • Be patient ; don’t fire off these search requests as quickly as possible. My crawling algorithm inserts a random delay of a few seconds in between each request. This can still yield hundreds of useful URLs per minute.
  • On harvesting the data : Even though you can parallelize this and download data as quickly as your connection can handle, it’s a good idea to randomize the URLs. If you hypothetically had 4 download processes running at once and they got to a point in the URL table which had many URLs from a single site, the server might be configured to reject too many simultaneous requests from a single client.

  Conclusion
  Anyway, that’s just the way I would (and did) do it. What did I do with all the data ? That’s a subject for a different post.

  Adorable spider drawing from here.