Monitoring Virtual Sports Product

Virtual Sports is one of our most popular products. Our tireless, HRNG-powered, CGI horses can be seen careering around a racetrack 24 hours a day, 7 days a week via RTMP or HLS.

A lot can go wrong with a live-stream that never ends: it can get out of sync, it can die completely, and it can even display the wrong thing entirely if a failover, erm, fails (don’t ask). Suffice to say, it’s something that needs to be monitored so we know when it needs fixing.

How does one monitor a video stream? There are some quick wins: you can check that your endpoints exist; for HLS you can check that your M3U8 files are changing using simple HTTP checks; but none of that can tell you that the stream is blank, or displaying an event from half an hour ago.

All of our Virtual Sports streams have something in common: they display the time of the current event, last event, or next event at various positions on the screen. If we could read those times programmatically then we could raise alarms when they look wrong, or don’t show up at all. Doing OCR on a video stream seems like a difficult problem, but by breaking it down and leveraging some well-established tools it’s perfectly doable.

A Plan

Various OCR tools can read text out of images without too much difficulty - Tesseract is one such tool - but we don’t have images, we have video. Enter FFmpeg. FFmpeg is a veritable Swiss Army Knife for dealing with video; it can consume, record and convert just about anything - including turning a video into discrete images.

So here’s a plan of attack:

Consume the video stream with FFmpeg and convert it to a series of images. Crop the images to regions where we know there will be a time. Run those images into Tesseract OCR to get the time from them as text. Expose the text we found in some way; maybe an HTTP API.

Checking the FFmpeg docs reveals that step 1 is pretty easy!

▶ ffmpeg -i rtmp://streamurl -r 1 frames/%04d-frame.png

That will consume the stream at rtmp://streamurl and output it as one PNG per second.

Step 3 is pretty easy too. On a manually cropped, cleaned and resized frame from the stream, Tesseract does just fine:

▶ tesseract horses-1506-region2.png stdout 15:06

That still leaves steps 2 and 4; but we’ve got a proof of concept for the difficult bits already.

Most general purpose programming languages should be able to handle the remaining steps without too much difficulty.

Go-Go-Gadget Go!

Go is a general purpose programming language; and it’s great for this sort of work.

First we need to make a temporary directory to store our images:

dir , err := ioutil . TempDir ( "" , "vsframes" ) if err != nil { log . Fatal ( "Failed to create temp directory" ) }

We need to fill that directory with images, so we’ll run FFmpeg in a Goroutine to do just that:

go func () { exec . Command ( "ffmpeg" , "-i" , "rtmp://streamurl" , "-r" , "1" , dir + "/frame-%04d.png" ) . Run () }()

We need somewhere to store our current state. It’s not going to get passed around, so an anonymous struct will do just fine:

state := struct { Time string sync . Mutex }{ Time : "" }

Note that the state struct embeds sync.Mutex . That makes the Lock and Unlock methods available on our struct so that we can safely update it in one place and read it in another.

We’ll expose it over HTTP as a blob of JSON:

http . HandleFunc ( "/" , func ( w http . ResponseWriter , r * http . Request ) { state . Lock () j , _ := json . Marshal ( state ) state . Unlock () w . Write ( j ) }) go http . ListenAndServe ( "0.0.0.0:1234" , nil )

Now to our main loop. We know we want to get all of the frames from the temp dir, do something with them, remove them, and then wait for a bit before repeating the process:

for { frames , _ := filepath . Glob ( dir + "/*.png" ) for _ , frame := range frames { // 'Something' // ... os . Remove ( frame ) } time . Sleep ( time . Millisecond * 100 ) }

That something we want to do is pull out the regions of interest and do OCR on them. Then for each bit of OCR output that is a valid time, we want to update the state:

// 'Something' regions := getRegions ( frame ) for _ , region := range regions { candidate := ocr ( region ) if validTime ( candidate ) { state . Lock () state . Time = candidate state . Unlock () } os . Remove ( region ) }

We’ve used a few user-defined functions there. getRegions uses the github.com/disintegration/imaging package to crop and clean some predefined regions of interest and write them to disk as PNGs. It returns a slice containing the filenames of the PNGs it created:

func getRegions ( path string ) [] string { // The coordinates for our regions of interest regions := [] image . Rectangle { image . Rect ( 83 , 66 , 132 , 78 ), image . Rect ( 96 , 162 , 148 , 174 ), image . Rect ( 160 , 21 , 213 , 33 ), image . Rect ( 100 , 93 , 147 , 108 ), } out := make ([] string , 0 ) // Open and decode the image r , _ := os . Open ( path ) defer r . Close () img , _ , err := image . Decode ( r ) if err != nil { return out } for i , region := range regions { // Crop and clean the image cropped := imaging . Crop ( img , region ) cleaned := cleanImage ( cropped ) // Write the cropped and cleaned image to disk regionPath := fmt . Sprintf ( "%s-region-%d.png" , path , i ) w , _ := os . Create ( regionPath ) png . Encode ( w , cleaned ) w . Close () out = append ( out , regionPath ) } return out }

The cleanImage function that getRegions calls makes the image easier for Tesseract to read by increasing its size, converting it to grayscale, and a few other things:

func cleanImage ( img image . Image ) image . Image { w := img . Bounds () . Size () . X h := img . Bounds () . Size () . Y p := imaging . Grayscale ( img ) p = imaging . Resize ( p , w * 3 , h * 3 , imaging . BSpline ) p = imaging . Invert ( p ) p = imaging . AdjustContrast ( p , 40 ) p = imaging . Sharpen ( p , 5 ) return p }

The ocr function just runs Tesseract against a given image, and returns any text it finds with non-number characters stripped off from either side:

func ocr ( path string ) string { raw , _ := exec . Command ( "tesseract" , path , "stdout" ) . Output () return strings . TrimFunc ( string ( raw ), func ( r rune ) bool { return ! unicode . IsNumber ( r ) }) }

Lastly, the validTime function just does a quick and dirty regex check against a string to see if it looks like a valid 24-hour time:

func validTime ( c string ) bool { v := regexp . MustCompile ( `^[0-2][0-9]:[0-5][0-9]$` ) return v . MatchString ( c ) }

That should be everything! Building a Go package is as simple as:

▶ go build

Running the resulting binary and issuing a quick check with curl confirms that everything is working as intended!

▶ curl http://localhost:1234/ { "Time" : "13:06" }

From here we could easily write a simple Nagios check (or similar) to set off an alarm when that time doesn’t look right.

Wrapping Up

In the name of getting things working quickly we’ve ignored a bunch of failure scenarios and generally haven’t paid much attention to error handling, but that’s nothing out of the ordinary for code destined for a blog post. It could do with some logging and proper signal handling too, but those things are - as is tradition - left as an exercise for the reader.

The original problem of “do OCR on a video stream” seemed like a difficult one, but we’ve not had to do anything particularly difficult to solve it.