Preparing videos for FutureLearn courses

This week we started up our new online course, Motion Capture: The Art of Studying Human Activity, and we are also rerunning Music Moves: Why Does Music Make You Move? for the seventh time. Most of the material for these courses is premade, but we record a new wrap-up video at the end of each week. This makes it possible to answer questions that have been posed during the week and add some new and relevant material.

To simplify making these wrap-up videos, I am this time around recording them with my Samsung Galaxy s21 Ultra and a set of Røde Wireless GO II microphones. Time is limited when making these videos, so I have decided to quickly trim the files with FFmpeg instead of spending time in video editing software.

I have started shooting videos in 4K, not necessarily because I need it right now, but all my equipment supports 4K these days, and it feels more future-proof. However, FutureLearn does not like 4K and is rather picky about the files to be uploaded:

  • File format: .mp4 / .mov / .m4v
  • File size: up to 5GB
  • Codec: H264
  • Frame rate: 25 fps
  • Bit rate: min 2 Mbps constant bit rate
  • Sound: AAC 44khz stereo

So how do you go about creating such files? Well, FFmpeg comes to the rescue again:

ffmpeg -i input.mp4 -vf "scale=1920:1080 , fps=25" -ar 44100 -b:v 8M -minrate 2M input.mp4

The one-liner is relatively self-explanatory. First, I apply a video filter that scales down the video to 1080p and reduces the framerate to 25fps. Then I specify that the audio should be reduced to 44100 Hz. FutureLearn wants a bitrate of 2 Mbps but does not specify a preferred bitrate. I decided to go for 8 Mbps, the suggested bitrate for 1080p uploads to YouTube. I added a minimum bitrate of 2 Mbps at the end, but I don’t think it is necessary since the bitrate used for MP4 files is constant.

All in all, this means that I can do the complete video editing with two simple one-liners, one for trimming the file and the one above for converting to the correct format. That way, I should manage to create two such wrap-up videos each week for the coming weeks.

Frame differencing with FFmpeg

I often want to create motion videos, that is, videos that only show what changed between frames. Such videos are nice to look at, and so-called “frame differencing” is also the start point for many computer vision algorithms.

We have made several tools for creating motion videos (and more) at the University of Oslo: the standalone VideoAnalysis app (Win/Mac) and the different versions of the Musical Gestures Toolbox. These are all great tools, but sometimes it would be nice also to create motion videos in the terminal using FFmpeg.

I have previously written about the tblend function in FFMPEG, which I thought would be a good starting point. However, it turned out to be slightly more challenging to do than I had expected. Hence, this blog post is to help others looking to do the same.

Here is the source video I used for testing:

Source video of dance improvisation.

First, I tried with this oneliner:

ffmpeg -i input.mp4 -filter_complex "tblend=all_mode=difference" output.mp4

It does the frame differencing, but I end up with a green image:

The result of the tblend filter.

I spent quite some time looking for a solution. Several people report a similar problem, but there are few answers. Finally, I found this explanation suggesting that the source video is in YUV while the filter expects RGB. To get the correct result, we need to add a format=gbrp to the filter chain:

ffmpeg -i input.mp4 -filter_complex "format=gbrp,tblend=all_mode=difference" output.mp4
The final motion video after RGB conversion.

I have now also added this to the function mgmotion in the Musical Gestures Toolbox for Terminal.

Try not to headbang challenge

I recently came across a video of the so-called Try not to headbang challenge, where the idea is to, well, not to headbang while listening to music. This immediately caught my attention. After all, I have been researching music-related micromotion over the last years and have run the Norwegian Championship of Standstill since 2012.

Here is an example of Nath & Johnny trying the challenge:

As seen in the video, they are doing ok, although they are far from sitting still. Running the video through the Musical Gestures Toolbox for Python, it is possible to see when and how much they moved clearly.

Below is a quick visualization of the 11-minute long sequence. The videogram (similar to a motiongram but of the original video) shows quite a lot of motion throughout. There is no headbanging, but they do not sit still.

A videogram of the complete video recording (top) with a waveform of the audio track. Two selected frames from the sequence and “zoomed-in” videograms show the motion of specific passages.

There are many good musical examples listed here. We should consider some of them for our next standstill championship. If corona allows, we plan to run a European Championship of Standstill in May 2022. More information soon!

Pre-processing Garmin VIRB 360 recordings with FFmpeg

I have previously written about how it is possible to “flatten” a Ricoh Theta+ recording using FFmpeg. Now, I have spent some time exploring how to process some recordings from a Garmin VIRB camera.

Some hours of recordings

The starting point was a bunch of recordings from our recent MusicLab Copenhagen featuring the amazing Danish String Quartet. A team of RITMO researchers went to Copenhagen and captured the quartet in both rehearsal and performance. We have data and media from motion capture, eye tracking, physiological sensing, audio, video, and more. The plan is to make it all available on OSF.

When it comes to video, we have many different recordings, ranging from small GoPro cameras hanging around the space to professional streaming cameras operated by a camera crew. In addition, we have one recording from a Garmin VIRB 360 camera hanging in the chandelier close to the musicians. Those recordings are what I will explore in this post.

An upside 360 recording

The Garmin VIRB camera records a 360-degree video using two 180-degree lenses. Unlike Ricoh Theta’s stereo-spherical videos, the Garmin stores the recording with an equirectangular projection. Here is a screenshot from the original recording:

An image from the original video recorded from a Garmin VIRB camera.

There are some obvious problems with this recording. First, the recording is upside down since the camera was hanging upside down from a chandelier above the musicians. The panning and tilting of the camera are also slightly off concerning the placement of the musicians. So it is necessary to do some pre-processing before analysing the files.

Most 360-degree cameras come with software for adjusting the image. The Garmin app can do it, but I already have all the files on a computer. It could also be done in video editing software, although I haven’t explored that. In any case, I look for an option that allows me to batch process a bunch of videos (yes, we have hours of recordings, and they are split up into different files).

Since working on the Ricoh files last year, I have learned that FFmpeg’s new 360 filter is part of the regular release. So I wanted to give it a spin. Along the way, I learned more about different image projections types that I will outline in the following.

Equirectangular projection

The starting point was the equirectangular projection coming out of the Garmin VIRB. The first thing to make it more useful is to flip the video around and place the musicians in the centre of the image.

Rotating, flipping, panning, and tilting the image to place the musicians in the centre.

The different functions of the v360 filter in FFmpeg are documented but not explained very well. So it took me quite some time to figure out how to make the adjustments. This is the one-liner I ended up with to create the image above:

ffmpeg -i input.mp4 -vf "v360=input=e:output=e:yaw=100:pitch=-50:v_flip=1:h_flip=1" output.mp4

There are some tricks I had to figure out to make this work. First, I use the v360 filter with equirectangular (shortened to e) as both the input and output of the filter. The rotation was done using both the v_flip and h_flip commands, which rotate around both the horizontal and vertical axes. In the original image, the cellist was on the edge. So I also had to turn the whole image horizontally using yaw and move the entire image down a bit using pitch. It took me some manual testing to figure out the correct numbers here.

Since the analysis will be focused on the musicians, I have also cropped the image using the general crop filter (note that you can add multiple filters with a comma in FFmpeg if you try to add another filter, only the last one will be used):

ffmpeg -i input.mp4 -vf "v360=input=e:output=e:yaw=100:pitch=-50:v_flip=1:h_flip=1, crop=1700:1000:1000:550" output_crop.mp4

This gives us a nicely cropped video of the musicians:

Cropped equirectangular image.

This video already looks quite good and could be used for analysis (for example, in one of the versions of Musical Gestures Toolbox). But I wanted to explore if other projections may work better.

Gnomonic projection

An alternative projection is called gnomonic in fancy terminology and “flat” in more plain language. It looks like this:

A gnomonic projection of the video.
ffmpeg -i input.mp4 -vf "v360=input=e:output=flat:v_flip=1:h_flip=1:yaw=90:pitch=-30:h_fov=150:v_fov=150" output_flat.mp4

Here I used the flat output type in FFmpeg and did the same flipping, panning and tilting as above. I had to use slightly different numbers for yaw and pitch to make it work, though. Also, here I added some cropping to focus on the musicians:

ffmpeg -i input.mp4 -vf "v360=input=e:output=flat:v_flip=1:h_flip=1:yaw=90:pitch=-30:h_fov=150:v_fov=150, crop=3800:1100:0:800" output_flat_crop.mp4

This left me with the final video:

Cropped gnomonic projection.

There are many problems with this projection, and the most obvious is the vast size difference between the musicians. So I won’t use this version for anything, but it was still interesting to explore.

Cube map

A different projection is the cube map. Here is an illustration of how it relates to the equirectangular projection:

Overview of different projection types (from Sizima).

The v360 filter also allows for creating such projections. It has multiple versions of this idea. I found a nice blog post by Anders Jirås that helped me understand how this function works.

First, I tested the c6x1 output function:

ffmpeg -i input.mp4 -vf "v360=input=e:output=c6x1:out_forder=frblud:yaw=50:pitch=-30:roll=50:v_flip=1:h_flip=1" output_c6x1.mp4

I changed the order of images using out_forder (as documented here) and (again) played around with the yaw, pitch, and roll to make something that worked well. This resulted in an image like this:

A cube map projection of the video. Here with a 6×1 cube layout.

There is also a function called c3x2, which will generate an image like this:

A 3×2 cube projection.

Adding some cropping to the 3×2 projection:

ffmpeg -i input.mp4 -vf "v360=input=e:output=c3x2:out_forder=frblud:yaw=50:pitch=-30:roll=50:v_flip=1:h_flip=1, crop=1500:1080:150:0" output_c3x2_crop.mp4

Then we end up with an image like this:

A cropped 3×2 projection.

This looks quite weird, mainly because the cellist wraps into a different cube than the others.

Equi-angular cubemap

Finally, I wanted to test a new projection invented by Google a couple of years ago: the Equi-Angular Cubemap. The idea has been to create a projection with fewer artefacts on the edges:

SaturationComp
Equirectangular Projection (left), Standard Cubemap (middle), Equi-Angular Cubemap (right) (from a Google blog post).

In FFmpeg, this can be achieved with the eac function:

ffmpeg -i input.mp4 -vf "v360=input=e:output=eac:yaw=100:pitch=-50:roll=0:v_flip=1:h_flip=1" output_eac.mp4

The resultant image looks like this:

An equi-angular cubemap projection.

Only the top part of the image is useful for my analysis, which can be cropped out like this:

ffmpeg -i input.mp4 -vf "v360=input=e:output=eac:yaw=100:pitch=-50:roll=0:v_flip=1:h_flip=1, crop=2200:1200:750:0" output_eac_crop.mp4

The final image looks like this:

A cropped equi-angular projection.

The equi-angular cubemap should have better projection overall because it avoids too much distortion on the edges. However, that comes at the cost of some more artefacts in the central parts of the image. So when cropping into the image as I did above, the equirectangular may actually work best.

Summing up

After quite some time fiddling around with FFmpeg and trying to understand the various parts of the new v360 function, I can conclude that the original equidistant projection is probably the best one to use for my analysis. The other projections probably work better for various types of 3D projections. Still, it was useful to learn how to run these processes using FFmpeg. This will surely come in handy when I am going to process a bunch of these files in the near future.

Kayaking motion analysis

Like many others, I bought a kayak during the pandemic, and I have had many nice trips in the Oslo fiord over the last year. Working at RITMO, I think a lot about rhythm these days, and the rhythmic nature of kayaking made me curious to investigate the pattern a little more.

Capturing kayaking motion

My spontaneous investigations into kayak motion began with simply recording a short video of myself kayaking. This was done by placing an action camera (a GoPro Hero 8, to be precise) on my life vest. The result looks like this:

In the future, it would be interesting to also test with a proper motion capture system (see this article for an overview of different approaches). However, as they say, the best motion capture system is the one you have at hand, and cameras are by far the easiest one to bring around.

Analysing kayaking motion

For the analysis, I reached for the Musical Gestures Toolbox for Python. It has matured nicely over the last year and is also where we are putting in most new development efforts these days.

The first step of motion analysis is to generate a motion video:

From the motion video, MGT will also create a motiongram:

Motiongram of a kayaking video.

From the motiongram, it is pretty easy to see the regularity of the kayaking strokes. This may be even easier from the videogram:

Videogram of a kayaking video.

We also get information about the centroid and quantity of motion:

Centroid and quantity of motion of the kayaking video.

The quantity of motion can be used for further statistical analysis. But for now, I am more interested in exploring how it is possible to better visualise the rhythmic properties of the video itself. It was already on the list to implement directograms in MGT, and this is even higher on the list now.

The motion average image (generated from the motion video) does not reveal much about the motion.

Motion average image of the kayaking video.

It is generated by calculating the average of all the frames. What is puzzling is the colour artefacts. I wonder whether that is coming from some compression error in the video or a bug somewhere in MGT for Python. I cannot see the same artefacts in the average image:

Average image of the kayaking video.

Analysing the sound of kayaking

The video recording also has sound, so I was curious to see if this could be used for anything. True, kayaking is a quiet activity, so I didn’t have very high hopes. Also, GoPros don’t have particularly good microphones, and they compress the sound a lot. Still, there could be something in the signal. To begin with, the waveform display of the sound does not tell that much:

A waveform of the sound of kayaking.

The spectrogram does not reveal that much either, although it is interesting to see the effects of the sound compression done by the GoPro (the horizontal lines from 5k and upward).

A spectrogram of the sound of kayaking.

Then the tempogram is more interesting.

A tempogram of the sound of kayaking.

It is exciting to see that it estimates the tempo to be 122 BPM, and this resonates with theories about 120 BPM being the average tempo of moderate human activity.

This little investigation into the sound and video of kayaking made me curious about what else can be found from such recordings. In particular, I will continue to explore approaches to analysing the rhythm of audiovisual recordings. It also made me look forward to a new kayaking season!