It is really incredible how they manage to coordinate the sticks and make it into a beautiful performance. Given my interest in the visual aspects of music performance, I reached for the Musical Gestures Toolbox to create some video visualisations.
I started with creating an average image of the video:
This image is not particularly interesting. The performers moved around quite a bit, so the average image mainly shows the stage. An alternative spatial summary is the creation of a keyframe history image of the video file. This is created by extracting the keyframes of the video (approximately 50 frames) and combining these into one image:
The keyframe history image summarizes how the performers moved around on stage and explained the spatial distribution of activity over time. But to get more into the temporal distribution of motion, we need to look at a spatiotemporal visualization. This is where motiongrams are useful:
If you click on the images above, you can zoom in to look at the visual beauty of the performance.
The problem with this approach, and many similar that I found by googling around, is that it samples frames with a specific interval. In the above code it looks up every 200th frame, which gives this image:
The problem is that the image only contains information about the 1600 first frames, or more specifically frames 0, 200, 400, 600, 800, 1000, 1200, 1400, 1600. I want to include frames that represent the whole video.
I see that many people create such displays by sampling based on scene changes in the video. There are two problems with this. First, it requires that there are scene changes in the video. This is usually not the case in the videos that I study, which are primarily recorded with a stationary camera in which only the “foreground” changes. The second problem with sampling one “salient” frames, is that we loose information about the temporal unfolding of the video file. From an analysis point of view, it is actually quite useful to know more or less when things happened in the video. That is not so easy if the sampling is uneven.
I was therefore happy to find a nice script made by Martin Sikora, which is based on looking up the duration of the file and use this to calculate the frames to export from the file. Running this script on the original video gives this image:
The 9 frames in the display above reveal that there is little dance in the first one third of the video file (can see the arm of the dancer enter in the third image). It also shows how the dancer moved around in the space. It is possible to get some idea about her spatial distribution, but there is little information about her actual motion throughout the sequence. I was therefore curious to try out making such a grid-based display from a history video, which actually shows some more of the actual motion.
It is possible to make (motion) history videos in both the Matlab and Python versions of the Musical Gestures Toolbox, but today I was curious as to whether it could be done simply with FFmpeg. And it turns out to be quite simple using a filter called tmix:
I played around for a while with the settings before ending up with these ones. Here I average over 30 frames (which is half a second for this 60fps video). I also use weight feature to give preference to the current frame. This makes it easier to follow the dancer, as the trajectories of past motion become more blurred.
Running the above grid-script on this video results in a keyframe display that shows more of the motion happening in the frames in question. This is useful to see, for example, when she moved more than in other frames.
I am quite happy with the above-mentioned, but it is not particularly fast. Creating the history video is time-consuming, since it has to process all the frames in the entire video. I therefore tested speeding up the video 8 times, using this command (the -an flag is used to remove the audio):
Running the history video function on this then runs quite a bit faster, and results in this hi-speed history video:
Running this through the grid-script gives a keyframe display that is both similar and different to the one above:
It is quite a lot quicker to generate, and also gives more information about the motion sequence.
The conclusion is that it is, indeed, possible to make a lot of interesting video visualizations using “only” FFmpeg. Several of these scripts are also much faster than the scripts I have previously used in Matlab and Python. So I will definitely continue to explore FFmpeg, and look at how it can be integrated with the other toolboxes.
The Musical Gestures Toolbox for Matlab (MGT) aims at assisting music researchers with importing, preprocessing, analyzing, and visualizing video, audio, and motion capture data in a coherent manner within Matlab.
Most of the concepts in the toolbox are based on the Musical Gestures Toolbox that I first developed for Max more than a decade ago. A lot of the Matlab coding for the new version was done in the master’s thesis by Bo Zhou.