Hi, welcome back.
So firstly, the reason I split this lesson into two videos is because what I'm going to showcase next
is the body pose estimation models that belong to the that that run to the library.
And I didn't want to mix it too much with the mass, you know, because it's a separate topic.
So I'm going to create a separate video entitled so that if you're searching for body pause in discourse,
you can easily find it in its own separate video here.
So, yes, you can see this is the image of me looking fit test image of a footballer.
Next, we can follow the same patterns that we did before where we lowered.
Basically, this is the config file, and then we set it to visualize and create the default predictor
as well.
And we can run this here, and you can see we can quickly identify that this is a person here.
You can see this is a very cool.
You can see each limb is in a different color, so you can see this is a very cool way.
This is bodies pause and the angle is actually correct as well.
Similarly, here between the shoulders, the eyes, fierce nose arms is probably a person in the background
doing something I actually don't recall.
Yeah, there is.
There's a person right there in the background and actually who's quite leery.
And while it got the body, police estimate quite wrong.
But it is quite cool that it actually tried it anyway.
So you can see this is how we inference on a body pose estimation.
Now back to basically panopticon segmentation, which basically does every object different.
A different color effectively can.
You can see that you have separate persons, each person is in a different color as well.
It is a bit like instance segmentation.
However, it goes into more granular detail and you can see how much detail it tries to predict here.
Playing field right sportsbook.
Pretty cool.
It should be football, but I mean, it's possible.
It's fair enough.
It's probably a generic category of basketballs and footballs and tennis balls, cricket balls.
Perhaps, so you can see that looks quite well.
Now what I actually can do, although I didn't have it, don't have a dunk.
It takes quite some time to generate.
Is that you can actually run a pin up tick segmentation video so you can load this video here and you
can just cut.
Is an f ffmpeg cut the first six seconds of it here.
Actually, let's try it and see if it works.
It just takes a bit of time to do.
But let's see.
Oh, it's the.
I guess this YouTube Library is broken, so I'll probably fix that for you guys.
And you can hopefully on a notebook, it works well, but that's it for this lesson.
Thank you for watching.
And in the next section, we'll take a look at creating a mask are seen in a different mask, our CNN
to recognize various ships.
So stay tuned for that lesson.
Thank you for watching.
But.