Hi and welcome back to the course in this section, we'll take a look at using deep sort with YOLO V
five to implement and integrate the tracking module with our object detector, which is YOLO V5.
So let's get started.
So open notebook 58, and we'll begin.
And firstly, I will just say this YOLO V5 is the ultimate X Torch v4 YOLO integration.
So also, just make sure you're using a GPU again for this lesson.
You can check it here because it's a lot faster.
It works with the CPU, but you know, GPUs are necessary in this deep learning computer vision world.
So run this first block here.
This Clunes, the report that we'll be using gets to model and installs the other required packages
that we need.
So it takes about 25 to 30 seconds.
Next, skip this line.
This was the previous code that we used to work with.
I cleaned it up and now posted everything here.
So this downloads the test video file, as well as the models actually the model to download it here
next.
So this takes about six to seven 10 seconds.
Next, all you need to do is just run.
So what we did before is that we don't let it a video calling it YouTube output because that was just
a video on YouTube and per license free video I should add just to clarify.
And yeah, it's downloaded here.
So we just have that file.
So all this track file needs is you need to point to the your five model that we'll be using to just
point to the directory here.
And we just point to where this YouTube file is stored and then we just go save it to see if the output.
So this takes just under a minute to run, and it generates about seven seconds of video.
And next, we could just convert it now to an MPEG4.
But so we take the API committed to output the MPEG four and the six, about 30 seconds, roughly.
And now we can display that video in our output, but by running this block here as well as this block.
This takes about 20 seconds to load the video player.
But let's check it out so you can see how well this is working.
Carnival one is consistently kind of a one one that is sexist as well.
Let's restore the video, and let's take a look at this person here.
You can see this woman, this person of five to six seven.
So you can see it's it's working quite well.
Watch it one more time.
Let's look at it.
Cause again, the motorcycle.
See, even though he disappears, it's 5:09.
Let's take a look at that one more time so you can see the motorcycle for a moment 22 28.
Which is a mistake, though, but generally this is looking quite low because this is a very cluttered
cynicism.
London Piccadilly Circus, which is a jam packed area and it's working quite well.
So that's it for this lesson.
What I will say is take a look.
The code in this report because it is quite simple and easy to understand the track, the API file.
And let's bring it up here so you can take a look.
This is where everything is integrated here.
It's well, commented the well labeled.
And it's all of the it's quite easy to understand.
In my opinion, they did a good job with this, and you can set all the parameters as well for different
thresholds, but you'll be five as well as deep sort.
So if you wanted to integrate deep sort deal of your five, you can just use this repo and build it
there.
OK, so thank you for watching.
And in the next section, we'll take a look at making deepfakes, which is a very cool and fun, sometimes
immoral thing to do.
So I'll see you in the next lesson.
Thank you.