OK.
So in twenty one point one we deal with a t i install and setup.
So let's talk a bit about tensor flows object action.
The TFT API is one of the more mature and relatively easy to use object action frameworks.
Most of them are actually quite finicky and tricky to use.
Typically most other obligation frameworks are finicky.
As I just said and difficult to use and they brick quite easily as I have a lot of moving parts tend
to flows of detection attempts to solve that by creating a framework API that uses tensor flow.
No surprise there.
To create an object detection modules using both our CNN family as well as the SSD family.
So while the TI FOTA Woody API makes it far easier than until the remittance it still has a bit of a
learning curve by the way.
This is actually an output of the tensor flow optimization API with an SSD.
It's quite cool isn't it.
So now let's talk about it install and setup.
Now if you're using the visual machine with this already installed you don't have to go through this.
However it's not that hard to do so I'm just gonna go through it step by step.
I'm not going to do it with you because I really have it installed on my machine and I don't I think
if I try to reinstall it I could mess things up.
But this is how I did it documented everything and it worked perfectly.
So what you do basically activate activated computer visual library.
We're not going to we're not going to install a disk because they can have a lot of clashes with packages
and libraries being you know messy with each other not mixing well.
So let's clone this environment.
So we go into the terminal and go condo create and let's call this TFT we named this environment that
so in future when you want to activate it you just go source activate TFT and it's there.
So anyway copy this line in your terminal and clone your directory your sorry CV environment and then
run this line here suited apt get install put above compiler Python pipe Perl Python Alexa Mel and python.
Okay okay.
Then we do pip install system pip install context lib Jupiter mapped lib and then go back.
So just go back to old man home directory make up for local models and then get clone.
This basically from here from this getup.
Link here fanciful models and go back again to directory and get cloned this as well and now as we go
back here go into this directory cocoa API Python API and end to make this will compile and build some
stuff that you need and basically just copy this line here and then decide comments here so don't actually
run this in a terminal.
So this is where we get the intensify models from so you use w get put above zip and you download this
link here and unzip this file and then you can delete this file afterwards.
It's fine.
And then what you do you get to part that we need to get everything working.
So we defined this part here and then we go to our protection bills and we run the tests and if the
stuff's tests run run successfully by opening this file we will know if this install works correctly.
So go ahead and try it on your own.
See if it works if there's any problems don't hesitate to contact me and all the times things change.
We did the updates.
So maybe just check this link if this isn't work first before you contact me.
See if there's anything here.
Menu maybe a new vision or a new dependency.
You never know.
Running the demo so download the python file in this folder.
I actually do have this file in the in the in my Python the book files here.
However they do work you don't don't run them from there they're actually copy and paste them into this
territory here.
So let me go to our virtual machine and I'll show you exactly where to find us.
Okay so we're back in a virtual machine and directory I want you to go to is.
Remember I told you based on our presentation here I wanted you to go to models models research oblique
detection and put this file here or resources file here that you don't want it.
Oh it's actually stored in your folder as well and your notebooks folder.
But anyhow let's go to the surgery and I'll show you where to put this file since models models research
and object let's type it in that type of it.
Object detection and you'll see it is a notebook somewhere.
This one here that is a file even a run from no one.
Okay.
So in the next chapter we're going to run this file and go to the Detroit tutorial.