Original subtitles

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Hello and welcome back to module one face recognition you had your intuition lectures with Curiel to

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understand how Cascades work.

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So now we're going to put them into action and we're going to put them into action to do some face recognition

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and we're going to do it through the web cam that is that I'm going to put on my web cam and we're going

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to try to protect my face and my eyes and some extra stuff that will be for the homework but that's

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what we'll be doing we'll be working with the Cascades that is with open city to do this face recognition.

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So as you can see I launched my spider in my virtual platform environment which is a virtual environment.

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And now the first thing we have to do when launching spider is to find the right folder because this

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folder is going to contain the Cascades ex-MIL.

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And so we need to be in this folder because we will be calling the Cascades to execute our code.

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So let's do that right now we're going to go to File Explorer and we're going to go to our computer

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vision A.D.C. folder which is right now on my desktop.

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So you go into this folder and you go to module one face recognition the first module of discours.

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So I'm very excited to start this.

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And actually right now I want to tell you that the more we evolve in the course the more the models

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will be working with will be powerful and that means that actually open city is great but it's definitely

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not the most powerful models of discourse and of the virtual platform that some of you might have noticed

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that it is no longer in development.

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We have some much better models today we will see that with object detection for object detection we'll

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be using something much more powerful than open city.

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I will let you find out about the surprise but still open city is going to do some great stuff here

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for face recognition.

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But keep in mind that you should really follow the course until the end because we will be working with

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some much more powerful models.

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But let's start with open city.

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So this is the right folder.

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Make sure that you're in the right folder.

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And now I'm quickly going to describe the folder.

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So we have four files.

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The first file is the code that we're about to implement right now but the connected version of the

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code.

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So if you open this file you will have each line of the code commented.

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So that's in case you don't have time to watch the video.

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But really I strongly recommend to watch the video because I will be seeing much more than the comments

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and this is actually this face recognition that bites and fowl.

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And this is actually where we're going to implement the code.

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Then we have the cascade for the eye.

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That's the cascade that we'll use to detect the eyes and the web.

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That is my eyes.

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But you will use the code to detect your eyes too.

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And the cascade to detect the face.

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So these are X-amount files that's not very important.

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But this is what we'll use from open city to do these face protections.

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So there we go that's the folder.

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Make sure that you have all these files.

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And now we're going to start.

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So as you can see there is this first code section here in the libraries.

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And of course the only library that we need here is open city and that is silly to say Make sure you

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import too.

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I'm going to execute the whole code in the end but make sure that you import 3:2 that's entirely compulsory.

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And the reason why we don't import much more is that actually we're not doing some deep learning here.

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If you noticed in the intuition lectures the Cascades are actually not some neural networks.

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There is a deep series of filters but this is not a series of layers so open city cascades are not based

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on any neural networks.

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And that's why for open city we don't need to import any neural network libraries like the torched libraries

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or tenso flow or anything.

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We just need open city.

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And what we need from open city is mainly the Cascades.

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And that is exactly what we're going to do now.

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We're going to be loading the Cascades one cascade for the face and one cascade for the eyes and to

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do this we're going to create two objects because these are going to be some objects of some class that

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is called Cascade classifier.

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And this class will take as arguments exactly the SML file that contains the cascade.

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So the first object will take an argument while the frontal face cascade and the second object will

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take its argument.

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I Harker's Kate.

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So let's do this.

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We're going to create a first object here for the face and we're going to call it face cascade.

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All right so that's the object.

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And now since it's an object of some class we need to call the class.

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Now this class is actually a class of the CB2 module from the open library.

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So what.

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And here we type the name of the class which is cascade class C fire cascade classifier.

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All right.

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And as I just said in argument here we just need to put one argument which is the name of the ex-MIL

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Harker's Kate.

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And since we're starting with a face well we need to put exactly that heart cascade underscore frontal

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face underscore default.

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SNL but we need to put that in quotes.

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There we go.

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Let's do that har cascade underscore frontal face underscore default and there we go.

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That SNL and that's will import the cascade for the face.

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So as a reminder the cascade is a series of filters that will apply one after the other to detect the

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face.

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All right so now we're going to do exactly the same for the eye.

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So I'm copying this line to be efficient and basic in here and I'm just going to play face Cascades

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by I cascade.

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All right.

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And of course here I need to replace Hargus Cade's frontal face default SNL by hardcase gate.

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I am just selecting this and replacing it by.

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There we go.

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So now we have our two Cascades the cascades of the face and the cascade of the eye so perfect we were

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ready to move onto the next code section which will be about implementing a function that will do the

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detection itself that will use those cascades to detect the face and the eye.

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But you're going to see that there is a little trick which is about computational efficiency and that

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is we're going to do the phase detection on the global referential the main differential but we're going

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to do the detection and the referential of the face and that will save us some computation time.

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All right so let's do that in the next tutorial.

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And until then the computer vision.