Original subtitles

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Hello and welcome to listen to toil to they we're going to do the face recognition with the web cam

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by playing the detect function that we made in the previous toils on each of the images coming from

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the webcam.

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All right.

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So let's do this.

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And if that doesn't take a while we might actually see the results at the end of this tutorial.

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So the first thing we need is the last frame coming from the webcam because that's on this last frame

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that we will apply the detect function and so to get this last frame we're going to create an object

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of another class which is video capture.

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So that's a class working on the web cam and basically this object will contain several entities several

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informations and one of them will be the frame.

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The last image last frame coming from the webcam.

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So this object is going to be called Video underscore capture because this is actually an object from

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the video capture class which is a class of the CB2 much all.

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So let's not forget CB2 here video capture class and that class takes one argument actually which is

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0 0 1 0.

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If it's the webcam of the computer and one if it's a webcam coming from an external device that is if

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you have an external webcam that you plugged into your computer.

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I'm going to use the webcam of my computer the internal webcam so that's going to be zero.

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But if you have an external webcam you will have to put one here.

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All right.

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So that's basically all we need to do to get this video capture object that contains the last frame

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coming from the webcam.

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And now we are ready to start the loop to apply to detect function on all the images coming from the

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web cam that we get things through this video capture object and on all these images.

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We will apply to detect function to detect the face and the eyes.

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So that's why we're going to start a loop now which is not going to be a for loop because it's going

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to be an infinite loop and we will break it by using the break trick.

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And by then you know you can end the loop by just typing break and actually break will be applied.

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If we type on q on the keyboard to turn off the webcam.

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So this while loop.

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The trick is to do well here and then trick which means we repeat infinitely until daybreak.

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All right.

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So what is the first step in it to do.

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Well the first step is to get the last frame coming from the webcam and to get it.

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We're going to use a method for more video capture object which is the read method and this read method

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returns two elements and the second element is the last frame coming from the webcam.

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And since we're only interested in that we're going to use this trick here underscore cummer frame because

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the read method returns two elements and we are only interested in this one.

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And the fact that we're putting an underscore here means that we won't get the first element returned

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by this read method.

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All right and so now we need to call this read method to get the last frame of the webcam and to do

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this since this method is a method of the video capture class.

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Well we need to take our video capture object then that and then read with some parenthesis.

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Perfect.

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That gets us the last frame of the webcam and now we are almost ready to apply to detect method.

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Remember the Digic method not only takes the frame as input but also the gray that is the black and

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white version of the frame and therefore we need to do something here to get this black and white version

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of this frame that we just got thanks to the read method of our video capture object.

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And to do this we're going to use the city color function CVG color is a function of our of each module

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that allows to do some transformations on some frames.

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So there will be some transformations on our original colored frame and this transformation will be

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of course to convert this color image into black and white so that we can get the second argument here

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Grey which will be the black and white version of this frame.

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So let's call it actually gray black and white version of the frame.

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And now we're going to take our city to Mudgal because we want to take the CVT color class which takes

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two arguments the first one is of course frame because that's the image we want to convert into black

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and white.

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And the second one will be civil to that all in capital letters color underscore b g are to gray.

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So what is this.

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This argument Sivi to that color B.G. are two gray cells.

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In fact to do an average on blue green and red and that's to take a scale of gray because you know cause

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the Cascades are applied on the gray images.

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But in order to get the right contrast of black and white well we need to do some average on the blue

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red and green to get this contrast.

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You know if we have some parts of the images that are with dark colors and some dark red blue green

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Well we will get some dark gray in the great image.

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So that's just a trick to obtain the right shades of gray.

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All right then I think you guess what we have to do we have to frame the original color image and gray

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which is the black and white version of the image.

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So we are ready to apply the detect function.

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We have the two arguments grand frame.

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So basically we're ready to get the original image with the detector rectangles detecting the face and

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the eyes.

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So we're going to introduce a new voice will here that we're going to call canvas and that will be the

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result of the detect function applied to our frame which is the last frame captured by the webcam and

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gray the black and white version of this frame.

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So what we simply need to do here is to call our detect function and then apply gray and frame gray

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and frame and that will get us the last streamed image of the web cam with the rectangle detectors Okay.

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Then we need to use some more tricks.

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The first week we need to do is to display in an animated way all the successive outputs that is all

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the successive processed images in a window.

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And to do this we're going to use from RACV to Mudgal the eum show function.

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So that's that is just a function that will do that for you.

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Display all these processed images in an animated way in a window.

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And so this show function takes two arguments.

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The first one is video and the second one is the canvas canvas which is the output of the detect function.

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That is the original image coming from the webcam but with the detector rectangles right then we need

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to use another trick which will be used to stop the webcam and face the election process if we press

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on cue on the keyboard.

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So the trick to do that is to blame if conditions saying that if see that wait key and you put one and

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0 x f f equals equals or in some quotes.

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Q That's Q On the keyboard.

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So is this then what do we want to do.

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Remember where I told you about the while loop the infinite loop.

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Well we want to break the process that is set stop everything.

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So this well loop will be over and that will allow us to end the process.

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So basically it's not very important to understand the techniques and syntax of this.

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This just means that if we press on cue on the keyboard Well this will break the process.

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No more facial recognition.

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OK.

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Perfect.

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So actually the while loop is over so we can just go back to the main code and we have only two things

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to do left.

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The first thing is to turn off the webcam and to do this we take our video capture object and we're

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going to use this release method to exactly turn up the webcam and eventually we need to destroy the

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window inside which all the images were displayed and to do this we take our to Mudgal and then dart

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and then we're going to use to destroy all Windows function and there we go the code is over.

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Congratulations.

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Now we're ready to detect our face and our eyes.

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So let's do it right now because everything is ready.

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So here we go.

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I'm selecting all this.

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You have to select all this code.

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Make sure that you are in this fuller to be able to load the Cascades.

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And if that's the case just press enter.

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There we go.

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The code is well executed and hi guys.

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Welcome to my place.

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This is actually where I'm recording all these tutorials for you.

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So super happy to have you here.

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As you can see well we are detecting very well my face and my eyes.

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I think that's the case for you to execute your code.

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So of course sometimes we see some false positive.

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We can see some rectangle around my mouth detecting the eyes are open to the things that's my eye but

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if I don't make too much expression or if I don't talk yet there we go.

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We have this blue rectangle detects in my face and these two green rectangles detects in my eyes.

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So there we go.

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That works pretty well.

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If you are not 100 percent convinced of the power of this model Well remember that in the next modules

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we'll be using some much more powerful models open.

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Definitely doesn't contain the most powerful models.

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We actually saw it from module to module 2 will be about object detection and we actually tried Open

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survie to detect some very cute bouncing dog that we actually filmed ourself.

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But it really turned out to work very bad.

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So instead we implemented a much more powerful model based on neural networks.

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So hold onto that.

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This is going to be interesting and mostly much more powerful.

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So I was very happy to show you this.

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This was just a warm up.

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Keep up the good energy for what's coming next because it's going to be much more challenging but also

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much more fascinating.

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So I'll see you soon for the next module object detection.

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But first I'll give you a little homework.

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This will be the homework of this module one face detection and this will be about detecting some of

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the features in the face.

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So good luck with that.

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And until then enjoy computer vision.