Original subtitles

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Hello and welcome to this new tutorial in the previous tutorial we loaded the Cascades and now that

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we did that we were ready to define a function that will do the detections.

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So we will make that function in two or three tutorials.

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We will start by defining it and getting the coordinates of the rectangle that will detect the face

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and then the eyes then we will have a for loop that will iterate through the different faces there are

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detected in the video.

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And so for each of these faces we will draw a rectangle indicating that's a face.

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And also inside each of these faces we will detect some eyes.

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So that's what this function is about.

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And let's implement it right now to define a function in python we have to start with Teff and then

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we need to give a name to this function we we're going to call it detect that we go detect and now we

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need to input some arguments.

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So since this function is going to be applied on single images and that is going to return for each

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of these images the same image with the rectangle that is detecting the faces and the eyes.

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Well it's going to take as inputs an image it's going to be the different images of the video coming

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from the webcam.

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But this function works on single images one by one.

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So that's why we can put the video directly we have to play it on images.

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But as you saw in the intuition lectures a cascade works on black and white image.

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So what we need to do is first get the black and white version of the image but also the original image

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because at the end this function will return the original image with the rectangles.

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It will now return the image in black and white.

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So that's why right now what we need to take as arguments is grey which is the black and white image

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that is image and black and white and also the original frame the original image which we will call

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frame and that's are two arguments perfekt we need to add some color now and then enter to go inside

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the function and define what we want it to do.

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All right.

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So as I said at the beginning of this tutorial The first thing we need to do is get the coordinates

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of the rectangle that will detect the face.

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And so we're going to get some tables of four elements x y w h x and y are the coordinates of the upper

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left corner.

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W will be the width of the rectangle and age will be the height of the rectangle.

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But since we're going to get several tables of these four elements Well we're going to put all these

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troubles in a variable that we're going to call faces and we are going to get these supples things to

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a method of the Cascade classifier class which is to detect multi-skilled method.

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All right.

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And in order to get this method we need to take the object of the Cascade classifier class and since

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right now we're working with the faces we're trying to get the coordinates of the rectangles detecting

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the faces.

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Well we're going to get this method from Phase cascade.

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That's our object for the face to face cascade and then dart and then that's where we can use to detect

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multiday scale method that will get us to the coordinates of the upper left corner and the width and

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the height of the rectangles detecting the faces.

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So now does detect Martell's kill method takes several arguments.

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The first one is of course the emergent like in white.

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Because as we said Cascades work on black and white images.

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So Gray that's the first argument and then it's going to take two other arguments which are going to

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be first the scale factor which tells by how much the size of the image is going to be reduced or equivalently

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by how much the size of the filter is that it's the Colonel's will be increased.

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So that's the same and we are going to take a scaling factor of 1.3 which means that the size of the

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image will be reduced 1.3 times and then a last argument which is the minimum number of neighbors.

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So if you remember the intuition lectures you saw that in order for a zone of pixels to be accepted

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we all need to have at least a certain number of neighbor zones that are also accepted and that certain

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number is exactly the minimum number that we're going to input right now.

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And that is going to be five.

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So that means that in order for a zone of pixels to be accepted one at least five neighbor zones must

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also be accepted.

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So that's what this is about.

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And so now I suspect some of you will ask why 1.3 and five.

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Well the reason is that we get some really good results with these numbers and that's why we're getting

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them.

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But of course that's due to experience we experience them and it turns out that 1.3 in five is a good

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combo to detect some faces with the web cam.

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All right so there we go.

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We have our faces so as a reminder faces our troubles of four elements x and y which are ordinated the

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upper left corner of the rectangle that will detect the face and W and age which are respectively the

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width and height of these rectangles.

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Perfect.

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So now we're going to start a full loop and actually we're going to iterate through the different faces

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because we have several tables and for each of these faces we will draw the rectangle and inside these

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rectangles will detect some eyes but we'll do that in the next tutorial.

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So until then enjoy computer vision.