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These are the user uploaded subtitles that are being translated: 1 00:00:00,550 --> 00:00:05,560 Hello and welcome to this new tutorial in the previous tutorial we loaded the Cascades and now that 2 00:00:05,560 --> 00:00:09,480 we did that we were ready to define a function that will do the detections. 3 00:00:09,610 --> 00:00:12,960 So we will make that function in two or three tutorials. 4 00:00:13,060 --> 00:00:18,970 We will start by defining it and getting the coordinates of the rectangle that will detect the face 5 00:00:19,060 --> 00:00:24,520 and then the eyes then we will have a for loop that will iterate through the different faces there are 6 00:00:24,520 --> 00:00:26,530 detected in the video. 7 00:00:26,560 --> 00:00:31,620 And so for each of these faces we will draw a rectangle indicating that's a face. 8 00:00:31,810 --> 00:00:36,330 And also inside each of these faces we will detect some eyes. 9 00:00:36,340 --> 00:00:38,000 So that's what this function is about. 10 00:00:38,050 --> 00:00:44,320 And let's implement it right now to define a function in python we have to start with Teff and then 11 00:00:44,320 --> 00:00:50,500 we need to give a name to this function we we're going to call it detect that we go detect and now we 12 00:00:50,500 --> 00:00:52,270 need to input some arguments. 13 00:00:52,270 --> 00:00:58,630 So since this function is going to be applied on single images and that is going to return for each 14 00:00:58,630 --> 00:01:04,880 of these images the same image with the rectangle that is detecting the faces and the eyes. 15 00:01:05,110 --> 00:01:10,750 Well it's going to take as inputs an image it's going to be the different images of the video coming 16 00:01:10,750 --> 00:01:11,680 from the webcam. 17 00:01:11,830 --> 00:01:15,720 But this function works on single images one by one. 18 00:01:15,850 --> 00:01:20,540 So that's why we can put the video directly we have to play it on images. 19 00:01:20,860 --> 00:01:26,730 But as you saw in the intuition lectures a cascade works on black and white image. 20 00:01:26,740 --> 00:01:32,440 So what we need to do is first get the black and white version of the image but also the original image 21 00:01:32,440 --> 00:01:36,940 because at the end this function will return the original image with the rectangles. 22 00:01:36,940 --> 00:01:39,790 It will now return the image in black and white. 23 00:01:39,790 --> 00:01:46,000 So that's why right now what we need to take as arguments is grey which is the black and white image 24 00:01:46,000 --> 00:01:52,360 that is image and black and white and also the original frame the original image which we will call 25 00:01:52,360 --> 00:01:58,960 frame and that's are two arguments perfekt we need to add some color now and then enter to go inside 26 00:01:58,960 --> 00:02:01,800 the function and define what we want it to do. 27 00:02:02,230 --> 00:02:02,530 All right. 28 00:02:02,530 --> 00:02:07,810 So as I said at the beginning of this tutorial The first thing we need to do is get the coordinates 29 00:02:07,960 --> 00:02:10,400 of the rectangle that will detect the face. 30 00:02:10,540 --> 00:02:17,650 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 31 00:02:17,650 --> 00:02:18,460 left corner. 32 00:02:18,490 --> 00:02:23,060 W will be the width of the rectangle and age will be the height of the rectangle. 33 00:02:23,260 --> 00:02:28,510 But since we're going to get several tables of these four elements Well we're going to put all these 34 00:02:28,510 --> 00:02:35,680 troubles in a variable that we're going to call faces and we are going to get these supples things to 35 00:02:35,680 --> 00:02:41,710 a method of the Cascade classifier class which is to detect multi-skilled method. 36 00:02:41,710 --> 00:02:42,430 All right. 37 00:02:42,430 --> 00:02:48,010 And in order to get this method we need to take the object of the Cascade classifier class and since 38 00:02:48,010 --> 00:02:53,060 right now we're working with the faces we're trying to get the coordinates of the rectangles detecting 39 00:02:53,060 --> 00:02:53,950 the faces. 40 00:02:54,190 --> 00:02:58,730 Well we're going to get this method from Phase cascade. 41 00:02:58,750 --> 00:03:06,430 That's our object for the face to face cascade and then dart and then that's where we can use to detect 42 00:03:07,120 --> 00:03:15,110 multiday scale method that will get us to the coordinates of the upper left corner and the width and 43 00:03:15,110 --> 00:03:18,560 the height of the rectangles detecting the faces. 44 00:03:18,580 --> 00:03:21,920 So now does detect Martell's kill method takes several arguments. 45 00:03:22,090 --> 00:03:25,080 The first one is of course the emergent like in white. 46 00:03:25,090 --> 00:03:29,620 Because as we said Cascades work on black and white images. 47 00:03:29,740 --> 00:03:35,350 So Gray that's the first argument and then it's going to take two other arguments which are going to 48 00:03:35,350 --> 00:03:43,450 be first the scale factor which tells by how much the size of the image is going to be reduced or equivalently 49 00:03:43,870 --> 00:03:48,290 by how much the size of the filter is that it's the Colonel's will be increased. 50 00:03:48,290 --> 00:03:54,670 So that's the same and we are going to take a scaling factor of 1.3 which means that the size of the 51 00:03:54,670 --> 00:04:01,700 image will be reduced 1.3 times and then a last argument which is the minimum number of neighbors. 52 00:04:01,900 --> 00:04:07,480 So if you remember the intuition lectures you saw that in order for a zone of pixels to be accepted 53 00:04:07,650 --> 00:04:13,310 we all need to have at least a certain number of neighbor zones that are also accepted and that certain 54 00:04:13,370 --> 00:04:17,110 number is exactly the minimum number that we're going to input right now. 55 00:04:17,110 --> 00:04:18,420 And that is going to be five. 56 00:04:18,640 --> 00:04:26,110 So that means that in order for a zone of pixels to be accepted one at least five neighbor zones must 57 00:04:26,110 --> 00:04:27,360 also be accepted. 58 00:04:27,580 --> 00:04:29,310 So that's what this is about. 59 00:04:29,320 --> 00:04:33,850 And so now I suspect some of you will ask why 1.3 and five. 60 00:04:34,000 --> 00:04:39,340 Well the reason is that we get some really good results with these numbers and that's why we're getting 61 00:04:39,340 --> 00:04:39,820 them. 62 00:04:39,820 --> 00:04:45,970 But of course that's due to experience we experience them and it turns out that 1.3 in five is a good 63 00:04:45,970 --> 00:04:49,540 combo to detect some faces with the web cam. 64 00:04:49,540 --> 00:04:51,170 All right so there we go. 65 00:04:51,190 --> 00:05:00,450 We have our faces so as a reminder faces our troubles of four elements x and y which are ordinated the 66 00:05:00,520 --> 00:05:07,530 upper left corner of the rectangle that will detect the face and W and age which are respectively the 67 00:05:07,530 --> 00:05:10,020 width and height of these rectangles. 68 00:05:10,350 --> 00:05:10,800 Perfect. 69 00:05:10,800 --> 00:05:15,240 So now we're going to start a full loop and actually we're going to iterate through the different faces 70 00:05:15,240 --> 00:05:22,290 because we have several tables and for each of these faces we will draw the rectangle and inside these 71 00:05:22,290 --> 00:05:26,300 rectangles will detect some eyes but we'll do that in the next tutorial. 72 00:05:26,310 --> 00:05:28,200 So until then enjoy computer vision. 8216