1 00:00:00,210 --> 00:00:05,730 Welcome to Lesson one of the section going forward we'll be using a text editor whether it's Adam sublime 2 00:00:05,730 --> 00:00:09,420 whatever you choose to work with it if you still don't have a text editor installed. 3 00:00:09,420 --> 00:00:15,090 Make sure to refer back to the text editor installation tutorial in section 2 before proceeding any 4 00:00:15,090 --> 00:00:16,020 further. 5 00:00:16,020 --> 00:00:21,890 The purpose of this section is to build a program that can identify lane lines in a picture or a video. 6 00:00:21,990 --> 00:00:26,930 When you and I drive a car we can see where the lane lines are using our ice. 7 00:00:27,000 --> 00:00:30,920 A car doesn't have any ice and that's where a computer vision comes in. 8 00:00:31,020 --> 00:00:35,670 Which through complex algorithms helps the computer see the world as we do. 9 00:00:35,880 --> 00:00:41,750 In our case we'll be using it to see the road and identify lane lines in a series of camera images. 10 00:00:41,760 --> 00:00:46,500 This lesson in particular will be quite simple as all we're going to do is set up the initial stages 11 00:00:46,500 --> 00:00:52,500 of our project and display an image onto which we'll be identifying lane lines. 12 00:00:52,500 --> 00:00:58,080 You'll start by opening up your terminal or command prompt and navigate to the desktop directory with 13 00:00:58,080 --> 00:01:06,270 the command CD desktop changed directory desktop inside of desktop will make a new folder with the command 14 00:01:06,300 --> 00:01:14,970 M-K Diyar make directory finding Laine's this folder you just made inside of desktop. 15 00:01:14,970 --> 00:01:24,640 You're going to open up without them by going to file open desktop find new layouts instead of finding 16 00:01:24,640 --> 00:01:28,280 Layne's make a new python file called Elayne's. 17 00:01:28,300 --> 00:01:35,400 Doc Putois inside of the Layne's file We're going to start by writing a program that can identify lines 18 00:01:35,400 --> 00:01:37,320 in a jpeg image. 19 00:01:37,320 --> 00:01:40,080 I posted the image on my get hub Taxus that. 20 00:01:40,170 --> 00:01:48,850 Sure to go to the following link and so once you get to this page click on test image Doug j peg and 21 00:01:48,940 --> 00:01:51,740 we're going to do is actually download the image. 22 00:01:51,760 --> 00:01:57,930 Or better yet just save the image and make sure to save it as a cheap image. 23 00:01:57,930 --> 00:02:03,580 It doesn't matter where you save it and make sure this says test image such that there are no extra 24 00:02:03,580 --> 00:02:09,510 ones or Couzin there so that your naming remains consistent with what I have in the videos. 25 00:02:09,720 --> 00:02:10,180 Right. 26 00:02:10,180 --> 00:02:11,860 Save your image. 27 00:02:12,310 --> 00:02:17,470 Once you download it wherever you have it downloaded make sure to drag it into your project folder like 28 00:02:17,470 --> 00:02:18,170 so. 29 00:02:18,510 --> 00:02:24,940 And now to display the image we're going to use open VI an open source computer vision library. 30 00:02:24,940 --> 00:02:33,370 So inside of your terminal we're going to write the command Pipp install will make use of the package 31 00:02:33,370 --> 00:02:39,100 manager Pepp to install open C-v contrib dush Python 32 00:02:45,930 --> 00:02:53,130 once you're finished installing it back to Visual Studio we're going to import the library CV to and 33 00:02:53,130 --> 00:02:59,920 from the library for now we will access two functions read and show to load our image. 34 00:02:59,940 --> 00:03:07,450 We'll first make use of the read function by setting image is equal to see me to read. 35 00:03:07,880 --> 00:03:13,080 And in this argument is where you will specify the image the file name as a string. 36 00:03:13,440 --> 00:03:19,060 Is is test image jpeg. 37 00:03:19,860 --> 00:03:25,470 And what this function will do is read the image from our file and return it as a multi-dimensional 38 00:03:25,640 --> 00:03:30,740 PI array containing the relative intensities of each pixel in the image. 39 00:03:30,780 --> 00:03:33,470 We now have our image data and an umpire right. 40 00:03:33,480 --> 00:03:37,110 The next step is to actually render it with the M show function. 41 00:03:37,170 --> 00:03:46,100 So we're right CB2 in show this takes two arguments the first one is the name of the one that we're 42 00:03:46,100 --> 00:03:48,920 going to open up we'll just call that result. 43 00:03:49,100 --> 00:03:52,720 And the second argument is the image that we want to show itself. 44 00:03:54,230 --> 00:04:01,430 If I run the code now to my terminal navigate to my project folder by reading CD Our project is named 45 00:04:01,490 --> 00:04:07,890 finding lanes and run the python file Python Elayne's dot p y. 46 00:04:08,390 --> 00:04:11,440 Notice that nothing is going to happen. 47 00:04:11,690 --> 00:04:18,730 That's because this function should be followed by the weight key function CB2 weight key. 48 00:04:19,190 --> 00:04:24,620 And what this function does is it displays the image for a specified amount of milliseconds. 49 00:04:24,620 --> 00:04:26,660 We'll set a time of zero. 50 00:04:26,690 --> 00:04:33,070 What this will do is it will display our window result to window infinitely until we press anything 51 00:04:33,080 --> 00:04:34,330 in our keyboard. 52 00:04:34,670 --> 00:04:41,780 If we rerun the code Python Laine's the image is displayed and notice our window name. 53 00:04:41,840 --> 00:04:44,740 Results will keep this lesson short and stop here. 54 00:04:44,750 --> 00:04:50,450 You learn how to load and display images using the open CB library and the next lesson we'll start discussing 55 00:04:50,450 --> 00:04:56,870 canny edge detection a technique that will use to write a program that can detect edges in an image 56 00:04:57,290 --> 00:04:59,450 and thereby single out the lane lights.