1 00:00:00,260 --> 00:00:06,420 Well I hope you solved the exercise and I believe it was not a difficult one. 2 00:00:06,420 --> 00:00:13,290 My purpose there was to get you used to it with the OpenCV code, and also why 3 00:00:13,290 --> 00:00:19,410 not to practice the For Loop, so as you can imagine this exercise would be solved 4 00:00:19,410 --> 00:00:25,800 by using a For Loop, so let me go through the code line by line quickly. 5 00:00:25,800 --> 00:00:32,520 So we have to import OpenCV, so cv2 and the glob library if you can recall it 6 00:00:32,520 --> 00:00:39,809 what the globe does is that it finds the path names of some files given a certain 7 00:00:39,809 --> 00:00:46,820 pattern, so in this case for instance I have this JPG files here, so, 1, 2, 3, 4, 5 8 00:00:46,820 --> 00:00:55,760 images and I said ok, create a list of file names that contain everything in 9 00:00:55,760 --> 00:01:05,369 first part and then jpg as the extension. So that will create a list such as say like 10 00:01:05,369 --> 00:01:12,659 C and then path here and then galaxy, the jpg and then the other image path and so on. 11 00:01:12,659 --> 00:01:18,630 You get the idea. And then what we need to do is iterate through this list. For each 12 00:01:18,630 --> 00:01:24,960 path image path in in the list, in this list, we'll do these operations for each 13 00:01:24,960 --> 00:01:33,329 of these items, so first we we'll read that image path, okay that image file 14 00:01:33,329 --> 00:01:40,200 actually as a black and white image so 0 is the flag, is the argument which implies 15 00:01:40,200 --> 00:01:45,060 an image to read as black and white, in the grayscale actually, and then we 16 00:01:45,060 --> 00:01:51,210 create a variable where we will store the resized images, so the image is 100 17 00:01:51,210 --> 00:01:57,299 by 100. So I'm passing the original image here and the size, the new size that the 18 00:01:57,299 --> 00:02:03,149 image will get. Then we want to show the image just for demonstration. This is not 19 00:02:03,149 --> 00:02:07,160 really important, but it lets you check out the images that are being resized. 20 00:02:07,160 --> 00:02:14,150 And this is the name of the window and then I pass here a wait key method and 21 00:02:14,150 --> 00:02:20,610 500 means 500 milliseconds, so each image will show and it will wait for half 22 00:02:20,610 --> 00:02:24,540 a second, so 500 milliseconds and then after this half second, Python will go 23 00:02:24,540 --> 00:02:28,770 to the next line, and then to the next, and then it will go to the next 24 00:02:28,770 --> 00:02:32,970 item of the list and so on. So wait key 500 milliseconds and then we 25 00:02:32,970 --> 00:02:38,880 have destroy all windows after this time passes, and then we write the resized 26 00:02:38,880 --> 00:02:46,230 image so re was a variable that holds the image object, the resized image object 27 00:02:46,230 --> 00:02:55,650 and this here, all this is the new name of the file, so what we get here is so we will 28 00:02:55,650 --> 00:03:00,720 have resized in the beginning of the image name and then just after that 29 00:03:00,720 --> 00:03:07,410 we will have the name of the original image, so for instance we would have, for 30 00:03:07,410 --> 00:03:15,060 galaxy would have resized galaxy dot jpg. So image here would be galaxy dot jpg 31 00:03:15,060 --> 00:03:21,920 here which is this one here, and yeah because the script is inside this folder, 32 00:03:21,920 --> 00:03:32,990 this list actually the image list would look something like galaxy dot jpg and then 33 00:03:32,990 --> 00:03:42,600 kangaroos in Australia dot jpg and so on. So this gets the relative paths of the files. 34 00:03:42,600 --> 00:03:48,060 We're not getting the full path. Okay, I hope that is clear, so resized galaxy. 35 00:03:48,060 --> 00:03:54,090 We could add another score so that we discriminate the resized word from the 36 00:03:54,090 --> 00:04:07,440 other image name, great. Let me execute this. Python script, so half a second, half a second, 37 00:04:07,440 --> 00:04:15,000 and it's done. And let's check the images. So from here up are the original images 38 00:04:15,000 --> 00:04:19,890 and these are the image products, so you can see here that all these 39 00:04:19,890 --> 00:04:25,979 are 100 by 100, and they are in a grayscale. Okay. 40 00:04:25,979 --> 00:04:28,599 That's it. See you in the next lecture.