1 00:00:00,780 --> 00:00:02,500 So let's talk a bit about transitions. 2 00:00:02,550 --> 00:00:07,080 Transitions are actually very simple and it's basically moving an image in one direction can be left 3 00:00:07,080 --> 00:00:09,400 right up down or even diagonally. 4 00:00:09,400 --> 00:00:13,020 If you implement an x and y traslation at the same time. 5 00:00:13,380 --> 00:00:19,950 So to perform a translation we actually use Open see these C-v to walk a fine function but that function 6 00:00:19,950 --> 00:00:23,250 requires what we call the translation matrix. 7 00:00:23,250 --> 00:00:29,280 So we are getting into too much high school geometry a translation matrix basically is is in this form 8 00:00:29,780 --> 00:00:33,020 and takes an x and y value as these elements here. 9 00:00:33,450 --> 00:00:39,570 Now what misrepresents here is a shift along the x axis horizontally and Y is shift along the y axis 10 00:00:39,570 --> 00:00:40,370 vertically. 11 00:00:40,590 --> 00:00:43,180 And these are the directions each shift takes place. 12 00:00:43,360 --> 00:00:50,130 So images all the start of being at the top left corner and retranslated in any direction using the 13 00:00:50,130 --> 00:00:51,680 transition matrix here. 14 00:00:51,750 --> 00:00:53,660 So let's implement this and quite quickly. 15 00:00:55,760 --> 00:00:58,750 So let's implement transitions using open C-v now. 16 00:00:58,760 --> 00:01:00,230 So we're going through line by line. 17 00:01:00,290 --> 00:01:02,580 I'll quickly show you what's being done here. 18 00:01:02,600 --> 00:01:06,940 The important thing to note is that are using to see V2 warp and function. 19 00:01:07,020 --> 00:01:12,920 And that's been implemented down here so quickly going through the image as we've done before we extract 20 00:01:12,920 --> 00:01:17,390 the height and the width of the image using non-pay see function taking only the first two elements 21 00:01:17,540 --> 00:01:21,050 of the ship three that it retains. 22 00:01:21,290 --> 00:01:25,340 Next I have a line here where we extract quarter of the height and width of do it. 23 00:01:25,340 --> 00:01:29,660 That's going to be a T x and y value in our translation Matrix. 24 00:01:29,710 --> 00:01:35,400 That's the direction or sorry the amount of pixels we're going to shift to image. 25 00:01:35,990 --> 00:01:42,140 And we actually use not by fluke to the two that actually defines the read data type for where translations 26 00:01:42,290 --> 00:01:43,680 matrix. 27 00:01:44,060 --> 00:01:48,660 And by using some square brackets here we actually created a T matrix here. 28 00:01:48,770 --> 00:01:53,600 It may or may not be important for you understand this but just take note of the form of this t matrix 29 00:01:53,600 --> 00:01:54,260 here. 30 00:01:54,770 --> 00:01:57,270 So the warp find function takes away image. 31 00:01:57,270 --> 00:02:04,370 So if we look at it in the matrix that we created and all within a height as a table and it actually 32 00:02:04,820 --> 00:02:06,590 returns the translated image. 33 00:02:06,650 --> 00:02:09,230 So let's actually run this and see what it looks like. 34 00:02:11,040 --> 00:02:20,650 Tulla this is a translated image here as you can see it's shifted image of an extraction a quarter of 35 00:02:20,650 --> 00:02:22,840 the initial dimensions. 36 00:02:23,050 --> 00:02:27,740 And similarly for the White image and we're just done with. 37 00:02:27,760 --> 00:02:32,110 So it's important to know that we should just take a look at a T matrix to give give you an understanding 38 00:02:32,110 --> 00:02:33,620 of what we have done here. 39 00:02:34,150 --> 00:02:36,870 So this is exactly what we wanted in 0 2 metrics. 40 00:02:36,890 --> 00:02:44,800 It's 1 0 in disorder anti-X and the way this being a quarter of the height and a quarter of the weight 41 00:02:45,100 --> 00:02:45,720 respectively.