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Let's take a step back and look at fine and none fine transforms again.

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So imagine you have an image like this here where we have this is clearly not fine.

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Transform off an original top down flat image here you can see the lines here.

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They're not parallel anymore and they're actually going to join in some feature spot maybe some way

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up here.

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FLATOW So what it means is that if we have four points here we can actually use an open civic function

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to actually watch this transfer this image here and get an image like this.

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So clearly we can take a skewed image once we're aware of the four points of the corner and get back

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to your original image.

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So let's take a look at that implementing that in some code.

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OK.

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So let's look at implementing that perspective transform that we just saw.

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So you remember we firstly need the coordinates of the four corners and the original image.

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So that's what we actually have here.

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These are the four points here.

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We're basically combining it into points 3 here comprise of these four quadrants and then so it is that

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these will be called points then we have points B which is do up which is output which is a point that

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we let's go back to the image here.

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These are the points that we want to define here.

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So that's a typical for standard size.

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We need to get the data so that it actually knows what to transform it to.

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So what we do know we actually use these two points is two sets of points here with this open CV function

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called Get perspective transform.

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What this does it actually generates a matrix and this matrix here is is basically the transform that

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basically transforms points to points B.

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And then we use what perspective that function you may have seen previously to actually generate the

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or to use this sort of matrix here and generate the output image to be called warped here.

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And this is the final size of the image we want to see.

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So as you can see it's actually lines up with this here.

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These would have four coordinates of the output image that we wanted.

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So let's run this good and voila we get the actual warped page here.

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If you wanted to do something smarter you'd probably write an algorithm that gets to CONTO of this image

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that automatically gets the locations of the corner points then feeds it in and then we run everything

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automatically.

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It's actually not too hard to build is actually a low some blogs online that actually do it for you.

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So we've just seen how we generated Imitrex for none I transform.

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But what about affine transform that should be simpler right.

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And indeed it is you only need tree coordinates to actually generate the find transform.

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So similarly we have points in B.

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We are.

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We actually use a different image here and there's a reason for that.

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Also we use shortly.

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So we have tree coordinates for points in b tree coordinates for Point B.

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We use get affine transform as opposed to get perspective transform and we similarly implement those

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transforms using Wolpoff when we just switched to see I would call them Andrews which is similar to

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using the actual dimensions here.

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We just take it directly from the image and let's run this code here.

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So as you can see these are two

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ships that we wish to transform.

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So let's press any key and continue.

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And this is the affine transform as you can see it as I find transform actually rotated the image slightly

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didn't just scale anything but as you can see clearly all the lanes Montie and parallelism here.

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So that's one of the main points of transforms.

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So I hope you get a feel of using these functions here are actually quite handy and quite important

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when doing more complicated stuff in open C-v.