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OK so let's not talk about trolling and dribbling is actually a very important open C-v function that

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is quite useful and you'll find we use it in so many of for many projects going forward.

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So what exactly is troubling troubling is converting an image to its binary form.

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But what does that mean exactly.

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So if you look at the function DCV to the Trishul function this gives you an idea of what to expect.

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So imagine we have the input image here.

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It Trishul value which is very important a max value and a threshold type.

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So I'm just going to quickly illustrate what happens in this function here.

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So let's look at his gray scale image here.

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And also please note all Trishul functions use grayscale images so images need to be converted.

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Integral scale before.

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Thresholding here.

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So this is grayscale image.

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It goes all the way from 0 to 255 at the bottom here.

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Let's assume this is a wheel midway point 127 that's over Trishul value.

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So and max value here is a value we want to set everything above that threshold.

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So in this example if this line here is 1:27 everything above it when we are going to 2:55 becomes 255.

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So this portion here becomes white and this push in above here becomes black which is zero.

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So it is different forms of travels which we see in the code but that just illustrates exactly what's

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happening here.

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There's also a type of Trishul and call adaptive thresholding which will come to an accord as well.

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And you may have noticed I used the word binary station here biner is Ishan or troubling refusing to

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see him doing it means converting to a fully greyscale image into just two points Max point and a loop

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when traditionally it's zero for a little point and to 55 for the max point.

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So white or black.

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OK.

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So let's take a look at implementing some of those Trishul methods or we just discuss.

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So as you can see it has five different thresholding methods.

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We're going to use here is binary binary inverse truncation Trisha's 0 tresses trash to zero inverse.

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So those are undiscovered and we can actually figure out exactly what each is doing.

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Or just to know that I actually said to Windows specifically here it isn't an open civies and so nice

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to automatically or do those windows.

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There are some C-v to move into functions that allow you to do that.

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But I'm not going to discuss that right now.

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So anyway back to thresholding.

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So this is our original image here.

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And remember from the slides I said we said 127 to be the Trishul value.

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So in Trishul binary everything that's below 127 which is of value be used in our code.

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Hopefully you remember that goes to black and everything above that goes to white.

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So Trishul binary Inv. actually and undecidedly does it do.

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Opposite the inverse of that.

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So everything docket and 1:27 goes to white and everything higher than 127 goes to black.

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Now trust trunk there's something a little bit different.

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What it does is that it takes everything that's actually brighter or higher than 1:27 going to Swee

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and Cupps it at 127 which is why everything just stops a disgrace here and continues along the screen.

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So what about G-0 0 0 actually does something of the opposite here.

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Everything that's actually below 127 actually gets capped to black similarly to binary here.

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However it leaves everything above 127 Lewan.

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So it's a nice effect can be useful sometimes and Trisha's 0.

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Invis actually does the opposite.

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So as you can see it maintains this color here.

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This is the initial 0 to 127 part but everything above 127 just like in Trishul binary invis goes to

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black.

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So that's close these windows now.

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And we can actually just quickly look at it could you remember the input images of Fu's here.

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1:27 is a Trishul value 255 is the max value set.

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That's de-valued cause too when it's above that Trishul or vice versa.

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Zero is actually used in the opposite case for most of these Tresor log algorithms.

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And this here fresh binary This is where we defined threshold type.

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We want to use.

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So it's a simple argument function CB2 Trishul.

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Hope you have fun using it.

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So you may have fun with that one weakness of these Tressell algorithms is ID all require us to set

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the value at 127 or whatever value we wish to use.

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No that's not convenient when you're actually doing it with Lexie's scanned documents or anything of

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the sort.

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So is there a better way of doing this.

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And yes it is.

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It's called adaptive thresholding and I have a slide in a presentation.

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We'll discuss these methods here.

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But there's quite a few adaptive thresholding methods.

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Use an open C-v is adaptive mean trash is OS2 his method which is quite popular and is Gosia or Austin's

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method.

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So let's run this code and we can actually see what's going on here.

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So this is the original image we're looking at.

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It's Charles Darwin version of species.

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So that's the original image.

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This here is a regular Trishna binary Trishul that previously used as you can see it's good.

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It's not bad but if you wanted to get this text book we would have had to use maybe a low or high value

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of 127.

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So this is it here with adaptive martially as you can see you can compare directly to this image and

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you can see the text is a lot more clear.

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So it setting a Trishul a value of 127 or anything of the like.

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It actually does a better job already.

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So we can all his method do a better job.

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Perhaps it can.

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I mean there's not much difference separating these images.

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But in my experience his method actually is quite handy.

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What about Astor's Gaussian method.

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Again this actually looks very similar it is on the onto the differences between these images here.

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However in your own image data set you may want to actually play and try these different logarithms.

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So let's listen a bit more about the adaptive treacly methods that we just saw.

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So the first one we saw was adaptive Trishul.

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And as you can.

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As you remembered adaptive turtling has a big advantage in that it reduces the uncertainty in setting

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a Trishul value.

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So the input does nutritional value in these parameters here is a max value which is 2:55 the adaptive

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type Trishul type block size which needs to be in odd numbers.

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One of those weird pensively quirks and a constant and lot of open civic work that needs to be said

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that faith Well traditionally five you can experiment with different values and if you get better results

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that's good.

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So that's quickly going.

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So these are the values here.

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So this is the adaptive type and I believe this is the only type available for dysfunction right now

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and this is the troubling type we use here as well.

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And these are the blocks and the constant here and that give us the results we saw earlier.

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So let's go back to a presentation now

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and they actually just misspoke when I just said adaptive trust means see was the only adaptive threshold

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type we could use in this function is actually adaptive Shesh Gaussians see which as you may remember

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is a weighted sum of neighborhood pixels under that Gaussian window.

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So let's discuss OS2 which is actually one of the best adaptive freshly methods.

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So what OS2 does is that it tries to find.

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Well it looks at the histogram of the range of intensities in the image here and it tries to find the

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peaks or the moods of that of the histogram here and then it actually finds a value that separates these

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histograms.

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The best value I can separate is two histograms that you actually get the best thresholding value here.

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So in most cases where you have changes in light intensity I would encourage you to use one of these

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thresholding adaptive gentling algorithms AWST Atsuko is actually quite good.

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However if your application can use a simple thresholding algorithm that we saw before Dewes maybe faster

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so maybe you should just go for one of those.

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Again it depends on the use case and you'll see going forward.

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We actually do use different methods in many projects.