Original subtitles

So how that can be just to much it's not in many ways this can be done or whether we're going to look

into that default method utilized by NCB which is R.G. arguably science for red green and blue.

So imagine this 10 by 10 grid I have here represented an image and each pixel point which is what these

cells are.

We have tree values one for red one for green one from Blue.

Now open can see these stories these values in it as an integer.

It allows us to go from zero to 255.

Giving us 256 values.

So no we represent colors here by mixing different intensities of red green and blue.

So as we can see in this pretty childish drawing here all these values here are going to be white.

White is represented by 255 in each color.

So that's two of the five for red green and blue.

Next if we have a green over here.

So 0 4 is going to be have zero red 0 blue but for.

They'll make colors like brown and yellow.

Now if we mix colors as you may have done in kindergarten I guess you can see that they're mixing red

and green.

You actually create yellow.

No that's actually how you represent color spectrum using RGV by mixing different combinations of colors

of different intensities.

And that's what these values are.

By the way are called intensities.

So the value of red that's low is going to be a dim dark value and one that's two fifty five is going

to be a bright red.

Similar to the shade here.

Now this picture of the Eiffel Tower which a blue letter actually resized we actually have a very long

green pixel count.

You can see the individual pixels here and you can actually see that there are different shades of yellow

and brown and black.

So that's exactly how we use RGV to represent images.

Now I've talked a lot about how our jobi is used but I haven't talked a lot about how it's stored in

a computer.

So that's what we're going to look at next.

So this scary looking thing here is actually hold still images.

This mess basically represents honored by a hundred pixel image where each point goes from zero to fifty

five as we saw previously.

And these are the colors for any color intensities for blue green and red.

If you're familiar with programming you'd quickly realize that this data can be easily stored in areas.

And that's exactly open C.V stores images if you're unfamiliar with the.

I'll give you a quick introduction before we go into any coding.

First think of an array is a table which is so then if I'd best location which is called its index in

a one dimensional array you have one row of information and each cell is identified by index number

which is 0 1 2 and so on.

In a two dimensional array we have multiple rows now so no we need two numbers to identify individual

cells.

So imagine this cell right here with midmost this is over.

It's going to be identified by position 1 1.

Think of it like an x y coordinate system.

So now we move on to treat emotional areas which is what we use is an open city to store images.

So in a three dimensional array think of this seeming two dimensional array but these are stacks behind

them.

So as you can see and this is stuck here we have to read values.

Then behind it degree values and certain values and so on.

So what this means now is that each cell isn't then defined by tree numbers.

Now we have 0 0 and 0 here 0 1 and 0 to represent a red.

If you wanted to find the green value it would be 0 1 1.

So that's essentially how R.G. images are stored in an array is open C-v.

So what about black and white or grayscale images.

Now these are actually simpler.

These do have a two dimension.

In fact they're just stored in a two dimensional array.

So instead of having multiple layers you just have a single layer like this.

And with each and with each coordinate position there's a value associated with it and that value in

a greyscale image basically represents sheets of three under 256 sheets not 50 and

as you can see here darker colors are represented by lower values previously so white being represents

represented by high values.

And that follows suit here.

So now we have a full spectrum of gray for an image.

This is an image of a number one by the way.

So what about binary images binary images can also be called black and white images.

However they're just two values use 2:55 and zero binary meaning two.

So it's either Maximin type system.