In the last lecture, we saw that we were not able to achieve high value addition that could I see due
to overfitting.
In this lecture.
In this lecture, we will apply different image processing to avoid or what fitting, and we will also
introduce a global player in our modern architecture.
And inmates preprocessing.
We will uplay shearing rotation.
Shift, height, shift and zoom to create dummy data from orbit.
Or is nilita?
Now, shearing looks like this.
So this is over what is no limit shearing means.
We are pulling any even ed off over photo.
And converting a script and do a rhombus rotation means rotating the image.
Which shift means we are shifting of a whole image?
Left or right?
Hide shift means we are shifting of wholly made up or down and zooming means we are zooming in at any
particular section of our image.
Now, it is very easy to randomly apply all these features to over images.
Again, we will be using meat doesn't litter.
The data will flow from that ACRI.
So we will be using flow from Derek Cremator.
And here we are reading the data.
We are reading the data from our train directory.
The target size.
Of the images we want is one 50 by one 50.
And this time we want images in the bed size of potato.
Earlier, we were using Betsey's of 20.
Now we are using Betsey's of today.
And the last more is binary.
Since we have two classes and the images of these glasses are in separate folders in our working directory.
Now, this is the code to convert over inmate data into handsets now using inmates due to and later
we can apply preprocessing here.
So earlier we were only using rescaling to rescale RGV values from zero to 255 to zero to one.
No, we can use a patient range parameter.
This the parameter to give rotation ranges.
So here my inmates can rotate from minus 40 to 40 degree.
For each inmate.
That inmates detergent network will automatically randomly choose a value between minus 40 to 40 and
applied that probation to the audience limit.
Similarly, we are using overt word shift Grange of point to.
Point two means we are allowing inmates due to the need to shift our images left or right.
By 20 percent of the total work.
Similarly, we are using hyd shift grainge of zero point two percent.
This means 20 percent of total height.
We are allowing inmates who died in return to shift.
Now, note that these sultriness, we are not seeing that.
Applied 20 percent of high tariffs to agreement.
We are just mentioning the upper limit.
So he data generator will randomly choose a number between zero and zero point to.
And applied that which shift or hardship to over or regional limits.
Similarly, we are using those sheer range of zero point to a zoom range of zero point two and horizontal
flip equate to crew.
Horizontal flip just means the mirror image along the vertical axis.
So right now, if this is our original image, the camera is pointing towards right horizontal flip
means.
The mirror image of this image along this axis.
So the camera in point to the left.
So the code is very similar to last thing.
We are creating green underscore doesn't object.
And which we are mentioning all this parameters.
And in this train underscored details in the details flowing from Betty Currie.
And here we are mentioning the train directory, train size, bed size and plus more.
Now we will be dreaming of a model only on the creating data dataset.
So there is no need to uplay all this transformation on the validation side.
Therefore, when we are creating pest detection, we are just rescaling our data.
We are not applying all this transformations to our data.
So if we execute this code, we will be creating who doesn't let us first to the train.
And that's code generator.
And second is the validation underscored in data and validation.
We are using test detection.
We are we are not applying any of these transformations.
So this are the sample of images that will be generated by our train doesn't return.
So here you can see that all these three images are from a single original image.
So you can see that that is a horizontal flip here.
Between these two images.
So in the first image, the cat is pointing towards left in the second limit.
The cat is pointing towards right.
There is some work shift as well, and this two images in the tournament.
You can see that there is some percentage of sheer applied to the image.
And some zoom is also there.