Hello, everyone.
Welcome to the third section in this course.
Now in this section of the course, we're going to generate Bob Ross paintings.
Now the key difference here is that we're going to load in our own data.
Now, as you can see, I have this folder with a bunch of Bob Ross paintings.
It's a beautiful folder.
I don't know if there's many beautiful folders out there, but this folder in particular is a beautiful
folder.
I'm loving the paintings here.
So, yeah, I have this folder full of Bob Ross paintings, which is very cool, and I just want to
show you how you can generate any dataset that you want.
So really, all you need is a folder with images and you can generate that data.
So before we start, I do want to show a couple of things.
So let's say you want to get Bob Ross paintings.
Now, obviously, there's not many Bob Ross paintings, but I want to show you a couple of tricks that
you can do.
So as you can see, you can search up Bob Ross paintings and you can find a bunch of paintings.
But I have this really cool extension here called download all images.
And so if you search up, download all images, extension Google, you'll find this extension here.
And what this does is it downloads all images on this web page, so you can scroll down a couple to
get more images.
And once you have all your images, obviously there's a lot of noise in here and you'll have to clean
out that noise.
But I've done that for you guys, so don't worry about that.
If you want to learn how to do that, I can definitely make a video in this course on that.
But once you have all the photos here, just click the zip file and it's going to download all these
images.
I just thought that was a very neat extension, and it definitely helps in this process in collecting
that data.
Or you can very well go on, go and find any sort of data there.
OK, so that's great.
So now that that's done with, we have this folder here with a bunch of paintings, right?
And so what we need to do is we need to compress this file into a zip file so that we can upload it
to Google collab.
And that's how you upload your datasets to Google CoLab.
It's you have to compress it into a zip file and you upload that zip file to Google collab.
And then in Google collab, you unzip it into a folder with all the images.
And then once we're there, we just need to load in the data in the same way that we load it up the
last data into a nampai array.
And that's really why we have no pages so we can load our data into an umpire.
And then that's where we train our gain off of.
So now that we've extracted this, now there is a link associated with this video where you can get
this dataset so you don't have to worry about extracting it.
But if you had your own folder.
This is the process you would follow.
And then we would go back to Google CoLab.
And so make sure, yeah, this is the right one.
And so, yeah, so then the next process is really just uploading the data here.
So let me just do that in the moment and I'll see you in a second.
OK, so I'm back here.
So what I'm going to do is just drag and drop the zip file into Google.
Call up just like that, and you'll see that we now have this loading icon here.
Now there's different ways you can upload your data.
The first way is to mount your Google Drive and so on.
Mount it right now, and I'll show you another trick.
OK, so I just put in my authorization code.
I'm going to press enter.
And once this is ready?
OK.
We now have this dry folder, and what you can do is you can just upload your Bob Ross zip file into
my drive, and that's just a way of storing your data onto a hard drive so that you don't have to constantly
load it in each time you log into Google up so you can use your drive to store the data into somewhere
that's always going to be there right now.
If you are a very large dataset with, for example, 15000 images, which is what we're going to do
in the next example or fifth 5K, depending on what dataset you want.
There's different datasets with different amounts.
You may need to upload it in a different way where you log in to Google, drive on your window and upload
that very large dataset in the Google Drive website.
OK.
So depending on how big the data is, you may not be able to just drag and drop it into Google Cloud,
and you may need to do it on the actual Google Drive website.
OK.
So I just want to give those tricks about stuff I've learned over the last couple of years.
And yeah, so I'm going to let this data load in, and once it's done, we're going to unzip it and
I'll see you in a moment.
All right.
So my data just uploaded now in order to unzip it.
We just need this one line of code here, and I gave you some guidance here, but let's write it out
ourselves.
So we're going to do unzip very simply unzip this zip file so we can trust these three dots coffee path
and zip this and store it.
Or actually, before we do that, we need this dashti and we need to store it into a folder called Bob
Ross.
OK, so this is the zip file that we want.
And then this is the folder that's going to be created where we're going to store all the images within.
And so if we run this, you'll see a bunch of inflating also known as uncompressed.
And that'll will be done very quickly.
And when we refresh it, we now have this Bob Ross folder, which has this Bob Ross two folder.
And in there we have all of our beautiful images.
Now we'll take some time to load the images here because there are quite a bit.
But we can double click it and we have some beautiful Bob Ross paintings here, which is awesome.
So we got the dataset into Google up.
The next thing that we need to do is to load in the data into a format that's similar to X train here.
OK.
So that's what we're going to do in the upcoming videos.
And yeah, let's actually do one more thing.
We just need the image path.
So let's go and let's get this Barbaro's to copy path and just paste it here.
And yeah, so this concludes this video where we loaded in the data and I just showed you some techniques
and how you can get data.
But in the next video, we're going to move on and I'll see you in the next video.