hey my name is Felipe and welcome to my
channel in this video we are going to make an image classifier. Image classification involves
classifying an image into different categories and is one of the most important fields in computer
vision I'm going to show you how to make an image classifier which is very simple, very robust and
it works 100% in Python, I'm going to walk you step by step through the entire process how to prepare
the data how to train the classifier and how to test its performance so following the steps of
this tutorial you will be able to build a very robust image classifier in only a few minutes
so let's get started so this is the process in which we are going to be working today in today's
tutorial you can see that this is a four steps process so training this image classifier will
take only four steps this will be a very easy and a very straightforward process now let me
show you the requirements we are going to use in this project we are going to use scikit learn,
scikit image and numpy and the image classifier we're going to use comes from the library scikit
learn this is the library which is very popular to solve machine learning related problems and
if you want to work with computer vision or with machine learning you will definitely need to be
familiar with scikit learn so this will be a very good example this will be a very good opportunity
in order to get more familiar with scikit learn now let's start with this project let's start with
this tutorial and the first step is preparing the data we are going to use in order to train this
image classifier and now let me show you the data we are going to use in today's tutorial this
is the data we are going to use you can see we have two different categories the categories are
empty or not empty this sounds very strange but let me show you exactly how these two categories
look like or how the data in each one of these categories look like and you can see from the not
empty category we have something that looks like this so basically we have cars these are images
from cars and if I show you... if I show you many many different pictures you can see that in all
of them we can see pretty much the same situation it's like a car that's basically the not
empty category and then if I show you the empty category this is pretty much an empty something
this is pretty much all the images containing empty...ness containing a completely empty... these are
completely empty images in some of them there are very small objects but you can see that all of the
images are mostly empty and these are... this is how these two categories look like this
is how these two categories look like and this data comes from one of my previous videos where
I showed you how to create a parking slot detector and counter using python and computer vision in
this other video I showed you how to take a video exactly like this... a video from a huge parking
lot like this and to make it look like this let me execute the code I showed you how to build in
this tutorial and the idea was to go from here to here the idea was to build something like this
where we detect absolutely all the parking slots and for each one of the parking slots we classify
if the parking slot is empty or not and if it's empty we plot it in green and if it's not empty we
plot it in red so this is exactly where the data comes from the data we are going to use in today's
tutorial you can see that for the not empty category this is how the data looks like these are
images from parking slots containing cars and from the empty category we have images from parking
slots which are completely and absolutely empty so this is the data we are going to use today
and the image classifier I'm going to show you how to build in today's tutorial it's a very
robust classifier in situations like this in situations where the data we have in situations
where the classification we want to make on our data it's very easy it's very simple right where
the different categories we want to tell apart the different categories we want to classify they are
visually super super super distinct they are super different right in this case we have images from
cars and in this other category we have absolutely empty images right, the image classifier I'm
going to show you today it's very robust and it works super super well in situations like this
and the reason I'm saying this is because this may not be a state-of-the-art image classifier
if you look for papers or if you look for the most recent techniques obviously you are not going
to find the classifier we are going to make today we're going to build today... and it's
definitely not the most robust classifier in the machine learning industry but if you want to
solve like a very very simple problem like the one we are going to solve today this image specifier
will be more than enough and I'm going to show you exactly what's the performance we achieve
with this classifier but that's later on in this tutorial now let's get started I already showed you
the data we are going to use and the first thing I'm going to do is to import os which is a library
which we definitely need in order to work with data and to read data and to load data from our
computer so I'm going to define a directory which is input directory and in this case I'm just
going to hardcode where the input directory is located which is here this will be something like
this and then I'm going to Define our categories and I'm also going to hard code it and I'm going
to say something like this our categories are empty and not empty okay and then the only thing I'm
going to do is to Define two lists which are the lists which are going to contain our data one
of them will be data and then the other one will be labels and I'm going to show you exactly how
we're going to use these two objects later on this tutorial and now what we're going to do is
to iterate in absolutely all the images in these two directories and we are going to load these
images we are going to read these images and we are going to format these images... this data in
a way that fits well the classifier we're going to use later today so let's start with it I'm going
to say something like for category in categories and then for file in something
like os listdir input dir and then I'm going to define the
image path like os path join and then input directory and category uh sorry this will
be os dot path dot join input dir and category okay because we are walking in absolutely all
the files for each one of our categories right something like this okay image path join input
dir category and then file and then we have defined exactly what's the location of our images
now the only thing we need to do is to read our images and to save our images into this list so
in order to read our images I am going to import from scikit mage dot IO Import imread in this
channel in all the videos I had made so far we usually used opencv as an image processing library
and obviously opencv it's by far the most popular and the most comprehensive of all image processing
libraries is the one I use the most in my projects but there are also other libraries which are
more convenient in different situations and for example in this case we are going to use scikit
image we're going to use this image processing library in order to read the images from our disk
from our computer so I'm going to call imread from image path and this will be image okay
and now I'm going to resize this image and for that I am going to import another
function from scikit image which is called from scikit image dot transform import
resize so I'm going to call resize I need to input my image and I need to input
the new size... which... the new size I'm going to resize all my images to which is 15 x 15
and this will be my image so I'm walking through absolutely all the images in these two
directories I'm taking these images I'm reading these images from my computer and I'm resizing
these images from this size to 15 x 15 okay now let's continue I have resized all of our
images and what I need to do now is to append this data into the list I have created
in order to contain all my data so data.append and we're not going to append the image... like the
image we have read and we have resized, but we are going to append the flatted image we are going
to take our image and we are going to make it into an array right because currently our image
is something like a matrix right it's something like a matrix of size for example in this
case 61 x 26 and three channels it's BGR we have resized this image now it's 15 x 15
and three channels now we want to take all of that information and we want to make it into
only one very very long array right we want to make it unidimensional so that's why we are
calling flatted and the reason we are doing this is because that's what we need to do in order to
use the image classifier we're going to use today when we are working with machine learning you
will notice different classifiers and different models and different libraries and different so on
different everything require different formats or require we format the data differently in order
to be fed into the machine learning model into the classifier or into the whatever we are using
and in this particular case we need to flat the data we need to make the data into an array
before we input this data into our classifier so that's all for the data and we also need
to append the category for this data the label and in order to do that I'm going to walk through
the categories slightly in a slightly different way I'm going to call this is category
index category in enumerate categories and now I will append category index to these
labels arrray to this label list and that's pretty much all we need to do the only thing I'm
going to do next is to cast each one of these lists into a numpy array so I'm going to say asarray
and then data okay and I'm going to do the same with labels np asarray labels I know what you're
thinking hey Felipe this is not going to work because you have not imported numpy and you're
completely right so I am going to import numpy now import numpy as np okay so that is it that's all we
need to do in order to load our data and in order to prepare our data to be fed into the classifier
or in order to move to the next step that is it we have completed the first step in our process so
congratulations we are one step closer to reach our goal of training our image classifier now in
order to make sure everything works properly and we don't have any error I'm going to execute
the code as it is now and numpy has no flatted um maybe it's flatten I think it's flatten maybe
I made a mistake so let's see what happens now it seems we don't have any errors so yeah I made a
mistake now we have to wait a few minutes because remember we are walking through absolutely all
the images in these two directories and we have among the two directories we have 6090 images so
this is going to take some time but yeah now it's completed and now it's time to move to the next
step where we are going to split our data into two different sets we are going to create a training
set and we are also going to create a test set the training set is the one we are going to use in
order to train our image classifier and then the test set is the one we're going to use to test
the performance of the classifier we are going to train so we definitely need to split all of our
data into these two sets and this is the function we are going to use, we need to import another
function from scikit learn and this function will be something like from scikit learn dot model
selection if I'm not mistaken import train test split yeah so this is how I'm going... we're going
to use this function I'm just going to write some things and then I'm going to explain it so this
will be something like X train X test y train y test... calling our data X and Y it's a very
popular convention in machine learning so we are going to do it too and this will be our train
test split function and we need to input data labels and then there will be other parameters
as test size I'm going to set this in 20... in 0.2 don't worry I'm going to explain exactly why
I'm setting all these parameters in a couple of minutes then another parameter is stratify
this will be according to labels and then Shuffle I'm going to call Shuffle here and this will be
true okay so I have called train test split I have input our data and our labels and then I have
specified a few parameters in order to show you exactly what these parameters mean... I'm going to
start with test size I told you we were splitting our data into two different sets the training
set... and the test set now let me show you an image to show you... what
this means, to show you exactly how this looks like so this is an image I found online this is an
image I found on Google or actually on duckduckgo and basically you can see that we have an array which
is called data which contains all of our data and we're creating two different arrays from it we are
creating X train and X test and we are specifying this size this split size which is 0.2 so what we
are doing is creating two different sets which are training set and test set from the total amount of
our samples from all of our data we are splitting all of our data into two different sets and the
way we decide what's the size of the test set is by specifying this parameter So currently we are
telling train test split to make this split in a way that 20% of all of our samples
are in the test set right now moving to the next argument Shuffle equal true this is
something that we always want to do when we are creating data when we are preparing data in order
to be fed into a machine learning classifier we definitely want to shuffle this data first so we
can avoid absolutely every bias we had when we were reading the data when we were creating
these data arrays... we definitely want to shuffle the data first this is a very good practice
sometimes there are some biases which we are not aware of sometimes the way we are reading
data or the way we are creating these arrays we we are making some mistakes or we are... I don't
know the way this walking is done maybe it's alphabetical order or maybe it's whatever we
always want to shuffle the data first that's a very good practice to avoid any type of bias when
we were reading the data and creating these arrays then stratify equals labels I would say it's always
a good practice when we are splitting our data into two different sets to Define exactly how
we are stratifying this split and in order to show you exactly what stratify means I'm going to
show you this picture which I also found online on duckduckgo and basically you can see that we have a
data set in this case is these 12 people and you can see three of these people are blue three
of these people are green and then six of these people are red and in this example we are taking
a a sample of four elements of four people and the way this sampling is done is by stratifying
by the different labels so if you look at the sample you can see that two of these samples are
red one of the samples is green and the other one is blue so we are keeping the same proportion of
the different labels in our original data set... that's exactly what we are doing when we are
stratifying according to the labels it's like a way to make sure that the all the different labels
are going to be in exactly the same proportion as in the original data set so it's definitely a good
practice... this is something we always always want to do now let's continue and you can
see that we have completed the second step in our process we are one step closer we have only two
steps left two steps to go we are one step closer of training our image classifier and that's going
to be all for splitting our data into a training set and a test set now it's time to train our
image classifier now it's time to do like the the actual training and this is how we are going
to do it I'm going to import another function which is called... if I remember
correctly from sklearn I think it's model selection as well import
gridsearchcv then from sklearn dot svm import SVC okay so the first thing I'm going to do is
to Define an object which is called classifier and this will be something like this this is
the classifier we are going to use uh it's SVC let's see if it's okay is sklearn svm import so I
think it's okay for some reason it didn't found it now I see what's the error SVC should
be in capital letters and now everything should be okay okay so what I'm doing here is creating
a new instance of this object and I'm calling this object classifier and this is the classifier
we are going to use in order to train our image classifier so what I'm going to do next is to
Define another object which is parameters and this will be something like this I'm just going to
write it and I'm going to explain it in a couple of minutes I'm going to define something like
a list of only one element which is going to be a dictionary with two keys one of these keys
will be gamma and then the other key will be C and that's pretty much all and then I'm going
to define a list for each one of these uh items and for gamma it will be something like 0.01
0.001 and 0.0001 okay and for C I'm going to do something like 1 10 100 and 1000 okay and
then it's when I'm going to call grid search I'm going to define a new object which is called grid
search and this will be grid search cv and I'm going to input these two objects I just created the
classifier and the parameters okay and now let me show you exactly what I'm doing I'm creating a
new instance of SVC and I'm calling this instance classifier and this is the classifier we are
going to use and I'm creating this object which is called parameters which is a list containing
only a dictionary and this section has only two keys one of them is gamma and the other one is
C and each one of these keys has a list of values okay now I'm going to to show you the scikit
learn documentation and going to... and going back to scikit learn and I'm going to show you how the
documentation looks like for SVC for the object we are using as our classifier and you may notice
all the different parameters we have in this classifier we have C kernel degree gamma cov0
shrinking probability and many many many other parameters we have many many many many different
parameters to choose from but when we were creating our object we are just calling this empty
Constructor we are not specifying absolutely any parameter we are just using all the default values
for SVC okay and you may notice as well that we are getting this other object and these two keys
we are creating are exactly two parameters from our... from our object from the
object we are going to use as our classifier so what we are going to do is we are not going
to train only one image classifier we are going to train many many many many different image
classifiers and we are going to train an image classifier for each one... for each combination we
have for C and Gamma so we have three values for gamma and we have four values for C this means we
are going to train 12 image classifiers right the way this process is going to work is that we are
not going to train only one image classifier but we are going to train many many and as many as
different combinations we have for C and Gamma in this case we have three values for gamma
four values for C three times four it's twelve so we are going to train 12 image classifiers
that's the process we are going to take in this tutorial and the way it works is that
we are going to choose the best of all of these different classifiers we are going to train so...
and the way we are going to do this training the way we are going to train as many classifiers
at once is by calling grid search by calling this other object I have specified over
here so this is how we are going to use it I have already defined this grid search and then the
only thing we need to do is to call grid search dot fit and I'm going to input X train which
is my training set and then y train which is which are the labels of our training set and
that's pretty much all that's pretty much all in order to train our image classifier that's pretty
much all to train all these 12 image classifiers right let's see what happens when I press play
now remember the way this works is that we are loading the data first and this is going to take a
few minutes because ithis is a lot a lot of data these are a lot of images and then we are taking
this process now the training also takes some time because remember we are training many many many
many different classifiers so this is going to take some time so I'm just going to wait a couple
of minutes and I'm going to see if something happens or if the execution is successful okay
so the execution is now completed we don't have any errors so we can continue and and this was
the third step in our four steps process this means we are pretty much there the only thing we
need to do now is to test the performance of the model we trained but we are almost there we have
almost completed this process and most importantly we have already trained our image classifier, our
image classifier is now trained the only thing we need to do now is to test how it performs to see
if we can use it or not okay and this is how we are going to do I mentioned that we were training
not only... not only one image classifier but we are training 12 different image classifiers one for
each one of... one for each combination of C and gamma so we are training many many different classifiers
and the way we are going to select one of all of these image classifiers if is by calling a member
of grid search which is called grid search dot base_estimator_ so by calling this
member is that we are going to get the best of all the different image classifiers that were
trained right we are training 12 different image classifiers and we are just choosing the
best one and the way we are choosing for the best one is by calling this member of grid search
that's basically what we are doing so this is our model this is our classifier best estimator this
is our model now let's see how it performs let's see if it's really like a good classifier let's
see what's the performance of this classifier on our... test data on the data we have created
in order to test the performance of this algorithm so what I'm going to do is to call base estimator
dot predict and I'm going to input my test data so I'm going to input X test and I'm going to call
the output from this prediction y prediction right and then I need to import another function which
is from sklearn dot metrics import accuracy score and let's see what's our performance
and I'm going to make a print I'm going to Define another value which is score and
score will be accuracy score y prediction and y test right I'm taking this which are the
labels of the test set and I'm just comparing against our predictions and now I'm going to
print something like I'm going to format this super super nicely so we get like a... like a very
clear measure of how well this performs so I'm going to express this as a percentage
and I will say something like of samples were correctly classified something like this and
this will be format string score times 100 right because score is going to give us a number
which ranges between 0 and 1 and it's going to be a measure of our score of how accurate our classifier
is on the test set now this is a value between 0 and 1 and it's very very useful and that contains
absolutely all the information but what I'm going to do is just reshaping where I'm going to do
like a reformat of this number into a percentage right because it's going to be much much cleaner
in order to see how it performs and all I have to do now is to execute this code again and
let's see what happens let's see what's our performance so I'm just going to press play and
I am going to wait a couple of minutes just like before and I will come back with our results
let's see how it performs the execution is now completed and this is the accuracy we got
with the best estimator from all the different image classifiers we trained we are getting a
99.9 percent accuracy this means that this is absolutely perfect this is a pretty much perfect
classifier a 99.9 accuracy is like a super super high performance it's like a very good performance
so we can definitely use this classifier later on we can definitely use this classifier in order
to use it in our project so the only thing we need to do next the only thing we have to do
now in order to complete this tutorial is to save this classifier to save this model because
we want to load this model from another project or we want to load this model in a different...
code or in a different whatever from a different location we definitely want to save the weights
or we want to save exactly all the information which is related to this model so we can use it
later in a different project and the way we are going to save this model is by using pickle which
is another python Library so I'm going to import pickle and then I'm just going to call pickle dot
dump and I'm going to specify the model I want to to save the object I want to save and also I need
to specify the file which is going to be something like model.p and then I need to open this file
as wb okay and that's going to be pretty much all in order to save our model in order to have a
file with our model so we can use it later on, on our project or from our location or we can use
this file in whatever way we want so I'm going to press play and that's going to be pretty much
all after the training process is completed and after everything it's done we should have a file
in exactly the location we have specified which in my case is here... we should have a file which
is called model.p so this is it this is going to be all for this tutorial we have absolutely
completed all these steps in our process we have completed all four steps in our process and
we have trained an image classifier using Python and scikit learn so if you enjoyed this video
I invite you to click the like button and I also invite you to write me a message in the comments
section below telling me what do you think about this video telling me what do you think about this
tutorial and also telling me your ideas or your recommendations for other videos or other projects
we could work next on this channel my name is Felipe I'm a computer vision developer and in this
channel I make tutorials coding tutorials exactly like this one and I also share my experience and
my resources the resources I use as a computer vision developer so if these are the type of
videos you are into I invite you to subscribe to my channel this is going to be all for today
and see you on the next video
