A 9000 Feet Overview of Entire AI Field + Semi & Self Supervised Learning | Episode 6

A 9000 Feet Overview of Entire AI Field + Semi & Self Supervised Learning | Episode 6

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In the previous episode of the Computer Vision For Everyone (CVFE) course, we discussed different branches of machine learning in detail with examples. Now in today’s episode, we’ll further dive in, by learning about some interesting hybrid branches of AI.

We’ll also learn about AI industries, AI applications, applied AI fields, and a lot more, including how everything is connected with each other. Believe me, this is one tutorial that will tie a lot of AI Concepts together that you’ve heard out there, you don’t want to skip it.

By the way, this is the final part of Artificial Intelligence 4 levels of explanation. All the four posts are titled as:

This tutorial is built on top of the previous ones so make sure to go over those parts first if you haven’t already, especially the last one in which I had covered the core branches of machine learning.  If you already know about a high-level overview of supervised, unsupervised, and reinforcement learning then you’re all good.

Alright, so without further ado, let’s get into it.

We have already learned about Core ML branches, Supervised Learning, Unsupervised Learning, and Reinforcement Learning, so now it’s time to explore hybrid branches, which use a mix of techniques from these three core branches. The two most useful hybrid fields are; Semi-Supervised Learning and Self-Supervised Learning. And both of these hybrid fields actually fall in a category of Machine Learning called Weak SupervisionDon’t worry I’ll explain all the terms.

The aim of hybrid fields like Semi-Supervised and Self-Supervised learning is to come up with approaches that bypass the time-consuming manual data labeling process involved in Supervised Learning.

So here’s the thing supervised learning is the most popular category of machine learning and it has the most applications in the industry and In today’s era where an everyday people are uploading images, text, blogposts in huge quantities, we’re at a point where we could train supervised models for almost anything with reasonable accuracy but here’s the issue, even though we have lots and lots of data, it’s actually very costly and time-consuming to label all of it. 

So what we need to do is somehow use methods that are as effective as supervised learning but don’t require us, humans, to label all the data. This is where these hybrid fields come up, and almost all of these are essentially trying to solve the same problem.

There are some other approaches out there as well, like the Multi-Instance Learning and some others that also, but we won’t be going over those in this tutorial as Semi-Supervised and Self-Supervised Learning are more frequently used than the other approaches.

Semi-Supervised Learning

Now let’s first talk about Semi-Supervised Learning. This type of learning approach lies in between Supervised Learning and Unsupervised Learning as in this approach, some of the data is labeled but most of it is still unlabelled.

Unlike supervised or unsupervised learning, semi-supervised learning is not a full-fledged branch of ML rather it’s just an approach, where you use a combination of supervised and unsupervised learning techniques together.

Let’s try to understand this approach with the help of an example; suppose you have a large dataset with 3 classes, cats, dogs, and reptiles. First, you label a portion of this dataset, and train a supervised model on this small labeled dataset.

After training, you can test this model on the labeled dataset and then use the output predictions from this model as labels for the unlabeled examples.

And then after performing prediction on all the unlabeled examples and generating the labels for the whole dataset, you can train the final model on the complete dataset.

Awesome right? With this trick, we’re cutting down the data annotation effort by 10x or more. And we’re still training a good mode.

But there is one thing that I left out, since the initial model was trained on a tiny portion of the original dataset it wouldn’t be that accurate in predicting new samples. So when you’re using the predictions of this model to label the unlabelled portion of the data, an additional step that you can take is to ignore predictions that have low confidence or confidence below a certain threshold.

This way you can perform multiple passes of predicting and training until your model is confident in predicting most of the examples. This additional step will help you avoid lots of mislabeled examples.

Note, what I’ve just explained is just one Semi-Supervised Learning approach and there are other variations of it as well.

It’s called semi-supervised since you’re using both labeled data and unlabeled data and this approach is often used when labeling all of the data is too expensive or time-consuming. For example, If you’re trying to label medical images then it’s really expensive to hire lots of doctors to label thousands of images, so this is where semi-supervised learning would help.

When you search on google for something, google uses a semi-supervised learning approach to determine the relevant web pages to show you based on your query.

Self-Supervised Learning

Alright now let’s talk about the Self-Supervised Learning, a hybrid field that has gotten a lot of recognition in the last few years, as mentioned above, it is also a type of a weak supervision technique and it also lies somewhere in between unsupervised and supervised learning.

Self-supervised learning is inspired by how we humans as babies pick things up and build up complex relations between objects without supervision, for example, a child can understand how far an object is by using the object’s size, or tell if a certain object has left the scene or not and we do all this without any external information or instruction.

Supervised AI algorithms today are nowhere close to this level of generalization and complex relation mapping of objects. But still, maybe we can try to build systems that can first learn patterns in the data like unsupervised learning and then understand relations between different parts of input data and then somehow use that information to label the input data and then train on that labeled data just like supervised learning.

This in summary is Self-Supervised Learning, where the whole intention is to somehow automatically label the training data by finding and exploiting relations or correlations between different parts of the input data, this way we don’t have to rely on human annotations. For example, in this paper, the authors successfully applied Self-Supervised Learning and used the motion segmentation technique to estimate the relative depth of scenes, and no human annotations were needed.

Now let’s try to understand this with the help of an example; Suppose you’re trying to train an object detector to detect zebras. Here are the steps you will follow; First, you will take the unlabeled dataset and create a pretext task so the model can learn relations in the data.

A very basic pretext task could be that you take each image and randomly crop out a segment from the image and then ask the network to fill this gap. The network will try to fill this gap, you will then compare the network’s result with the original cropped segment and determine how wrong the prediction was, and relay the feedback back to the network.

This whole process will repeat over and over again until the network learns to fill the gaps properly, which would mean the network has learned how a zebra looks like. Then in the second step; just like in semi-supervised learning, you will label a very small portion of the dataset with annotations and train the previous zebra model to learn to predict bounding boxes.

Since this model already knows how a zebra looks like, and what body parts it consists of, it can now easily learn to localize it with very few training examples.

This was a very basic example of a self-supervised learning pipeline and the pretext cropping task I mentioned was very basic, in reality, the pretext task for computer vision used in self-supervised learning is more complex.

Also If you know about Transfer Learning then you might wonder why not instead of using a pretext task, we instead use transfer learning. Now that could work but there are a lot of times when the problem we’re trying to solve is a lot different than the tasks that existing models were trained on and so in those cases transfer learning doesn’t work as efficiently with limited labeled data.

I should also mention that although self-supervised learning has been successfully used in language-based tasks, it’s still in the adoption and development stage in Computer vision tasks. This is because, unlike text, it’s really hard to predict uncertainty in images, the output is not discrete and there are countless possibilities meaning there is not just one right answer. To learn more about these challenges, watch Yan Lecun’s ICLR presentation on self-supervised learning.

2 years back, Google published the SimCLR network in which they demonstrated an excellent self-supervised learning framework for image data. I would strongly recommend reading this excellent blog post in order to learn more on this topic. There are some very intuitive findings in this article that I can’t cover here.

Besides Weak Supervision techniques, there are a few other methods like Transfer Learning and Active Learning. All of the techniques aim to partially or completely automate or reduce the data labeling or annotation process.

And this is a very active area of research these days, weak supervision techniques are closing the performance gap between them and supervised techniques. In the coming years, I expect to see wide adoption of Weak supervision and other similar techniques where manual data labeling is either no longer required or just minimally involved.

In Fact here’s what Yan LeCun, one of the pioneers of modern AI says:

“If artificial intelligence is a cake, self-supervised learning is the bulk of the cake,” “The next revolution in AI will not be supervised, nor purely reinforced”

Alright now let’s talk about Applied Fields of AI, AI industries, applications, and also let’s recap and summarize the entire field of AI and along with some very common issues.

So, here’s the thing … You might have read or heard these phrases.

Branches of AI, sub-branches of AI, Fields of AI, Subfields of AI, Domains of AI, or Subdomains of AI, Applications of AI,  Industries of AI, AI paradigms.

Sometimes these phrases are accompanied by words like Applied AI Branches or Major AI Branches etc. And here’s the issue, I’ve seen numerous blog posts and people that used these phrases interchangeably. And I might be slightly guilty of that too. But the thing is, there is no strong consensus on what is major, applied branches, or sub Fields of AI. It’s a huge clutter of terminology out there.

In Fact, I actually googled some of these phrases and clicked to see images. But believe me, it was an abomination, to say the least.

I mean the way people had done categorization of AI Branches was an absolute mess. I mean seriously, the way people had mixed up AI applications with AI industries with AI branches …. it was just chaos… I’m not lying when I say I got a headache watching those graphs.

So here’s what I’m gonna do! I’m going to try to draw an abstract overview of the complete field of AI along with branches, subfields, applications, industries, and other things in this episode.

Complete Overview of AI Field

Now what I’m going to show you is just my personal overview and understanding of the AI field, and it can change as I continue to learn so I don’t expect everyone to agree with this categorization.

One final note, before we start: If you haven’t subscribed then please do so now. I’m planning to release more such tutorials and by subscribing you will get an email every time we release a tutorial.

Alright, now let’s summarize the entire field of Artificial Intelligence. First off, We have Artificial Intelligence, I’m talking about Weak AI  Or ANI (Artificial Narrow Intelligence), since we have made no real progress in AGI or ASI, we won’t be talking about that.

Inside AI, there is a subdomain called Machine Learning, now the area besides Machine learning is called Classical AI, this consists of rule-based Symbolic AI, Fuzzy logic, statistical techniques, and other classical methods. The domain of Machine learning itself consists of a set of algorithms that can learn from the data, these are SVM, Random Forest, KNN, etc.

Inside machine learning is a subfield called Deep Learning, which is mostly concerned with Hierarchical learning algorithms called Deep Neural Networks. Now there are many types of Neural nets, e.g. Convolutional networks, LSTM, etc. And each type consists of many architectures which also have many variations.

Now machine learning (Including Deep learning) has 3 core branches or approaches, Supervised Learning, Unsupervised Learning, and Reinforcement Learning, we also have some hybrid branches which combine supervised and unsupervised methods. All of these can be categorized as Weak Supervision methods.

Now when studying machine learning, you might also come across learning approaches like Transfer Learning, Active Learning, and others. These are not broad fields but just learning techniques used in specific circumstances.

Alright now let’s take a look at some applied fields of AI, now there is no strong consensus but according to me there are 4 Applied Fields of AI; Computer Vision, Natural Language Processing, Speech, and Numerical Analysis. All 4 of these Applied fields use algorithms from either Classical AI, Machine Learning, or Deep Learning.

Let’s further look into these fields, Computer Vision can be split into 2 categories, Image Processing where we manipulate, process, or transform images. And Recognition, where we analyze content in images and make sense out of it. A lot of the time when people are talking about computer vision they are only referring to the recognition part.

Natural Language Processing can be broadly split into 2 parts; Natural Language Understanding; where you try to make sense of the textual data, interpret it, and understand its true meaning. And Natural Language Generation; where you try to generate meaningful text.

Btw the task of  Language translation like in Google Translate uses both NLU & NLG

Speech can also be divided into 2 categories, Speech Recognition or Speech to text (STT); where you try to build systems that can understand speech and correctly predict the right text for it, and Speech Generation or text-to-speech (TTS); where you try to build systems able to generate realistic human-like speech.

And Finally Numerical Analytics; where you analyze numerical data to either gain meaningful insights or do predictive modeling, meaning you train models to learn from data and make useful predictions based on it.

Now I’m calling this numerical analytics but you can also call this Data Analytics or Data Science. I avoided the word “data” because Image, Text, and Speech are also data types.

And if you think about it, even data types like images, and text are converted to numbers at the end but, right now I’m defining numerical analytics as the field that analyzes numerical data other than these three data types.

Now since I work in Computer Vision, let me expand the computer vision field a bit.

So both of these categories (Image Processing and Recognition) can be further split into two types; Classical vision techniques and Modern vision techniques.

The only difference between the two types is that modern vision techniques use only Deep Learning based methods whereas Classical vision does not. So for example, Classical Image Processing can be things like image resizing, converting an image to grayscale, Canny edge detection, etc.

And Modern Image Processing can be things like Image Colorization via deep learning etc.

Classical Recognition can be things like: Face Detection with Haar cascades, and Histogram based Object detection.

And Modern Recognition can be things like Image Classification, Object Detection using neural networks, etc.

So these were Applied Fields of AI, Alright now let’s take a look at some Applied SubFields of AI. I’m defining Applied subfields as those fields that are built around certain specialized topics of any of the 4 applied fields I’ve mentioned.

For example, Extended Reality is an applied subfield of AI built around a particular set of computer vision algorithms. It consists of Virtual Reality;

Augmented Reality;

and Mixed Reality;

You can even consider Extended Reality as a subdomain of Computer Vision. It’s worth mentioning that most of the computer vision techniques used in Extended reality itself fall in another domain of Computer Vision called Geometric Computer Vision, these algorithms deal with geometric relations between the 3D world and its projection into a 2D image.

There are many applied AI Subfields, another example of this would be Expert Systems which is an AI system that emulates the decision-making ability of a human expert.

So consider a Medical Diagnostic app that can take pictures of your skin and then a computer vision algorithm evaluates the picture to determine if you have any skin diseases.

Now, this system is performing a task that a dermatologist (skin expert) does, so it’s an example of an Expert system.

Rule-based Expert Systems became really popular in the 1980s and were considered a major feat in AI. These systems had two parts, a knowledge base, (A database containing all the facts provided by a human expert) and an inference engine that used the knowledge base and the observations from the user to give out results.

Although these types of expert systems are still used today, they have serious limitations. Now the example of the Expert system I just gave is from the Healthcare Industry and Expert systems can be found in other industries too.

Speaking of industries, let’s talk about AI applications used in industries. So these days AI is used in almost any industry you can think of, some popular categories are Automotive, Finance, Healthcare, Robotics, and others.

Within each Industry, you will find AI applications like self-driving cars, fraud detection, etc. All these applications are using methods & techniques from one of the 4 Applied AI Fields.

There are many applications that fail in multiple industries, for example, a humanoid robot built for amusement will fall in robotics and the entertainment industry. While the Self Driving car technologies fall into the transportation and automotive industry.

Also, an industry may split into subcategories. For example, Digital Media can be split into social media, streaming media, and other niche industries. By the way, most media sites use Recommendation Systems, which is yet another applied AI subdomain.

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Alright, so this was a high-level overview of the complete field of AI. Not everyone would agree with this categorization, but this categorization is necessary when you’re deciding which area of AI to focus on and how all the fields are connected to each other, and personally, I think this is one of the simplest and most intuitive abstract overviews of the AI field that you’ll find out there. Obviously, It was not meant to cover everything, but a high-level overview of the field.

This Concludes the 4th and final part of our Artificial Intelligence – 4 levels Explanation series. If you enjoyed this episode of computer vision for everyone then do subscribe to the Bleed AI YouTube channel and share it with your colleagues. Thank you.

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Designing Advanced Image Filters in OpenCV | Creating Instagram Filters – Pt 3⁄3

Designing Advanced Image Filters in OpenCV | Creating Instagram Filters – Pt 3⁄3

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In the previous tutorial of this series, we had covered Look Up Tables in-depth and utilized them to create some interesting lighting effects on images/videos. Now in this one, we are gonna level up the game by creating 10 very interesting and cool Instagram filters.

The Filters which are gonna be covered are; Warm Filter, Cold Filter, Gotham Filter, GrayScale Filter, Sepia Filter, Pencil Sketch Filter, Sharpening Filter, Detail Enhancing Filter, Invert Filter, and Stylization Filter.

You must have used at least one of these and maybe have wondered how these are created, what’s the magic (math) behind these. We are gonna cover all this in-depth in today’s tutorial and you will learn a ton of cool image transformation techniques with OpenCV so buckle up and keep reading the tutorial.

This is the last tutorial of our 3 part Creating Instagram Filters series. All three posts are titled as:

3-4 Filters in this tutorial use Look Up Tables (LUT) which were explained in the previous tutorial, so make sure to go over that one if you haven’t already. Also, we have used mouse events to switch between filters in real-time and had covered mouse events in the first post of the series, so go over that tutorial as well if you don’t know how to use mouse events in OpenCV.

The tutorial is pretty simple and straightforward, but for a detailed explanation you can check out the YouTube video above, although this blog post alone does have enough details to help you follow along.

Download Code:


We will be creating the following filters-like effects in this tutorial.

  1. Warm Filter
  2. Cold Filter
  3. Gotham Filter
  4. GrayScale Filter
  5. Sepia Filter
  6. Pencil Sketch Filter
  7. Sharpening Filter
  8. Detail Enhancing Filter
  9. Invert Filter
  10. Stylization Filter

Alright, so without further ado, let’s dive in.

Import the Libraries

We will start by importing the required libraries.

Creating Warm Filter-like Effect

The first filter is gonna be the famous Warm Effect, it absorbs blue cast in images, often caused by electronic flash or outdoor shade, and improves skin tones. This gives a kind of warm look to images that’s why it is called the Warm Effect. To apply this to images and videos, we will create a function applyWarm() that will decrease the pixel intensities of the blue channel and increase the intensities of the red channel of an image/frame by utilizing Look Up tables ( that we learned about in the previous tutorial).

So first, we will have to construct the Look Up Tables required to increase/decrease pixel intensities. For this purpose, we will be using the scipy.interpolate.UnivariateSpline() function to get the required input-output mapping.


First 10 elements from the increase table:
[7.32204295e-15 1.03827895e+00 2.08227359e+00 3.13191257e+00
4.18712454e+00 5.24783816e+00 6.31398207e+00 7.38548493e+00
8.46227539e+00 9.54428209e+00]

First 10 elements from the decrease table::
[-5.69492230e-15 7.24142824e-01 1.44669675e+00 2.16770636e+00
2.88721627e+00 3.60527107e+00 4.32191535e+00 5.03719372e+00
5.75115076e+00 6.46383109e+00]

Now that we have the Look Up Tables we need, we can move on to transforming the red and blue channel of the image/frame using the function cv2.LUT(). And to split and merge the channels of the image/frame, we will be using the function cv2.split() and cv2.merge() respectively. The applyWarm() function (like every other function in this tutorial) will display the resultant image along with the original image or return the resultant image depending upon the passed arguments.

Now, let’s utilize the applyWarm() function created above to apply this warm filter on a few sample images.

Woah! Got the same results as the Instagram warm filter, with just a few lines of code. Now let’s move on to the next one.

Creating Cold Filter-like Effect

This one is kind of the opposite of the above filter, it gives coldness look to images/videos by increasing the blue cast. To create this filter effect, we will define a function applyCold() that will increase the pixel intensities of the blue channel and decrease the intensities of the red channel of an image/frame by utilizing the same LookUp tables, we had constructed above.

For this one too, we will be using the cv2.split()cv2.LUT() and cv2.merge() functions to split, transform, and merge the channels.

Now we will test this cold filter effect utilizing the applyCold() function on some sample images.

Now we’ll use the look up table creat

Nice! Got the expected results for this one too.

Creating Gotham Filter-like Effect

Now the famous Gotham Filter comes in, you must have heard or used this one on Instagram, it gives a warm reddish type look to images. We will try to apply a similar effect to images and videos by creating a function applyGotham(), that will utilize LookUp tables to manipulate image/frame channels in the following manner.

  • Increase mid-tone contrast of the red channel
  • Boost the lower-mid values of the blue channel
  • Decrease the upper-mid values of the blue channel

But again first, we will have to construct the Look Up Tables required to perform the manipulation on the red and blue channels of the image. We will again utilize the scipy.interpolate.UnivariateSpline() function to get the required mapping.

First 10 elements from the midtone contrast increase table:
[0.09416024 0.75724879 1.39938782 2.02149343 2.62448172 3.20926878
3.77677071 4.32790362 4.8635836 5.38472674]

First 10 elements from the lowermids increase table:
[0.15030475 1.31080448 2.44957754 3.56865611 4.67007234 5.75585842
6.82804653 7.88866883 8.9397575 9.98334471]

First 10 elements from the uppermids decrease table::
[-0.27440589 0.8349419 1.93606131 3.02916902 4.11448171 5.19221607
6.26258878 7.32581654 8.38211602 9.4317039 ]

Now that we have the required mappings, we can move on to creating the function applyGotham() that will utilize these LookUp tables to apply the required effect.

Now, let’s test this Gotham effect utilizing the applyGotham() function on a few sample images and visualize the results.

Now w

Stunning results! Now, let’s move to a simple one.

Creating Grayscale Filter-like Effect

Instagram also has a Grayscale filter also known as 50s TV Effect, it simply converts a (RGB) color image into a Grayscale (black and white) image. We can easily create a similar effect in OpenCV by using the cv2.cvtColor() function. So let’s create a function applyGrayscale() that will utilize cv2.cvtColor() function to apply this Grayscale filter-like effect on images and videos.

Now let’s utilize this applyGrayscale() function to apply the grayscale effect on a few sample images and display the results.

Cool! Working as expected. Let’s move on to the next one.

Creating Sepia Filter-like Effect

I think this one is the most famous among all the filters we are creating today. This gives a warm reddish-brown vintage effect to images which makes the images look a bit ancient which is really cool. To apply this effect, we will create a function applySepia() that will utilize the cv2.transform() function and the fixed sepia matrix (standardized to create this effect, that you can easily find online) to serve the purpose.

Now let’s check this sepia effect by utilizing the applySepia() function on a few sample images.

Spectacular results! Reminds me of the movies, I used to watch in my childhood ( Yes, I am that old 😜 ).

Creating Pencil Sketch Filter-like Effect

The next one is the Pencil Sketch Filter, creating a Pencil Sketch manually requires hours of hard work but luckily in OpenCV, we can do this in just one line of code by using the function cv2.pencilSketch() that give a pencil sketch-like effect to images. So lets create a function applyPencilSketch() to convert images/videos into Pencil Sketches utilizing the cv2.pencilSketch() function.

We will use the following funciton to applythe pencil sketch filter, this function retruns a grayscale sketch and a colored sketch of the image

This filter is a type of edge preserving filter, these filters have 2 Objectives, one is to give more weightage to pixels closer so that the blurring can be meaningfull and second to average only the similar intensity valued pixels to avoid the edges, so in this both of these objectives are controled by the two following parameters.

sigma_s Just like sigma in other smoothing filters this sigma value controls the area of the neighbourhood (Has Range between 0-200)

sigma_r This param controls the how dissimilar colors within the neighborhood will be averaged. For example a larger value will restrcit color variation and it will enforce that constant color stays throughout. (Has Range between 0-1)

shade_factor This has range 0-0.1 and controls how bright the final output will be by scaling the intensity.

Now we will apply this pencil sketch effect by utilizing the applyPencilSketch() function on a few sample images and visualize the results.

Now let’s check how the changeIntensity() functi

Amazing right? we created this effect with just a single line of code. So now, instead of spending hours manually sketching someone or something, you can take an image and apply this effect on it to get the results in seconds. And you can further tune the parameters of the cv2.pencilSketch() function to get even better results.

Creating Sharpening Filter-like Effect

Now let’s try to create the Sharpening Effect, this enhances the clearness of an image/video and decreases the blurriness which gives a new interesting look to the image/video. For this we will create a function applySharpening() that will utilize the cv2.filter2D() function to give the required effect to an image/frame passed to it.

Now, let’s see this in action utilizing the applySharpening() function created above on a few sample images.

Nice! this filter makes the original images look as if they are out of focus (blur).

Creating a Detail Enhancing Filter

Now this Filter is another type of edge preserving fitler and has the same parameters as the pencil sketch filter.This filter intensifies the details in images/videos, we’ll be using the function called cv2.detailEnhance(). let’s start by creating the a wrapper function applyDetailEnhancing(), that will utilize the cv2.detailEnhance() function to apply the needed effect.

Now, let’s test the function applyDetailEnhancing() created above on a few sample images.

Satisfying results! let’s move on to the next one.

Creating Invert Filter-like Effect

This filter inverts the colors in images/videos meaning changes darkish colors into light and vice versa, which gives a very interesting look to images/videos. This can be accomplished using multiple approaches we can either utilize a LookUp table to perform the required transformation or subtract the image by 255 and take absolute of the results or just simply use the OpenCV function cv2.bitwise_not(). Let’s create a function applyInvert() to serve the purpose.

Let’s check this effect on a few sample images utilizing the applyInvert() function.

Looks a little scary, lets’s try it on a few landscape images.

Interesting effect! but I will definitely not recommend using this one on your own images, except if your intention is to scare someone xD.

Creating Stylization Filter-like Effect

Now let’s move on to the final one, which gives a painting-like effect to images. We will create a function applyStylization() that will utilize the cv2.stylization() function to apply this effect on images and videos. This one too will only need a single line of code.

Now, as done for every other filter, we will utilize the function applyStylization() to test this effect on a few sample images.

Again got fascinating results! Wasn’t that fun to see how simple it is to create all these effects?

Apply Instagram Filters On a Real-Time Web-cam Feed

Now that we have created the filters and have tested them on images, let’s move to apply these on a real-time webcam feed, first, we will have to create a mouse event callback function mouseCallback(), similar to the one we had created for the Color Filters in the previous tutorial, the function will allow us to select the filter to apply, and capture and store images into the disk by utilizing mouse events in real-time.

Now that we have a mouse event callback function mouseCallback() to select a filter to apply, we will create another function applySelectedFilter() that we will need, to check which filter is selected at the moment and apply that filter to the image/frame in real-time.

Now that we will the required functions, let’s test the filters on a real-time webcam feed, we will be switching between the filters by utilizing the mouseCallback() and applySelectedFilter() functions created above and will overlay a Camera ROI over the frame and allow the user to capture images with the selected filter applied, by clicking on the Camera ROI in real-time.

Output Video:

Awesome! working as expected on the videos too.

Assignment (Optional)

Create your own Filter with an appropriate name by playing around with the techniques you have learned in this tutorial, and share the results with me in the comments section.

And I have made something similar in our latest course Computer Vision For Building Cutting Edge Applications too, by Combining Emotion Recognition with AI Filters, so do check that out, if you are interested in building complex, real-world and thrilling AI applications.


In today’s tutorial, we have covered several advanced image processing techniques and then utilized these concepts to create 10 different fascinating Instagram filters-like effects on images and videos.

This concludes the Creating Instagram Filters series, throughout the series we learned a ton of interesting concepts. In the first post, we learned all about using Mouse and TrackBars events in OpenCV, in the second post we learned to work with Lookup Tables in OpenCV and how to create color filters with it, and in this tutorial, we went even further and created more interesting color filters and other types of effects.

If you have found the series useful, do let me know in the comments section, I might publish some other very cool posts on image filters using deep learning.
We also provide AI Consulting at Bleed AI Solutions, by building highly optimized and scalable bleeding-edge solutions for our clients so feel free to contact us if you have a problem or project that demands a cutting-edge AI/CV solution.

Working With Lookup Tables & Applying Color Filters on Images & Videos | Creating Instagram Filters – Pt ⅔

Working With Lookup Tables & Applying Color Filters on Images & Videos | Creating Instagram Filters – Pt ⅔

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In the previous tutorial of this series, we learned how the mouse events and trackbars work in OpenCV, we went into all the details needed for you to get comfortable with using these. Now in this tutorial, we will learn to create a user interface similar to the Instagram filter selection screen using mouse events & trackbars in OpenCV.

But first, we will learn what LookUp Tables are, why are they preferred along with their use cases in real-life, and then utilize these LookUp Tables to create some spectacular photo effects called Color Filters a.k.a. Tone Effects.

This Tutorial is built on top of the previous one so if you haven’t read the previous post and don’t know how to use mouse events and trackbars in OpenCV, then you can read that post here. As we are gonna utilize trackbars to control the intensities of the filters and mouse events to select a Color filter to apply.

This is the second tutorial in our 3 part Creating Instagram Filters series (in which we will learn to create some interesting and famous Instagram filters-like effects). All three posts are titled as:

  1. Part 1: Working With Mouse & Trackbar Events in OpenCV 
  2. Part 2: Working With Lookup Tables & Applying Color Filters on Images & Videos (Current tutorial)
  3. Part 3: Designing Advanced Image Filters in OpenCV

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The tutorial is divided into the following parts:

Alright, without further ado, let’s dive in.

Import the Libraries

First, we will import the required libraries.

Introduction to LookUp Tables

LookUp Tables (also known as LUTs) in OpenCV are arrays containing a mapping of input values to output values that allow replacing computationally expensive operations with a simpler array indexing operation at run-time.* Don’t worry in case the definition felt like mumbo-jumbo to you, I am gonna break down this to you in a very digestible and intuitive manner. Check the image below containing a LookUp Table of Square operation.

So it’s just a mapping of a bunch of input values to their corresponding outputs i.e., normally outcomes of a certain operation (like square in the image above) on the input values. These are structured in an array containing the output mapping values at the indexes equal to the input values. Meaning the output for the input value 2 will be at the index 2 in the array, and i.e., 4 in the image above. Now that we know what exactly these LookUp Tables are, so let’s move to create one for the square operation.

First 10 mappings: [ 0 1 4 9 16 25 36 49 64 81]

This is how a LookUp Table is created, yes it’s that simple. But you may be thinking how and for what are they used for? Well as mentioned in the definition, these are used to replace computationally expensive operations (in our example, Square) with a simpler array indexing operation at run-time.

So in simple words instead of calculating the results at run-time, these allow to transform input values into their corresponding outputs by looking up in the mapping table by doing something like this:

Square of 10 is: 100

This eliminates the need of performing a computationally expensive operation at run-time as long as the input values have a limited range which is always true for images as they have pixels intensities [0-255].

Almost all the image processing operations can be performed much more efficiently using these LookUp Tables like increasing/decreasing image brightness, saturation, contrast, even changing specific colors in images like the black and white color shift done in the image below.

Stunning! right? let’s try to perform this color shift on a few sample images. First, we will construct a LookUp Table mapping all the pixel values greater than 220 (white) to 0 (black) and then transform an image according to the lookup table using the cv2.LUT() function.

Function Syntax:

dst = cv2.LUT(src, lut)


  • src: – It is the input array (image) of 8-bit elements.
  • lut: – It is the look-up table of 256 elements.


  • dst: – It is the output array of the same size and number of channels as src, and the same depth as lut.

Note: In the case of a multi-channel input array (src), the table (lut) should either have a single channel (in this case the same table is used for all channels) or the same number of channels as in the input array (src).

As you can see it worked as expected. Now let’s construct another LookUp Table mapping all the pixel values less than 50 (black) to 255 (white) and then transform another sample image to switch the black color in the image with white.

The Black to white shift is also working perfectly fine. You can perform a similar shift with any color you want and this technique can be really helpful in efficiently changing green background screens from high-resolution videos and creating some interesting effects.

But we still don’t have an idea how much computational power and time these LookUp Tables save and are they worth trying? Well, this completely depends upon your use case, the number of images you want to transform, the resolution of the images you are working on, etc.

How about we perform a black to white shift on a few images with and without LookUp Tables and note the execution time to get an idea of the time difference? You can change the number of images and their resolution according to your use case.

First, let’s do it without using LookUp Tables.

Wall time: 194 ms

We have the execution time without using LookUp Tables, now let’s check the difference by performing the same operation utilizing LookUp Tables. First we will create the look up Table, this only has to be done once.

Now we’ll use the look up table created above in action

Wall time: 81.2 ms

So the time taken in the second approach (LookUp Tables) is significantly lesser while the results are the same.

Applying Color Filters on Images/Videos

Finally comes the fun part, Color Filters that give interesting lighting effects to images, simply by modifying pixel values of different color channels (R,G,B) of images and we will create some of these effects utilizing LookUp tables.

We will first construct a lookup table, containing the mapping that we will need to apply different color filters.

[128, 128, 129, 129, 130, 130, 131, 131, 132, 132] Length of table: 256

Now we will create a function applyColorFilter() that will utilize the lookup table we created above, to increase pixel intensities of specified channels of images and videos and will display the resultant image along with the original image or return the resultant image depending upon the passed arguments.

Now we will utilize the function applyColorFilter() to apply different color effects on a few sample images and display the results.

Cool! right? the results are astonishing but some of them are feeling a bit too much. So how about we will create another function changeIntensity() to control the intensity of these filters, again by utilizing LookUpTables. The function will simply increase or decrease the pixel intensities of the same color channels that were modified by the applyColorFilter() function and will display the results or return the resultant image depending upon the passed arguments.

For modifying the pixel intensities we will use the Gamma Correction technique, also known as the Power Law Transform. Its a nonlinear operation normally used to correct the brightness of an image using the following equation:


Here γ<1 will increase the pixel intensities while γ>1 will decrease the pixel intensities and the filter effect. To perform the process, we will first construct a lookup table using the equation above.

And then we will create the changeIntensity() function, which will use the table we have constructed and will re-construct the table every time the gamma value changes.