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A Crash Course with Dlib Library, 101 to Mastery

A Crash Course with Dlib Library, 101 to Mastery

Download Dlib Resource Guide

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This tutorial will serve as a crash course to dlib library. Dlib is another powerful computer vision library out there. It is not as extensive as OpenCV but still, there is a lot you can do with it.

This crash course assumes you’re somewhat familiar with OpenCV, if not then I’ve also published a crash course on OpenCV too. Make sure to download Dlib Resource Guide above which includes all important links in this post.

Side Note: I missed publishing a tutorial last week as I tested covoid positive and was ill, still not 100% but getting better 🙂

Dlib is created and maintained by Davis King, It’s a C++ toolkit containing machine learning & Computer Vision algorithms for a number of important tasks including, Facial Landmark detection, Deep Metric Learning, Object tracking and more. It also has a python API.

Note: It’s worth noting that the main power of dlib is in numerical optimization but today I’m only going to focus on applications, you can look at optimization examples here.

Its a popular library which is used by people in both industry and academia in a wide range of domains including robotics, embedded devices and other areas.

I plan to cover most of the prominent features and algorithms present in dlib so this blog post alone can give you the best overview of dlib and its functionality. Now, this is a big statement, If I had to explain most of dlib features in a single place then I would probably be writing a book or making a course on it but rather I plan to explain it all in this post.

So how am I going to accomplish that?

So here’s the thing I’m not going to write and explain the code for each algorithm with dlib, because I don’t want to write several thousand’s of words worth of a blog post and also because almost all of the features of dlib have been explained pretty well in several posts on the internet.

So if everything is out there then why the heck am I trying to make a crash course out of it ?

So here’s the real added value of this crash course:

In this post, I will connect all the best and the most important tutorials on different aspects of dlib out there in a nice hierarchical order. This will not only serve as a golden Dlib 101 to Mastery post for people just starting out with dlib but will also serve as a well-structured reference guide for dlib users.

The post is split into various sections, in each section, I will briefly explain a useful algorithm or technique present in dlib. If that explanation intrigues you and you feel that you need to explore that particular algorithm further then in each section I provide links to high-quality tutorials that goes in-depth about that function, the links would mostly be from Pyimagesearch, LearnOpenCV as these are golden sites when it comes to Computer Vision Tutorials. 

When learning some topic, ideally we prefer these two things:

  • A Collection of all the useful material regarding the topic presented at one place in a nice and neat hierarchical order.
  • Each material presented and delivered in a high-quality format preferably by an author who knows how to teach it the right way.

In this post, I’ve made sure both of these points are true, all the information is presented in a nice order and the posts that I link to will be of high quality. Other than that I will also try to include other extra resources where I feel necessary. 

Now let’s get started

Here’s the outline for this crash course:

Installation:

The easiest way to install dlib is to do:

pip install dlib

This will only work if you have Visual Studio (i.e. you need a C++ compiler) and CMake installed as dlib will build and compile first before installing. If you don’t have these then you can use my OpenCV’s source installation tutorial to install these two things.

If you don’t want to bother installing these then here’s what you can do, if you have a python version greater then 3.6 then create a virtual environment for python 3.6 using Anaconda or virtualenv.

After creating a python 3.6 environment you can do:

pip install dlib==19.8.1

This will let you directly install pre-built binaries of dlib but this currently only works with python 3.6 and below.

Extra Resources:

Installing dlib in Mac, Raspi & Ubuntu.

Face Detection:

Now that we have installed dlib, let’s start with face detection.

Why face detection ?

Well, most of the interesting use cases in dlib for computer vision is with faces, like facial landmark detection, face recognition, etc so before we can detect facial landmarks, we need to detect faces in the image.

Dlib not only comes with a face detector but it actually comes with 2 of them. If you’re a computer vision practitioner then you would most likely be familiar with the old Haar cascade based face detector. Although this face detector is a lot popular, it’s almost 2 decades old and not very effective when it comes to different orientations of the faces.

Dlib comes with 2 face detection algorithms that are way more effective than the haar cascade based detectors.

These 2 detectors are:

HOG (histogram of oriented gradients) based detection: This detector uses HOG and Support vector machines, its slower than haar cascades but its more accurate and able to handle different orientations
CNN Based Detector: This is a really accurate deep learning based detector but its extremely slow on a CPU, you should only use this if you’ve compiled dlib with GPU.

You can learn more about these detectors here. Other than that I published a library called bleedfacedetector which lets you use these 2 detectors using just a few lines of the same code, and the library also has 2 other face detectors including the haar cascade one. You can look at bleedfacedetector here.

Extra Resources:

Here’s a tutorial on different Face detection methods including the dlib ones.


Facial Landmark Detection:

Now that we have learned how to detect faces in images, we will now learn the most common use case of dlib library which is facial landmark detection, with this method you will be able to detect key landmarks/features of the face like eyes, lips, etc.

The detection of these features will allow you to do a lot of things like track the movement of eyes, lips to determine the facial expression of a person, control a virtual Avatar with your facial expressions, understand 3d facial pose of a person, virtual makeover, face swapping, morphing, etc.

Remember those smart Snapchat overlays which trigger based on the facial movement, like that tongue that pops out when you open your mouth, well you can also make that using facial landmarks.

So its suffice to say that Facial landmark detection has a lot of interesting applications.

The landmark detector in dlib is based on the paper “One Millisecond Face Alignment with an Ensemble of Regression Trees”, its robust enough to correctly detect landmarks in different facial orientations and expressions. And it easily runs in real-time.

The detector returns 68 important landmarks, these can be seen in below image.

The 68 specific human face landmarks | Download Scientific Diagram

You can read a detailed tutorial on Facial Landmark detection here.

After reading the above tutorial the next step is to learn to manipulate the ROI of these landmarks so, you can modify or extract the individual features like the eyes, nose lips, etc. You can learn that by reading this Tutorial.

After you have gone through both of the above tutorials then you’re ready for running the landmark detector in real time but if you’re still confused about the exact process then take a look at this tutorial

Extra Resources:

Here’s another great tutorial on Facial Landmark Detection.

Facial Landmark Detection Applications (Blink, yawn, smile detection & Snapchat filters):

After you’re fully comfortable working with facial landmarks that’s when the fun starts. Now you’re ready to make some exciting applications, you can start by making a blink detection system by going through the tutorial here. 

The main idea for a blink detection system is really simple, you just look at 2 vertical landmark points of the eyes and take the distance between these points, if the distance is too small (below some threshold) then that means the eyes are closed.

Of course, for a robust estimate, you won’t just settle for the distance between two points but rather you will take a smart average of several distances. One smart approach is to calculate a metric called Eye aspect ratio (EAR) for each eye. This metric was introduced in a paper called “ Real-Time Eye Blink Detection using Facial Landmarks

This will allow you to utilize all 6 x,y landmark points of the eyes returned by dlib, and this way you can accurately tell if there was a blink or not.

Here’s the equation to calculate the EAR.

The full implementation details are explained in the tutorial linked above.

You can also easily extend the above method to create a drowsiness detector that alerts drivers if they feel drowsy, this can be done by monitoring how long the eyes are closed for. This is a really simple extension of the above and have real-world applications and could be used to save lives. Here’s a tutorial that explains how to build a step by step drowsiness detection system.

Interestingly you can take the same blink detection approach above and apply it to lips instead of the eyes, and create a smile detector. Yeah, the only thing you would need to change would be the x,y point coordinates (replace eye points with lip points), the EAR equation (use trial and error or intuition to change this), and the threshold.

Few years back I created this smile camera application with only a few lines of code, it takes a picture when you smile. You can easily create that by modifying the above tutorial.

What more can you create with this ?

How about a yawn detector, or a detector that tells if the user’s mouth is opened or not. You can do this by slightly modifying the above approach, you will be using the same lips x,y landmark points, the only difference would be how you’re calculating the distance between points.

Here’s a cool application I built a while back, its the infamous google dino game that’s controlled by me opening and closing the mouth.

The only drawback of the above application is that I can’t munch food while playing this game.

Taking the same concepts above you can create interesting snapchat overlay triggers. 

Here’s an eye bulge and fire throw filter I created that triggers when I glare or open my mouth.

Similarly you can create lots of cool things using the facial landmarks.

Facial Alignment & Filter Orientation Correction:

Doing a bit of math with the facial landmarks will allow you to do facial alignment correction. Facial alignment allows you to correctly orient a rotated face.

Why is facial alignment important?

One of the most important use case for facial alignment is in face recognition, there are many classical face recognition algorithms that will perform better if the face is oriented correctly before performing inference on them.

Here’s a full tutorial on facial Alignment.

One other useful thing concerning facial alignment is that you can actually extract the angle of the rotated face, this is pretty useful when you’re working with an augmented reality filter application as this will allow you to rotate the filters according to the orientation of the face.

Here’s an application I built that does that. 

Head Pose Estimation:

A problem similar to facial alignment correction could be head pose estimation. In this technique instead of determining the 2d head rotation, you will learn to extract the full 3d head pose orientation. This is particularly useful when you’re working with an augmented reality application like overlaying a 3d mask on the face. You will only be able to correctly render the 3d object on the face if you know the face’s 3d orientation.

Here’s a great tutorial that teaches you head pose estimation in great detail.



Single & Multi-Object Tracking with Dlib:

Landmark detection is not all dlib has to offer, there are other useful techniques like a correlation tracking algorithm for Object Tracking that comes packed with dlib.

The tracker is based on Danelljan et al’s 2014 paper, Accurate Scale Estimation for Robust Visual Tracking

This tracker works well with changes in translation and scale and it works in real time.

Object Detection VS  Object Tracking:

If you’re just starting out in your computer vision journey and have some confusion regarding object detection vs tracking then understand that in Object Detection, you try to find an instance of the target object in the whole image. And you perform this detection in each frame of the video. There can be multiple instances of the same object and you’ll detect all of them with no differentiation between those object instances.

What I’m trying to say above is that a single image or frame of a video can contain multiple objects of the same class for e.g. multiple cats can be present on the same image and the object detector will see it as the same thing CAT with no difference between the individual cats throughout the video.

Whereas an Object Tracking algorithm will track each cat separately in each frame and will recognize each cat by a unique ID throughout the video. 

You can read this tutorial that goes over Dlib correlation tracker.

After reading the above tutorial you can go ahead and read this tutorial for using the correlation tracker to track multiple objects.



Face Swapping, Averaging & Morphing:

Here’s a series of cool facial manipulations you can do by utilizing facial landmarks and some other techniques.

Face Morphing:

What you see in the above video is called facial morphing. I’m sure you have seen such effects in other apps and movies. This effect is a lot more than a simple image pixel blending or transition.

To have a morph effect like the above, you need to do image alignment, establish pixel correspondences using facial landmark detection and more.

Here’s a nice tutorial that teaches you face morphing step by step.

By understanding and utilizing facial morphing techniques you can even do morphing between dissimilar objects like a face to a lion.

Face Swapping:

After you’ve understood face morphing then another really interesting you can do is face swapping, where you take a source face and put it over a destination face. Like putting Modi’s face over Musharaf’s above.

The techniques underlying face swapping is pretty similar to the one used in face morphing so there is not much new here.

The way this swapping is done makes the results look real and freakishly weird. See how everything from lightning to skin tone is matched.

Here’s a full tutorial on face swapping.

Tip: If you want to make the above code work in real-time then you would need to replace the seamless cloning function with some other faster cloning method, the results won’t be as good but it’ll work in real-time.

Alternative Tutorial:
Switching eds with python

Note: It should be noted this technique although gives excellent results but the state of the art in face swapping is achieved by deep learning based methods (deepfakes, FaceApp etc).

Face Averaging:

Average face of: Aiman Khan, Ayeza Khan, Mahira Khan, Mehwish Hayat, Saba Qamar & Syra Yousuf 

Similar to above methods there’s also Face averaging where you smartly average several faces together utilizing facial landmarks.

The face image you see above is the average face I created using 6 different Pakistani female celebrities.

Personally speaking out of all the applications here I find face averaging the least useful or fun. But Satya has written a really interesting Tutorial on face averaging here that is worth a read.

Face Recognition:

It should not come as a surprise that dlib also has a face recognition pipeline, not only that but the Face recognition implementation is really robust one and is a modified version of  ResNet-34, based on the paper “ Deep Residual Learning for Image Recognition paper by He et al.”, it has an accuracy of 99.38% on the Labeled Faces in the Wild (LFW) dataset. This dataset contains ~3 million images.

The model was trained using deep metric learning and for each face, it learned to output a 128-dimensional vector. This vector encodes all the important information about the face. This vector is also called a face embedding.

First, you will store some face embeddings of target faces and then you will test on different new face images. Meaning you will extract embedding from test images and compare it with the saved embeddings of the target faces.

If two vectors are similar (i.e. the euclidean distance between them is small) then it’s said to be a match. This way you can make thousands of matches pretty fast. The approach is really accurate and works in real-time.

Dlib’s Implementation of face recognition can be found here. But I would recommend that you use the face_recognition library to do face recognition.This library uses dlib internally and makes the code a lot simpler.

You can follow this nice tutorial on doing face recognition with face_recognition library.

Extra resources:

An Excellent Guide on face recognition by Adam Geitgey.


Face Clustering:

Image Credit: Dlib Blog

Consider this, you went to a museum with a number of friends, all of them asked you to take their pictures behind several monuments/statues such that each of your friend had several images of them taken by you. 

Now after the trip, all your friends ask for their pictures, now you don’t want to send each of them your whole folder. So what can you do here?

Fortunately, face clustering can help you out here, this method will allow you to make clusters of images of each unique individual.

Consider another use case: You want to quickly build a face recognition dataset for 10 office people that reside in a single room. Instead of taking manual face samples of each person, you instead record a short video of everyone together in the room, you then use a face detector to extract all the faces in each frame, and then you can use a face clustering algorithm to sort all those faces into clusters/folders. Later on, you just need to name these folders and your dataset is ready.

Clustering is a useful unsupervised problem and has many more use cases.
Face clustering is built on top of face recognition so once you’ve understood the recognition part this is easy.

You can follow this tutorial to perform face clustering.

Training a Custom Landmark Predictor:

Just like the Dlib’s Facial Landmark detector, you can train your own custom landmark detector. This detector is also called a shape predictor. Now you aren’t restricted to only facial landmarks but you can go ahead and train a landmark detector for almost anything, body joints of a person, some key points of a particular object, etc. 

As long as you can get sufficient annotated data for the key points, you can use dlib to train a landmark detector on it.

Here’s a tutorial that teaches you how to train a custom Landmark detector.

After going through the above tutorial, you may want to learn how to further optimize your trained model in terms of model size, accuracy, and speed. 

So there are multiple Hyperparameters that you can tune to get better performance, here’s a tutorial that lets you automate the tuning process, also take a look a this too.

Extra Resources:

Here’s another tutorial on training a shape predictor.

Training a Custom Object Detector:

Just like a custom landmark detector, you can train a custom Object detector with dlib. Dlib uses Histogram of Oriented Gradients (HOG) as features and a Support Vector Machine (SVM) Classifier. This combined with sliding windows and image pyramids, you’ve got yourself an Object detector. The only limitation is that you can train it to detect a single object at a time.

The Object detection approach in dlib is based on the same series of steps used in the sliding window based object detector first published by Dalal and Triggs in 2005 in the Histograms of Oriented Gradients for Human Detection.

HOG + SVM based detector are the strongest non Deep learning based approach for object detection, Here’s a hand detector I built using this approach a few years back. 

I didn’t even annotated nor collected training data for my hands but instead made a sliding window application that automatically collected my hand pictures as it moved on the screen and I placed my hands in the bounding box.

Afterward, I took this hand detector created a  Video car game controller, so now I was steering the Video game car with my hands literally. To be honest, that wasn’t a pleasant experience, my hand was sore afterwards. Making something cool is not hard but it would take a whole lot effort to make a practical VR or AR-based application. 

Here’s Dlib Code for Training an Object Detector and here’s a blog post that teaches you how to do that.

Extra Resources:
Here’s another Tutorial on training the detector.



Dlib Optimizations For Faster & Better Performance:

Here’s a bunch of techniques and tutorials that will help you get the most out of dlib’s landmark detection.

Using A Faster Landmark Detector:

Beside’s the 68 point landmark detector, dlib also has 5 point landmark detector that is 10 times smaller and faster (about 10%) than the 68 point one. If you need more speed and the 5 landmark points as visualized above is all you need then you should opt for this detector. Also from what I’ve seen its also somewhat more efficient than the 68 point detector.

Here’s a tutorial that explains how to use this faster landmark detector.

Speeding Up the Detection Pipeline:

There are a bunch of tips and techniques that you can use to get a faster detection speed, now a landmark detector itself is really fast, the rest of the pipeline takes up a lot of time. Some tricks you can do to increase speed are:

Skip Frames:

If you’re reading from a high fps camera then it won’t hurt to perform detection on every other frame, this will effectively double your speed.

Reduce image Size: 

If you’re using Hog + Sliding window based detection or a haar cascade + Sliding window based one then the face detection speed depends upon the size of the image. So one smart thing you can do is reduce the image size before face detection and then rescale the detected coordinates for the original image later.

Both of the above techniques and some others are explained in this tutorial.

Tip: The biggest bottleneck you’ll face in the landmark detection pipeline is the HOG based face detector in dlib which is pretty slow. You can replace this with haar cascades or the SSD based face detector for faster performance.

What’s Next?

computer vision

If you want to go forward from here and learn more advanced things and go into more detail, understand theory and code of different algorithms then be sure to check out our Computer Vision & Image Processing with Python Course (Urdu/Hindi). In this course, I go into a lot of detail regarding vision fundamentals and cover a plethora of algorithms and techniques to help you master Computer Vision.

If you want to start a career in Computer Vision & Artificial Intelligence then this course is for you. One of the best things about this course is that the video lectures are in Urdu/Hindi Language without any compromise on quality, so there is a personal/local touch to it.

Summary:

Let’s wrap up, in this tutorial we went over a number of algorithms and techniques in dlib.

We started with installation, moved on to face detection and landmark prediction, and learned to build a number of applications using landmark detection. We also looked at other techniques like correlation tracking and facial recognition.

We also learned that you can train your own landmark detectors and object detectors with dlib.

At the end we learned some nice optimizations that we can do with our landmark predictor. 

Extra Resources:

Final Tip: I know most of you won’t be able to go over all the tutorials linked here in a single day so I would recommend that you save and bookmark this page and tackle a single problem at a time. Only when you’ve understood a certain technique move on to the next.

It goes without saying that Dlib is a must learn tool for serious computer vision practitioners out there.

I hope you enjoyed this tutorial and found it useful. If you have any questions feel free to ask them in the comments and I’ll happily address it.




Emotion / Facial Expression Recognition with OpenCV.

Emotion / Facial Expression Recognition with OpenCV.

Download Source Code

Facial Recognition

A few weeks ago we learned how to do Super-Resolution using OpenCV’s DNN module, in today’s post we will perform Facial Expression Recognition AKA Emotion Recognition using the DNN module. Although the term emotion recognition is technically incorrect (I will explain why) for this problem but for the remainder of this post I’ll be using both of these terms, since emotion recognition is short and also good for SEO since people still search for emotion recognition while looking for facial expression recognition xD.

The post is structured in the following way:

  • First I will define Emotion Recognition & its importance.
  • Then I will discuss different approaches to tackle this problem.
  • Finally, we will Implement an Emotion Recognition pipeline using OpenCV’s DNN module. 

Emotion Recognition Or Facial Expression Recognition

Now let me start by clarifying what I meant when I said this problem is incorrectly quoted as Emotion recognition. So you see by saying that you’re doing emotion recognition you’re implying that you’re actually finding the emotion of a person whereas in a typical AI-based emotion recognition system you’ll find around and the one that we’re gonna built looks only at a single image of a person’s face to determine the emotion of that person. Now, in reality, our expression may at times exhibit what we feel but not always. People may smile for a picture or someone may have a face that inherently looks gloomy & sad but that doesn’t represent the person’s emotion. 

So If we were to build a system that actually recognizes the emotions of a person then we need to do more than look at a simple face image. We would also consider the body language of a person through a series of frames, so the network would be a combination of an LSTM & a CNN network. Also for a more robust system, we may also incorporate a voice tone recognition AI as the tone of a voice, and speech patterns tell a lot about the person’s feelings.

Watch this part of the interview of Lisa Feldman Barret who debunks these so-called Emotion recognition systems.

Since today we’ll only be looking at a single face image so it’s better to call our task Facial Expression Recognition rather than Emotion recognition.

Facial Expression Recognition Applications:

Monitoring facial expressions of several people over a period of time provides great insights if used carefully, so for this reason we can use this technology in the following applications.

1: Smart Music players that play music according to your mood:

Think about it, you come home after having a really bad day, you lie down on the bed looking really sad & gloomy and then suddenly just the right music plays to lift up your mood.

2: Student Mood Monitoring System:

Now a system that cleverly averages the expressions of multiple students over a period of time can get an estimate of how a particular topic or teacher is impacting students, does the topic being taught stresses out the students, is a particular session from a teacher a joyful experience for students. 

3: Smart Advertisement Banners:

Think about smart advertisement banners that have a camera attached to it, when a commercial airs, it checks real-time facial expressions of people consuming that ad and informing the advertiser if the ad had the desired effect or not. Similarly, companies can get feedback if customers liked their products or not without even asking them.

Also, check out this video in which the performance of a new Ice Cream flavor is tested on people using their expressions.

These are just some of the applications from top of my head, if you start thinking about it you can come up with more use cases. One thing to remember is that you have to be really careful as how you use this technology. Use it as an assistive tool and do not completely rely on it. For e.g don’t deploy on Airport and start interrogating every other black guy who triggers Angry expressions on the system for a couple of frames.

Facial Expression Recognition Approaches:

So let’s talk about the ways we could go about recognizing someone’s facial expressions. We will look at some classical approaches first then move on to deep learning.

Haar Cascades based Recognition:

Perhaps the oldest method that could work are Haar Cascades. So essentially these Haar Cascades also called viola jones Classifier is an outdated Object detection technique by Paul Viola and Michael Jones in 2001. It is a machine learning-based approach where a cascade is trained from a lot of positive and negative images. It is then used to detect objects in images.

The most popular use of these cascades is as a face detector which is still used today, although there are better methods available. 

Now instead of using face detection, we could train a cascade to detect expressions. Since you can only train a single class with a cascade so you’ll need multiple cascades. A better way to go about is to first perform face detection then look for different features inside the face ROI, like detecting a smile with this smile detection cascade. You can also train a frown detector and so on.

Truth be told, this method is so weak that I wouldn’t even try experimenting with this in this time and era but since people have used this in the past so I’m just putting it there.

Fisher, Eigen & LBPH based Recognition:

OpenCV’s built-in face_recognition module has 3 different face recognition algorithms, Eigenfaces face recognizer,  Fisherfaces face recognizer and Local binary patterns histograms (LBPH) Face Recognizer.

If you’re wondering why am I mentioning face recognition algorithms on a facial expression recognition post, So understand this,  these algorithms can extract some really interesting features like principal components and local histograms which you can then feed into an ML classifier like SVM, so in theory, you can repurpose them for emotion recognition, only this time the target classes are not the identities of people but some facial expressions. This will work best if you have a few classes, ideally 2-3. I haven’t seen many people work on emotion like this but take a look at this post in which a guy uses Fisher faces for facial expression recognition.

Again I would mention this is not a robust approach, but would work better than the previous one.

Histogram Oriented Gradients based Recognition (HOG):

Now similar to the above approach instead of using the face_recognizer module to extract features you can extract HOG features of faces, HOG based features are really effective. After extracting HOG features you can train an SVM or any other Machine learning classifier on top of it.

Custom Features with Landmark Detection:

One of the easiest and effective ways to create an emotion recognition system is to use a landmark detector like the one in dlib which allows you to detect 68 important landmarks on the face.

By using this detector you can extract facial features like eyes, eyebrows, mouth, etc. Now you can take custom measurements of these features like measuring the distance between the lip ends to detect if the person is smiling or not. Similarly, you can measure if the eyes are wide open or not, indicating surprise or shock.

Now there are two ways to go about it, either you can send these custom measurements to an ML classifier and let it learn to predict emotions based on these measurements or you use your own heuristics to determine when to call it happy, sad etc based on the measurements.

I do think the former approach is more effective than the latter. But if you’re just determining a singular emotion like if a person is smiling or not then it’s easier to use heuristics.

Deep Learning based Recognizer:

It should not come as a surprise that the State of the Art approach to detect emotions would be a deep learning-based approach. Let me explain how you would create a simple yet effective emotion recognizer system. So what you would simply do is train a Convolutional Neural Network (CNN) on different facial expression images (Ideally thousands of images for each class/emotion) and after the training showed it new samples and if done right it would perform better than all the above approaches I’ve mentioned.

Now that we have discussed different approaches, let’s move on to the coding part of the blog. 

Facial Expression Recognition in OpenCV

We will be using a deep learning classifier that will be loaded to the OpenCV DNN module. The authors trained this model using MS Cognitive Toolkit (formerly CNTK) and then converted this model to ONNX (Open neural network exchange ) format.

ONNX format allows developers to move models between different frameworks such as CNTK, Caffe2, Tensorflow, PyTorch etc.

There is also a javascript version of this model (version 1.2) with a live demo which you can check out here. In this post we will be using version 1.3 which has a better performance.

You can also look at the original source code used to train this model here, the authors explained the architectural details of their model in their research paper titled Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution.

In the paper, the authors demonstrate training a deep CNN using 4 different approaches: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. The model that we are going to use today was trained using cross-entropy loss, which according to the author’s conclusion was one of the best performing models.

The model was trained on FER+ dataset,  FER dataset was the standard dataset for emotion recognition task but in FER+ each image has been labeled by 10 crowd-sourced taggers, which provides a better quality of ground truth label for still image emotion than the original FER labels.

More information about the ONNX version of the model can be found here.

The input to our emotion recognition model is a grayscale image of 64×64 resolution. The output is the probabilities of 8 emotion classes: neutral, happiness, surprise, sadness, anger, disgust, fear, and contempt.

Here’s the architecture of the model.

Here are the steps we would need to perform:

  1.  Initialize the Dnn module.
  2. Read the image.
  3. Detect faces in the image.
  4. Pre-process all the faces.
  5. Run a forward pass on all the faces.
  6. Get the predicted emotion scores and convert them to probabilities.
  7. Finally get the emotion corresponding to the highest probability

Make sure you have the following Libraries Installed.

  • OpenCV ( possibly Version 4.0 or above)
  • Numpy
  • Matplotlib
  • bleedfacedetector

Bleedfacedetector is my face detection library which can detect faces using 4 different algorithms. You can read more about the library here.

You can install it by doing:

pip install bleedfacedetector

Before installing bleedfacedetector make sure you have OpenCV & Dlib installed.

pip install opencv-contrib-python

To install dlib you can do:

pip install dlib
OR
pip install dlib==19.8.1

Directory Hierarchy

You can go ahead and download the source code from the download code section. After downloading the zip folder, unzip it and you will have the following directory structure.

You can now run the Jupyter notebook Facial Expression Recognition.ipynb and start executing each cell as follows.

Import Libraries



Initialize DNN Module

To use Models in ONNX format, you just have to use cv2.dnn.readNetFromONNX(model) and pass the model inside this function.



Read Image

This is our image on which we are going to perform emotion recognition.

  • Line 2: We’re reading the image form disk.
  • Line 5-6 : We’re setting the figure size and showing the image with matplotlib, [:,:,::-1] means to reverse image channels so we can show OpenCV BGR images properly in matplotlib. OpenCV BGR images.



Define the available classes / labels

Now we will create a list of all 8 available emotions that we need to detect.



Detect faces in the image

The next step is to detect all the faces in the image, since our target image only contains a single face so we will extract the first face we find. 

Line 4: We’re using an SSD based face detector with 20% filter confidence to detect faces, you can easily swap this detector with any other detector inside bleedfacedetector by just changing this line.

Line 7: We’re extracting the x,y,w,h coordinates from the first face we found in the list of faces.

Line 10-13: We’re padding the face by a value of 3, now this expands the face ROI boundaries, this way the model takes a look at a larger face image when predicting. I’ve seen this improves results in a lot of cases, Although this is not required.


Padded Vs Non Padded Face

Here you can see what the final face ROI looks like when it’s padded and when it’s not padded.

Pre-Processing Image

Before you pass the image to a neural network you perform some image processing to get the image in the right format. So the first thing we need to do is convert the face from BGR to Grayscale then we’ll resize the image to be of size 64x64. This is the size that our network requires. After that we’ll reshape the face image into (1, 1, 64, 64), this is the final format which the network will accept.

Line 2: Convert the padded face into GrayScale image
Line 5: Resize the GrayScale image into 64 x 64
Line 8: Finally we are reshaping the image into the required format for our model


Input the preprocessed Image to the Network



Forward Pass

Most of the Computations will take place in this step, This is the step where the image goes through the whole neural network.



Check the output

As you can see, the model outputs scores for each emotion class.

Shape of Output: (1, 8)
[[ 0.59999390 -0.05662632 7.5.22 -3.5109.508 -0.33268.593 -3.967.581.5 9.2001578 -3.1812003 ]]



Apply Softmax function to get probabilities:

We will convert the model scores to class probabilities between 0-1 by applying a Softmax function on it.

[9.1010029e-04 4.7197891e-04 9.6490067e-01 1.491846e-05
3.5819356e-04 9.4487186e-06 3.3313509e-02 2.1165248e-05]


Get Predicted emotion

Predicted Emotion is: Surprise


Display Final Result

We already have the correct prediction from the last step but to make it more cleaner we will display the final image with the predicted emotion, we will also draw a bounding box over the detected face.

Creating Functions

Now that we have seen a step by step implementation of the network, we’ll create the 2 following python functions.

Initialization Function: This function will contain parts of the network that will be set once, like loading the model.

Main Function: This function will contain all the rest of the code from preprocessing to postprocessing, it will also have the option to either return the image or display it with matplotlib.

Furthermore, the Main Function will be able to predict the emotions of multiple people in a single image, as we will be doing all the operations in a loop.

Initialization Function

Main Function

Set returndata = True when you just want the image. I usually do this when working with videos.



Initialize the Emotion Recognition

Call the initialization function once.



Calling the main function

Now pass in any image to the main function 

Real time emotion recognition on Video:

You can also take the above main function that we created and put it inside a loop and it will start detecting facial expressions on a video, below code detects emotions using your webcam in real time. Make sure to set returndata = True

Conclusion:

Here’s the confusion matrix of the model from the author’s paper. As you can see this model is not good at predicting Disgust, Fear & Contempt classes.

You can try running the model on different images and you’ll also agree with the above matrix, that the last three classes are pretty difficult to predict, particularly because It’s also hard for us to differentiate between these many emotions based on just facial expression, a lot of micro expressions overlap between these classes and so it’s understandable why the algorithm would have a hard time differentiating between 8 different emotional expressions.

Improvement Suggestions:

Still, if you really want to detect some expressions that the model seems to fail on then the best way to go about is to train the model yourself on your own data. Ethnicity & color can make a lot of difference. Also, try removing some emotion classes so the model can focus only on those that you care about.

You can also try changing the padding value, this seems to help in some cases.

If you’re working on a live video feed then try to average the results of several frames instead of giving a new result on every new frame. 

What’s Next?

computer vision

If you want to go forward from here and learn more advanced things and go into more detail, understand theory and code of different algorithms then be sure to check out our Computer Vision & Image Processing with Python Course (Urdu/Hindi). In this course, I go into a lot of detail regarding vision fundamentals and cover a plethora of algorithms and techniques to help you master Computer Vision.

If you want to start a career in Computer Vision & Artificial Intelligence then this course is for you. One of the best things about this course is that the video lectures are in Urdu/Hindi Language without any compromise on quality, so there is a personal/local touch to it.

Summary:

In this tutorial we first learned about the Emotion Recognition problem, why it’s important, and what are the different approaches we could take to develop such systems.

Then we learned to perform emotion recognition using OpenCV’s DNN module. After that, we went over some ways on how to improve our results.

I hope you enjoyed this tutorial. If you have any questions regarding this post then please feel free to comment below and I’ll gladly answer them.


Checkout Bleed AI Premium Subscribers. This will give you access to Graded Quizzes, Premium Colab Notebooks, Priority Support, Course discounts & Practice Assignments You can Sign Up for the membership here. It’s Free.




Computer Vision Crash Course with OpenCV & Python

Computer Vision Crash Course with OpenCV & Python

Download Source Code

computer vision

If you’re looking for a single stand-alone Tutorial that will give you a good overall taste of the exciting field of Computer Vision using OpenCV then this is it. This Tutorial will serve as a Crash Course to learn the basics of OpenCV Library. 

What is OpenCV:

OpenCV (Open Source Computer Vision ) is the biggest library for Computer Vision which contains more than 2500 optimized algorithms that can be used to do face detection, action recognition, image stitching, extracting 3d models, generating point clouds, augmented reality and a lot more.

So if you’re planning to perform Computer Vision weather on a deep learning project or on a Raspberry pie or you want to make a career in it then at some point you will definitely cross paths with this library. So it’s better that you get started with it today.

About this Crash Course Course:

Since I’m a member of the Official OpenCV.org Course team and this blog Bleed AI is all about making you master Computer Vision, so I feel I’m in a very good position to teach you about this library and that too in a single post.

Of Course, we won’t be able to cover a whole lot as I said it contains over 2500 algorithms, still, after going through this course you will be able to get a grip on fundamentals and built some interesting things.

Prerequisite: To follow along with this course it’s important that you are familiar with Python language & you have python installed in your system.

Make sure to download the Source code above to try out the code.

Let’s get Started

Crash Course Outline:

Here’s the outline for this course.

Installing OpenCV-python:

The easiest way to install OpenCV is by using a package manager like e.g. with pip.

So you can just Open Up the command prompt and run the following command:

pip install opencv-contrib-python

By doing the above, you will install opencv along with its contrib package which contains some extra algorithms. If you don’t need the extra algorithms then you can also run the following command:

pip install opencv-python

Make sure to install Only one of the above packages, not both. There are also some headless versions of OpenCV which do not contain any GUI functions, you can look those here.

The other Method to install OpenCV is installing it from the source. Now installing from the source has its perks but it’s much harder and I recommend only people who have prior experience with OpenCV attempting this. You can look at my tutorial of installing from the source here.

Note: Before you can install OpenCV, you must have numpy library installed on your system. You can install numpy by doing:

pip install numpy

After Installing OpenCV you should check your installation by opening up the command prompt or anaconda prompt, launching python interpreter by typing python and then importing OpenCV by doing: import cv2

Reading & Displaying an Image:

After installing OpenCV we will see how we can read & display an image in OpenCV. You can start by running the following cell in the jupyter notebook you downloaded from the source code section.

In OpenCV you can read an image by using the cv2.imread() function. 

image = cv2.imread(filename, [flag])

Note: The Square brackets i.e. [ ] are used to denote optional parameters

Params:

  • filename: Name of the image or path to the image.
  • flag: There are numerous flags but three most important ones are these: 0,1,-1

If you pass in 1 the image is read in Color, if 0 is passed the image is read in Grayscale (Black & white) and if -1 is used to read transparent images which we will learn in the next chapter, If no parameter is passed the image will be read in Color.

Line 1-5: Importing Opencv and numpy library.
Line 8: We are reading our image in grayscale, this function will read the image in a numpy array format.
Line 11: We are printing our image.

Output:

Now just by looking at the above output, you can get a lot of information about the image we used.

Take a guess on what’s written in the image

Go ahead …I’ll wait.

If you guessed the number 2 then congrats you’re right. In fact, there is a lot more information that you can extract from the above output. For e.g, I can tell that the size of the image is (21x22). I can also say that number 2 is written in white on a black image and is written in the middle of the image.

How was I able to get all that…especially considering I’m no Sherlock?

The size of the image can easily be extracted by counting the number of rows & columns. And since we are working with a single channel grayscale image, the values in the image represent the intensity of the image, meaning 0 represents black and 255 white color, and all the numbers between 0 and 255 are different shades of gray.

You can look at the colormap below of a Grayscale image to understand it better.

Beside’s counting the rows and columns, you can just use the shape function on a numpy array to find its shape

Output: 

(21, 22)

The values returned are in rows, columns or you can call it height, width, or x,y. If we were dealing with a color image then img.shape would have returned height, width, channels.

Now it’s not ideal to print images, especially when they are 100s of pixels in width and height, so let’s see how we can show images with OpenCV.

To display an Image there are generally 3 steps. There are generally 3 steps involved in displaying an image properly.

Step 1: Use cv2.imshow() to show images.

cv2.imshow(Window_Name, img)

Params:

  • Window_Name: Any custom name you  assign to your window
  • img: Your image either be in uint8 datatype or float datatype having range 0-1.

Step 2: Also with cv2.imshow() you will have to use the cv2.waitKey() function. This function is a keyboard binding function. Its argument is the time in milliseconds. The function waits for specified milliseconds. If you press any key in that time frame, the program continues. If 0 is passed, it waits indefinitely for a keystroke. This function returns the ASCII value of the keyboard key pressed, for e.g. if you press ESC key then it will return 27 which is the ASCII value for the ESC key. For now, we won’t be using this returned value.

cv2.waitKey(delay=0)

Note: The default delay is 0 which means wait forever until the user presses a key.

Step 3: The last step is to destroy the window we created so the program can end, now this is not required to view the image but if you don’t destroy the window then you can’t proceed to end the program and it can crash, so to destroy the windows you will do:

cv2.destroyAllWindows()

This will destroy all present image windows, there is also a function to destroy a specific window.
Now let’s see the full code in action.

Line 5: I’m resizing the image by 1000%  or by 10 times in both x and y direction using the function cv2.resize() since the original size of the image is too small. I will later discuss this function.
Line 8-14:  Showing the image and waiting for a keypress. Destroying the image when there is a keypress.

Output: 

Accessing & Modifying Image Pixels and ROI:

For this example, I will be reading this image which is from one of my favorite Anime series.

You can access individual pixels of the image and modify them. Now before we get into that lets understand how an image is represented in OpenCV. We already know it’s a numpy array. But besides that you can find out other properties of the image.

Output:

The data type of the Image is: uint8
The dimensions of the Image is: 3

So the datatype of images you read with OpenCV is uint8 and if its a color image then it’s a 3-dimensional image.
Let’s talk about these dimensions. First 2 are the width and the height and the 3rd are the image channels. Now these are B (blue), G (green), & R (red) channels. In OpenCV due to historical reasons, colored images are stored in BGR instead of the common RGB format.

You can access any individual pixel value bypassing its x,y location in the image.

Output:

[143 161 168]

The tuple output above means that at location (300,300) the value for the blue channel is 143, the green channel is 161 and the red channel is 168.

Just like we can read individual image pixels, we can modify them too.

I’ve just made the pixel at location (300,300) black. Because I’ve only modified a single pixel the change is really hard to see. So now we will modify an ROI (Region of Interest) of the image so that we can see our changes.

Modifying a whole ROI is pretty similar to modifying a pixel, you just need to select a range instead of a single value.

Line 1-2: We are making a copy of the image so we don’t end up modifying the original.

Line 4-5: We are setting all pixels in x range 100-150 and in y range 80-120 equal to 0 (meaning black). Now, this should give us a black box in the image.

Line 8-10 :  Showing the image and waiting for a keypress. Destroying the image when there is a keypress.

Output:

Resizing an Image:

You can use cv2.resize() function to resize an image. You have 2 ways of resizing the image, either by passing in the new width & height values or by passing in percentages of those values using fx and fy params. We have already seen how we used the second method to resize the image to 10x its size so now I’m going to show you the first method.

You can see below both the original and the resized version of the image.

Result:

An obvious problem with the above approach you can see is that it’s not maintaining the aspect ratio of the image which is why the image looks distorted to you. A better approach would be to resize a single dimension at a time and shrink or expand the other dimension accordingly.

Resizing While Keeping the Aspect Ratio Constant:

So let’s resize the image while keeping the aspect ratio constant. This time we are going to resize the width to 300 and modify the height respectively.

Line 4-5: We are extracting the shape of the image, [:-1] indicates that I don’t want channels returned, just height and width. This ensures your code works for both color and grayscale images.

Line 7-11: We are calculating the ratio of the new width to the old width and then multiplying this ratio value with the height for getting the new value of the height. The logic behind is this, if we resized a 600 px width image to 300 px width then we would get a ratio of 0.5 and if the height was 200 px then by multiplying 0.5 with the height, we would get a new value of 100 px, by using these new values we won’t get any distortions.

Result:

Geometric Transformations:

You can apply different kinds of transformations to the image, now there are some complex transformations but for this post, I will only be discussing translation & rotation. Both of these are types of Affine transformations. So we will be using a function called warpAffine() to transform them.

transformed_ image = cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]])

Params:

  • src: input image.
  • M: 2×3 transformation matrix.
  • dsize: size of the output image.
  • flags: combination of interpolation methods.
  • borderMode: pixel extrapolation method (see BorderTypes) by default its constant border.
  • borderValue: value used in case of a constant border; by default, it is 0, which means replaced values will be black.

Now you pass in a 2×3 Matrix into the warpaffine function which does the required transformation, the first two 2 columns of the matrix control, rotation, scale and shear, and the last column encodes translation (shift) of image.

Again, we will only focus on translation and rotation in this post.

Translation:

Translation is the shifting of an object’s location, meaning the movement of image in x, y-direction. Suppose you want the image to move tx amount of pixels in the x-direction and ty amount of pixels in y-direction then you will construct below transformation matrix accordingly and pass it into the warpAffine function.

So now you just need to change the tx and ty values here for translation in x and y direction.

Line 6-10: We’re constructing the translation matrix so we move 120 px in x-direction and 40 px in the negative y-direction.

Output:

Rotation

Similarly, we can also rotate an image, by passing a matrix into the warpaffine function. Now instead of designing a matrix for rotation, I’m going to use a built-in function called cv2.getRotationMatrix2D() which will return a rotation matrix according to our specifications.

M = cv2.getRotationMatrix2D( center, angle, scale )

Params:

  • center: This is the center of the rotation in the source image.
  • Angle: The Rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the – top-left corner).
  • Scale: scaling factor.

Output:

Note: If you don’t like the black pixels that appear after translation or rotation then you can use a different border filling method, look at the available borderModes here.

Drawing on Image:

Let’s take a look at some drawing operations in OpenCV. So now we will learn how to draw a line, a circle, and a rectangle on an image. We will also learn to put text on the image. Since each drawing function will modify the image so we will be working on copies of the original image. We can easily make a copy of image by doing: img.copy()

Most of the drawing functions have below parameters in common.

  • img : Your Input Image
  • color : Color of the shape for a BGR image, pass it as a tuple i.e. (255,0,0), for Grayscale image just pass a single scalar value from 0-255.
  • thickness : Thickness of the line or circle etc. If -1 is passed for closed figures like circles, it will fill the shape. default thickness = 1
  • lineType : Type of line, popular choice is cv2.LINE_AA .

Drawing a Line:

We can draw a line in OpenCV by using the function cv2.line(). We know from basic geometry you can draw a line, you just need 2 points. So you’ll pass in coordinates of 2 points in this function.

cv2.line(img, pt1, pt2, color, [thickness])

Params:

  • pt1: First point of the line, this is a tuple of (x1,y1) point.
  • pt2: Second point of the line, this is a tuple of (x2,y2) point.

Output:

Drawing a Circle

We can draw a Circle in OpenCV by using the function cv2.Circle(). For drawing a circle we just need a center point and a radius.

cv2.circle(img, Center, radius, color, [thickness])

Params:

  • center: Center of the circle.
  • radius: Radius of the circle.

Output:

Drawing a Rectangle

We can also draw a rectangle in OpenCV by using the function cv2.rectangle(). You just have to pass two corners of a rectangle to draw it. It’s similar to the cv2.line function.

cv2.rectangle(img, pt1, pt2, color, [thickness])

Params:

  • pt1: Top left corner of the rectangle.
  • pt2: bottom right corner of the rectangle.

Output:

Putting Text:

Finally, we can also write Text by using the function cv2.putText(). Writing Text on images is an essential tool, you will be able to see real-time stats on image instead of just printing. This is really handy when you’re working with videos.

cv2.putText(img, text, origin, fontFace, fontScale, color, [thickness])

Params:

  • text: Text string to be drawn.
  • origin: Top-left corner of the text (x,y) origin position.
  • fontFace: Font type, we will use cv2.FONT_HERSHEY_SIMPLEX.
  • fontScale: Font scale, how large your text will be.

Output:

Cropping an Image:

We can also crop or slice an image, meaning we can extract any specific area of the image using its coordinates, the only condition is that it must be a rectangular area. You can segment irregular parts of images but the image is always stored as a rectangular object in the computer, this should not be a surprise since we already know that images are matrices. Now let’s say we wanted to crop naruto’s face then we would need four values namely X1 (lower bound on the x-axis),  X2 (Upper bound on y-axis), Y1 (lower bound on the y-axis) and Y2 (Upper bound on the y-axis)

After getting these values, you will pass them in like below.

face_roi = img [Start X : End X, Start Y: End Y]

Lets see the full script

Line 2: We are passing in the coordinates to crop naruto’s face, you can get these coordinates by several methods, some are of them are: by trial and error, by hovering the mouse over the image when using matplotlib notebook magic command, or by hovering over the image when you have installed OpenCV with QT support or by making a mouse click function that splits x,y coordinates. 

Result:

Note: If you’re gonna modify the cropped ROI, then it’s better to make a copy of it, otherwise modifying the cropped version would also affect the original. 

You can make a copy like this:

face_roi  = img[100:270,300:450].copy()

Image Smoothing/Blurring:

Smoothing or blurring an Image in OpenCV is really easy. If you’re thinking about why we would need to blur an image then understand that It’s very common to blur/smooth an image in vision applications, this reduces noise in the image. The noise can be present due to various factors like maybe the sensor by which you took the picture was corrupted or it malfunctioned, or environmental factors like the lightning was poor, etc. Now there are different types of blurring to deal with different types of noises and I have discussed each method in detail and even done a comparison between them inside our Computer Vision Course but for now, we will briefly look at just one method, the Gaussian Blurring method. This is the most common image smoothing technique used. It gets rid of Gaussian Noise. In simple words, this will work most of the time. 

Smoothed_image  = cv2.GaussianBlur(source-image, ksize, sigmaX)

Params:

  • source-image:  Our input image
  • ksize: Gaussian kernel size. kernel width and height can differ but they both must be positive and odd. 
  • sigmaX: Gaussian kernel standard deviation in X direction.

Again to keep this short, I won’t be getting into the math nor the parameter details for how this function works, although it’s really interesting. One thing you need to learn is that by controlling the kernel size you control the level of smoothing done. There is also a SigmaX and a SigmaY parameter that you can control.

Output: 

Thresholding an Image:

For this section, we will be using this image.

There are times when we need a binary black & white mask of the image, where our target object is in white and the background black or vice versa. The easiest way to get a mask of our image is to threshold our image. There are different types of thresholding methods, I’ve introduced most of them in our Computer Vision course but for now, we are going to discuss the most basic and most used one. So what thresholding does is that it checks each pixel in the image against a threshold value and If the pixel value is smaller than the threshold value, it is set to 0, otherwise, it is set to the maximum value, (this maximum value is usually 255 so white color).

ret, thresholded_image = cv2.threshold(source_image, thresh, max_value, type)

Params:

  • Source_image: This is your input image.
  • thresh: Threshold value. (If you use THRESH_BINARY then all values above this are set to max_value.)
  • max_value: Maximum value, normally this is set to be 255.
  • type: Thresholding type. The most common types are THRESH_BINARY  & THRESH_BINARY_INV
  • ret: Boolean variable which tells us if thresholding was successful or not.

Now before you can threshold an image you need to convert the image into grayscale, now you could have loaded the image in grayscale but since we have a color image already we can convert to grayscale using cv2.cvtColor function. This function can be used to convert one color to different color formats for this post we are only concerned with the grayscale conversion.

Output:

Now that we have a grayscale image, we can apply our threshold.

Line 2: We are applying a threshold such that all pixels having an intensity above 220 are converted to 255 and all pixels below 220 become 0.

Output:

Now Let’s see the result of the inverted threshold, which just reverses the results above. For this you just need to pass in cv2.THRESH_BINARY_INV instead of cv2.THRESH_BINARY.

Output:

Edge Detection:

Now we will take a look at edge detection, why edge detection? Well edges encode the structure of the images and it encodes most of the information in the images so for this reason edge detection is an integral part of many Vision applications.

In OpenCV there are edge detectors such as Sobel filters and laplacian filters but the most effective is the Canny Edge detector. In our Computer Vision Course I go into detail of exactly how this detector works but for now let’s take a look at its implementation in OpenCV.

edges = cv2.Canny( image, threshold1, threshold2)

Params:

  • image: This is our input image.
  • edges: output edge map; single channels 8-bit image, which has the same size as image .
  • threshold1: First threshold for the hysteresis procedure.
  • threshold2: Second threshold for the hysteresis procedure.

Line 1-2: I’m detecting edges with lower and upper hysteresis values being 30 and 150. I can’t explain how these values work without going into the theory so, for now, understand that for any image you need to tune these 2 threshold values to get the correct results.

Output:

Contour Detection:

Contour detection is one of my most favorite topics because with just contour detection you can do a lot and I’ve built a number of cool applications using contours.

Contours can be defined simply as a curve joining all the continuous points (along the boundary), having the same color or intensity. In simple terms think of contours as white blobs on black background for e.g. in the output of threshold function or the edge detection function, each shape can be considered as an individual contour. So you can segment each shape, localize them or even recognize them.

The contours are a useful tool for shape analysis, object detection, and recognition, take a look at this detection and recognition application I’ve built using contour detection.

You can use this function to detect contours.

contours, hierarchy = cv2.findContours(image, mode, method[, offset])

Params:

  • image Source: This is your input image in binary format, this is either a black & white image obtained from a thresholding or a similar function or the output of a canny edge detector.
  • mode: Contour retrieval mode, for example  cv2.RETR_EXTERNAL mode lets you extract only external contours meaning if there is a contour inside a contour then that child contour will be ignored. You can see other RetrievalModes here
  • method: Contour approximation method, for most cases cv2.CHAIN_APPROX_NONE works just fine.

After you detect contours you can draw them on the image by using this function.

cv2.drawContours( image, contours, contourIdx, color, [thickness] )

Params:

  • image: original input image.
  • contours: This is a list of contours, each contour is stored as a vector.
  • contourIdx: Parameter indicating which contour to draw. If it is -1 then all the contours are drawn.
  • color: Color of the contours.

Line 7 Using an Inverted threshold as the shapes need to be white and background black.

Line 10: Detecting contours on the thresholded image and drawing it on the image copy.

Line 13: Draw detected Contours.

Output:

You can also get the number of objects or shapes present by counting the number of contours.

Output:

Total Shapes present in image are: 6

Since there are 6 shapes in the above image we are seeing 6 detected contours.

Like I said with contours you can build some really amazing things, In our Computer Vision course, I’ve discussed contours in a lot of depth and also created several steps by step applications with it. For e.g. take a look at this Virtual Pen & Eraser post that I created on LearnOpencv.

Morphological Operations:

In this section, we will take a look at morphological operations. This is one of the most used preprocessing techniques to get rid of noise in binary (black & white) masks. They need two inputs, one is our input image and a kernel (also called a structuring element) which decides the nature of the operation. Two very common morphological operations are Erosion and Dilation. Then there are other variants like Opening, Closing, Gradient, etc.

In this post, we will only be looking at Erosion & Dilation. These are all you need in most cases.

Erosion: 

The fundamental idea of erosion is just like how it sounds, it erodes (eats away or eliminates) the boundaries of foreground objects (Always try to keep foreground in white). So what happens is that a kernel slides through the image. A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise, it is eroded (made to zero).

Erosion decreases the thickness or size of the foreground object or you can simply say the white region of image decreases. It is useful for removing small white noises.

eroded_image = cv2.erode(source_image, kernel, [iterations] )

Params:

  • source_image: Input image.
  • kernel: Structuring element or filter used for erosion if None is passed then, a 3x3 rectangular structuring element is used. The bigger kernel you create the stronger the impact of erosion on the image.
  • iterations: Number of times erosion is applied, the larger the number, greater the effect. 

We will be using this image for erosion, Notice the white spots, with erosion we will attempt to remove this noise. 

Line 5: Making a 7x7 kernel, bigger the kernel the stronger the effect.

Line 8: Applying erosion with 2 iterations, the values for kernel and iterations should be tuned according to your own images.

Output:

As you can see the white noise is gone but there is a small problem, our object (person) has become thinner. We can easily fix this by applying dilation which is the opposite of erosion.

Dilation:

It is just the opposite of erosion. It increases the white region in the image or size of the foreground object increases. So essentially dilation expands the boundary of Objects. Normally, in cases like noise removal, erosion is followed by dilation. Because, erosion removes white noises, but it also shrinks our object like we have seen in our example. So now we dilate it. Since noise is gone, they won’t come back, but our object area increases.

Dilation is also useful for removing black noise or in other words black holes in our object. So it helps in joining broken parts of an object.

dilated_image  = cv2.dilate( source_image, kernel, [iterations])

The parameters are the same as erosion.

We will attempt to fill up holes/gaps in this image.

Output:

computer vision

See the black holes/gaps are gone. You will find a combination of erosion and dilation used across many image processing applications.

Working with Videos:

We have learned how to deal with images in OpenCV, now let’s work with Videos in OpenCV. First, it should be clear to you that any operation you perform on images can be done on videos too since a video is nothing but a series of images, for e.g. Consider a 30 FPS video, which means this video shows 30 Frames (images) each second.

There are multiple ways to work with images in OpenCV, you first you have to initialize the camera Object by doing this:

cap = cv2.VideoCapture(arg)

Now there are 4 ways we can use the videoCapture Object depending what you pass in as arg:

1. Using Live camera feed: You pass in an integer number i.e. 0,1,2 etc e.g. cap = cv2.VideoCapture(0), now you will be able to use your webcam live stream.

2. Playing a saved Video on Disk: You pass in the path to the video file e.g. cap = cv2.VideoCapture(Path_To_video).

3. Live Streaming from URL using Ip camera or similar: You can stream from a URL e.g. cap = cv2.VideoCapture(protocol://host:port/script_name?script_params|auth) Note, that each video stream or IP camera feed has its own URL scheme.

4. Read a sequence of Images: You can also read sequences of images but this is not used much.

The next step After Initializing is read from video frame by frame, we do this by using cap.read().

ret, frame = cap.read()

  • ret:: A boolean variable which either returns True if the frame was successfully read otherwise False if it fails to read the next frame, this is a really important param when working with videos since after reading the last frame from the video this parameter will return false meaning it can’t read the next frame so we know we can exit the program now.
  • frame: This will be a frame/image of our video. Now everytime we run cap.read() it will give us a new frame so we will put cap.read() in a loop and show all the frames sequentially , it will look like we are playing a video but actually we are just displaying frame by frame.

After exiting the loop there is one last thing you must do, you must release the cap object you created by doing cap.release() otherwise your camera will stay on even after the program ends. You may also want to destroy any remaining windows after the loop.

Line 2: Initializing the VideoCapture object, if you’re using a usb cam then this value can be 1, 2 etc instead of 0

Line 6-17: looping and reading frame by frame from the camera, making sure it’s not corrupted and then converting to grayscale.

Line 24-25:  Check if the user presses the q under 1 millisecond after the imshow function, if yes then exit the loop. The ord() method converts a character to its ASCII value so we can compare it with the returned ASCII value of waitKey() method.

Line 28: Release the camera otherwise your cameras will be left on and the program will exit, this will cause problems the next time you run this cell.

Face Detection with Machine Learning:

In this section we will work with a machine learning-based face detection model, the model we are going to use is a Haar cascade based face detector. It’s the oldest known face detection technique that is still used today at some capacity, although there are more effective approaches, for e.g. take a look at Bleedfacedetector, a python library that I built a year back. It lets users use 4 different types of face detectors by just changing a single line of code.

This Haar Classifier has been trained on several positive (images with faces) and negative (images without faces) images. After training it has learned to recognize faces. 

Before using the face detector, you first must initialize it.

cv2.CascadeClassifier(xml_model_file)

  • xml_model_file: This is your trained haar cascade model in a .xml file

Detected_faces = cv2.CascadeClassifier.detectMultiScale( image, [ scaleFactor], [minNeighbors])

Params:

  • Image: This is your input image.
  • scaleFactor: Parameter specifying how much the image size is reduced at each pyramid scale.
  • minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.

I’m not going to go into the details of this classifier so you can ignore the definitions of scaleFactor & minNeighbors and just remember that you should tune the value of scaleFactor for controlling speed/accuracy tradeoff. Also increase the number of minNeihbors if you’re getting lots of false detections. There is also a minSize & a maxSize parameter which I’m not discussing for now.

Let’s detect all the faces in this image.

computer vision

Line 8: We are performing face detection and obtaining a list of faces.

Line 11-13: Looping through each face in the list & drawing a rectangle using its coordinates on the image.The list of faces is an array, of x,y,w,h coordinates so an Object (face) is represented as 4 numbers, x,y is the top left corner of the object (face) and w,h is the width and height of the object (face). We can easily use these coordinates to draw a rectangle on the face.

Output:

computer vision

As you can see almost all faces were detected in the above image. Normally you don’t make deductions regarding a model based on a single image but if I were to make one then I’d say this model is racist or in ML terms this model is biased towards white people.

One issue with these cascades is that they will fail when the face is rotated or is tilted sideways or occupied but no worries you can use a stronger SSD based face detection using bleedfacedetecor.

There are also other Haar Cascades besides this face detector that you can use, take a look at the list here. Not all of them are good but you should try the eye & pedestrian cascades.

Image Classification with Deep Learning:

In this section, we will learn to use an image Classifier in OpenCV. We will be using OpenCV’s built-in DNN module. Recently I made a tutorial on performing Super Resolution with DNN module. The DNN module allows you to use pre-trained neural networks from popular frameworks like TensorFlow, PyTorch, ONNX etc. and use those models directly in OpenCV. One problem is that the DNN module does not allow you to train neural networks. Still, it’s a powerful tool, let’s take a look at an image classification pipeline using OpenCV.

Note: I will create a detailed post on OpenCV DNN module in a few weeks, for now I’m keeping this short.

DNN Pipeline 

Generally there are 4 steps when doing deep learning with DNN module.

  1. Read the image and the target classes.
  2. Initialize the DNN module with an architecture and model parameters.
  3. Perform the forward pass on the image with the module
  4. Post process the results.

Now for this we are using a couple of files, like the class labels file, the neural network model and its configuration file, all these files can be downloaded in the source code download section of this post.
We will start by reading the text file containing 1000 ImageNet Classes, and we extract and store each class in a python list.

Output:

Number of Classes 1000

Output:

[‘n01440764 tench, Tinca tinca’, ‘n01443537 goldfish, Carassius auratus’, ‘n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias’, ‘n01491361 tiger shark, Galeocerdo cuvieri’, ‘n01494475 hammerhead, hammerhead shark’]

All these classes are in the text file named synset_words.txt. In this text file, each class is in on a new line with its unique id, Also each class has multiple labels for e.g look at the first 3 lines in the text file:

  • ‘n01440764 tench, Tinca tinca’
  • ‘n01443537 goldfish, Carassius auratus’
  • ‘n01484850 great white shark, white shark

So for each line we have the Class ID, then there are multiple class names, they all are valid names for that class and we’ll just use the first one. So in order to do that we’ll have to extract the second word from each line and create a new list, this will be our labels list.

Here we will extract the labels (2nd element from each line) and create a labels list.

Output:

[‘tench’, ‘goldfish’, ‘great white shark’, ‘tiger shark’, ‘hammerhead’, ‘electric ray’, ‘stingray’, ‘cock’, ‘hen’, ‘ostrich’, ‘brambling’, ‘goldfinch’, ‘house finch’, ‘junco’, ‘indigo bunting’, ‘robin’, ‘bulbul’, ‘jay’, ‘magpie’, ‘chickadee’, ‘water ouzel’, ‘kite’, ‘bald eagle’, ‘vulture’, ‘great grey owl’, ‘European fire salamander’, ‘common newt’, ‘eft’, ‘spotted salamander’, ‘axolotl’, ‘bullfrog’, ‘tree frog’, ‘tailed frog’, ‘loggerhead’, ‘leatherback turtle’, ‘mud turtle’, ‘terrapin’, ‘box turtle’, ‘banded gecko’, ‘common iguana’, ‘American chameleon’, ‘whiptail’, ‘agama’, ‘frilled lizard’, ‘alligator lizard’, ‘Gila monster’, ‘green lizard’, ‘African chameleon’, ‘Komodo dragon’, ‘African crocodile’]

Now we will initialize our neural network which is a GoogleNet model trained in a caffe framework on 1000 classes of ImageNet. We will initialize it using cv2.dnn.readNetFromCaffe(), there are different initialization methods for different frameworks. 

This is the image upon which we will run our classification.

computer vision

Pre-processing the image:

Now before you pass an image in the network you need to preprocess it, this means resizing the image to the size it was trained on, for many neural networks this is 224x224, in pre-processing step you also do other things like Normalize the image (make the range of intensity values between 0-1) and mean subtraction, etc. These are all the steps the authors did on the images that were used during model training.

Fortunately In OpenCV you have a function called cv2.dnn.blobFromImage() which most of the time takes care of all the pre-processing for you.

blob = cv2.dnn.blobFromImage(image[, scalefactor,  [ size], [ mean])

Parameters:

  • Image: Input image.
  • Scalefactor: Used to normalize the image. This value is multiplied by the image, value of 1 means no scaling is done.
  • Size: The size to which the image will be resized to, this depends upon each model.
  • Mean: These are mean R,G,B Channel values from the whole dataset and these are subtracted from the image’s R,G,B channels respectively, this gives illumination invariance to the model.

There are other important parameters too but I’m skipping them for now.


Now this blob is our pre-processed image. It’s ready to be sent to the network but first you must set it as input


This is the most important step, now the image will go through the entire network and you will get an output. Most of the computation time will take place in this step.


Now if we check the size of Output predictions, we will see that it’s 1000. So the model has returned a list of probabilities for each of the 1000 classes in ImageNet dataset. The index of the highest probability is our target class index.

Output:

Total Number of Predictions are: 1000

You can try to print the predictions to understand it better, so we will print initial 50 predictions.

Output:

[9.21621759e-05 9.98483717e-01 5.40040501e-09 4.63205048e-08
 1.46981725e-08 9.53976155e-07 1.41102263e-09 9.43037321e-07
 1.11432279e-07 3.47782636e-11 1.09528010e-07 2.49071910e-08
 4.35386397e-07 1.09613385e-09 8.02263755e-10 5.40932188e-09
 2.00020311e-09 6.00099359e-10 5.15557423e-11 4.74516648e-10
 8.58448729e-11 1.90391162e-07 3.05192899e-10 1.51088759e-08
 1.51897750e-09 6.16360580e-07 5.98882507e-05 1.80176867e-04
 1.07785991e-06 2.55477469e-04 1.88719014e-07 5.45302964e-06
 2.64027094e-05 2.53552770e-07 5.08395566e-08 6.60280875e-07
 1.89136574e-06 8.86267983e-08 5.25031763e-04 3.21334414e-06
 6.80627727e-06 2.47660046e-06 1.29753553e-05 1.73194076e-06
 1.06492757e-06 3.31227341e-07 1.72065847e-06 4.86000363e-06
 3.48621292e-08 1.47009416e-07]

Now if I wanted to get the top most prediction or the highest probability then we would just need to do np.max() 

Output:

0.9984837

See, we got a class with 99.84% probability. This is really good, it means our network is pretty sure about the name of the target class.

If we wanted to check the index of the target class we can just do np.argmax()

Output:

1

Our network says the class with the highest probability is at index 1. We just have to use this index in the labels list to get the name of the actual predicted class

Output:

goldfish

So our target class is goldfish which has a probability of 99.84%

In the final step we are just going to put the above information over the image.

Output:

computer vision

So this was an Image classification pipeline, similarly there are a lot of other interesting neural nets for different tasks, Object Detection, Image Segmentation, Image Colorization etc. I cover using 13-14 Different Neural nets with OpenCV using Video Walkthroughs and notebooks inside our Computer Vision Course and also show you how to use them with Nvidia & OpenCL GPUs.

What’s Next?

computer vision

If you want to go forward from here and learn more advanced things and go into more detail, understand theory and code of different algorithms then be sure to check out our Computer Vision & Image Processing with Python Course (Urdu/Hindi). In this course I go into a lot more detail on each of the topics that I’ve covered above.

If you want to start a career in Computer Vision & Artificial Intelligence then this course is for you. One of the best things about this course is that the video lectures are in Urdu/Hindi Language without any compromise on quality, so there is a personal/local touch to it.

Summary: 

In this post, we covered a lot of fundamentals in OpenCV. We learn to work with images as well as videos, this should serve as a good starting point but keep on learning. Remember to refer to OpenCV documentation and StackOverflow if you’re stuck on something. In a few weeks, I’ll be sharing our Computer Vision Resource Guide, which will help you in your Computer Vision journey.

If you have any questions or confusion regarding this post, feel free to comment on this post and I’ll be happy to help.


Today we are also releasing  Beta Version of Bleed AI Premium Subscribers. This will give you access to Graded Quizzes, Premium Colab Notebook, Priority Support, Course discounts & Practice Assignments You can Sign Up for the membership here. It’s Free.




(Urdu/Hindi) Learn how  to use these 5 Google AI Experiments

(Urdu/Hindi) Learn how to use these 5 Google AI Experiments

In this single tutorial alone I go over 5 different Google AI Experiments, I show you how you can use them inside the browser and how most of them essentially work. As these applications are openSource you will also get the GitHub repo for these experiments.

Some seasoned practitioners among you can even build on top of these applications and make something more interesting out of it.

QUICK, DRAW

Magic Sketch-Pad  / Sketch RNN

Extra Links

AI DUET

Imaginary Soundscape

Semi-Conductor

I hope you found this tutorial useful. For future Tutorials by us, make sure to Subscribe to Bleed AI below

I’m offering a premium 3-month Comprehensive State of the Art course in Computer Vision & Image Processing with Python (Urdu/Hindi). This course is a must take if you’re planning to start a career in Computer vision & Artificial Intelligence, the only prerequisite to this course is some programming experience in any language.

This course goes into the foundations of Image processing and Computer Vision, you learn from the ground up what the image is and how to manipulate it at the lowest level and then you gradually built up from there in the course, you learn other foundational techniques with their theories and how to use them effectively.

(Urdu/Hindi) From Training to Deploying Classification, Pose Estimation & Sound Recognition models without coding.

(Urdu/Hindi) From Training to Deploying Classification, Pose Estimation & Sound Recognition models without coding.

In less than 10 minutes I teach you how to train an effective hand finger recognition classifier, a pose detection model & a sound recognition model and also show you multiple deployment options.

If you’re not impressed yet than let me tell you this: “You won’t need any programming knowledge or need to install anything to work with this, an internet connection with a browser is sufficient.”

Unless you’re using Internet Explorer 😐

So the tool we are using is Teachable Machine version 2.  A few months ago I made a video on Teachable machine version 1 but version 1 was more of a teaching tool. This version actually is a lot more powerful and allows you to export models in various ways.

I hope you found this tutorial useful. For future Tutorials by us, make sure to Subscribe to Bleed AI below

I’m offering a premium 3-month Comprehensive State of the Art course in Computer Vision & Image Processing with Python (Urdu/Hindi). This course is a must take if you’re planning to start a career in Computer vision & Artificial Intelligence, the only prerequisite to this course is some programming experience in any language.

This course goes into the foundations of Image processing and Computer Vision, you learn from the ground up what the image is and how to manipulate it at the lowest level and then you gradually built up from there in the course, you learn other foundational techniques with their theories and how to use them effectively.