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5 Awesome Computer Vision Project Ideas with Python, Machine Learning and Deep Learning!

Discussion on 5 cool computer vision projects to learn new skills and enhance your resume

Project Ideas

Photo by Simon Migaj on Unsplash
Photo by Simon Migaj on Unsplash

Computer Vision is a field of Artificial Intelligence that deals with images and pictures to solve real-life visual problems. The ability of the computer to recognize, understand and identify digital images or videos to automate tasks is the main goal which computer vision tasks seek to accomplish and perform successfully.

Humans have no problem to identify the objects and the surroundings around them. However, it is not so easy for computers to identify and distinguish the various patterns, visuals, images, and objects in the environment. The reason for this difficulty arises because the interpretability of the human brain and eyes differ from computers which interpret most of the outputs in either 0’s or 1’s i.e. in binary. The images are often times converted in arrays of three dimensions consisting of the colors red, blue, green. They have a range of values that can be computed from 0 to 255 and using this conventional means of arrays, we can write code exclusive to identify and recognize images. With the rising technology and advancements in machine learning, deep learning, and computer vision, modern computer vision projects can solve complicated tasks like image segmentation and classification, object detection, face recognition, and so much more.

We will be looking at two projects for beginners to get started with computer vision, then we will look at two more intermediate level projects to gain a more solid foundation of computer vision with Machine Learning and deep learning. Finally, we will look at one advanced level computer vision project using deep learning. For each project, we will briefly discuss the theory related to the particular project. After this, we will understand how these projects can be handled and optimized. I will try to provide at least one link to the resources that will help you to get started with each of these projects.


Photo by Daniil Kuželev on Unsplash
Photo by Daniil Kuželev on Unsplash

Beginner level computer vision projects:

1. Color Detection –

This is a basic project for beginners to get started with the computer vision module open-cv. Here, you can learn how exactly you can distinguish the various colors apart from each other. This starter project also helps in the understanding the concepts of masking and is perfect for a beginner level computer vision project. The task is to distinguish between the various colors like red, green, blue, black, white, etc. from the specific frame and display only the visible colors. This project allows the user to gain a better understanding of how exactly masking works for more complicated image classification and image segmentation tasks. This beginner project can be used to learn more detailed concepts of how exactly these images of numpy arrays are exactly stacked in the form of RGB images. You can also learn about the conversion of images from the color form into a form of grayscale images.

More complex projects can be achieved with the same task by using deep learning models such as UNET or CANET to solve more complex image segmentation and classification tasks along with the maskings of each image. There is a wide range of complex projects available with deep learning approaches if you want to learn more.

There are lots of free resources available online to get started with the color detection project of your choice. After researching and looking at the various resources and choices I found the below reference to be quite optimal because it has a YouTube video as well a detailed explanation of the code. Both the starter code and the video demonstration is provided by them.

Detecting colors (Hsv Color Space) – Opencv with Python – Pysource

2. Optical Character Recognition (OCR) –

This is another basic project best suited for beginners. Optical character recognition is the conversion of 2-Dimensional text data into a form of machine-encoded text by the use of an electronic or mechanical device. You use computer vision to read the image or text files. After reading the images, use the pytesseract module of Python to read the text data in the image or the PDF and then convert them into a string of data that can be displayed in python.

The installation of the pytesseract module might be slightly complicated so refer to a good guide to get started with the installation procedure. You can also look at the resource link provided below to make the overall installation process easier. It also guides you through an intuitive understanding of optical character recognition. Once you have an in-depth understanding of how OCR works and the tools required, you can proceed to compute more complex problems. This can be using sequence to sequence attention models to convert the data read by OCR from one language into another.

Here are two links that will help you to get started with Google text-to-speech and optical character recognition. View the references provided in the optical character recognition link to understand more concepts and learn about OCR in a more detailed approach.

How to get started with Google Text-to-Speech using Python

Getting Started with Optical Character Recognition using Python


Intermediate level computer vision projects:

1. Face Recognition using Deep Learning –

Face recognition is the procedural recognition of a human face along with the authorized name of the user. Face detection is a simpler task and can be considered as a beginner level project. Face detection is one of the steps that is required for face recognition. Face detection is a method of distinguishing the face of a human from the other parts of the body and the background. The haar cascade classifier can be used for the purpose of face detection and accurately detect multiple faces in the frame. The haar cascade classifier for frontal face is usually an XML file that can be used with the open-cv module for reading the faces and then detecting the faces. A machine learning model such as the histogram of oriented gradients (H.O.G) which can be used with labeled data along with support vector machines (SVM’s) to perform this task as well.

The best approach for face recognition is to make use of the DNN’s (deep neural networks). After the detection of faces, we can use the approach of deep learning to solve face recognition tasks. There is a huge variety of transfer learning models like VGG-16 architecture, RESNET-50 architecture, face net architecture, etc. which can simplify the procedure to construct a deep learning model and allow users to build high-quality face recognition systems. You can also build a custom deep learning model for solving the face recognition task. The modern models built for face recognition are highly accurate and provide an accuracy of almost over 99% for labeled datasets. The applications for the face recognition models can be used in security systems, surveillance, attendance systems, and a lot more.

Below is an example of a face recognition model built by me using the methods of VGG-16 transfer learning for face recognition after the face detection is performed by the haar cascade classifier. Check it out to learn a more detailed explanation of how exactly you can build your very own face recognition model.

Smart Face Lock System

2. Object Detection/Object Tracking –

This computer vision project could easily be considered a fairly advanced one but there are so many free tools and resources that are available that you could complete this task without any complications. The object detection task is the method of drawing a bounding box around the recognized object and identifying the recognized object according to the determined labels and predict these with specific accuracies. the object tracking is slightly different in comparison to the object detection, as you not only detect the particular object but also follow the object with the bounding box around it. Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them. An example of this can be either following a particular vehicle on a road path or tracking a ball in any sports game like golf, cricket, baseball, etc. The various algorithms to perform these tasks are R-CNN’s (Region-based convolutional neural networks), SSD (single shot detector), and YOLO (you only look once) among many others.

I am going to mention 2 of the best resources by two talented programmers. One method is more so for embedded systems like the raspberry pi and the other one is for PC related real-time webcam object detection. These two below resources are some of the best ways to get started with object detection/object tracking and they have YouTube videos explaining them in detail as well. Please do check out these resources to gain a better understanding of object detection.

EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi

theAIGuysCode/Object-Detection-API


Advanced level computer vision projects:

1. Human Emotion and Gesture Recognition –

This project uses computer vision and deep learning to detect the various faces and classify the emotions of that particular face. Not only do the models classify the emotions but also detects and classifies the different hand gestures of the recognized fingers accordingly. After distinguishing the human emotions or gestures a vocal response is provided by the trained model with the accurate prediction of the human emotion or gesture respectively. The best part about this project is the wide range of data set choices you have available to you.

The below link is a reference to one of the deep learning projects done by me by using methodologies of computer vision, data augmentation, and libraries such as TensorFlow and Keras to build deep learning models. I would highly recommend viewers to check the below 2-part series for a complete breakdown, analysis, and understanding of how to compute the following advanced computer vision task. Also, make sure to refer to the Google text-to-speech link provided in the previous section to understand how the vocal text conversion of text to speech works.

Human Emotion and Gesture Detector Using Deep Learning: Part-1


Photo by Anastasia Petrova on Unsplash
Photo by Anastasia Petrova on Unsplash

Conclusion:

These are the 5 awesome computer vision project ideas across various difficulty levels. The brief theory for each of the concepts along with a link to some helpful resources was provided accordingly. I hope this article helps the viewers to dive into the amazing field of computer vision and explore the various projects offered by the stream. If you are interested in learning everything about machine learning then feel free to check out my tutorial series that explains every concept about machine learning from scratch by referring to the link which is provided below. The parts of the series will be constantly updated on a weekly basis or sometimes even faster.

All About Ml – Towards Data Science

Thank you all for sticking on till the end and I hope you enjoyed the read. Have a wonderful day!


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