Computer vision is a pretty cool field that’s all about teaching computers to see and understand the world around them, just like we do. Imagine a computer that can look at an image or a video and be able to tell you what’s in it, or even what’s happening in it. That’s computer vision!
Computer vision is a branch of artificial intelligence that deals with how computers can be made to gain high-level understanding from digital images or videos. It involves the development of algorithms and models that can process, analyze, and understand visual data from the world.
It’s a rapidly growing field with a wide range of applications, from self-driving cars to medical imaging to facial recognition. There are many tools available all around to perform these tasks. Here we provide some of the popular and widely used tools, so hang tight and enjoy the ride!
OpenCV, short for “Open Source Computer Vision,” is a library that’s widely used in the field of computer vision. It’s packed with all sorts of features that can be used to do everything from basic image processing, like resizing and cropping, to more advanced tasks like object detection and facial recognition.
One of the great things about OpenCV is that it’s open source, which means that anyone can use it for free. This has led to a huge community of developers who have contributed to the library, making it one of the most comprehensive and widely-used computer vision libraries out there.
A project that uses this library is https://github.com/manojshridharbhat/text-detection-in-an-image-using-python. This project uses python to detect and extract text from images and uses a combination of color reduction and edge detection techniques, as well as localization, to get the job done. If you’re interested in text detection and extraction in images, check out this project!
TensorFlow Object Detection API
First up, we have the TensorFlow Object Detection library. It is a very powerful tool in the computer vision world. You can train models to recognize and categorize objects in images and videos; it’s insane. And the best part? TensorFlow’s so versatile you can use it with all kinds of programming languages, including Python. Plus, the object detection API makes it easy-peasy to train your own models using pre-trained models.
TensorFlow Object Detection Project
A fine example of a project using TensorFlow is https://github.com/AlvaroCavalcante/auto_annotate#how. This project tries to develop an auto annotation tool. It’s got this semi-supervised architecture concept behind it. Basically, you train a model with a small amount of labeled data and, boom, it generates new labels for the rest of the dataset.
It’s pretty straightforward – it uses this initial and basic object detection model to create XML files with the image annotations. And you can even set a confidence threshold for the detector; it’s like a balance between the generated predictions, and this is just one of many ways that this tool can be used.
Hugging Face’s Vision
With Hugging Face’s Vision library, you can train models to detect and classify objects in images and videos with ease. It’s super flexible – you can use it with a variety of programming languages, including Python. The object detection API makes it simple to train your own custom models using pre-trained models.
Hugging Face’s Vision Project
A project that uses of this library is https://github.com/lleviraz/nlp-questions-generation-from-relations. This project proposes an improved approach for identifying relationships within sentences. The central concept is to use questions – one or more per relationship – and feed them into a question and answer model. The relationship is then classified based on which question leads to the correct answer. Pretty neat, huh?
MATLAB Computer Vision Toolbox
The MATLAB Computer Vision Toolbox is a tool that allows you to process and analyze images using MATLAB. With this toolbox, you can do object detection, image segmentation, and even 3D vision.
One of the great things about this toolbox is that it’s super user-friendly. You don’t have to be an expert in computer vision to use it – it comes with a wide range of pre-built functions that make it easy to get started. It’s perfect for those who like a more hands-on approach to learning. The toolbox also comes with a bunch of examples and tutorials to help you get up to speed.
MATLAB Computer Vision Toolbox Project
One fun project with this toolbox is https://github.com/bartuakyurek/RealTime-TicTacToe. The software looks for a white rectangular board (like an A4 sheet of paper) and then checks inside for a game grid. If it finds one, it updates the game and makes a move. It mainly uses Hough transform to detect the board and grid. What a fun way to use this tool!
Scikit-image, also known as skimage, is a Python library that is dedicated to image processing. It’s built on top of other popular libraries, such as NumPy and SciPy, and provides a wide range of functionality for image processing tasks.
One nice thing about skimage is that it has a simple and intuitive interface, making it easy for even beginner programmers to get started with image processing. It also has a wide range of image processing functions, from basic tasks such as color conversion and image resizing, to more advanced tasks such as object detection and image restoration.
A cool project that uses this library is https://github.com/FeziweMelvin/cartoon-ify-an-image, which is about turning a regular image into a cartoon-like image much like any of the numerous filters on Snapchat or Instagram.
The method this project uses involves using k-means clustering to segment the input image and turn it into a cartoon. Whether you’re working on a simple image editing task or a complex research project, skimage provides a wide range of tools and functions to help you get the job done.
TorchVision is a library built on top of PyTorch. It provides functionalities for object detection and segmentation using popular frameworks like Faster R-CNN, Mask R-CNN, RetinaNet, and more. PyTorch being more extensible and flexible than other libraries, TorchVision also inherits those properties and allows developers to build complex models and architectures easily.
A project using this library is https://github.com/dksifoua/Neural-Image-Caption-Generator. This project aims to build a model that can take an image and generate a caption for it. It’s using the Flickr-8k dataset to train the model, which is a dataset of real-world images with corresponding captions.
OpenVINO (Open Visual Inference and Neural network Optimization) is a toolkit developed by Intel that helps developers optimize neural network models for a variety of hardware platforms. The toolkit allows developers to optimize their models for a variety of platforms including Intel CPUs, Intel GPUs, Intel VPUs and Intel FPGAs.
A key feature of OpenVINO is its ability to accelerate deep learning inference on a variety of hardware platforms. This allows developers to deploy their models on a range of devices; from powerful servers to low-power edge devices.
A project utilizing this library is https://github.com/prateeksawhney97/People-Counter-Application-Using-Intel-OpenVINO-Toolkit. This is an example of how to use Intel’s hardware and software tools to create a smart video IoT solution. It uses technology to detect individuals within a designated area and provide information such as the number of people present, the average amount of time they spend in the frame, and the overall count.
Emgu CV (.NET)
Emgu CV is known for its object detection capabilities. You can use the library to train a model to detect objects in images and videos, which opens up a whole world of possibilities. Imagine building a security system that can automatically detect intruders or creating a self-driving car that can detect other vehicles on the road. Using its motion tracking capabilities, you can track the movement of objects in a video, which can be useful for things like creating games or even just making fun animations.
Emgu CV Project
A project that uses this library is https://github.com/kryffin/EmguCV_Project, where the contributors replicate the game of Pong.
They use a script that receives an image captured by a webcam. This image is then converted to the HSV color space. They then use a Canny filter to detect the contours of the image and locate the center of each contour, and they use its Y-coordinate to move the player if the shape’s area is above a certain threshold. Pretty out-of-the-box, eh?
KerasCV is a library built on top of the popular deep learning library, Keras, that simplifies building computer vision models. It’s basically a set of pre-built, high-level functions that you can use to quickly and easily build a variety of different types of computer vision models. It also provides pre-trained models for some of the most common computer vision tasks, so you can get started right away.
A project with this library is https://github.com/stared/stable-diffusion-keras-m1-gpu. This project is focused on Stable diffusion image generation with KerasCV, which means that the computer can create novel images seemingly out of nothing! Worth checking out if you want to know more about using this library.
The Albumentations library is a computer vision library that helps enhancing the performance of deep convolutional neural networks. A great feature of albumentations is that it has a wide range of image augmentation techniques built-in, from simple things like flipping and cropping to more advanced techniques like grid distortion and elastic transformations. This makes it easy to find the right augmentation techniques for your specific project and dataset.
A project that makes use of this library is https://github.com/AlanWake41/Image-Segmentation-with-Pytorch. The project started off with the Segmentation Dataset and created their own custom dataset class for Image-mask dataset. To improve the dataset, they utilized segmentation augmentation to augment both the images and their masks using the albumentation library.
Computer vision is a fascinating field that’s constantly evolving and improving, and it has the potential to change the way we interact with technology and the world around us. It’s a great field to explore if you’re interested in artificial intelligence and image processing.