Computer vision is a fast-growing field of science that deals with the extraction of information from digital images and videos to gain a high-level understanding of the environment.
The technology is predominantly applied in complex problems in robotics, augmented reality, and self-driving cars, such as object detection, space measurements for navigation, face recognition, action and activity recognition, powering vision, and human pose estimation.
The key objective is to understand how human vision works in a three-dimensional world and transfer it to build algorithms that can determine the structure and type of the object before a digital camera, control a computer system, or provide people with information about the object.
Here is a non-exhaustive list of applications of computer vision.
- Automatic face recognition, and interpretation of the expression
- Visual guidance of autonomous vehicles
- Automated medical image analysis, interpretation, and diagnosis
- 3D urban modeling of a city using drone pictures.
- Robotic manufacturing: manipulation, grading, and assembly of parts
- Optical recognition of characters and numbers such as zip codes, or license plates.
- OCR: recognition of printed or handwritten characters and words
- Agricultural robots: visual grading and harvesting of produce
- Smart offices: tracking of persons and objects; understanding gestures
- Biometric-based visual identification of persons
- Visually endowed robotic helpers
- Vision-based interaction, allowing players to interact directly with a game through moves.
- Security monitoring and alerting; detection of an anomaly
- Intelligent interpretive prostheses for the blind
- Virtual reality allows us to know the position of a user and the positions of all the objects around.
- Tracking of moving objects; collision avoidance; stereoscopic depth
- Object-based (model-based) compression of video streams
- General scene recognition allows us to identify the location where a photo was taken, by comparing it with billions of photos on Google to find the best matches.
This post will look at the top 10 computer vision frameworks you need to know in 2021.
1. Google Cloud’s Vision API
Google Cloud’s Vision API is an easy-to-use image recognition technology that lets developers understand the content of an image by applying powerful machine learning models. It offers powerful pre-trained machine learning models through REST and RPC APIs. It also lets developers easily integrate key vision detection features within an application, including the face, and landmark detection, image labeling, optical character recognition (OCR), and explicit content tagging. It also allows us to assign labels to images and quickly classify them into millions of predefined categories. It can help us detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.
YOLO (You Only Look Once) is a state-of-the-art, real-time object detection system among the most widely used deep learning-based object detection methods. It considers object detection as a regression problem, directly predicting the class probabilities and bounding box oﬀsets from full images with a single feed-forward convolution neural network. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. YOLOv3 eliminates region proposal generation and feature resampling and encapsulates all stages in a single network to form a true end-to-end detection system.
Tensorflow is a free, open-source framework for creating algorithms to develop a user-friendly Graphical Framework called TensorFlow Graphical Framework (TF-GraF) for object detection API, which is widely applied to solve complex tasks efficiently in agriculture, engineering, and medicine. The TF-GraF provides independent virtual environments for amateurs and beginners to design, train, and deploy machine intelligence models without coding or command-line interface (CLI) in the client-side.
The TF-GraF supports the flexible model selection of SSD, Faster-RCNN, RFCN, and Mask-RCNN, including convolutional neural networks (inceptions and ResNets). TF-GraF takes care of setting and configuration, allowing anyone to use deep learning technology for their project without installing complex software and the environment.
libfacedetection is an open-source library for face detection in images. It provides a pre-trained convolutional neural network for CNN-based face detection in images, enabling users to detect faces that have a size greater than 10×10 pixels. In C source files, the CNN model has been converted to static variables. The source code is not dependant on any other libraries. You need a C++ compiler that can compile the source code under Windows, Linux, ARM, and any platform. SIMD instructions are used to speed up detection. In case you use Intel CPU or NEON for ARM, You can enable AVX2.
5. Raster Vision
Raster Vision is an open-source Python framework to build computer vision models on satellite, aerial, and other large sets of images (including oblique drone imagery). It allows users without any expertise in deep learning or machine learning workflow to quickly and repeatedly configure experiments, including analyzing training datasets, creating training chips, training models, creating predictions, evaluating models, bundling the model files and deployment.
Raster Vision has built-in support for chip classification, object detection, and semantic segmentation with backbends using PyTorch and Tensorflow. Users can execute experiments on CPUs and GPUs with built-in support for running in the cloud using AWS Batch. The framework is also extensible to new data sources, tasks (e.g., object detection), backend (e.g., TF Object Detection API), and cloud providers.
SOD is an embedded, modern cross-platform computer vision and machine learning software library. It exposes a set of APIs for deep-learning, advanced media analysis, and processing, including real-time, multi-class object detection, and model training on embedded systems with limited computational resource and IoT devices.
SOD was designed to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well as commercial products. Designed for computational efficiency and with a strong focus on real-time applications, SOD includes a comprehensive set of both classic and state-of-the-art deep-neural networks with their pre-trained models.
Face_recognition is the world’s simplest facial recognition API for Python and the command line. Built with deep learning using dlib60‘s state-of-the-art face recognition, it can recognize and manipulate faces from Python or from the command line. The model has an accuracy of 99.38% on the Labeled Faces in the Wild61 benchmark. It provides a simple face_recognition command-line tool that lets you do face recognition on a folder of images from the command line!
DeepFaceLab is an open-source deep fake system that utilizes machine learning for photo-realistic face swapping in videos. It provides an imperative and easy-to-use pipeline, including data loader and processing, model training, and post-processing, for people to create deep fake videos with no comprehensive understanding of deep learning framework or without writing complicated boilerplate code. This state-of-the-art framework provides a complete command-line tool with every aspect of the pipeline and functions like a point-and-shoot camera. Notably, more than 95% of deep fake videos are created with DeepFaceLab.
OpenCV is an open-source computer vision and machine learning software library, built to provide a common infrastructure for computer vision applications and accelerate the use of machine perception in commercial products. A BSD-licensed product, OpenCV, makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, including a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms.
These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects and produce 3D point clouds from stereo cameras. It can stitch images together to produce a high-resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality.