Computer-Vision-Guide
Computer Vision Guide
https://github.com/mikeroyal/Computer-Vision-Guide
Last synced: 11 days ago
JSON representation
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Game Development Learning Resources
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Game Development Tools
- Amazon Lumberyard
- Metal - level GPU programming framework used for rendering 2D and 3D graphics on Apple platforms such as iOS, iPadOS, macOS, watchOS and tvOS.
- Unigine - platform game engine designed for development teams (C++/C# programmers, 3D artists) working on interactive 3D apps.
- Panda3D - source and free for any purpose, including commercial ventures.
- Source 2 - Life: Alyx.
- Havok
- Houdini
- AppGameKit
- Open Graphics Library(OpenGL) - accelerated rendering of 2D/3D vector graphics currently developed by the [Khronos Group](https://www.khronos.org/).
- OpenCL
- Mesa 3D Graphics Library - source implementation of the OpenGL specification. A system for rendering interactive 3D graphics. Mesa ties into several other open-source projects: the [Direct Rendering Infrastructure](https://dri.freedesktop.org/), [X.org](https://x.org/), and [Wayland](https://wayland.freedesktop.org/) to provide OpenGL support on Linux, FreeBSD, and other operating systems.
- OpenGL ES
- EGL
- VDPAU
- VA API - source library and API specification, which provides access to graphics hardware acceleration capabilities for video processing.
- XvMC
- AMD Radeon ProRender - based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
- Superpowers - time collaborative projects . You can use it solo like a regular offline game maker, or setup a password and let friends join in on your project through their Web browser.
- URHO3D - platform 2D and 3D game engine implemented in C++ and released under the MIT license. Greatly inspired by OGRE and Horde3D.
- Vivox
- HGIG
- GameBlocks - Cheat & Middleware software.
- Unity - platform game development platform. Use Unity to build high-quality 3D and 2D games, deploy them across mobile, desktop, VR/AR, consoles or the Web, and connect with loyal and enthusiastic players and customers.
- Vivox
- Unreal Engine - time 3D creation tool. Continuously evolving to serve not only its original purpose as a state-of-the-art game engine, today it gives creators across industries the freedom and control to deliver cutting-edge content, interactive experiences, and immersive virtual worlds.
- DirectX 12 Ultimate
- LibGDX - platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
- cocos2d-x - platform framework for building 2d games, interactive books, demos and other graphical applications. It is based on cocos2d-iphone, but instead of using Objective-C, it uses C++. It works on iOS, Android, macOS, Windows and Linux.
- MonoGame - platform games. The spiritual successor to XNA with thousands of titles shipped across desktop, mobile, and console platforms. MonoGame is a fully managed .NET open source game framework without any black boxes.
- AppGameKit
- Blender
- Godot - packed, cross-platform game engine to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools, so that users can focus on making games without having to reinvent the wheel. Games can be exported in one click to a number of platforms, including the major desktop platforms (Linux, Mac OSX, Windows) as well as mobile (Android, iOS) and web-based (HTML5) platforms.
- High Level Shading Language(HLSL) - like programmable shaders for the Direct3D pipeline. HLSL was first created with DirectX 9 to set up the programmable 3D pipeline.
- MoltenVK
- OpenGL Shading Language(GLSL) - style language, so it covers most of the features a user would expect with such a language. Such as control structures (for-loops, if-else statements, etc) exist in GLSL, including the switch statement.
- Unigine - platform game engine designed for development teams (C++/C# programmers, 3D artists) working on interactive 3D apps.
- Superpowers - time collaborative projects . You can use it solo like a regular offline game maker, or setup a password and let friends join in on your project through their Web browser.
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Game Engines
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Learning Resources for ML
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viii. Linear Regression
- Machine Learning by Stanford University from Coursera
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Learning Machine learning and artificial intelligence from Google Cloud Training
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LiDAR Learning Resources
- Introduction to Lidar Course - NOAA
- Lidar 101:An Introduction to Lidar Technology, Data, and Applications(PDF) - NOAA
- Understanding LiDAR Technologies - GIS Lounge
- LiDAR University Free Lidar Training Courses on MODUS AI
- LiDAR | Learning Plan on ERSI
- Light Detection and Ranging Sensors Course on Coursera
- Quick Introduction to Lidar and Basic Lidar Tools(PDF)
- LIDAR - GIS Wiki
- OpenStreetMap Wiki
- OpenStreetMap Frameworks
- Back to the Top
- Light Detection and Ranging Sensors Course on Coursera
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LiDAR Tools & Frameworks
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground. Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- National Map Data Download and Visualization Services
- Mola
- MOLA
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- LASer(LAS) - dimensional point cloud data data between data users. Although developed primarily for exchange of lidar point cloud data, this format supports the exchange of any 3-dimensional x,y,z tuplet. This binary file format is an alternative to proprietary systems or a generic ASCII file interchange system used by many companies. The problem with proprietary systems is obvious in that data cannot be easily taken from one system to another. There are two major problems with the ASCII file interchange. The first problem is performance because the reading and interpretation of ASCII elevation data can be very slow and the file size can be extremely large even for small amounts of data. The second problem is that all information specific to the lidar data is lost. The LAS file format is a binary file format that maintains information specific to the lidar nature of the data while not being overly complex.
- 3D point cloud - dimensional coordinates system.. Point clouds can be produced directly by 3D scanner which records a large number of points returned from the external surfaces of objects or earth surface. These data are exchanged between LiDAR users mainly through LAS format files (.las).
- ArcGIS Desktop - effective desktop geographic information system (GIS) software. It is the essential software package for GIS professionals. ArcGIS Desktop users can create, analyze, manage, and share geographic information so decision-makers can make intelligent, informed decisions.
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
- National Geospatial Program
- USGS Lidar Base Specification(LBS) online edition
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground. Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- USGS
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
- National Geospatial Program
- USGS Lidar Base Specification(LBS) online edition
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MATLAB Learning Resources
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viii. Linear Regression
- MATLAB
- MATLAB Documentation
- Getting Started with MATLAB
- MATLAB Online Courses from Udemy
- MATLAB Online Courses from Coursera
- MATLAB Online Courses from edX
- Building a MATLAB GUI
- MATLAB Style Guidelines 2.0
- Setting Up Git Source Control with MATLAB & Simulink
- Pull, Push and Fetch Files with Git with MATLAB & Simulink
- Create New Repository with MATLAB & Simulink
- PRMLT
- MathWorks Certification Program
- PRMLT
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MATLAB Tools, Libraries, Frameworks
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viii. Linear Regression
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Simulink Online™
- MATLAB Drive™
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Computer Vision Toolbox™
- Statistics and Machine Learning Toolbox™
- Mapping Toolbox™
- UAV Toolbox
- Partial Differential Equation Toolbox™
- ROS Toolbox
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- SoC Blockset™
- Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
- ThingSpeak™ - of-concept IoT systems that require analytics.
- hctsa - series analysis using Matlab.
- YALMIP
- hctsa - series analysis using Matlab.
- MATLAB Schemer
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- SEA-MAT
- Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
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ML Frameworks, Libraries, and Tools
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viii. Linear Regression
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- PyTorch
- Amazon SageMaker
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- Anaconda
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache PredictionIO
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- XGBoost
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- nGraph - of-use to AI developers.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Cluster Manager for Apache Kafka(CMAK)
- Tensorman
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- cuML - learn.
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
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Photogrammetry Learning Resources
- Terrestrial(Close-range) photogrammetry
- Top Photogrammetry Courses Online | Udemy
- Photogrammetry With Drones: In Mapping Technology | Udemy
- Introduction to Photogrammetry Course | Coursera
- Photogrammetry Online Classes and Training | Linkedin Learning
- Pix4D training and certification for mapping professionals
- Drone mapping and photogrammetry workshops with Pix4D
- Digital Photogrammetric Systems Course | Purdue Online Learning
- Photogrammetry Training | Deep3D Photogrammetry
- ASPRS Certification Program
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Photogrammetry Tools, Libraries, and Frameworks
- Autodesk® ReCap™
- Autodesk® ReCap™ Photo - connected solution tailored for drone/UAV photo capturing workflows. Using ReCap Photo, you can create textured meshes, point clouds with geolocation, and high-resolution orthographic views with elevation maps.
- Pix4D
- PIX4Dmapper
- Adobe Scantastic - based photogrammetry pipeline), users can easily scan objects in their physical environment and turn them into 3D models which can then be imported into tools like [Adobe Dimension](https://www.adobe.com/products/dimension.html) and [Adobe Aero](https://www.adobe.com/products/aero.html).
- Adobe Aero - party apps like Cinema 4D, or asset libraries like Adobe Stock and TurboSquid. Aero optimizes a wide array of assets, including OBJ, GLB, and glTF files, for AR, so you can visualize them in real time.
- Agisoft Metashape - alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales.
- MicroStation
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- Terramodel
- COLMAP - purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections.
- Multi-View Environment (MVE) - view datasets and to support the development of algorithms based on multiple views. It features Structure from Motion, Multi-View Stereo and Surface Reconstruction. MVE is developed at the TU Darmstadt.
- PhotoModeler - effective way for accurate 2D or 3D measurement, photo-digitizing, surveying, 3D scanning, and reality capture.
- ODM
- WebODM - friendly, commercial grade software for drone image processing. Generate georeferenced maps, point clouds, elevation models and textured 3D models from aerial images. It supports multiple engines for processing, currently [ODM](https://github.com/OpenDroneMap/ODM) and [MicMac](https://github.com/dronemapper-io/NodeMICMAC/).
- NodeODM
- FIELDimageR
- Regard3D - from-motion program. It converts photos of an object, taken from different angles, into a 3D model of this object.
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- MicMac - source photogrammetry software tools for 3D reconstruction.
- AliceVision - of-the-art computer vision algorithms that can be tested, analyzed and reused.
- Meshroom - source 3D Reconstruction Software based on the AliceVision framework.
- MicroStation
Categories
Robotics Tools and Frameworks
149
Autodesk Tools and Frameworks
78
R Tools, Libraries, and Frameworks
45
Game Development Tools
37
C/C++ Tools and Frameworks
32
MATLAB Tools, Libraries, Frameworks
30
C/C++ Learning Resources
29
ML Frameworks, Libraries, and Tools
28
Python Frameworks, Libraries, and Tools
25
Photogrammetry Tools, Libraries, and Frameworks
24
LiDAR Tools & Frameworks
18
Augmented Reality (AR) & Virtual Reality (VR)
17
Game Development Learning Resources
16
Robotics Learning Resources
15
MATLAB Learning Resources
14
Learning Resources for ML
14
CUDA Tools Libraries, and Frameworks
14
Autodesk Learning Resources
14
Python Learning Resources
12
LiDAR Learning Resources
12
R Learning Resources
11
Photogrammetry Learning Resources
10
Game Engines
7
CUDA Learning Resources
6
Robotic Arms & Dev Kits
3
License
1
Sub Categories
viii. Linear Regression
345
vi. Determinants
2
v. Fundamental vector spaces
2
iv. Row space, columns space, and rank of a matrix
2
i. Basis
2
i. Using row operations
2
iv. Linear transformations
2
iii. Matrix-vector product
2
i. Vector operations
2
i. Solving systems of equations
2
ii. Systems of equations as matrix equations
2
ii. Matrix operations
2
iii. Dimension and Basis for Vector Spaces
1
iii. Transpose of a Matrix
1
vii. Eigenvalues and eigenvectors
1
ii. Matrix representations of linear transformations
1
ii. Using elementary matrices
1
Keywords
python
14
deep-learning
9
cpp
8
cuda
8
machine-learning
7
ros
7
gpu
6
nvidia
4
cross-platform
4
azure
4
computer-vision
4
data-science
4
julia
4
game-development
3
game-engine
3
gamedev
3
cpp11
3
windows
3
neural-networks
3
cpp14
3
c
3
tensorflow
3
pytorch
3
deep-reinforcement-learning
3
matlab
3
artificial-intelligence
3
ai
3
c-plus-plus
3
neural-network
3
cxx14
3
alicevision
2
visualization
2
azure-sdk
2
camera-tracking
2
research
2
iot
2
statistics
2
tensor
2
cxx
2
meshroom
2
docker
2
autonomous-vehicles
2
ros2
2
cpp20
2
cxx11
2
simulator
2
structure-from-motion
2
self-driving-car
2
photogrammetry
2
nvidia-hpc-sdk
2