Movatterモバイル変換

• Algorithms
• API Wrappers
• Bayesian Nets
• Compression
• Containers
• Graph Tools
• Image Processing
• Linear Algebra
• Machine Learning
• Metaprogramming
• Miscellaneous
• Networking
• Optimization
• Parsing

• Dlib Blog
• Examples: C++
  • 3D Point Cloud
  • Assignment Learning
  • Base64 Encoder
  • Bayesian Network From Disk
  • Bayesian Network GUI
  • Bayesian Network
  • Bridge
  • BSP
  • C-Support Vector Machine
  • Cmd Line Parser
  • Compress Stream
  • Config File Reader
  • Custom Trainers
  • Deep Face Recognition
  • Deep Learning DCGAN
  • Deep Learning Dog Hipsterizer
  • Deep Learning Face Detection
  • Deep Learning Imagenet Classifier
  • Deep Learning Imagenet Trainer
  • Deep Learning Inception
  • Deep Learning Instance Segmentation Trainer
  • Deep Learning Instance Segmentation
  • Deep Learning Introduction Part 1
  • Deep Learning Introduction Part 2
  • Deep Learning Introduction Part 3
  • Deep learning Language Modeling
  • Deep Learning Max-Margin Object Detection
  • Deep Learning Multi-Class Vehicle Detection
  • Deep Learning Semantic Segmentation Trainer
  • Deep Learning Semantic Segmentation
  • Deep Learning Vehicle Detection Trainer
  • Deep Learning Vehicle Detection
  • Deep Metric Learning Introduction
  • Deep Metric Learning on Images
  • Directory Navigation
  • Empirical Kernel Map
  • Face Detection
  • Face Landmark Detection
  • FFMPEG: capture audio to file
  • FFMPEG: capture audio
  • FFMPEG: decode video another example
  • FFMPEG: decode video
  • FFMPEG: demux images and audio
  • FFMPEG: encode video with muxer
  • FFMPEG: encode video
  • FFMPEG: face pose from webcam
  • FFMPEG: query codec capability
  • FFMPEG: read video with demuxer
  • FFMPEG: RTSP
  • FFMPEG: screen grabbing
  • FHOG Feature Extraction
  • FHOG Object Detection
  • Graph Labeling
  • GUI
  • Hough Transform
  • HTTP Server
  • Image
  • IO Socket Streams
  • IO Streams Server
  • Kernel Centroid
  • Kernel K-Means Clustering
  • Kernel Ridge Regression
  • Kernel RLS Filtering
  • Kernel RLS Regression
  • KRR Classification
  • Learning to Track
  • Linear Assignment Problems
  • Linear Manifold Regularizer
  • Linear Model Predictive Control
  • Logger Advanced
  • Logger Custom Output
  • Logger
  • Matrix Expressions
  • Matrix
  • Member Function Pointer
  • Model Selection
  • Multiclass Classification
  • Multithreaded Object
  • Neural Network
  • Non-Linear Least Squares
  • Nu-Support Vector Machine
  • Numerical Integration
  • Object Detector Advanced
  • Object Detector
  • One Class Classifiers
  • Online SVM
  • Optimization
  • Parallel For Loops
  • Pipe 2
  • Pipe
  • Quantum Computing
  • Queue
  • Random Cropper
  • Rank Features
  • Relevance Vector Classification
  • Relevance Vector Regression
  • Running Stats
  • Self-supervised deep feature learning
  • Sequence Labeling
  • Sequence Segmentation
  • Sockets
  • Sockstreambuf
  • Sparse Vectors
  • SQLite
  • Std C++ Allocator
  • Structural Support Vector Machines
  • Support Vector Regression
  • SURF
  • SVM-Rank
  • Thread Function
  • Thread Pool
  • Threaded Object
  • Threads
  • Timer
  • Train Object Detector
  • Train Shape Predictor
  • Training a YOLO object detector
  • Using Custom Kernels
  • Video Object Tracking
  • Webcam Face Pose Estimation
  • XML Parser
• Examples: Python
  • Binary Classification
  • CNN Face Detector
  • Face Alignment
  • Face Clustering
  • Face Detector
  • Face Jittering/Augmentation
  • Face Landmark Detection
  • Face Recognition
  • Find Candidate Object Locations
  • Global Optimization
  • Linear Assignment Problems
  • Sequence Segmenter
  • Structural Support Vector Machines
  • SVM-Rank
  • Train Object Detector
  • Train Shape Predictor
  • Video Object Tracking
• FAQ
• Home
• How to compile
• How to contribute
• Index
• Introduction
• License
• Python API
• Suggested Books
• Who uses dlib?

• Change Log
• Release Notes

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib'sopen source licensing allows you to use it in any application, free of charge.

To follow or participate in the development of dlib subscribe todlib on github. Also be sure to read thehow to contribute page if you intend to submit code to the project.

To quickly get started using dlib,follow these instructions to build dlib.

Major Features

• Documentation
  • Unlike a lot of open source projects, this one provides complete and precise documentation for every class and function. There are also debugging modes that check the documented preconditions for functions. When this is enabled it will catch the vast majority of bugs caused by calling functions incorrectly or using objects in an incorrect manner.
  • Lots of example programs are provided
  • I consider the documentation to be the most important part of the library. So if you find anything that isn't documented, isn't clear, or has out of date documentation, tell me and I will fix it.
• High Quality Portable Code
  • Good unit test coverage. The ratio of unit test lines of code to library lines of code is about 1 to 4.
  • The library is tested regularly on MS Windows, Linux, and Mac OS X systems. However, it should work on any POSIX system and has been used on Solaris, HPUX, and the BSDs.
  • No other packages are required to use the library. Only APIs that are provided by an out of the box OS are needed.
  • There is no installation or configure step needed before you can use the library. See theHow to compile page for details.
  • All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. The rest of the library is either layered on top of the OS abstraction layers or is pure ISO standard C++.
• Machine Learning Algorithms
  • Deep Learning
  • Conventional SMO based Support Vector Machines forclassification andregression
  • Reduced-rank methods for large-scaleclassification andregression
  • Relevance vector machines forclassification andregression
  • General purposemulticlass classification tools
  • AMulticlass SVM
  • A tool for solving the optimization problem associated withstructural support vector machines.
  • Structural SVM tools forsequence labeling
  • Structural SVM tools for solvingassignment problems
  • Structural SVM tools forobject detection in images as well as more powerful (but slower)deep learning tools for object detection.
  • Structural SVM tools forlabeling nodes in graphs
  • A large-scaleSVM-Rank implementation
  • An onlinekernel RLS regression algorithm
  • An onlineSVM classification algorithm
  • Semidefinite Metric Learning
  • An online kernelizedcentroid estimator/novelty detector and offline support vectorone-class classification
  • Clustering algorithms:linear orkernel k-means,Chinese Whispers, andNewman clustering.
  • Radial Basis Function Networks
  • Multi layer perceptrons
• Numerical Algorithms
  • A fastmatrix object implemented using the expression templates technique and capable of using BLAS and LAPACK libraries when available.
  • Numerous linear algebra and mathematical operations are defined for the matrix object such as thesingular value decomposition,transpose,trig functions, etc.
  • General purpose unconstrained non-linear optimization algorithms using theconjugate gradient,BFGS, andL-BFGS techniques
  • Levenberg-Marquardt for solving non-linear least squares problems
  • Box-constrained derivative-free optimization via theBOBYQA algorithm
  • An implementation of theOptimized Cutting Plane Algorithm
  • Severalquadraticprogramsolvers
  • Combinatorial optimization tools for solvingoptimal assignment andmin cut/max flow problems as well as theCKY algorithm for finding the most probable parse tree
  • Abig integer object
  • Arandom number object
• Graphical Model Inference Algorithms
  • Join tree algorithm for exact inference in a Bayesian network.
  • Gibbs sampler markov chain monte carlo algorithm for approximate inference in a Bayesian network.
  • Routines for performing MAP inference inchain-structured,Potts, orgeneral factor graphs.
• Image Processing
  • Routines forreading andwriting common image formats.
  • Automatic color space conversion between various pixel types
  • Common image operations such as edge finding and morphological operations
  • Implementations of theSURF,HOG, andFHOG feature extraction algorithms.
  • Tools fordetecting objects in images includingfrontal face detection andobject pose estimation.
  • High qualityface recognition
• Threading
  • The library provides a portable and simplethreading API
  • A message passingpipe for inter-thread andinter-process communication
  • Atimer object capable of generating events that are regularly spaced in time
  • Threaded objects
  • Threaded functions
  • Parallel for loops
  • Athread_pool with support for futures
• Networking
  • The library provides a portable and simpleTCP sockets API
  • An object to help you make TCP basedservers
  • iostream andstreambuf objects that enables TCP sockets to interoperate with the C++ iostreams library
  • A simpleHTTP server object you can use to embed a web server into your applications
  • A message passingpipe for inter-thread andinter-process communication
  • A tool used to implement algorithms using theBulk Synchronous Parallel (BSP) computing model
• Graphical User Interfaces
  • The library provides a portable and simple coreGUI API
  • Implemented on top of the core GUI API are numerouswidgets
  • Unlike many other GUI toolkits, the entire dlib GUI toolkit is threadsafe
• Data Compression and Integrity Algorithms
  • ACRC 32 object
  • MD5 functions
  • Various abstracted objects representing parts ofdata compression algorithms. Many forms of the PPM algorithm are included.
• Testing
  • A thread safelogger object styled after the popular Java logger log4j
  • A modularunit testing framework
  • Variousassert macros useful for testing preconditions
• General Utilities
  • A type-safeobject to convert between big and little endian byte orderings
  • Acommand line parser with the ability to parse and validate command lines with various types of arguments and options
  • AnXML parser
  • An object that can performbase64 conversions
  • Manycontainer classes
  • Serialization support
  • Manymemory manager objects that implement different memory pooling strategies
  • A tool that lets you easilycall C++ from MATLAB