opencv2函数使用手册.pdf

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Introduction

API Concepts

core. The Core Functionality

Basic Structures

Command Line Parser

Basic C Structures and Operations

Dynamic Structures

Operations on Arrays

Drawing Functions

XML/YAML Persistence

XML/YAML Persistence (C API)

Clustering

Utility and System Functions and Macros

OpenGL interoperability

Intel® IPP Asynchronous C/C++ Converters

imgproc. Image Processing

Image Filtering

Geometric Image Transformations

Miscellaneous Image Transformations

Histograms

Structural Analysis and Shape Descriptors

Motion Analysis and Object Tracking

Feature Detection

Object Detection

highgui. High-level GUI and Media I/O

User Interface

Reading and Writing Images and Video

Qt New Functions

video. Video Analysis

Motion Analysis and Object Tracking

calib3d. Camera Calibration and 3D Reconstruction

Camera Calibration and 3D Reconstruction

features2d. 2D Features Framework

Feature Detection and Description

Common Interfaces of Feature Detectors

Common Interfaces of Descriptor Extractors

Common Interfaces of Descriptor Matchers

Common Interfaces of Generic Descriptor Matchers

Drawing Function of Keypoints and Matches

Object Categorization

objdetect. Object Detection

Cascade Classification

Latent SVM

Scene Text Detection

ml. Machine Learning

Statistical Models

Normal Bayes Classifier

K-Nearest Neighbors

Support Vector Machines

Decision Trees

Boosting

Gradient Boosted Trees

Random Trees

Extremely randomized trees

Expectation Maximization

Neural Networks

MLData

flann. Clustering and Search in Multi-Dimensional Spaces

Fast Approximate Nearest Neighbor Search

Clustering

photo. Computational Photography

Inpainting

Denoising

HDR imaging

References

Decolorization

Seamless Cloning

Non-Photorealistic Rendering

stitching. Images stitching

Stitching Pipeline

References

High Level Functionality

Camera

Features Finding and Images Matching

Rotation Estimation

Autocalibration

Images Warping

Seam Estimation

Exposure Compensation

Image Blenders

nonfree. Non-free functionality

Feature Detection and Description

contrib. Contributed/Experimental Stuff

Stereo Correspondence

FaceRecognizer - Face Recognition with OpenCV

OpenFABMAP

legacy. Deprecated stuff

Motion Analysis

Expectation Maximization

Histograms

Planar Subdivisions (C API)

Feature Detection and Description

Common Interfaces of Descriptor Extractors

Common Interfaces of Generic Descriptor Matchers

cuda. CUDA-accelerated Computer Vision

CUDA Module Introduction

Initalization and Information

Data Structures

Object Detection

Camera Calibration and 3D Reconstruction

cudaarithm. CUDA-accelerated Operations on Matrices

Core Operations on Matrices

Per-element Operations

Matrix Reductions

Arithm Operations on Matrices

cudabgsegm. CUDA-accelerated Background Segmentation

Background Segmentation

cudacodec. CUDA-accelerated Video Encoding/Decoding

Video Decoding

Video Encoding

cudafeatures2d. CUDA-accelerated Feature Detection and Description

Feature Detection and Description

cudafilters. CUDA-accelerated Image Filtering

Image Filtering

cudaimgproc. CUDA-accelerated Image Processing

Color space processing

Histogram Calculation

Hough Transform

Feature Detection

Image Processing

cudaoptflow. CUDA-accelerated Optical Flow

Optical Flow

cudastereo. CUDA-accelerated Stereo Correspondence

Stereo Correspondence

cudawarping. CUDA-accelerated Image Warping

Image Warping

optim. Generic numerical optimization

Linear Programming

Downhill Simplex Method

Primal-Dual Algorithm

Nonlinear Conjugate Gradient

shape. Shape Distance and Matching

Shape Distance and Common Interfaces

Shape Transformers and Interfaces

Cost Matrix for Histograms Common Interface

EMD-L1

softcascade. Soft Cascade object detection and training.

Soft Cascade Classifier

Soft Cascade Training

CUDA version of Soft Cascade Classifier

superres. Super Resolution

Super Resolution

videostab. Video Stabilization

Introduction

Global Motion Estimation

Fast Marching Method

viz. 3D Visualizer

Viz

Widget

Bibliography

The OpenCV Reference Manual Release 3.0.0-dev June 25, 2014

CONTENTS 1 Introduction 1.1 API Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 . . . . . . . . . . 7 2 core. The Core Functionality 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Structures . . 2.1 64 Command Line Parser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 65 Basic C Structures and Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 . . 2.4 Dynamic Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 . . 2.5 Operations on Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2.6 Drawing Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 2.7 XML/YAML Persistence . 2.8 XML/YAML Persistence (C API) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 2.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 2.10 Utility and System Functions and Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 2.11 OpenGL interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 . 241 2.12 Intel® IPP Asynchronous C/C++ Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Image Filtering . 3 imgproc. Image Processing . . 243 3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 3.2 Geometric Image Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 3.3 Miscellaneous Image Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 3.4 Histograms . 3.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 3.6 Motion Analysis and Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 3.7 Feature Detection . 3.8 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Structural Analysis and Shape Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . 4 highgui. High-level GUI and Media I/O 345 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Reading and Writing Images and Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 4.1 User Interface 4.2 4.3 Qt New Functions . . . . . . . . . . . . . . . . 5 video. Video Analysis 369 5.1 Motion Analysis and Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 6 calib3d. Camera Calibration and 3D Reconstruction 6.1 Camera Calibration and 3D Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 . 393 7 features2d. 2D Features Framework 425 Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 7.1 i

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Common Interfaces of Feature Detectors 7.2 Common Interfaces of Descriptor Extractors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 7.3 Common Interfaces of Descriptor Matchers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 7.4 7.5 Common Interfaces of Generic Descriptor Matchers . . . . . . . . . . . . . . . . . . . . . . . . . . 448 7.6 Drawing Function of Keypoints and Matches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 7.7 Object Categorization . . . . . 8 objdetect. Object Detection 8.1 8.2 8.3 Cascade Classiﬁcation . Latent SVM . . . . Scene Text Detection . . . . . . . . . . . . . 9 ml. Machine Learning Statistical Models . . . . . . . . . . . Boosting . . Support Vector Machines . . . 9.1 9.2 Normal Bayes Classiﬁer . 9.3 K-Nearest Neighbors . . 9.4 . 9.5 Decision Trees . 9.6 . . 9.7 Gradient Boosted Trees 9.8 . 9.9 9.10 Expectation Maximization . . 9.11 Neural Networks . 9.12 MLData . . . . . . . . . . Random Trees . Extremely randomized trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 475 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 . . . . . . . . . . . . . . . 10 ﬂann. Clustering and Search in Multi-Dimensional Spaces 10.1 Fast Approximate Nearest Neighbor Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Clustering . 529 . 529 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 . . . . . . . . . . . . 11 photo. Computational Photography . . . . . . . . . . 11.1 Inpainting . . . . 11.2 Denoising . . . . . 11.3 HDR imaging . . . . 11.4 References . . . 11.5 Decolorization . . 11.6 Seamless Cloning . . 11.7 Non-Photorealistic Rendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 . . . . . . . . . . . . . . . . . . . . . . 12 stitching. Images stitching . . 553 . 12.1 Stitching Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 12.2 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 12.3 High Level Functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 12.4 Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 12.5 Features Finding and Images Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 12.6 Rotation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 . 12.7 Autocalibration . . . . 12.8 Images Warping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 12.9 Seam Estimation . . . . 12.10 Exposure Compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576 12.11 Image Blenders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 nonfree. Non-free functionality 581 13.1 Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 ii

14 contrib. Contributed/Experimental Stuff 587 14.1 Stereo Correspondence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 14.2 FaceRecognizer - Face Recognition with OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 14.3 OpenFABMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 . . . . . . . . . . . . . . . . 15 legacy. Deprecated stuff 669 . 15.1 Motion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 . 15.2 Expectation Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 15.3 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674 15.4 Planar Subdivisions (C API) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 15.5 Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 15.6 Common Interfaces of Descriptor Extractors . 690 . . . . . . . . . . . . . . . . . . . . . . . . . . 691 15.7 Common Interfaces of Generic Descriptor Matchers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 cuda. CUDA-accelerated Computer Vision 16.1 CUDA Module Introduction . 16.2 Initalization and Information . . 16.3 Data Structures . . 16.4 Object Detection . . 16.5 Camera Calibration and 3D Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712 . 718 . . . . . . . . . . . . . . . . 17 cudaarithm. CUDA-accelerated Operations on Matrices 17.1 Core Operations on Matrices . . 17.2 Per-element Operations 17.3 Matrix Reductions . . . 17.4 Arithm Operations on Matrices . . . . . . . . 721 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 18 cudabgsegm. CUDA-accelerated Background Segmentation 743 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 18.1 Background Segmentation . . . 19 cudacodec. CUDA-accelerated Video Encoding/Decoding 19.1 Video Decoding . 19.2 Video Encoding . . . . . . . . . . . . . . . . . 747 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 750 20 cudafeatures2d. CUDA-accelerated Feature Detection and Description 755 20.1 Feature Detection and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 21 cudaﬁlters. CUDA-accelerated Image Filtering 21.1 Image Filtering . . . . . . . . . 767 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 22 cudaimgproc. CUDA-accelerated Image Processing 22.1 Color space processing . 22.2 Histogram Calculation . 22.3 Hough Transform . . . . 22.4 Feature Detection . 22.5 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 775 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 780 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 23 cudaoptﬂow. CUDA-accelerated Optical Flow 23.1 Optical Flow . . . . . . . . . . . 793 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 24 cudastereo. CUDA-accelerated Stereo Correspondence 24.1 Stereo Correspondence . . . . . 799 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799 25 cudawarping. CUDA-accelerated Image Warping 25.1 Image Warping . . . . . . . . . 807 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 iii

26 optim. Generic numerical optimization . 26.1 Linear Programming . . . 26.2 Downhill Simplex Method . 26.3 Primal-Dual Algorithm . . . 26.4 Nonlinear Conjugate Gradient . . . . . . . 813 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 818 27 shape. Shape Distance and Matching 821 27.1 Shape Distance and Common Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 27.2 Shape Transformers and Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826 27.3 Cost Matrix for Histograms Common Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 828 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829 27.4 EMD-L1 . . . . . . . . . . . . 28 softcascade. Soft Cascade object detection and training. 831 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 28.1 Soft Cascade Classiﬁer . 28.2 Soft Cascade Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834 28.3 CUDA version of Soft Cascade Classiﬁer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 . . . . . . . . 29 superres. Super Resolution . . 29.1 Super Resolution . . . . 30 videostab. Video Stabilization . 30.1 Introduction . . 30.2 Global Motion Estimation . . 30.3 Fast Marching Method . . . . . . . 31 viz. 3D Visualizer . . . 31.1 Viz . 31.2 Widget . . . . . . . . . . . . . . . . . . . . 839 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839 841 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 841 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 841 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846 849 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 862 . . . . . . . . . . . . . Bibliography 887 iv

CHAPTER ONE INTRODUCTION OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms. The document describes the so-called OpenCV 2.x API, which is essentially a C++ API, as opposite to the C-based OpenCV 1.x API. The latter is described in opencv1x.pdf. OpenCV has a modular structure, which means that the package includes several shared or static libraries. The following modules are available: • core - a compact module deﬁning basic data structures, including the dense multi-dimensional array Mat and basic functions used by all other modules. • imgproc - an image processing module that includes linear and non-linear image ﬁltering, geometrical image transformations (resize, afﬁne and perspective warping, generic table-based remapping), color space conversion, histograms, and so on. • video - a video analysis module that includes motion estimation, background subtraction, and object tracking algorithms. • calib3d - basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction. • features2d - salient feature detectors, descriptors, and descriptor matchers. • objdetect - detection of objects and instances of the predeﬁned classes (for example, faces, eyes, mugs, people, cars, and so on). • highgui - an easy-to-use interface to video capturing, image and video codecs, as well as simple UI capabilities. • gpu - GPU-accelerated algorithms from different OpenCV modules. • ... some other helper modules, such as FLANN and Google test wrappers, Python bindings, and others. The further chapters of the document describe functionality of each module. But ﬁrst, make sure to get familiar with the common API concepts used thoroughly in the library. 1.1 API Concepts cv Namespace All the OpenCV classes and functions are placed into the cv namespace. Therefore, to access this functionality from your code, use the cv:: speciﬁer or using namespace cv; directive: #include "opencv2/core.hpp" ... 1

The OpenCV Reference Manual, Release 3.0.0-dev cv::Mat H = cv::findHomography(points1, points2, CV_RANSAC, 5); ... or #include "opencv2/core.hpp" using namespace cv; ... Mat H = findHomography(points1, points2, CV_RANSAC, 5 ); ... Some of the current or future OpenCV external names may conﬂict with STL or other libraries. In this case, use explicit namespace speciﬁers to resolve the name conﬂicts: Mat a(100, 100, CV_32F); randu(a, Scalar::all(1), Scalar::all(std::rand())); cv::log(a, a); a /= std::log(2.); Automatic Memory Management OpenCV handles all the memory automatically. First of all, std::vector, Mat, and other data structures used by the functions and methods have destructors that deallocate the underlying memory buffers when needed. This means that the destructors do not always deallocate the buffers as in case of Mat. They take into account possible data sharing. A destructor decrements the reference counter associated with the matrix data buffer. The buffer is deallocated if and only if the reference counter reaches zero, that is, when no other structures refer to the same buffer. Similarly, when a Mat instance is copied, no actual data is really copied. Instead, the reference counter is incremented to memorize that there is another owner of the same data. There is also the Mat::clone method that creates a full copy of the matrix data. See the example below: // create a big 8Mb matrix Mat A(1000, 1000, CV_64F); // create another header for the same matrix; // this is an instant operation, regardless of the matrix size. Mat B = A; // create another header for the 3-rd row of A; no data is copied either Mat C = B.row(3); // now create a separate copy of the matrix Mat D = B.clone(); // copy the 5-th row of B to C, that is, copy the 5-th row of A // to the 3-rd row of A. B.row(5).copyTo(C); // now let A and D share the data; after that the modified version // of A is still referenced by B and C. A = D; // now make B an empty matrix (which references no memory buffers), // but the modified version of A will still be referenced by C, // despite that C is just a single row of the original A B.release(); // finally, make a full copy of C. As a result, the big modified // matrix will be deallocated, since it is not referenced by anyone C = C.clone(); You see that the use of Mat and other basic structures is simple. But what about high-level classes or even user data types created without taking automatic memory management into account? For them, OpenCV offers the Ptr template 2 Chapter 1. Introduction

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