mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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620 lines
32 KiB
620 lines
32 KiB
% |
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% The OpenCV cheatsheet structure: |
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% |
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% opencv data structures |
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% point, rect |
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% matrix |
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% |
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% creating matrices |
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% from scratch |
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% from previously allocated data: plain arrays, vectors |
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% converting to/from old-style structures |
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% |
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% element access, iteration through matrix elements |
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% |
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% copying & shuffling matrix data |
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% copying & converting the whole matrices |
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% extracting matrix parts & copying them |
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% split, merge & mixchannels |
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% flip, transpose, repeat |
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% |
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% matrix & image operations: |
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% arithmetics & logic |
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% matrix multiplication, inversion, determinant, trace, SVD |
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% statistical functions |
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% |
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% basic image processing: |
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% image filtering with predefined & custom filters |
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% example: finding local maxima |
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% geometrical transformations, resize, warpaffine, perspective & remap. |
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% color space transformations |
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% histograms & back projections |
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% contours |
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% |
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% i/o: |
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% displaying images |
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% saving/loading to/from file (XML/YAML & image file formats) |
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% reading videos & camera feed, writing videos |
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% |
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% operations on point sets: |
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% findcontours, bounding box, convex hull, min area rect, |
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% transformations, to/from homogeneous coordinates |
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% matching point sets: homography, fundamental matrix, rigid transforms |
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% |
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% 3d: |
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% camera calibration, pose estimation. |
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% uncalibrated case |
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% stereo: rectification, running stereo correspondence, obtaining the depth. |
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% |
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% feature detection: |
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% features2d toolbox |
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% |
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% object detection: |
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% using a classifier running on a sliding window: cascadeclassifier + hog. |
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% using salient point features: features2d -> matching |
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% |
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% statistical data processing: |
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% clustering (k-means), |
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% classification + regression (SVM, boosting, k-nearest), |
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% compressing data (PCA) |
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% |
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\documentclass[10pt,landscape]{article} |
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\usepackage[usenames,dvips,pdftex]{color} |
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\usepackage{multicol} |
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\usepackage{calc} |
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\graphicspath{{./images/}} |
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{ \geometry{top=.5in,left=.5in,right=.5in,bottom=.5in} } |
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{\ifthenelse{ \lengthtest{ \paperwidth = 297mm}} |
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T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} |
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#1 |
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% ----------------------------------------------------------------------- |
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\begin{document} |
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\raggedright |
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\footnotesize |
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\begin{multicols}{3} |
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\begin{center} |
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\Large{\textbf{OpenCV 2.4 Cheat Sheet (C++)}} \\ |
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\end{center} |
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\newlength{\MyLen} |
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\settowidth{\MyLen}{\texttt{letterpaper}/\texttt{a4paper} \ } |
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%\section{Filesystem Concepts} |
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%\begin{tabular}{@{}p{\the\MyLen}% |
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% @{}p{\linewidth-\the\MyLen}@{}} |
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%\texttt{\href{http://www.ros.org/wiki/Packages}{package}} & The lowest level of ROS software organization. \\ |
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%\texttt{\href{http://www.ros.org/wiki/Manifest}{manifest}} & Description of a ROS package. \\ |
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%\texttt{\href{http://www.ros.org/wiki/Stack}{stack}} & Collections of ROS packages that form a higher-level library. \\ |
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%\texttt{\href{http://www.ros.org/wiki/Stack Manifest}{stack manifest}} & Description of a ROS stack. |
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%\end{tabular} |
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\emph{The OpenCV C++ reference manual is here: \url{http://docs.opencv.org}. Use \textbf{Quick Search} to find descriptions of the particular functions and classes} |
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\section{Key OpenCV Classes} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Point_}{Point\_}} & Template 2D point class \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Point3_}{Point3\_}} & Template 3D point class \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Size_}{Size\_}} & Template size (width, height) class \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Vec}{Vec}} & Template short vector class \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Matx}{Matx}} & Template small matrix class \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Scalar_}{Scalar}} & 4-element vector \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Rect_}{Rect}} & Rectangle \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Range}{Range}} & Integer value range \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Mat}{Mat}} & 2D or multi-dimensional dense array (can be used to store matrices, images, histograms, feature descriptors, voxel volumes etc.)\\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#sparsemat}{SparseMat}} & Multi-dimensional sparse array \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Ptr}{Ptr}} & Template smart pointer class |
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\end{tabular} |
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\section{Matrix Basics} |
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\begin{tabbing} |
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\textbf{Cr}\=\textbf{ea}\=\textbf{te}\={} \textbf{a matrix} \\ |
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\> \texttt{Mat image(240, 320, CV\_8UC3);} \\ |
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\textbf{[Re]allocate a pre-declared matrix}\\ |
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\> \texttt{image.\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-create}{create}(480, 640, CV\_8UC3);}\\ |
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\textbf{Create a matrix initialized with a constant}\\ |
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\> \texttt{Mat A33(3, 3, CV\_32F, Scalar(5));} \\ |
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\> \texttt{Mat B33(3, 3, CV\_32F); B33 = Scalar(5);} \\ |
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\> \texttt{Mat C33 = Mat::ones(3, 3, CV\_32F)*5.;} \\ |
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\> \texttt{Mat D33 = Mat::zeros(3, 3, CV\_32F) + 5.;} \\ |
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\textbf{Create a matrix initialized with specified values}\\ |
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\> \texttt{double a = CV\_PI/3;} \\ |
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\> \texttt{Mat A22 = (Mat\_<float>(2, 2) <<} \\ |
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\> \> \texttt{cos(a), -sin(a), sin(a), cos(a));} \\ |
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\> \texttt{float B22data[] = \{cos(a), -sin(a), sin(a), cos(a)\};} \\ |
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\> \texttt{Mat B22 = Mat(2, 2, CV\_32F, B22data).clone();}\\ |
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\textbf{Initialize a random matrix}\\ |
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\> \texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randu}{randu}(image, Scalar(0), Scalar(256)); }\textit{// uniform dist}\\ |
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\> \texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randn}{randn}(image, Scalar(128), Scalar(10)); }\textit{// Gaussian dist}\\ |
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\textbf{Convert matrix to/from other structures}\\ |
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\>\textbf{(without copying the data)}\\ |
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\> \texttt{Mat image\_alias = image;}\\ |
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\> \texttt{float* Idata=new float[480*640*3];}\\ |
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\> \texttt{Mat I(480, 640, CV\_32FC3, Idata);}\\ |
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\> \texttt{vector<Point> iptvec(10);}\\ |
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\> \texttt{Mat iP(iptvec); }\textit{// iP -- 10x1 CV\_32SC2 matrix}\\ |
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\> \texttt{IplImage* oldC0 = cvCreateImage(cvSize(320,240),16,1);}\\ |
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\> \texttt{Mat newC = cvarrToMat(oldC0);}\\ |
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\> \texttt{IplImage oldC1 = newC; CvMat oldC2 = newC;}\\ |
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\textbf{... (with copying the data)}\\ |
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\> \texttt{Mat newC2 = cvarrToMat(oldC0).clone();}\\ |
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\> \texttt{vector<Point2f> ptvec = Mat\_<Point2f>(iP);}\\ |
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\>\\ |
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\textbf{Access matrix elements}\\ |
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\> \texttt{A33.at<float>(i,j) = A33.at<float>(j,i)+1;}\\ |
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\> \texttt{Mat dyImage(image.size(), image.type());}\\ |
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\> \texttt{for(int y = 1; y < image.rows-1; y++) \{}\\ |
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\> \> \texttt{Vec3b* prevRow = image.ptr<Vec3b>(y-1);}\\ |
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\> \> \texttt{Vec3b* nextRow = image.ptr<Vec3b>(y+1);}\\ |
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\> \> \texttt{for(int x = 0; x < image.cols; x++)}\\ |
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\> \> \> \texttt{for(int c = 0; c < 3; c++)}\\ |
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\> \> \> \texttt{ dyImage.at<Vec3b>(y,x)[c] =}\\ |
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\> \> \> \texttt{ saturate\_cast<uchar>(}\\ |
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\> \> \> \texttt{ nextRow[x][c] - prevRow[x][c]);}\\ |
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\> \texttt{\} }\\ |
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\> \texttt{Mat\_<Vec3b>::iterator it = image.begin<Vec3b>(),}\\ |
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\> \> \texttt{itEnd = image.end<Vec3b>();}\\ |
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\> \texttt{for(; it != itEnd; ++it)}\\ |
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\> \> \texttt{(*it)[1] \textasciicircum{}= 255;}\\ |
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\end{tabbing} |
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\section{Matrix Manipulations: Copying, Shuffling, Part Access} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-copyto}{src.copyTo(dst)}} & Copy matrix to another one \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-convertto}{src.convertTo(dst,type,scale,shift)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ Scale and convert to another datatype \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-clone}{m.clone()}} & Make deep copy of a matrix \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-reshape}{m.reshape(nch,nrows)}} & Change matrix dimensions and/or number of channels without copying data \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-row}{m.row(i)}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-col}{m.col(i)}} & Take a matrix row/column \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-rowrange}{m.rowRange(Range(i1,i2))}} |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-colrange}{m.colRange(Range(j1,j2))}} & \ \ \ \ \ \ \ Take a matrix row/column span \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-diag}{m.diag(i)}} & Take a matrix diagonal \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Mat}{m(Range(i1,i2),Range(j1,j2)), m(roi)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ Take a submatrix \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#repeat}{m.repeat(ny,nx)}} & Make a bigger matrix from a smaller one \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#flip}{flip(src,dst,dir)}} & Reverse the order of matrix rows and/or columns \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#split}{split(...)}} & Split multi-channel matrix into separate channels \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#merge}{merge(...)}} & Make a multi-channel matrix out of the separate channels \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#mixchannels}{mixChannels(...)}} & Generalized form of split() and merge() \\ |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randshuffle}{randShuffle(...)}} & Randomly shuffle matrix elements \\ |
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\end{tabular} |
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\begin{tabbing} |
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Exa\=mple 1. Smooth image ROI in-place\\ |
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\>\texttt{Mat imgroi = image(Rect(10, 20, 100, 100));}\\ |
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\>\texttt{GaussianBlur(imgroi, imgroi, Size(5, 5), 1.2, 1.2);}\\ |
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Example 2. Somewhere in a linear algebra algorithm \\ |
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\>\texttt{m.row(i) += m.row(j)*alpha;}\\ |
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Example 3. Copy image ROI to another image with conversion\\ |
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\>\texttt{Rect r(1, 1, 10, 20);}\\ |
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\>\texttt{Mat dstroi = dst(Rect(0,10,r.width,r.height));}\\ |
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\>\texttt{src(r).convertTo(dstroi, dstroi.type(), 1, 0);}\\ |
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\end{tabbing} |
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\section{Simple Matrix Operations} |
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OpenCV implements most common arithmetical, logical and |
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other matrix operations, such as |
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\begin{itemize} |
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\item |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#add}{add()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#subtract}{subtract()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#multiply}{multiply()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#divide}{divide()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#absdiff}{absdiff()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-and}{bitwise\_and()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-or}{bitwise\_or()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-xor}{bitwise\_xor()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#max}{max()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#min}{min()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#compare}{compare()}} |
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-- correspondingly, addition, subtraction, element-wise multiplication ... comparison of two matrices or a matrix and a scalar. |
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\begin{tabbing} |
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Exa\=mple. \href{http://en.wikipedia.org/wiki/Alpha_compositing}{Alpha compositing} function:\\ |
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\texttt{void alphaCompose(const Mat\& rgba1,}\\ |
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\> \texttt{const Mat\& rgba2, Mat\& rgba\_dest)}\\ |
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\texttt{\{ }\\ |
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\> \texttt{Mat a1(rgba1.size(), rgba1.type()), ra1;}\\ |
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\> \texttt{Mat a2(rgba2.size(), rgba2.type());}\\ |
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\> \texttt{int mixch[]=\{3, 0, 3, 1, 3, 2, 3, 3\};}\\ |
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\> \texttt{mixChannels(\&rgba1, 1, \&a1, 1, mixch, 4);}\\ |
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\> \texttt{mixChannels(\&rgba2, 1, \&a2, 1, mixch, 4);}\\ |
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\> \texttt{subtract(Scalar::all(255), a1, ra1);}\\ |
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\> \texttt{bitwise\_or(a1, Scalar(0,0,0,255), a1);}\\ |
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\> \texttt{bitwise\_or(a2, Scalar(0,0,0,255), a2);}\\ |
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\> \texttt{multiply(a2, ra1, a2, 1./255);}\\ |
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\> \texttt{multiply(a1, rgba1, a1, 1./255);}\\ |
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\> \texttt{multiply(a2, rgba2, a2, 1./255);}\\ |
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\> \texttt{add(a1, a2, rgba\_dest);}\\ |
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\texttt{\}} |
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\end{tabbing} |
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\item |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#sum}{sum()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#mean}{mean()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#meanstddev}{meanStdDev()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#norm}{norm()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#countnonzero}{countNonZero()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#minmaxloc}{minMaxLoc()}}, |
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-- various statistics of matrix elements. |
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\item |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#exp}{exp()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#log}{log()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#pow}{pow()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#sqrt}{sqrt()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#carttopolar}{cartToPolar()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#polartocart}{polarToCart()}} |
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-- the classical math functions. |
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\item |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#scaleadd}{scaleAdd()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#transpose}{transpose()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#gemm}{gemm()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#invert}{invert()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#solve}{solve()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#determinant}{determinant()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#trace}{trace()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#eigen}{eigen()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#SVD}{SVD}}, |
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-- the algebraic functions + SVD class. |
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\item |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#dft}{dft()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#idft}{idft()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#dct}{dct()}}, |
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\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#idct}{idct()}}, |
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-- discrete Fourier and cosine transformations |
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\end{itemize} |
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For some operations a more convenient \href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#matrix-expressions}{algebraic notation} can be used, for example: |
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\begin{tabbing} |
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\texttt{Mat}\={} \texttt{delta = (J.t()*J + lambda*}\\ |
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\>\texttt{Mat::eye(J.cols, J.cols, J.type()))}\\ |
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\>\texttt{.inv(CV\_SVD)*(J.t()*err);} |
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\end{tabbing} |
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implements the core of Levenberg-Marquardt optimization algorithm. |
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\section{Image Processsing} |
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\subsection{Filtering} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#filter2d}{filter2D()}} & Non-separable linear filter \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#sepfilter2d}{sepFilter2D()}} & Separable linear filter \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#blur}{boxFilter()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#gaussianblur}{GaussianBlur()}}, |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#medianblur}{medianBlur()}}, |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#bilateralfilter}{bilateralFilter()}} |
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& Smooth the image with one of the linear or non-linear filters \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#sobel}{Sobel()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#scharr}{Scharr()}} |
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& Compute the spatial image derivatives \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#laplacian}{Laplacian()}} & compute Laplacian: $\Delta I = \frac{\partial ^ 2 I}{\partial x^2} + \frac{\partial ^ 2 I}{\partial y^2}$ \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#erode}{erode()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#dilate}{dilate()}} & Morphological operations \\ |
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\end{tabular} |
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\begin{tabbing} |
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Exa\=mple. Filter image in-place with a 3x3 high-pass kernel\\ |
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\> (preserve negative responses by shifting the result by 128):\\ |
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\texttt{filter2D(image, image, image.depth(), (Mat\_<float>(3,3)<<}\\ |
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\> \texttt{-1, -1, -1, -1, 9, -1, -1, -1, -1), Point(1,1), 128);}\\ |
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\end{tabbing} |
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\subsection{Geometrical Transformations} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#resize}{resize()}} & Resize image \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#getrectsubpix}{getRectSubPix()}} & Extract an image patch \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#warpaffine}{warpAffine()}} & Warp image affinely\\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#warpperspective}{warpPerspective()}} & Warp image perspectively\\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#remap}{remap()}} & Generic image warping\\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#convertmaps}{convertMaps()}} & Optimize maps for a faster remap() execution\\ |
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\end{tabular} |
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\begin{tabbing} |
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Example. Decimate image by factor of $\sqrt{2}$:\\ |
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\texttt{Mat dst; resize(src, dst, Size(), 1./sqrt(2), 1./sqrt(2));} |
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\end{tabbing} |
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|
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\subsection{Various Image Transformations} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#cvtcolor}{cvtColor()}} & Convert image from one color space to another \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#threshold}{threshold()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#adaptivethreshold}{adaptivethreshold()}} & Convert grayscale image to binary image using a fixed or a variable threshold \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#floodfill}{floodFill()}} & Find a connected component using region growing algorithm\\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#integral}{integral()}} & Compute integral image \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#distancetransform}{distanceTransform()}} |
|
& build distance map or discrete Voronoi diagram for a binary image. \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#watershed}{watershed()}}, |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#grabcut}{grabCut()}} |
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& marker-based image segmentation algorithms. |
|
See the samples \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/watershed.cpp}{watershed.cpp}} and \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/grabcut.cpp}{grabcut.cpp}}. |
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|
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\end{tabular} |
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|
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\subsection{Histograms} |
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\begin{tabular}{@{}p{\the\MyLen}% |
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@{}p{\linewidth-\the\MyLen}@{}} |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#calchist}{calcHist()}} & Compute image(s) histogram \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#calcbackproject}{calcBackProject()}} & Back-project the histogram \\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#equalizehist}{equalizeHist()}} & Normalize image brightness and contrast\\ |
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\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#comparehist}{compareHist()}} & Compare two histograms\\ |
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\end{tabular} |
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|
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\begin{tabbing} |
|
Example. Compute Hue-Saturation histogram of an image:\\ |
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\texttt{Mat hsv, H;}\\ |
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\texttt{cvtColor(image, hsv, CV\_BGR2HSV);}\\ |
|
\texttt{int planes[]=\{0, 1\}, hsize[] = \{32, 32\};}\\ |
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\texttt{calcHist(\&hsv, 1, planes, Mat(), H, 2, hsize, 0);}\\ |
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\end{tabbing} |
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|
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\subsection{Contours} |
|
See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/contours2.cpp}{contours2.cpp}} and \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/squares.cpp}{squares.cpp}} |
|
samples on what are the contours and how to use them. |
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\section{Data I/O} |
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\href{http://docs.opencv.org/modules/core/doc/xml_yaml_persistence.html\#xml-yaml-file-storages-writing-to-a-file-storage}{XML/YAML storages} are collections (possibly nested) of scalar values, structures and heterogeneous lists. |
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|
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\begin{tabbing} |
|
\textbf{Wr}\=\textbf{iting data to YAML (or XML)}\\ |
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\texttt{// Type of the file is determined from the extension}\\ |
|
\texttt{FileStorage fs("test.yml", FileStorage::WRITE);}\\ |
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\texttt{fs << "i" << 5 << "r" << 3.1 << "str" << "ABCDEFGH";}\\ |
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\texttt{fs << "mtx" << Mat::eye(3,3,CV\_32F);}\\ |
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\texttt{fs << "mylist" << "[" << CV\_PI << "1+1" <<}\\ |
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\>\texttt{"\{:" << "month" << 12 << "day" << 31 << "year"}\\ |
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\>\texttt{<< 1969 << "\}" << "]";}\\ |
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\texttt{fs << "mystruct" << "\{" << "x" << 1 << "y" << 2 <<}\\ |
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\>\texttt{"width" << 100 << "height" << 200 << "lbp" << "[:";}\\ |
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\texttt{const uchar arr[] = \{0, 1, 1, 0, 1, 1, 0, 1\};}\\ |
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\texttt{fs.writeRaw("u", arr, (int)(sizeof(arr)/sizeof(arr[0])));}\\ |
|
\texttt{fs << "]" << "\}";} |
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\end{tabbing} |
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|
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\emph{Scalars (integers, floating-point numbers, text strings), matrices, STL vectors of scalars and some other types can be written to the file storages using \texttt{<<} operator} |
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|
|
\begin{tabbing} |
|
\textbf{Re}\=\textbf{ading the data back}\\ |
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\texttt{// Type of the file is determined from the content}\\ |
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\texttt{FileStorage fs("test.yml", FileStorage::READ);}\\ |
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\texttt{int i1 = (int)fs["i"]; double r1 = (double)fs["r"];}\\ |
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\texttt{string str1 = (string)fs["str"];}\\ |
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|
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\texttt{Mat M; fs["mtx"] >> M;}\\ |
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|
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\texttt{FileNode tl = fs["mylist"];}\\ |
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\texttt{CV\_Assert(tl.type() == FileNode::SEQ \&\& tl.size() == 3);}\\ |
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\texttt{double tl0 = (double)tl[0]; string tl1 = (string)tl[1];}\\ |
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\texttt{int m = (int)tl[2]["month"], d = (int)tl[2]["day"];}\\ |
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\texttt{int year = (int)tl[2]["year"];}\\ |
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\texttt{FileNode tm = fs["mystruct"];}\\ |
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\texttt{Rect r; r.x = (int)tm["x"], r.y = (int)tm["y"];}\\ |
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\texttt{r.width = (int)tm["width"], r.height = (int)tm["height"];}\\ |
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|
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\texttt{int lbp\_val = 0;}\\ |
|
\texttt{FileNodeIterator it = tm["lbp"].begin();}\\ |
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|
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\texttt{for(int k = 0; k < 8; k++, ++it)}\\ |
|
\>\texttt{lbp\_val |= ((int)*it) << k;}\\ |
|
\end{tabbing} |
|
|
|
\emph{Scalars are read using the corresponding FileNode's cast operators. Matrices and some other types are read using \texttt{>>} operator. Lists can be read using FileNodeIterator's.} |
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|
|
\begin{tabbing} |
|
\textbf{Wr}\=\textbf{iting and reading raster images}\\ |
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imwrite}{imwrite}("myimage.jpg", image);}\\ |
|
\texttt{Mat image\_color\_copy = \href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 1);}\\ |
|
\texttt{Mat image\_grayscale\_copy = \href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 0);}\\ |
|
\end{tabbing} |
|
|
|
\emph{The functions can read/write images in the following formats: \textbf{BMP (.bmp), JPEG (.jpg, .jpeg), TIFF (.tif, .tiff), PNG (.png), PBM/PGM/PPM (.p?m), Sun Raster (.sr), JPEG 2000 (.jp2)}. Every format supports 8-bit, 1- or 3-channel images. Some formats (PNG, JPEG 2000) support 16 bits per channel.} |
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|
|
\begin{tabbing} |
|
\textbf{Re}\=\textbf{ading video from a file or from a camera}\\ |
|
\texttt{VideoCapture cap;}\\ |
|
\texttt{if(argc > 1) cap.open(string(argv[1])); else cap.open(0)};\\ |
|
\texttt{Mat frame; namedWindow("video", 1);}\\ |
|
\texttt{for(;;) \{}\\ |
|
\>\texttt{cap >> frame; if(!frame.data) break;}\\ |
|
\>\texttt{imshow("video", frame); if(waitKey(30) >= 0) break;}\\ |
|
\texttt{\} } |
|
\end{tabbing} |
|
|
|
\section{Simple GUI (highgui module)} |
|
|
|
\begin{tabular}{@{}p{\the\MyLen}% |
|
@{}p{\linewidth-\the\MyLen}@{}} |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#namedwindow}{namedWindow(winname,flags)}} & \ \ \ \ \ \ \ \ \ \ Create named highgui window \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#destroywindow}{destroyWindow(winname)}} & \ \ \ Destroy the specified window \\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#imshow}{imshow(winname, mtx)}} & Show image in the window \\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#waitkey}{waitKey(delay)}} & Wait for a key press during the specified time interval (or forever). Process events while waiting. \emph{Do not forget to call this function several times a second in your code.} \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#createtrackbar}{createTrackbar(...)}} & Add trackbar (slider) to the specified window \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#setmousecallback}{setMouseCallback(...)}} & \ \ Set the callback on mouse clicks and movements in the specified window \\ |
|
|
|
\end{tabular} |
|
|
|
See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/camshiftdemo.cpp}{camshiftdemo.cpp}} and other \href{https://github.com/Itseez/opencv/tree/master/samples/}{OpenCV samples} on how to use the GUI functions. |
|
|
|
\section{Camera Calibration, Pose Estimation and Depth Estimation} |
|
|
|
\begin{tabular}{@{}p{\the\MyLen}% |
|
@{}p{\linewidth-\the\MyLen}@{}} |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#calibratecamera}{calibrateCamera()}} & Calibrate camera from several views of a calibration pattern. \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findchessboardcorners}{findChessboardCorners()}} & \ \ \ \ \ \ Find feature points on the checkerboard calibration pattern. \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#solvepnp}{solvePnP()}} & Find the object pose from the known projections of its feature points. \\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereocalibrate}{stereoCalibrate()}} & Calibrate stereo camera. \\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereorectify}{stereoRectify()}} & Compute the rectification transforms for a calibrated stereo camera.\\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#initundistortrectifymap}{initUndistortRectifyMap()}} & \ \ \ \ \ \ Compute rectification map (for \texttt{remap()}) for each stereo camera head.\\ |
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|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoBM}{StereoBM}}, \texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoSGBM}{StereoSGBM}} & The stereo correspondence engines to be run on rectified stereo pairs.\\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#reprojectimageto3d}{reprojectImageTo3D()}} & Convert disparity map to 3D point cloud.\\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findhomography}{findHomography()}} & Find best-fit perspective transformation between two 2D point sets. \\ |
|
|
|
\end{tabular} |
|
|
|
To calibrate a camera, you can use \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/calibration.cpp}{calibration.cpp}} or |
|
\texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/stereo\_calib.cpp}{stereo\_calib.cpp}} samples. |
|
To get the disparity maps and the point clouds, use |
|
\texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/stereo\_match.cpp}{stereo\_match.cpp}} sample. |
|
|
|
\section{Object Detection} |
|
|
|
\begin{tabular}{@{}p{\the\MyLen}% |
|
@{}p{\linewidth-\the\MyLen}@{}} |
|
\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/object_detection.html\#matchtemplate}{matchTemplate}} & Compute proximity map for given template.\\ |
|
|
|
\texttt{\href{http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html\#cascadeclassifier}{CascadeClassifier}} & Viola's Cascade of Boosted classifiers using Haar or LBP features. Suits for detecting faces, facial features and some other objects without diverse textures. See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/c/facedetect.cpp}{facedetect.cpp}}\\ |
|
|
|
\texttt{{HOGDescriptor}} & N. Dalal's object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detecting people, cars and other objects with well-defined silhouettes. See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/peopledetect.cpp}{peopledetect.cpp}}\\ |
|
|
|
\end{tabular} |
|
|
|
% |
|
% feature detection: |
|
% features2d toolbox |
|
% |
|
% object detection: |
|
% using a classifier running on a sliding window: cascadeclassifier + hog. |
|
% using salient point features: features2d -> matching |
|
% |
|
% statistical data processing: |
|
% clustering (k-means), |
|
% classification + regression (SVM, boosting, k-nearest), |
|
% compressing data (PCA) |
|
|
|
\end{multicols} |
|
\end{document}
|
|
|