├── .gitignore ├── LICENSE ├── README.md ├── cv2cheatsheet.pdf └── cv2cheatsheet.tex /.gitignore: -------------------------------------------------------------------------------- 1 | ## Core latex/pdflatex auxiliary files: 2 | *.aux 3 | *.lof 4 | *.log 5 | *.lot 6 | *.fls 7 | *.out 8 | *.toc 9 | *.fmt 10 | *.fot 11 | *.cb 12 | *.cb2 13 | .*.lb 14 | 15 | ## Intermediate documents: 16 | *.dvi 17 | *.xdv 18 | *-converted-to.* 19 | # these rules might exclude image files for figures etc. 20 | # *.ps 21 | # *.eps 22 | # *.pdf 23 | 24 | ## Generated if empty string is given at "Please type another file name for output:" 25 | .pdf 26 | 27 | ## Bibliography auxiliary files (bibtex/biblatex/biber): 28 | *.bbl 29 | *.bcf 30 | *.blg 31 | *-blx.aux 32 | *-blx.bib 33 | *.run.xml 34 | 35 | ## Build tool auxiliary files: 36 | *.fdb_latexmk 37 | *.synctex 38 | *.synctex(busy) 39 | *.synctex.gz 40 | *.synctex.gz(busy) 41 | *.pdfsync 42 | 43 | ## Build tool directories for auxiliary files 44 | # latexrun 45 | latex.out/ 46 | 47 | ## Auxiliary and intermediate files from other packages: 48 | # algorithms 49 | *.alg 50 | *.loa 51 | 52 | # achemso 53 | acs-*.bib 54 | 55 | # amsthm 56 | *.thm 57 | 58 | # beamer 59 | *.nav 60 | *.pre 61 | *.snm 62 | *.vrb 63 | 64 | # changes 65 | *.soc 66 | 67 | # comment 68 | *.cut 69 | 70 | # cprotect 71 | *.cpt 72 | 73 | # elsarticle (documentclass of Elsevier journals) 74 | *.spl 75 | 76 | # endnotes 77 | *.ent 78 | 79 | # fixme 80 | *.lox 81 | 82 | # feynmf/feynmp 83 | *.mf 84 | *.mp 85 | *.t[1-9] 86 | *.t[1-9][0-9] 87 | *.tfm 88 | 89 | #(r)(e)ledmac/(r)(e)ledpar 90 | *.end 91 | *.?end 92 | *.[1-9] 93 | *.[1-9][0-9] 94 | *.[1-9][0-9][0-9] 95 | *.[1-9]R 96 | *.[1-9][0-9]R 97 | *.[1-9][0-9][0-9]R 98 | *.eledsec[1-9] 99 | *.eledsec[1-9]R 100 | *.eledsec[1-9][0-9] 101 | *.eledsec[1-9][0-9]R 102 | *.eledsec[1-9][0-9][0-9] 103 | *.eledsec[1-9][0-9][0-9]R 104 | 105 | # glossaries 106 | *.acn 107 | *.acr 108 | *.glg 109 | *.glo 110 | *.gls 111 | *.glsdefs 112 | *.lzo 113 | *.lzs 114 | 115 | # uncomment this for glossaries-extra (will ignore makeindex's style files!) 116 | # *.ist 117 | 118 | # gnuplottex 119 | *-gnuplottex-* 120 | 121 | # gregoriotex 122 | *.gaux 123 | *.gtex 124 | 125 | # htlatex 126 | *.4ct 127 | *.4tc 128 | *.idv 129 | *.lg 130 | *.trc 131 | *.xref 132 | 133 | # hyperref 134 | *.brf 135 | 136 | # knitr 137 | *-concordance.tex 138 | # TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files 139 | # *.tikz 140 | *-tikzDictionary 141 | 142 | # listings 143 | *.lol 144 | 145 | # luatexja-ruby 146 | *.ltjruby 147 | 148 | # makeidx 149 | *.idx 150 | *.ilg 151 | *.ind 152 | 153 | # minitoc 154 | *.maf 155 | *.mlf 156 | *.mlt 157 | *.mtc[0-9]* 158 | *.slf[0-9]* 159 | *.slt[0-9]* 160 | *.stc[0-9]* 161 | 162 | # minted 163 | _minted* 164 | *.pyg 165 | 166 | # morewrites 167 | *.mw 168 | 169 | # nomencl 170 | *.nlg 171 | *.nlo 172 | *.nls 173 | 174 | # pax 175 | *.pax 176 | 177 | # pdfpcnotes 178 | *.pdfpc 179 | 180 | # sagetex 181 | *.sagetex.sage 182 | *.sagetex.py 183 | *.sagetex.scmd 184 | 185 | # scrwfile 186 | *.wrt 187 | 188 | # sympy 189 | *.sout 190 | *.sympy 191 | sympy-plots-for-*.tex/ 192 | 193 | # pdfcomment 194 | *.upa 195 | *.upb 196 | 197 | # pythontex 198 | *.pytxcode 199 | pythontex-files-*/ 200 | 201 | # tcolorbox 202 | *.listing 203 | 204 | # thmtools 205 | *.loe 206 | 207 | # TikZ & PGF 208 | *.dpth 209 | *.md5 210 | *.auxlock 211 | 212 | # todonotes 213 | *.tdo 214 | 215 | # vhistory 216 | *.hst 217 | *.ver 218 | 219 | # easy-todo 220 | *.lod 221 | 222 | # xcolor 223 | *.xcp 224 | 225 | # xmpincl 226 | *.xmpi 227 | 228 | # xindy 229 | *.xdy 230 | 231 | # xypic precompiled matrices and outlines 232 | *.xyc 233 | *.xyd 234 | 235 | # endfloat 236 | *.ttt 237 | *.fff 238 | 239 | # Latexian 240 | TSWLatexianTemp* 241 | 242 | ## Editors: 243 | # WinEdt 244 | *.bak 245 | *.sav 246 | 247 | # Texpad 248 | .texpadtmp 249 | 250 | # LyX 251 | *.lyx~ 252 | 253 | # Kile 254 | *.backup 255 | 256 | # gummi 257 | .*.swp 258 | 259 | # KBibTeX 260 | *~[0-9]* 261 | 262 | # TeXnicCenter 263 | *.tps 264 | 265 | # auto folder when using emacs and auctex 266 | ./auto/* 267 | *.el 268 | 269 | # expex forward references with \gathertags 270 | *-tags.tex 271 | 272 | # standalone packages 273 | *.sta 274 | 275 | # Makeindex log files 276 | *.lpz 277 | 278 | # xwatermark package 279 | *.xwm 280 | 281 | # REVTeX puts footnotes in the bibliography by default, unless the nofootinbib 282 | # option is specified. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # python-opencv 2 | Cheat sheet for OpenCV 4.x (Python) 3 | 4 | The objective of this cheat sheet is not to exhaustively list all possible available OpenCV functions. The objective is to provide a quick reference for some of the most used operations. 5 | 6 | Note that many parameters are not included and example arguments are, in many cases, provided. For more details on OpenCV functions, and respective parameters, check: https://docs.opencv.org/master/. 7 | 8 | Let me know if you've found a bug, or if you have suggestions on how to improve this cheat sheet. 9 | -------------------------------------------------------------------------------- /cv2cheatsheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/a-anjos/python-opencv/9ab29c76388daf7bfe530a67e1b46dc141804b9e/cv2cheatsheet.pdf -------------------------------------------------------------------------------- /cv2cheatsheet.tex: -------------------------------------------------------------------------------- 1 | % Base on http://wch.github.io/latexsheet/}{http://wch.github.io/latexsheet/ 2 | \documentclass[10pt,landscape, a4paper]{article} 3 | \usepackage{multicol} 4 | \usepackage{calc} 5 | \usepackage{ifthen} 6 | \usepackage[landscape]{geometry} 7 | \usepackage{hyperref} 8 | \usepackage{minted} 9 | \usepackage{amsmath} 10 | % rescale the whole thing 11 | \usepackage[]{datetime2} 12 | 13 | 14 | % This sets page margins to .5 inch if using letter paper, and to 1cm 15 | % if using A4 paper. (This probably isn't strictly necessary.) 16 | % If using another size paper, use default 1cm margins. 17 | \ifthenelse{\lengthtest { \paperwidth = 11in}} 18 | { \geometry{top=.5in,left=.5in,right=.5in,bottom=.5in} } 19 | {\ifthenelse{ \lengthtest{ \paperwidth = 297mm}} 20 | {\geometry{top=1cm,left=1cm,right=1cm,bottom=1cm} } 21 | {\geometry{top=1cm,left=1cm,right=1cm,bottom=1cm} } 22 | } 23 | 24 | % Turn off header and footer 25 | \pagestyle{empty} 26 | 27 | 28 | 29 | % Redefine section commands to use less space 30 | \makeatletter 31 | \renewcommand{\section}{\@startsection{section}{1}{0mm}% 32 | {-1ex plus -.5ex minus -.2ex}% 33 | {0.5ex plus .2ex}%x 34 | {\normalfont\large\bfseries}} 35 | \renewcommand{\subsection}{\@startsection{subsection}{2}{0mm}% 36 | {-1explus -.5ex minus -.2ex}% 37 | {0.5ex plus .2ex}% 38 | {\normalfont\normalsize\bfseries}} 39 | \renewcommand{\subsubsection}{\@startsection{subsubsection}{3}{0mm}% 40 | {-1ex plus -.5ex minus -.2ex}% 41 | {1ex plus .2ex}% 42 | {\normalfont\small\bfseries}} 43 | \makeatother 44 | 45 | % Define BibTeX command 46 | \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em 47 | T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} 48 | 49 | % Don't print section numbers 50 | \setcounter{secnumdepth}{0} 51 | 52 | 53 | \setlength{\parindent}{0pt} 54 | \setlength{\parskip}{0pt plus 0.5ex} 55 | 56 | 57 | % ----------------------------------------------------------------------- 58 | 59 | \begin{document} 60 | 61 | \raggedright 62 | \footnotesize 63 | \begin{multicols}{2} 64 | 65 | 66 | % multicol parameters 67 | % These lengths are set only within the two main columns 68 | %\setlength{\columnseprule}{0.25pt} 69 | \setlength{\premulticols}{1pt} 70 | \setlength{\postmulticols}{1pt} 71 | \setlength{\multicolsep}{1pt} 72 | \setlength{\columnsep}{2pt} 73 | 74 | \begin{center} 75 | \Large{\textbf{OpenCV 4.x Cheat Sheet (Python version)}} \\ 76 | \small{A summary of: \url{https://docs.opencv.org/master/}} 77 | \end{center} 78 | 79 | \section{I/O} 80 | \begin{tabular}{@{}ll@{}} 81 | \mintinline{python}{i = imread("name.png")} & Loads image as BGR (if grayscale, \texttt{B=G=R})\\ 82 | \mintinline{python}{i = imread("name.png", IMREAD_UNCHANGED)} & Loads image as is (inc.\ transparency if available)\\ 83 | \mintinline{python}{i = imread("name.png", IMREAD_GRAYSCALE)} & Loads image as grayscale\\ 84 | \mintinline{python}{imshow("Title", i)} & Displays image $I$\\ 85 | \mintinline{python}{imwrite("name.png", i)} & Saves image $I$\\ 86 | \mintinline{python}{waitKey(500)} & Wait 0.5 seconds for keypress (0 waits forever)\\ 87 | \mintinline{python}{destroyAllWindows()} & Releases and closes all windows\\ 88 | \end{tabular} 89 | 90 | 91 | \subsection{Color/Intensity} 92 | \newlength{\MyLen} 93 | %\settowidth{\MyLen}{\texttt{letterpaper}/\texttt{a4paper} \ } 94 | %\begin{tabular}{@{}p{\the\MyLen}% 95 | % @{}p{\linewidth-\the\MyLen}@{}} 96 | \begin{tabular}{@{}ll@{}} 97 | \mintinline{python}{i_gray = cvtColor(i, COLOR_BGR2GRAY)}& BGR to gray conversion\\ 98 | \mintinline{python}{i_rgb = cvtColor(i, COLOR_BGR2RGB)}& BGR to RGB (useful for \mintinline{python}{matplotlib})\\ 99 | \mintinline{python}{i = cvtColor(i, COLOR_GRAY2RGB)}& Converts grayscale to RGB (\texttt{R=G=B})\\ 100 | \mintinline{python}{i = equalizeHist(i)}& Histogram equalization\\ 101 | \mintinline{python}{i = normalize(i, None, 0, 255, NORM_MINMAX, CV_8U)} & Normalizes $I$ between 0 and 255\\ 102 | \mintinline{python}{i = normalize(i, None, 0, 1, NORM_MINMAX, CV_32F)} & Normalizes $I$ between 0 and 1 103 | \end{tabular} 104 | \subsubsection{Other useful color spaces} 105 | \begin{tabular}{@{}ll@{}} 106 | \mintinline{python}{COLOR_BGR2HSV}& BGR to HSV (Hue, Saturation, Value)\\ 107 | \mintinline{python}{COLOR_BGR2LAB}& BGR to Lab (Lightness, Green/Magenta, Blue/Yellow)\\ 108 | \mintinline{python}{COLOR_BGR2LUV}& BGR to Luv ($\approx$ Lab, but different normalization)\\ 109 | \mintinline{python}{COLOR_BGR2YCrCb}& BGR to YCrCb (Luma, Blue-Luma, Red-Luma)\\ 110 | \end{tabular} 111 | 112 | \subsection{Channel manipulation} 113 | \begin{tabular}{@{}ll@{}} 114 | \mintinline{python}{b, g, r = split(i)}& Splits the image $I$ into channels\\ 115 | \mintinline{python}{b, g, r, a = split(i)}& Same as above, but $I$ has alpha channel\\ 116 | \mintinline{python}{i = merge((b, g, r))}& Merges channels into image\\ 117 | 118 | 119 | \end{tabular} 120 | 121 | \subsection{Arithmetic operations} 122 | \begin{tabular}{@{}ll@{}} 123 | \mintinline{python}{i = add(i1, i2)}& $\min(I_1 + I_2, 255)$, i.e.\ saturated addition if \texttt{uint8}\\ 124 | \mintinline{python}{i = addWeighted(i1, alpha, i2, beta, gamma)}& $\min(\alpha I_1 + \beta I_2 + \gamma, 255)$, i.e.\ image blending\\ 125 | \mintinline{python}{i = subtract(i1, i2)}& $\max(I_1 - I_2, 0)$, i.e.\ saturated subtraction if \texttt{uint8}\\ 126 | \mintinline{python}{i = absdiff(i1, i2)}& $\left| I_1 - I_2\right|$, i.e.\ absolute difference\\ 127 | \end{tabular} 128 | 129 | \textbf{Note:} one of the images can be replaced by a scalar. 130 | 131 | 132 | \subsection{Logical operations} 133 | \begin{tabular}{@{}ll@{}} 134 | \mintinline{python}{i = bitwise_not(i)}& Inverts every bit in $I$ (e.g.\ mask inversion)\\ 135 | \mintinline{python}{i = bitwise_and(i1, i2)}& Logical \textit{and} between $I_1$ and $I_2$ (e.g.\ mask image)\\ 136 | \mintinline{python}{i = bitwise_or(i1, i2)}& Logical \textit{or} between $I_1$ and $I_2$ (e.g.\ merge 2 masks)\\ 137 | \mintinline{python}{i = bitwise_xor(i1, i2)}& Exclusive \textit{or} between $I_1$ and $I_2$\\ 138 | \end{tabular} 139 | 140 | \subsection{Statistics} 141 | \begin{tabular}{@{}ll@{}} 142 | \mintinline{python}{mB, mG, mR, mA = mean(i)} & Average of each channel (i.e.\ BGRA)\\ 143 | \mintinline{python}{ms, sds = meanStdDev(i)} & Mean and SDev p/channel (3 or 4 rows each)\\ 144 | \mintinline{python}{h = calcHist([i], [c], None, [256], [0,256])} & Histogram of channel \texttt{c}, no mask, 256 bins (0-255)\\ 145 | %\mintinline{python}{h = calcHist([i], [0,1], None, [256,256], [0,256, 0,256])} & 2D histogram using channels 0, 1\\ 146 | \mintinline{python}{h = calcHist([i], [0,1], None, [256,256],} & 2D histogram using channels 0 and 1, with\\ 147 | \multicolumn{1}{r}{\mintinline{python}{[0,256, 0,256])}}&\phantom{ } ``resolution'' 256 in each dimension\\ 148 | \end{tabular} 149 | 150 | \subsection{Filtering} 151 | \begin{tabular}{@{}ll@{}} 152 | \mintinline{python}{i = blur(i, (5, 5))} & Filters $I$ with $5\times 5$ box filter (i.e.\ average filter)\\ 153 | \mintinline{python}{i = GaussianBlur(i, (5,5), sigmaX=0, sigmaY=0)} & Filters $I$ with $5\times 5$ Gaussian; auto $\sigma$s; ($I$ is \mintinline{python}{float})\\ 154 | \mintinline{python}{i = GaussianBlur(i, None, sigmaX=2, sigmaY=2)} & Blurs, auto kernel dimension\\ 155 | \mintinline{python}{i = filter2D(i, -1, k)} & Filters with 2D kernel using cross-correlation\\ 156 | \mintinline{python}{kx = getGaussianKernel(5, -1)} & 1D Gaussian kernel with length 5 (auto StDev)\\ 157 | \mintinline{python}{i = sepFilter2D(i, -1, kx, ky)} & Filter using separable kernel (same output type)\\ 158 | \mintinline{python}{i = medianBlur(i, 3)} & Median filter with size=3 (size $\geq 3$)\\ 159 | \mintinline{python}{i = bilateralFilter(i, -1, 10, 50)} & Bilateral filter with $\sigma_\text{r} = 10$, $\sigma_\text{s}=50$, auto size\\ 160 | \end{tabular} 161 | \subsubsection{Borders} 162 | All filtering operations have parameter \mintinline{python}{borderType} which can be set to: 163 | \begin{tabular}{@{}ll@{}} 164 | \mintinline{python}{BORDER_CONSTANT} & Pads with constant border (requires additional parameter \mintinline{python}{value})\\ 165 | \mintinline{python}{BORDER_REPLICATE} & Replicates the first/last row and column onto the padding\\ 166 | \mintinline{python}{BORDER_REFLECT} & Reflects the image borders onto the padding\\ 167 | \mintinline{python}{BORDER_REFLECT_101} & Same as previous, but doesn't include the pixel at the border (the default)\\ 168 | \mintinline{python}{BORDER_WRAP} & Wraps around the image borders to build the padding\\ 169 | \end{tabular} 170 | 171 | Borders can also be added with custom widths: 172 | \begin{tabular}{@{}ll@{}} 173 | \mintinline{python}{i = copyMakeBorder(i, 2, 2, 3, 1, borderType=BORDER_WRAP)} & Widths: top, bottom, left, right\\\\ 174 | \end{tabular} 175 | 176 | \subsection{Differential operators} 177 | \begin{tabular}{@{}ll@{}} 178 | \mintinline{python}{i_x = Sobel(i, CV_32F, 1, 0)} & Sobel in the x-direction: $I_x = \frac{\partial}{\partial x}I$\\ 179 | \mintinline{python}{i_y = Sobel(i, CV_32F, 0, 1)} & Sobel in the y-direction: $I_y = \frac{\partial}{\partial y}I$\\ 180 | \mintinline{python}{i_x, i_y = spatialGradient(i, 3)} & The gradient: $\nabla I$ (using $3\times 3$ Sobel): needs \mintinline{python}{uint8} image\\ 181 | \mintinline{python}{m = magnitude(i_x, i_y)} & $\lVert\nabla I\rVert$; $I_x, I_y$ must be float (for conversion, see \mintinline{python}{np.astype()})\\ 182 | \mintinline{python}{m, d = cartToPolar(i_x, i_y)} & $\lVert\nabla I\rVert$; $\theta \in [0, 2\pi]$; \mintinline{python}{angleInDegrees=False}; needs \mintinline{python}{float32} $I_x, I_y$\\ 183 | \mintinline{python}{l = Laplacian(i, CV_32F, ksize=5)} & $\Delta I$, Laplacian with kernel size of 5\\ 184 | \end{tabular} 185 | 186 | \subsection{Geometric transforms} 187 | \begin{tabular}{@{}ll@{}} 188 | \mintinline{python}{i = resize(i, (width, height))} & Resizes image to \texttt{width}$\times$\texttt{height}\\ 189 | \mintinline{python}{i = resize(i, None, fx=0.2, fy=0.1)} & Scales image to 20\% width and 10\% height\\ 190 | \mintinline{python}{M = getRotationMatrix2D((xc, yc), deg,} & Returns $2\times 3$ rotation matrix \texttt{M}, arbitrary $(x_c, y_c)$\\ 191 | \multicolumn{1}{r}{\mintinline{python}{scale)}} &\\ 192 | \mintinline{python}{M = getAffineTransform(pts1,pts2)} & Affine transform matrix \texttt{M} from 3 correspondences\\ 193 | \mintinline{python}{i = warpAffine(i, M, (cols,rows))} & Applies Affine transform \texttt{M} to $I$, output size=(\texttt{cols}, \texttt{rows}) \\ 194 | \mintinline{python}{M = getPerspectiveTransform(pts1,pts2)} & Perspective transform matrix \texttt{M} from 4 correspondences\\ 195 | \mintinline{python}{M, s = findHomography(pts1, pts2)} & Persp transf mx \texttt{M} from all $\gg 4$ corresps (Least squares)\\ 196 | \mintinline{python}{M, s = findHomography(pts1, pts2, RANSAC)} & Persp transf mx \texttt{M} from best $\gg 4$ corresps (RANSAC)\\ 197 | \mintinline{python}{i = warpPerspective(i, M, (cols, rows))} & Applies perspective transform \texttt{M} to image $I$\\ 198 | \end{tabular} 199 | \subsubsection{Interpolation methods} 200 | \mintinline{python}{resize}, \mintinline{python}{warpAffine} and \mintinline{python}{warpPerspective} use bilinear interpolation by default. It can be changed by parameter \mintinline{python}{interpolation} for \mintinline{python}{resize}, and \mintinline{python}{flags} for the others: 201 | \begin{tabular}{@{}ll@{}} 202 | \mintinline{python}{flags=INTER_NEAREST} & Simplest, fastest (or \mintinline{python}{interpolation=INTER_NEAREST})\\ 203 | \mintinline{python}{flags=INTER_LINEAR} & Bilinear interpolation: Default\\ 204 | \mintinline{python}{flags=INTER_CUBIC} & Bicubic interpolation\\ 205 | \end{tabular} 206 | 207 | 208 | \subsection{Segmentation} 209 | \begin{tabular}{@{}ll@{}} 210 | \mintinline{python}{_, i_t = threshold(i, t, 255, THRESH_BINARY)} & Manually thresholds image $I$ given threshold level $t$\\ 211 | \mintinline{python}{t, i_t = threshold(i, 0, 255, THRESH_OTSU)} & Returns thresh level and thresholded image using Otsu\\ 212 | \mintinline{python}{i_t = adaptiveThreshold(i, 255, } & \\ 213 | \multicolumn{1}{r}{\mintinline{python}{ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, b, c)}}& Adaptive mean-c with block size $b$ and constant $c$\\ 214 | \mintinline{python}{bp = calcBackProject([i_hsv], [0,1], h,} & Back-projects histogram $h$ onto the image \texttt{i\_hsv}\\ 215 | \multicolumn{1}{r}{\mintinline{python}{ [0,180, 0,256], 1)}}&\phantom{ } using only hue and saturation; no scaling (i.e.\ 1)\\ 216 | \mintinline{python}{cp, la, ct = kmeans(feats, K, None, crit, 10,} & Returns the labels \texttt{la} and centers \texttt{ct} of \texttt{K} clusters,\\ 217 | \multicolumn{1}{r}{\mintinline{python}{KMEANS_RANDOM_CENTERS)}}&\phantom{ } best compactness \texttt{cp} out of 10; 1 feat/column\\ 218 | \end{tabular} 219 | 220 | 221 | \subsection{Features} 222 | \begin{tabular}{@{}ll@{}} 223 | \mintinline{python}{e = Canny(i, tl, th)} & Returns the Canny edges (\texttt{e} is binary)\\ 224 | \mintinline{python}{l = HoughLines(e, 1, pi/180, 150)} & Returns all $(\rho, \theta) \geq 150$ votes, Bin res: $\rho = 1$ pix, $\theta = 1\deg$\\ 225 | \mintinline{python}{l = HoughLinesP(e, 1, pi/180, 150,}&\\ 226 | \multicolumn{1}{r}{\mintinline{python}{None, 100, 20)}} & Probabilistic Hough, min length=100, max gap=20\\ 227 | \mintinline{python}{c = HoughCircles(i, HOUGH_GRADIENT, 1,} & Returns all $(x_c, y_c, r)$ with at least 18 votes, bin resolution=1,\\ 228 | \multicolumn{1}{r}{\mintinline{python}{minDist=50, param1=200, param2=18,}} & \phantom{ } param1 is the $t_h$ of Canny, and the centers must be at least\\ 229 | \multicolumn{1}{r}{\mintinline{python}{minRadius=20, maxRadius=60)}} & \phantom{ } 50 pixels away from each other\\ 230 | \mintinline{python}{r = cornerHarris(i, 3, 5, 0.04)} & Harris corners' $R$s per pixel, window=3, Sobel=5, $\alpha=0.04$\\ 231 | \end{tabular} 232 | \begin{tabular}{@{}ll@{}} 233 | \mintinline{python}{f = FastFeatureDetector_create()} & Instantiates the Star feature detector\\ 234 | \mintinline{python}{k = f.detect(i, None)} & Detects keypoints on grayscale image $I$\\ 235 | \mintinline{python}{i_k = drawKeypoints(i, k, None)} & Draws keypoints \texttt{k} on color image $I$\\ 236 | \mintinline{python}{d = xfeatures2d.BriefDescriptorExtractor_create()} & Instantiates a BRIEF descriptor\\ 237 | \mintinline{python}{k, ds = d.compute(i, k)} & Computes the descriptors of keypoints \texttt{k} over $I$\\ 238 | \mintinline{python}{dd = AKAZE_create()} & Instantiates the AKAZE detector/descriptor\\ 239 | \mintinline{python}{m = BFMatcher.create(NORM_HAMMING,} & Instantiates a brute-force matcher,\\ 240 | \multicolumn{1}{r}{\mintinline{python}{crossCheck=True)}} & \phantom{ }with x-checking, and Hamming distance\\ 241 | \mintinline{python}{ms = m.match(ds_l, ds_r)} & Matches the left and right descriptors\\ 242 | \mintinline{python}{i_m = drawMatches(i_l, k_l, i_r, k_r, ms, None)} & Draws matches from the left keypoints \texttt{k\_l} on\\ 243 | &\phantom{ } left image $I_l$ to right $I_r$, using matches \texttt{ms}\\ 244 | \end{tabular} 245 | 246 | \subsection{Detection} 247 | \begin{tabular}{@{}ll@{}} 248 | \mintinline{python}{ccs = matchTemplate(i, t, TM_CCORR_NORMED)} & Matches template $T$ to image $I$ (normalized X-correl)\\ 249 | \mintinline{python}{m, M, m_l, M_l = minMaxLoc(ccs)} & Min, max values and respective coordinates in \texttt{ccs}\\ 250 | \mintinline{python}{c = CascadeClassifier()} & Creates an instance of an ``empty'' cascade classifier\\ 251 | \mintinline{python}{r = c.load("file.xml")}& Loads a pre-trained model from file; \texttt{r} is \mintinline{python}{True/False}\\ 252 | \mintinline{python}{objs = c.detectMultiScale(i)} & Returns 1 tuple \texttt{(x, y, w, h)} per detected object\\ 253 | \end{tabular} 254 | 255 | \subsection{Motion and Tracking} 256 | \begin{tabular}{@{}ll@{}} 257 | \mintinline{python}{pts = goodFeaturesToTrack(i, 100, 0.5, 10)} & Returns 100 Shi-Tomasi corners with, at least, 0.5\\ 258 | &\phantom{ }quality, and 10 pixels away from each other\\ 259 | \mintinline{python}{pts1, st, e = calcOpticalFlowPyrLK(i0, i1,}& New positions of pts from estimated optical\\ 260 | \multicolumn{1}{r}{\mintinline{python}{pts0, None)}}&flow between $I_0$ and $I_1$; \texttt{st[i]} is 1 if flow\\ 261 | &\phantom{ }for point \texttt{i} was found, or 0 otherwise\\ 262 | \mintinline{python}{t = TrackerCSRT_create()} & Instantiates the CSRT tracker\\ 263 | \mintinline{python}{r = t.init(f, bbox)} & Initializes tracker with frame and bounding box\\ 264 | \mintinline{python}{r, bbox = t.update(f)} & Returns new bounding box, given next frame\\ 265 | \end{tabular} 266 | 267 | 268 | 269 | 270 | 271 | \subsection{Drawing on the image} 272 | \begin{tabular}{@{}ll@{}} 273 | \mintinline{python}{line(i,(x0, y0),(x1, y1), (b, g, r), t)}& Line\\ 274 | \mintinline{python}{rectangle(i, (x0, y0), (x1, y1), (b, g, r), t)}& Rectangle\\ 275 | \mintinline{python}{circle(i,(x0, y0), radius, (b, g, r), t)}& Circle\\ 276 | \mintinline{python}{polylines(i,[pts], True, (b, g, r), t)}& Closed (\mintinline{python}{True}) polygon (\mintinline{python}{pts} is array of points)\\ 277 | \mintinline{python}{putText(i, "Hi", (x,y), FONT_HERSHEY_SIMPLEX,}\\ 278 | \multicolumn{1}{r}{\mintinline{python}{1, (r,g,b), 2, LINE_AA)}}& Writes ``Hi'' at $(x, y)$, font size=1, thickness=2\\ 279 | 280 | \end{tabular} 281 | \subsubsection{Parameters} 282 | \begin{tabular}{@{}ll@{}} 283 | \mintinline{python}{(x0, y0)} & Origin/Start/Top left corner (note that it's not (row,column))\\ 284 | \mintinline{python}{(x1, y1)} & End/Bottom right corner\\ 285 | \mintinline{python}{(b, g, r)} & Line color (\mintinline{python}{uint8})\\ 286 | \mintinline{python}{t} & Line thickness (fills, if negative) 287 | \end{tabular} 288 | 289 | 290 | \subsection{Calibration and Stereo} 291 | \begin{tabular}{@{}ll@{}} 292 | %\mintinline{python}{s = cv2.StereoSGBM_create(minDisparity = 0, numDisparities = 32, blockSize = 11)} & initializes \\ 293 | % 294 | \mintinline{python}{r, crns = findChessboardCorners(i, (n_x,n_y))} & 2D coords of detected corners; \mintinline{python}{i} is gray; \mintinline{python}{r} is\\ 295 | \multicolumn{1}{r}{} & \phantom{ }the status; \mintinline{python}{(n_x, n_y)} is size of calib target\\ 296 | \mintinline{python}{crnrs = cornerSubPix(i, crns, (5,5), (-1,-1), crit)} & Improves coordinates with sub-pixel accuracy\\ 297 | \mintinline{python}{r, K, D, ExRs, ExTs = calibrateCamera(crns_3D,}& Calculates intrinsics (inc. distortion coeffs), \&\\ 298 | \multicolumn{1}{r}{\mintinline{python}{crns_2D, i.shape[:2], None, None)}} &\phantom{ }extrinsics (i.e.\ \texttt{1 R+T} per target view); \mintinline{python}{crns_3D}\\ 299 | \multicolumn{1}{r}{} & \phantom{ }contains 1 array of 3D corner coords p/target\\ 300 | \multicolumn{1}{r}{} & \phantom{ }view; \mintinline{python}{crns_2D} contains the respective arrays of\\ 301 | \multicolumn{1}{r}{} & \phantom{ }2D corner coordinates (i.e.\ 1 \mintinline{python}{crns} p/target view)\\ 302 | \mintinline{python}{drawChessboardCorners(i, (n_x, n_y), crns, r)} & Draws corners on $I$ (may be color); \texttt{r} is status\\ 303 | \multicolumn{1}{r}{} & \phantom{ } from corner detection\\ 304 | \mintinline{python}{u = undistort(i, K, D)} & Undistorts $I$ using the intrinsics\\ 305 | 306 | \mintinline{python}{s = StereoSGBM_create(minDisparity = 0,}&\\ 307 | \multicolumn{1}{r}{\mintinline{python}{numDisparities = 32, blockSize = 11)}} & Instantiates Semi-Global Block Matching method\\ 308 | \mintinline{python}{s = StereoBM_create(32, 11)} & Instantiates a simpler block matching method\\ 309 | \mintinline{python}{d = s.compute(i_L, i_R)} & Computes disparity map ($\propto^{-1}$ depth map)\\ 310 | \end{tabular} 311 | 312 | \subsection{Termination criteria (used in e.g.\ K-Means, Camera calibration)} 313 | \begin{tabular}{@{}ll@{}} 314 | \mintinline{python}{crit = (TERM_CRITERIA_MAX_ITER, 20, 0)}& Stops after 20 iterations\\ 315 | \mintinline{python}{crit = (TERM_CRITERIA_EPS, 0, 1.0)}& Stop if ``movement'' is less than 1.0\\ 316 | \mintinline{python}{crit = (TERM_CRITERIA_MAX_ITER | TERM_CRITERIA_EPS, 20, 1.0)}& Stops whatever happens first\\ 317 | \end{tabular} 318 | 319 | 320 | \subsection{Useful stuff} 321 | \subsubsection{Numpy (\mintinline{python}{np.})} 322 | \begin{tabular}{@{}ll@{}} 323 | \mintinline{python}{m = mean(i)} & Mean/average of array $I$\\ 324 | \mintinline{python}{m = average(i, weights)} & Weighted mean/average of array $I$\\ 325 | \mintinline{python}{v = var(i)} & Variance of array/image $I$\\ 326 | \mintinline{python}{s = std(i)} & Standard deviation of array/image $I$\\ 327 | \mintinline{python}{h,b = histogram(i.ravel(),256,[0,256])} & \texttt{numpy} histogram also returns the bins \texttt{b}\\ 328 | \mintinline{python}{i = clip(i, 0, 255)} & \texttt{numpy}'s saturation/clamping function\\ 329 | \mintinline{python}{i = i.astype(np.float32)} & Converts the image type to \mintinline{python}{float32} (vs.\ \mintinline{python}{uint8, float64})\\ 330 | \mintinline{python}{x, _, _, _ = linalg.lstsq(A, b)} & Solves the least squares problem $\frac{1}{2}\lVert Ax - b\rVert^2$\\ 331 | \mintinline{python}{i = hstack((i1, i2))} & Merges $I_1$ and $I_2$ side-by-side\\ 332 | \mintinline{python}{i = vstack((i1, i2))} & Merges $I_1$ above $I_2$ \\ 333 | \mintinline{python}{i = fliplr(i)} & Flips image left-right\\ 334 | \mintinline{python}{i = flipud(i)} & Flips image up-down\\ 335 | \mintinline{python}{i = pad(i, ((1, 1), (3, 3)), 'reflect')} & Alternative to \mintinline{python}{copyMakeBorder} (also top, bottom, left, right)\\ 336 | \mintinline{python}{idx = argmax(i)} & Linear index of maximum in $I$ (i.e.\ index of flattened $I$)\\ 337 | \mintinline{python}{r, c = unravel_index(idx, i.shape)} & 2D coordinate of the index with respect to shape of \texttt{i}\\ 338 | \texttt{b = any(M > 5)} & Returns \mintinline{python}{True} if any element in array $M$ is greater than 5\\ 339 | \texttt{b = all(M > 5)} & Returns \mintinline{python}{True} if all elements in array $M$ are greater than 5\\ 340 | \texttt{rows, cols = where(M > 5)} & Returns indices of the rows and cols where elems in $M$ are >5\\ 341 | \mintinline{python}{coords = list(zip(rows, cols))} & Creates a list with the elements of \texttt{rows} and \texttt{cols} paired\\ 342 | \mintinline{python}{M_inv = linalg.inv(M)} & Inverse of $M$\\ 343 | \mintinline{python}{rad = deg2rad(deg)} & Converts degrees into radians\\ 344 | \end{tabular} 345 | \subsubsection{Matplotlib.pyplot (\mintinline{python}{plt.})} 346 | \begin{tabular}{@{}ll@{}} 347 | \mintinline{python}{imshow(i, cmap="gray", vmin=0, vmax=255)} & \mintinline{python}{matplotlib}'s \mintinline{python}{imshow} preventing auto-normalization\\ 348 | \mintinline{python}{quiver(xx, yy, i_x, -i_y, color="green")} & Plots the gradient direction at positions \mintinline{python}{xx, yy}\\ 349 | \mintinline{python}{savefig("name.png")} & Saves the plot as an image\\ 350 | \end{tabular} 351 | 352 | 353 | 354 | 355 | 356 | \rule{0.3\linewidth}{0.25pt} 357 | \scriptsize 358 | 359 | Copyright \copyright\ 2019 António Anjos (Rev: \today)\\ 360 | Most up-to-date version: \url{https://github.com/a-anjos/python-opencv} 361 | 362 | 363 | 364 | \end{multicols} 365 | \end{document} 366 | --------------------------------------------------------------------------------