\documentclass[10pt,a4paper]{article}

% Packages
\usepackage{fancyhdr}           % For header and footer
\usepackage{multicol}           % Allows multicols in tables
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%\usepackage[utf8x]{inputenc}    % For unicode character support
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\usepackage{lastpage}           % Needed for total page number
\usepackage{seqsplit}           % Splits long words.
%\usepackage{opensans}          % Can't make this work so far. Shame. Would be lovely.
\usepackage[normalem]{ulem}     % For underlining links
% Most of the following are not required for the majority
% of cheat sheets but are needed for some symbol support.
\usepackage{amsmath}            % Symbols
\usepackage{MnSymbol}           % Symbols
\usepackage{wasysym}            % Symbols
%\usepackage[english,german,french,spanish,italian]{babel}              % Languages

% Document Info
\author{Plus5754}
\pdfinfo{
  /Title (opencv-notes.pdf)
  /Creator (Cheatography)
  /Author (Plus5754)
  /Subject (opencv-notes Cheat Sheet)
}

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\newcommand{\mymulticolumn}[3]{\multicolumn{#1}{>{\columncolor{\RowColorName}}#2}{#3}} % For coloured multi-cols
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% Header and Footer
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\fancyhead{} % Set header to blank
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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
    \SetRowColor{DarkBackground}
    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
    }
\end{tabulary}
\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{opencv-notes Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Plus5754} via \textcolor{DarkBackground}{\uline{cheatography.com/183380/cs/38184/}}}
\end{tabulary}
\end{multicols}}

\fancyfoot[L]{ \footnotesize
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Plus5754 \\
  \uline{cheatography.com/plus5754} \\
  \end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 13th May, 2023.\\
   Updated 11th April, 2023.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
  \SetRowColor{white}
  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
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% from this page to resize cheat sheet text:
% www.emerson.emory.edu/services/latex/latex_169.html
\footnotesize % Small font.


\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Drawing on Images:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{cv2.line(img,(0,0),(200,300),(255,0,0),5) \#Draw a blue line with thickness of 5 \newline cv2.rectangle(img,(50,50),(200,300),(0,255,0),3) \#Draw a green rectangle with thickness of 3 \newline cv2.circle(img,(200,200), 50, (0,0,255), -1) \#Draw a filled red circle \newline cv2.putText(img,'OpenCV',(10,500), \seqsplit{cv2.FONT\_HERSHEY\_SIMPLEX}, 4,(255,255,255),2,cv2.LINE\_AA) \#Add text} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Feature Detection:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# identify distinct regions of an image that contain important information. Used for object  \newline \# recognition, image registration, and 3D reconstruction. Common methods: Harris corner  \newline \# detection, Shi-Tomasi corner detection, FAST, SURF, SIFT. \newline img = cv2.imread('image.jpg', 0)  \#Read image in grayscale \newline fast = \seqsplit{cv2.FastFeatureDetector\_create()}  \#\#FAST feature detection \newline keypoints\_fast = fast.detect(img) \newline orb = cv2.ORB\_create()  \#\#ORB feature detection \newline keypoints\_orb, descriptors = orb.detectAndCompute(img, None) \newline sift = \seqsplit{cv2.xfeatures2d.SIFT\_create()}    \#\#SIFT feature detection \newline keypoints\_sift, descriptors = \seqsplit{sift.detectAndCompute(img}, None) \newline surf = \seqsplit{cv2.xfeatures2d.SURF\_create()}   \#\#SURF feature detection \newline keypoints\_surf, descriptors = \seqsplit{surf.detectAndCompute(img}, None) \newline  \newline cv2.drawKeypoints(img, keypoints\_fast, img, color=(255, 0, 0)) \newline cv2.drawKeypoints(img, keypoints\_orb, img, color=(0, 255, 0)) \newline cv2.drawKeypoints(img, keypoints\_sift, img, color=(0, 0, 255)) \newline cv2.drawKeypoints(img, keypoints\_surf, img, color=(255, 255, 0))} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Perspective Transformation:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \newline pts1 = np.float32({[}{[}56,65{]},{[}368,52{]},{[}28,387{]},{[}389,390{]}{]}) \#Four points on original image \newline pts2 = np.float32({[}{[}0,0{]},{[}300,0{]},{[}0,300{]},{[}300,300{]}{]}) \#Four points on destination image \newline M = \seqsplit{cv2.getPerspectiveTransform(pts1},pts2) \#Perspective transformation matrix \newline dst = cv2.warpPerspective(img,M,(300,300)) \#Apply perspective transformation \newline cv2.imshow('image',dst) \newline cv2.waitKey(0) \newline cv2.destroyAllWindows()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Feature Description:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# extract numerical representation of detected feature in image. Important for object  \newline \# recognition and image matching. Common methods: BRIEF, ORB, SIFT, SURF. \newline img = cv2.imread('image.jpg', 0)  \# Read image in grayscale \newline fast = \seqsplit{cv2.FastFeatureDetector\_create()}   \#FAST feature detection \newline brief = \seqsplit{cv2.xfeatures2d.BriefDescriptorExtractor\_create()}  \#BRIEF descriptor \newline keypoints\_fast = fast.detect(img) \newline keypoints\_fast, descriptors\_fast = brief.compute(img, keypoints\_fast) \newline orb = cv2.ORB\_create()  \#ORB feature detection and descriptor \newline keypoints\_orb, descriptors\_orb = orb.detectAndCompute(img, None) \newline sift = \seqsplit{cv2.xfeatures2d.SIFT\_create()}   \#SIFT feature detection and descriptor \newline keypoints\_sift, descriptors\_sift = \seqsplit{sift.detectAndCompute(img}, None) \newline surf = \seqsplit{cv2.xfeatures2d.SURF\_create()}  \#SURF feature detection and descriptor \newline keypoints\_surf, descriptors\_surf = \seqsplit{surf.detectAndCompute(img}, None)} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Haar Cascade Classifiers (Object Detection):}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# object detection using image patterns. Trains on features of positive and negative samples.  \newline \# Identifies object in new images by matching pattern. Can detect faces, eyes, and other objects. \newline face\_cascade = \seqsplit{cv2.CascadeClassifier('haarcascade\_frontalface\_default.xml')} \#Load face cascade classifier \newline img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \newline gray = cv2.cvtColor(img, cv2.COLOR\_BGR2GRAY) \#Convert to grayscale \newline faces = \seqsplit{face\_cascade.detectMultiScale(gray}, 1.3, 5) \#Detect faces \newline for (x,y,w,h) in faces: \newline     cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) \#Draw rectangle around face} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Contour Detection:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{gray = cv2.cvtColor(img, cv2.COLOR\_BGR2GRAY) \#Convert to grayscale \newline ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH\_BINARY) \#Threshold \newline contours, hierarchy = cv2.findContours(thresh, cv2.RETR\_TREE, \seqsplit{cv2.CHAIN\_APPROX\_SIMPLE)} \#Find contours \newline cv2.drawContours(img, contours, -1, (0,255,0), 3) \#Draw contours} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Loading and Saving Images:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \#Load image \newline cv2.imshow('image',img) \#Show image \newline cv2.imwrite('output.jpg',img) \#Save image \newline cv2.waitKey(0) \#Wait for a keyboard event \newline cv2.destroyAllWindows() \#Close all windows} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Image Properties:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \newline print('Image Shape:', img.shape) \#Prints (height, width, channels) \newline print('Image Size:', img.size) \#Prints the total number of pixels \newline print('Image Datatype:', img.dtype) \#Prints the datatype of the pixels} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Accessing a Camera/ Video Capture:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{cap = cv2.VideoCapture(0) \#Open default camera \newline while(True): \newline     ret, frame = cap.read() \#Read frame from camera \newline     cv2.imshow('frame',frame) \#Show frame \newline     if cv2.waitKey(1) \& 0xFF == ord('q'): \#Quit when 'q' is pressed \newline         break} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Accessing a Video File:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{cap = \seqsplit{cv2.VideoCapture('video.mp4')} \#Open video file \newline while(cap.isOpened()): \newline     ret, frame = cap.read() \#Read frame from video \newline     cv2.imshow('frame',frame) \#Show frame \newline     if cv2.waitKey(25) \& 0xFF == ord('q'): \#Quit when 'q' is pressed \newline         break} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Video Writer:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{cap = cv2.VideoCapture(0) \newline fourcc = \seqsplit{cv2.VideoWriter\_fourcc(*'XVID')} \#Codec \newline out = \seqsplit{cv2.VideoWriter('output.avi'},fourcc, 20.0, (640,480)) \#Create video writer \newline while(cap.isOpened()): \newline     ret, frame = cap.read() \#Capture frame-by-frame \newline     if ret==True: \newline         out.write(frame) \#Write frame to output video \newline         cv2.imshow('frame',frame) \#Display frame \newline         if cv2.waitKey(1) \& 0xFF == ord('q'): \#Exit loop on 'q' key press \newline             break \newline     else: \newline         break \newline cap.release() \#Release camera \newline out.release() \#Release video writer \newline cv2.destroyAllWindows()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Colorspace Conversion:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \newline gray\_img = cv2.cvtColor(img, cv2.COLOR\_BGR2GRAY) \#Convert to grayscale \newline hsv\_img = cv2.cvtColor(img, cv2.COLOR\_BGR2HSV) \#Convert to HSV colorspace} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Blurring:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# reduces image noise and sharpness by averaging pixel values in a neighborhood. \newline gaussian\_blur = cv2.GaussianBlur(img, (5, 5), 0)  \newline median\_blur = cv2.medianBlur(img, 5) \newline bilateral\_filter = cv2.bilateralFilter(img, 9, 75, 75)} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Image Filtering:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# modifies pixel values to improve image quality. Common methods include 2D convolution,  \newline \#blurring, and edge detection. \newline img = cv2.imread('image.jpg') \newline kernel = np.ones((5,5),np.float32)/25 \newline convolution = cv2.filter2D(img,-1,kernel)  \#2D Convolution \newline blur = cv2.blur(img,(5,5))  \# Image Blurring (averaging) \newline gaussian\_blur = cv2.GaussianBlur(img, (5,5),0)   \# Gaussian Blurring \newline median\_blur = cv2.medianBlur(img, 5)   \# Median Blurring \newline bilateral\_filter = cv2.bilateralFilter(img, 9, 75, 75)  \#Bilateral Filtering} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Edge Detection:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', 0)  \# Read image in grayscale \newline \# Canny edge detection \newline edges\_canny = cv2.Canny(img, 100, 200) \newline \# Sobel operator \newline edges\_sobel = cv2.Sobel(img, cv2.CV\_64F, 1, 0) + cv2.Sobel(img, cv2.CV\_64F, 0, 1) \newline \# Laplacian of Gaussian (LoG) \newline edges\_log = \seqsplit{cv2.Laplacian(cv2.GaussianBlur(img}, (3, 3), 0), cv2.CV\_64F) \newline cv2.imshow('Original', img) \newline cv2.imshow('Canny Edges', edges\_canny) \newline cv2.imshow('Sobel Edges', edges\_sobel) \newline cv2.imshow('LoG Edges', edges\_log) \newline cv2.waitKey(0) \newline cv2.destroyAllWindows()} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Morphological Operations:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{kernel = np.ones((5,5),np.uint8) \#5x5 kernel \newline erosion = cv2.erode(img,kernel,iterations = 1) \#Erosion \newline dilation = cv2.dilate(img,kernel,iterations = 1) \#Dilation \newline opening = cv2.morphologyEx(img, cv2.MORPH\_OPEN, kernel) \#Opening \newline closing = cv2.morphologyEx(img, cv2.MORPH\_CLOSE, kernel) \#Closing} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Histogram Equalization:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# improves contrast by redistributing pixel values in an image.  \newline \# It's useful for image preprocessing and analysis \newline eq\_img = cv2.equalizeHist(img) \#Apply histogram equalization} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Hough Transform:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# detect simple shapes such as lines, circles, and ellipses in an image \newline img = cv2.imread('image.jpg', cv2.IMREAD\_GRAYSCALE) \newline edges = cv2.Canny(img,50,150,apertureSize = 3) \#Canny edge detection \newline lines = cv2.HoughLines(edges,1,np.pi/180,200) \#Hough transform \newline for rho,theta in lines{[}0{]}: \newline     a = np.cos(theta) \newline     b = np.sin(theta) \newline     x0 = a{\emph{rho \newline     y0 = b}}rho \newline     x1 = int(x0 + 1000{\emph{(-b)) \newline     y1 = int(y0 + 1000}}(a)) \newline     x2 = int(x0 - 1000*(-b)) \newline     y2 = int} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Thresholding:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{\# Thresholding separates object from background by setting pixel values above or below a  \newline \# threshold. It creates a binary image. Helps with object detection, segmentation, and feature  \newline \# extraction. Methods: global, adaptive, and Otsu's. \newline img = cv2.imread('image.jpg', cv2.IMREAD\_GRAYSCALE) \newline ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH\_BINARY) \#Binary thresholding \newline ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH\_BINARY\_INV) \#Inverse binary thresholding \newline ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH\_TRUNC) \#Truncated thresholding \newline ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH\_TOZERO) \#Threshold to zero \newline ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH\_TOZERO\_INV) \#Inverse threshold to zero \newline adaptive\_thresh = \seqsplit{cv2.adaptiveThreshold(img},255,cv2.ADAPTIVE\_THRESH\_GAUSSIAN\_C,cv2.THRESH\_BINARY,11,2) \#Adaptive thresholding} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{Image Pyramids:}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{img = cv2.imread('image.jpg', cv2.IMREAD\_COLOR) \newline lower\_reso = cv2.pyrDown(img) \#Reduce resolution \newline higher\_reso = cv2.pyrUp(img) \#Increase resolution} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}



\end{document}