contour

I have a customized function for plotting filled contours which is heavily based on Carey McGilliard and Bridget Ferris work ( http://wiki.cbr.washington.edu/qerm/sites/qerm/images/1/16/Filled.contour3.R ) and http://wiki.cbr.washington.edu/qerm/index.php/R/Contour_Plots . the filled.contour3 function runs perfectly in R 2.15.3 but throws an error in R 3.0.x Error in .Internal(filledcontour(as.double(x), as.double(y), z, as.double(levels), : there is no .Internal function 'filledcontour' Could you please help me with a solution or a workarround so that I can use the filled.contour3() function

I want to output a blue-filled hand but get the incorrect output. I've included the input picture, incorrect output picture and code below. i think the code below does not fill the whole image because the image isn't closed yet at the right boundary. how do i close the shape and fill it with blue properly? #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <iostream> #include <stdio.h> #include <stdlib.h> using namespace cv; using namespace std; void drawStuff(); void showInputWindow(); void showCannyWindow(); void

When filling a grid with color such as when using contourf in pyplot, I need to find a way to change the color that pyplot uses to fill data that exceed the specified range of the colorbar. I wish to have a static colorbar that does not automatically change its range to fit the max/min of the data, so having occasional extreme values that exceed its bounds is inevitable, and colors need to be specified for such values. The default color for values exceeding the bounds of the colorbar is white, which can glaringly clash with the surrounding data if the colormap does not have white as its end

I am plotting data as described in a previous Stackoverflow question: gnuplot 2D polar plot with heatmap from 3D dataset - possible? Mostly it is working well for me, and I am down to some small details. One of these is how to exert control over the contour line colors and linewidths. There are lots of posts on the web regarding using set style increment user followed by definition of user style via set style line 1 lc rgb "blue" lw 2 etc. In theory this was supposed to force splot to plot lines using the newly defined styles. I tried it and it did not work. In addition, when I went to the

I need to use cv::FindContours() in a program and I have to know the algorithm behind that. What algorithm does openCV use to find contours? How does it work? fireant If you read the documentation it is mentioned this function implements the algorithm of: Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985) OpenCV is open source if you want to see how this is implemented just need to read the code: https://github.com/opencv/opencv/blob/master/modules/imgproc/src/contours.cpp#L1655 One of the answers to this question

I am trying to create a contour plot. I would like to have depth on the Y-axis and time on the X-axis. Right now this is the code that I am using: par <- ggplot(up_PAR, aes(Time.hour.of.the.day., Depth, z = PAR)) parplot <- par + stat_contour(bins=20, aes(colour=..level..))+ scale_colour_gradient(limits=c(0.000842, 0.00000000195),low="black", high="black") + scale_y_reverse()+ theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ xlab("Hour of the Day")+ ylab("Depth(m)")+ ggtitle("Downwelling PAR (photons/m2/s), January 22nd") direct.label(parplot) However,

I am working on Retinal fundus images.The image consists of a circular retina on a black background. With OpenCV, I have managed to get a contour which surrounds the whole circular Retina. What I need is to crop out the circular retina from the black background. It is unclear in your question whether you want to actually crop out the information that is defined within the contour or mask out the information that isn't relevant to the contour chosen. I'll explore what to do in both situations. Masking out the information Assuming you ran cv2.findContours on your image, you will have received a