contour

问题 I am trying to draw contour plot with ggplot2 and it is proving to be a little harder than I imagined. Using the iris dataset I am able to produce this plot: ggplot(iris, aes(x=Petal.Width, y=Petal.Length, fill=Sepal.Width)) + stat_density2d(geom="polygon", aes(fill=..level..)) My issue is that I can't seem to figure out how to display- rather than the density values -the raw Sepal.Width values. Here is what I've tried: ggplot(iris, aes(x=Petal.Width, y=Petal.Length, z=Sepal.Width)) + geom

问题 I am trying to draw contour plot with ggplot2 and it is proving to be a little harder than I imagined. Using the iris dataset I am able to produce this plot: ggplot(iris, aes(x=Petal.Width, y=Petal.Length, fill=Sepal.Width)) + stat_density2d(geom="polygon", aes(fill=..level..)) My issue is that I can't seem to figure out how to display- rather than the density values -the raw Sepal.Width values. Here is what I've tried: ggplot(iris, aes(x=Petal.Width, y=Petal.Length, z=Sepal.Width)) + geom

问题 I feel like I've been endlessly searching for a solution to this and cannot find one anywhere. Basically, I need to overlay coloured contour maps as different layers (with different colours) onto a ggmap and cannot get this to work for my life. What I'm trying to do is take the example illustrated here: Overlay two ggplot2 stat_density2d plots with alpha channels only instead of mapping this onto a ggplot(), map it onto a ggmap(). I've read here: ggmap with geom_map superimposed that part of

问题 how to recognise a zebra crossing from top view using opencv? in my previous question the problem is to find the curved zebra crossing using opencv. now I thought that the following way would be much easier way to detect it, (i) canny it (ii) find the contours in it (iii) find the black stripes in it, in my case it is slightly oval in shape now my question is how to find that slightly oval shape?? look here for images of the crossing: www.shaastra.org/2013/media/events/70/Tab/422/Modern

问题 here is the code that produces the plot below: filled.contour(x,y,z, col=colors2, levels=breakpoints, plot.axes={axis(1); axis(2); map(add=TRUE, interior=FALSE)} ) A sample can be created using: z=matrix(rnorm(7008),nrow=96) x=seq(-176.25,180, by=3.75) y=seq(-90,90, by=2.5) filled.contour(x,y,z, plot.axes={axis(1); axis(2); map(add=TRUE, interior=FALSE)} ) However I can't seem to figure out how to crop/move the projected world map (from the 'maps' package) 3.75 degrees on/to the right side so

问题 I am using openCV with python to extract contours from an image. Now I need to export these contour paths (list) as an svg paths. How can I achieve this ? code: ret,thresh = cv2.threshold(imgray,27,25,0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_TC89_L1) print(type(contours)) #type list 回答1: the problem has been solved as follows: c = max(contours, key=cv2.contourArea) #max contour f = open('path.svg', 'w+') f.write('<svg width="'+str(width)+'" height

问题 I am using openCV with python to extract contours from an image. Now I need to export these contour paths (list) as an svg paths. How can I achieve this ? code: ret,thresh = cv2.threshold(imgray,27,25,0) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_TC89_L1) print(type(contours)) #type list 回答1: the problem has been solved as follows: c = max(contours, key=cv2.contourArea) #max contour f = open('path.svg', 'w+') f.write('<svg width="'+str(width)+'" height

问题 I have a image with about 50 to 100 small contours. I wish to find the average intensity[1] of each of these contours in real-time[2]. Some of the ways I could think of was Draw contour with FILLED option for each contour; use each image as a mask over the original image, thus find the average. But I presume that this method won't be real-time at first glance. Study OpenCV implementation of drawContour function with the FILLED option and access the pixels enclosed by the contour in the same

问题 So here is the code that i used to detect the contours : IplImage* DetectAndDrawQuads(IplImage* img) { CvSeq* contours; CvSeq* result; CvMemStorage *storage = cvCreateMemStorage(0); IplImage* ret = cvCreateImage(cvGetSize(img), 8, 3); IplImage* temp = cvCreateImage(cvGetSize(img), 8, 1); cvCvtColor(img, temp, CV_BGR2GRAY); cvFindContours(temp, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0)); while(contours) { result = cvApproxPoly(contours, sizeof

问题 I am trying to replicate the code from Andrew Ng's Machine Learning course on Coursera in R (as the course is in Octave). Basically I have to plot a non linear decision boundary (at p = 0.5) for a polynomial regularized logistic regression. I can easily replicate the plot with the base library: contour(u, v, z, levels = 0) points(x = data$Test1, y = data$Test2) where: u <- v <- seq(-1, 1.5, length.out = 100) and z is a matrix 100x100 with the values of z for every point of the grid. Dimension