Pixsets are covered elsewhere, in the vignette vignette('pixsets'), and in the morphology tutorial. Internally a pixset is just a array of logicals, so it’s no different from what you’d get from running a test on a vector, e.g.: 1:4 >= 2 FALSE TRUE TRUE TRUEĬompared to logical arrays, however, pixsets come with many convenience functions, for plotting, splitting, morphological operations, etc.Īs an example, the following bit of code generates random shapes by filtering & thresholding a random image, splitting it into connected components, and removing the smaller ones: set.seed(1) Width: 768 pix Height: 512 pix Depth: 1 Colour channels: 1 A pixset is a set of pixels, represented as a binary image, and that’s what you get when you test properties on images, e.g.: grayscale(parrots) >. The reverse is possible as well: you can convert a cimg object to a ame head(as.ame(parrots)) x y cc value Width: 10 pix Height: 10 pix Depth: 1 Colour channels: 1 You can also be specific by setting the “dims” argument explicitly: mutate(df,value=cos(sin(x+y)^2)) %>% as.cimg(dims=c(10,10,1,1)) Image. Width: 10 pix Height: 10 pix Depth: 1 Colour channels: 1īy default as. will try to guess image size from the input. The reverse is possible as well: if you have a ame with columns x,y,z,cc,value, you can turn it into a cimg object: df % as.cimg Image. Which tries to guess what sort of image dimensions you want (see tutorial). Width: 3 pix Height: 3 pix Depth: 1 Colour channels: 1 Width: 10 pix Height: 10 pix Depth: 1 Colour channels: 1Īnd vectors: 1:9 %>% as.cimg Image. To promote an array to a “cimg” object, use as.cimg: noise % as.cimg Image. “cimg” is just a regular 4d array with an S3 class tacked on so we can have custom plot, print, etc. Imager uses the “cimg” class for its images. Here’s an example with a grayscale image: parrots <- load.example('parrots')Īnd a colour image: dim(parrots) 768 512 1 3Īnd finally a video, also in colour: dim(tennis) 352 240 150 3 In R the object is represented as a 4D array. All in all the different dimensions are represented in the x,y,z,c order. If we have several colour channels, then once we’re done with the first colour channel we move to the next one. Once we hit the end of the screen, we move to the next frame (increasing z) and repeat the process. Once we hit the end of the scanline, we move to the next line. Pixels are stored in the following manner: we scan the image beginning at the upper-left corner, along the x axis. Your objects will still be officially 4 dimensional, with two trailing flat dimensions. If you only have grayscale images then the two extra dimensions are obviously pointless, but they won’t bother you much. Remember the order, it will be used consistently in imager. The first two are the usual spatial dimensions, the third one will usually correspond to depth or time, and the fourth one is colour. The four dimensions are labelled x,y,z,c. Images are represented as 4D numeric arrays, which is consistent with CImg’s storage standard (it is unfortunately inconsistent with other R libraries, like spatstat, but converting between representations is easy). See also the animation package for more along these lines. Invisible(purrr::map2(iml,fnames,function(im,f) imager::save.image(im,f))) lets you make a video from a directory containing images representing individual frames: dd <- tempdir() One problem with videos is that they very quickly won’t fit in memory anymore, so you might have to deal with them piecewise. To save videos, use : f % imsplit("z") %>% plot Use fps to set the frame acquisition rate: vid % plot If you need to load and save videos please install ffmpeg for videos. #Load the first four, return as image list, display Urls % html_nodes("img") %>% html_attr("src") #Get urls of parrot pictures file tags, get their "src" attribute, a URL to an image If you need to access images in other formats you’ll need to install ImageMagick. imager supports PNG, JPEG and BMP natively. In an interactive session you can run play(combined) to view the results. Tennis % imappend("x") #Paste the two videos together #If ffmpeg is present, we can load videos as well: Grayscale(im) %>% imgradient("xy") %>% plot(layout="row") #Another example of chaining: image gradient along x and y axes Here’s an example of imager in action: library(imager)įile % deriche(2,order=2,axis="y") %>% plot
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