That's why we need to group or scale a number of pixels and replace them with one single "average" pixel. For example image 400 by 400 pixels is a rather small image, however you will not find a terminal with 400 colums or 400 rows. Therefore we need to do some type conversion here.ġ:20 - obviously one pixel of a real image cannot be mapped one-to-one to an ASCII character as ASCII character on the screen takes much more space. Note that Golang does not allow to multiply int (or in this case uint32) with float64 constants. There are several ways how to do it and in this case we will use a formula that was used for NTSC analogue television encoding system. Note that we are importing image/png package - this way we instruct Golang which decoder should be used for loading the image.įor consistency we are checking that there are no errors and the image is loaded properly.ġ:07 - define grayscale function that will convert RGB color to a grayscale component. Usually such type of images give best results for ASCII art.Ġ:40 - define loadImage function that will load image from a file system. This image is nice as it has some contrast - some light and dark areas with clear borders. Let's break down the solution and comment on some complex or interesting things.Ġ:20 - As starting point we'll use an image with Golang logo. Thanks to Wolfram Team and Mikayel Egibyan in particular for mentoring the internship.Disclaimer: never use this code in production. ASCII?: Convert Images and GIFs into ASCII Art.Overall this approach cannot recover the partition structure and the character raster (since it only uses the mean intensity value as a distance measure). Also, we modified the procedures to import already rasterized characters, so only algorithm complexity is considered:Īs we can see, the Intensity-based approach is way faster. Approaches used the same values for similar parameters (the time computation uses AbsoluteTiming). dimensions, resizing the input image to match 500 in one dimensionĪs we can see, the results are similar, but in the Intensity-based approach, edges are more precise.įor time-complexity Comparison, a square image, which resolution was changing from 100 to 2000. A workaround to accelerate the character rasters' generation is to call Rasterize once, via grouping all characters as a batch on a grid: rasters = Rasterize[ We could do it as simple as Rasterize CharacterRange, but this approach will require Rasterize to be called 95 times, which is pretty time-consuming. The approach uses the basic idea to partition an image into a rectangular grid and use intensities to assign a character raster to each cell (the result will always be a grayscale image).įirst of all, we need to rasterize all 95 ASCII characters ( CharacterRange). In this case, the similarity measure is the mean intensity. When thinking about ASCII art generation, the first thing that comes to mind is to partition an image and see the partitions' similarity to the possible character set. This post is about the first approach called an Intensity-based approach (the notebook is available as an attachment to the post). We designed and developed four approaches to perform ASCII art. Doing an internship with Wolfram Research Inc., we worked on the ASCII art project.
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