Tag Archives: imager

Pencil Scribbles

Con las bombas que tiran los fanfarrones, se hacen las gaditanas tirabuzones (Palma y corona, Carmen Linares)

This time I draw Franky again using an algorithm to solve the Travelling Salesman Problem as I did in my last post. On this occasion, instead of doing just one single line drawing, I overlap many of them (250 concretely), each of them sampling 400 points on the original image (in my previous post I sampled 8.000 points). Last difference is that I don’t convert the image to pure black and white withĀ threshold function: now I use the gray scale number of each pixel to weight the sample.

Once again, I use ggplot2 package, and its magical geom_path, to generate the image. The pencil effect is obtained giving a very high transparency to the lines. This is the result:

I love when someone else experiment with my experiments as Mara Averick did:

Or Erik-Jan van Kesteren:

You can do it as well with this one, since you will find the code here. Please, let me know your own creations if you do. You can find me on twitter or by email.

P.S.: Although it may seems otherwise, I’m not obsessed with Frankenstein šŸ™‚

The Travelling Salesman Portrait

I have noticed even people who claim everything is predestined, and that we can do nothing to change it, look before they cross the road (Stephen Hawking)

Imagine a salesman and a set of cities. The salesman has to visit each one of the cities starting from a certain one and returning to the same city. The challenge is finding the route which minimizes the total length of the trip.Ā This is theĀ Travelling Salesman ProblemĀ (TSP): one of the most profoundly studied questions in computational mathematics. Since you can find a huge amount of articles about the TSP in the Internet, I will not give more details about it here.

In this experiment I apply an heuristic algorithm to solve the TSP to draw a portrait. The idea is pretty simple:

  • Load a photo
  • Convert it to black and white
  • Choose a sample of black points
  • Solve the TSP to calculate a route among the points
  • Plot the route

The result is a single line drawing of the image that you loaded. To solve the TSP I used the arbitrary insertion heuristic algorithmĀ (Rosenkrantz et al. 1977), which is quite efficient.

To illustrate the idea, I have used again this image of Frankenstein (I used it before inĀ this other experiment). This is the result:

You can find the code here.


Remember me, remember me, but ah! forget my fate (Dido’s Lament, Henry Purcell)

A Voronoi diagramĀ divides a plane based on a set of original points. Each polygon, or Voronoi cell, contains an original point and all that are closer to that point than any other.

This is a nice example of a Voronoi tesselation. You can find good explanations of Voronoi diagrams and Delaunay triangulationsĀ here (in English) or here (in Spanish).

A grayscale image is simply a matrix where darknessĀ of pixel located in coordinates (i, j) is represented by the value of its corresponding element of the matrix:Ā a grayscale image is a dataset. This is a Voronoi diagraman of Frankenstein:

To do it I followed the next steps:

  1. Read this image
  2. Convert it to gray scale
  3. Turn it into a pure black and white image
  4. Obtain a random sample of black pixels (previous image corresponds to a sample of 6.000 points)
  5. Computes the Voronoi tesselation

Steps 1Ā to 3 were done with imager, a very appealingĀ package to proccess and analiceĀ images. Step 5 was done with deldir, also a convenientĀ package which computes Delaunay triangulation and the Dirichlet or Voronoi tessellations.

The next grid shows tesselations for sample size from 500 to 12.000 points and step equal toĀ 500:

I gathered all previous images in this gif created with magick, another amazing package of R I discovered recently:

This is the code:


# Download the image
download.file(file, destfile = "frankenstein.jpg", mode = 'wb')

# Read and convert to grayscale
load.image("frankenstein.jpg") %>% grayscale() -> x

# This is just to define frame limits
x %>% 
  as.data.frame() %>% 
  group_by() %>% 
  summarize(xmin=min(x), xmax=max(x), ymin=min(y), ymax=max(y)) %>% 

# Filter image to convert it to bw
x %>%
  threshold("45%") %>% 
  as.cimg() %>% 
  as.data.frame() -> df

# Function to compute and plot Voronoi tesselation depending on sample size
doPlot = function(n)
  #Voronoi tesselation
  df %>% 
  sample_n(n, weight=(1-value)) %>% 
  select(x,y) %>% 
  deldir(rw=rw, sort=TRUE) %>% 
  .$dirsgs -> data

  # This is just to add some alpha to lines depending on its longitude
  data %>% 
         alpha=findInterval(long, quantile(long, probs = seq(0, 1, length.out = 20)))/21)-> data

  # A little bit of ggplot to plot results
  data %>% 
    ggplot(aes(alpha=(1-alpha))) +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color="black", lwd=1) +
    scale_y_continuous(expand=c(0,0), trans=reverse_trans())+
    theme(legend.position  = "none",
            panel.background = element_rect(fill="white"),
            axis.ticks       = element_blank(),
            panel.grid       = element_blank(),
            axis.title       = element_blank(),
            axis.text        = element_blank())->plot


# I call the previous function and store resulting plot in jpeg format
jpeg(name, width = 600, height = 800, units = "px", quality = 100)

# Once all images are stored I can create gif

for (i in length(images):1)
  x=image_scale(x, "300")
  c(x, frames) -> frames
animation=image_animate(frames, fps = 2)
image_write(animation, "Frankenstein.gif")