Drawing Region Adjacency Graphs

A lot of Image Processing algorithms are based on intuition from visual cues. Region Adjacency Graphs would also benefit if they were somehow drawn back on the images they represent. If we are able to see the nodes, edges, and the edges weights, we can fine tune our parameters and algorithms to suit our needs. I had written a small hack in this blog post to help better visualize the results. Later, Juan suggested I port if for scikit-image. It will indeed be a very helpful tool for anyone who wants to explore RAGs in scikit-image.

Getting Started

You will need to pull for this Pull Request to be able to execute the code below. I’ll start by defining a custom show_image function to aid displaying in IPython notebooks.

from skimage import graph, data, io, segmentation, color
from matplotlib import pyplot as plt
from skimage.measure import regionprops
import numpy as np
from matplotlib import colors

def show_image(img):
    width = img.shape[1] / 50.0
    height = img.shape[0] * width/img.shape[1]
    f = plt.figure(figsize=(width, height))

We will start by loading a demo image just containing 3 bold colors to help us see how the draw_rag function works.

image = io.imread('/home/vighnesh/Desktop/images/colors.png')


We will now use the SLIC algorithm to give us an over-segmentation, on which we will build our RAG.

labels = segmentation.slic(image, compactness=30, n_segments=400)

Here’s what the over-segmentation looks like.

border_image = segmentation.mark_boundaries(image, labels, (0, 0, 0))


Drawing the RAGs

We can now form out RAG and see how it looks.

rag = graph.rag_mean_color(image, labels)
out = graph.draw_rag(labels, rag, border_image)


In the above image, nodes are shown in yellow whereas edges are shown in green. Each region is represented by its centroid. As Juan pointed out, many edges will be difficult to see because of low contrast between them and the image, as seen above. To counter this we support the desaturate option. When set to True the image is converted to grayscale before displaying. Hence all the image pixels are a shade of gray, while the edges and nodes stand out.

out = graph.draw_rag(labels, rag, border_image, desaturate=True)


Although the above image does very well to show us individual regions and their adjacency relationships, it does nothing to show us the magnitude of edges. To give us more information about the magnitude of edges, we have the colormap option. It colors edges between the first and the second color depending on their weight.

blue_red = colors.ListedColormap(['blue', 'red'])
out = graph.draw_rag(labels, rag, border_image, desaturate=True,


As you can see, the edges between similar regions are blue, whereas edges between dissimilar regions are red. draw_rag also accepts a thresh option. All edges above thresh are not considered for drawing.

                     colormap=blue_red, thresh=10)


Another clever trick is to supply a blank image, this way, we can see the RAG unobstructed.

cyan_red = colors.ListedColormap(['cyan', 'red'])
out = graph.draw_rag(labels, rag, np.zeros_like(image), desaturate=True,


Ahhh, magnificent.

Here is a small piece of code which produces a typical desaturated color-distance RAG.

image = data.coffee()
rag = graph.rag_mean_color(image, labels)
cmap = colors.ListedColormap(['blue', 'red'])
out = graph.draw_rag(labels, rag, image, border_color=(0,0,0), desaturate=True,


If you notice the above image, you will find some edges crossing over each other. This is because, some regions are convex. Hence their centroid lies outside their boundary and edges emanating from it can cross other edges.


I will go over some examples of RAG drawings, since most of it is similar, I won’t repeat the code here. The Ncut technique, wherever used, was with its default parameters.

Color distance RAG of Coffee on black background


Color distance RAG of Coffee after applying NCut


Notice how the centroid of the white rim of the cup is placed at its centre. It is the one adjacent to the centroid of the gray region of the upper part of the spoon, connected to it via a blue edge. Notice how this edge crosses others.

Color distance RAG of Lena


A futuristic car and its color distance RAG after NCut



Coins Image and their color distance RAG after NCut


Further Improvements

  • A point that was brought up in the PR as well is that thick lines would immensely enhance the visual
    appeal of the output. As and when they are implemented, rag_draw should be modified to support drawing
    thick edges.
  • As centroids don’t always lie in within an objects boundary, we can represent regions by a point other than their centroid, something which always lies within the boundary. This would allow for better visualization of the actual RAG from its drawing.