# A thing or two about the Fourier Transform

Recently I have been reading up on frequency domain image processing. I am still just beginning to understand how it works. Over the last few weeks I have been trying to understand the ** Fourier Transform **. Although the gist of  Fourier Series is easy to understand from its formula, that of the Fourier Transform isn’t (to me at least). This blog post describes the Fourier Transform as I understood it, along with some helpful diagrams. I am using NumPy and matplotlib for plotting. As a reference for all the equations mentioned in this post, I am using Digital Image Processing (3rd Edition).

## The Fourier Series

I haven’t done any digging into its theory, but the formula for Fourier Series is pretty straight forward. Given $f$ has a period of $T$

$f(t) = \sum\limits_{n=-\infty}^\infty c_{n} e^{i\frac{2 \pi n}{T}}$

where

$c_n = \frac{1}{T} \int\limits_{\frac{-T}{2}}^{\frac{T}{2}} f(t)e^{-i\frac{2 \pi n}{T}}$

for
$n = 0,\pm 1, \pm2, .....$

To simplify this, we can replace $\omega=\frac{2\pi}{T}$. $f$ now becomes.

$f(t) = \sum\limits_{n=-\infty}^\infty c_n e^{i \omega n}$

Using Euler’s Formula

$f(t) = \sum\limits_{n=-\infty}^\infty c_n (cos(n \omega t) + i. sin(n \omega t))$

We can say that, $f$ is now exactly equal to an infinite sum of certain sines and cosines. The Wikipedia Page has some pretty nice visualizations of the concept.

## The Discrete Fourier Transform

This is what took me a while to understand. The discrete fourier transform, is derived from the Fourier Transform. Given a discrete function $f$ whose $M$ samples have been collected, we have.

$F(u) = \sum\limits_{x=0}^{M-1} f(x)e^{-i\frac{2\pi u x}{M}}$

for

$u = 0, 1, 2, ..., M -1$

As you can see, the function $f$ of $x$ is transformed into the function $F$ of $u$.
We can get $f$ back by the inverse Fourier Transform

$f(x) = \frac{1}{M} \sum\limits_{u=0}^{M-1} F(u)e^{i\frac{2\pi u x}{M}}$
for
$x = 0, 1, 2, ... , M -1$

Although the relationship of $f$ and $F$ is mathematically proven, the underlying meaning was not immediately apparent to me. What puzzled me was, why $F$ is computed at all, and how can it tell us anything about $f$. The way I looked at it to understand it better is as follows.

Let
$\omega = \frac{2\pi}{M}$

$f(x) = \frac{1}{M} \sum\limits_{u=0}^{M-1} F(u)e^{i\omega u x}$

Using Euler’s formula we have

$f(x) = \frac{1}{M}[{F(0).(cos(0) + i.sin(0)) + F(1).(cos(1\omega x) + i.sin(1\omega x)) + F(2).(cos(2\omega x) + i.sin(2\omega x)) + ...} ]$

From the above equation, it is clear why the Fourier Transform is important. The function $F$, has given us coefficients to express $f$ as a sum of sines and cosines. I will try to demonstrate this with some code. I wrote a small helper function to help me input curves. The following function taken an image of a curve drawn in black on white and saves it into a .npy file. You will need the scikit-image library to use this.

from skimage import io, color, util
from matplotlib import pyplot as plt

pylab.rcParams['figure.figsize'] = (8.0, 8.0)

def img2curve(imgfile, arrayfile):
""" Converts an image of a curve into numpy array and saves it"""

img = color.rgb2gray(img)
img = util.img_as_ubyte(img)
array = np.zeros((img.shape[1], ))

for r in range(img.shape[0]):
for c in range(img.shape[1]):
if img[r, c] < 128:
array[c] = img.shape[0] - r
np.save(arrayfile, array)

def show_curve(array):
""" Plots real and imaginary parts of an array """

ax = plt.subplot(111)
ax.set_xlim(0, len(array))
ax.set_ylim(-10, len(array))
plt.plot(np.real(array), color='blue')
plt.plot(np.imag(array), color='red')


### Original Image

img = io.imread("curve.png")
io.imshow(img)


### Derived Curve

img2curve("curve.png", "curve.npy")
show_curve(curve)


If you’d like to, you can download the original image and the curve.npy file.

### Computing the DFT

We will compute the discrete fourier transform using NumPy’s np.fft.fft method and observe the first 5 terms.

curve_fft = np.fft.fft(curve)
print curve_fft[:5]


curve_fft is our function $F$ as described above. Applying the inverse transform to curve_fft should give us back curve exactly. But let’s see what happens by considering an element in $F$ at a time. First we take only $F(0)$ and apply the inverse transform by using np.fft.ifft.

### Contribution of F(0)

$t_0(x) = \frac{1}{M}[F(0).(cos(0) + i.sin(0))]$

tmp = curve_fft.copy()
tmp[1:] = 0

array = np.fft.ifft(tmp)
show_curve(array)


As you can see, it is a real constant shown in blue.

### Contribution of F(1)

$t_1(x) = \frac{1}{M}[F(1).(cos(1\omega x) + i.sin(1\omega x))]$

tmp = curve_fft.copy()
tmp[2:] = 0
tmp[0:1] = 0

array = np.fft.ifft(tmp)
show_curve(array)


### Contribution of F(2)

$t_2(x) = \frac{1}{M}[F(2).(cos(2\omega x) + i.sin(2\omega x))]$

tmp = curve_fft.copy()
tmp[3:] = 0
tmp[0:2] = 0

array = np.fft.ifft(tmp)
show_curve(array)


### Contribution of F(0), F(1) and F(2)

$t_0(x) + t_1(x) + t_2(x)$

tmp = curve_fft.copy()
tmp[3:] = 0

array = np.fft.ifft(tmp)
show_curve(array)


As you can see the resultant curve is trying to match the original curve. The imaginary part in red, will eventually get zeroed out when all the terms of $F$ are considered.

## Fourier Transform in action

The code snippet below generates successive images of contribution of each term in $F$. In nth still, the blue and red lines show the real and imaginary part of the curve so far, and the dotted lines show the contribution of $F(n)$.

import numpy as np
from matplotlib import pyplot as plt

curve_fft = np.fft.fft(curve)

result = np.zeros_like(curve, dtype=np.complex)

for i in range(curve_fft.shape[0]):

tmp = np.zeros_like(curve, dtype=np.complex)
tmp[i] = curve_fft[i]

tmp_curve = np.fft.ifft(tmp)
result += tmp_curve

fig = plt.figure()
ax = plt.subplot(111)
ax.set_xlim(0, len(curve))
ax.set_ylim(-10, len(curve))

plt.plot(np.real(result), color='blue')
plt.plot(np.imag(result), color='red')

plt.title("Adding F(%d)" % i)
plt.plot(np.real(tmp_curve), 'r--', color='blue')
plt.plot(np.imag(tmp_curve), 'r--', color='red')

fig.savefig('out/' + str(i) + ".png")



I ran the command

avconv -f image2 -r 1 -i %d.png -r 20 fft.mp4


to generate the final video.

As I understand DFT more, I’ll keep posting. Till then, thanks for reading.

# GSoC 2014 – Signing off

This years GSoC coding period has nearly come to an end. This post aims to briefly summarize everything that happened during the last three months. My task was to implement Region Adjacency Graph based segmentation algorithms for scikit-image. This post provides a good explanation about them. Below I will list out my major contributions.

## Contributions

Fixing the API for RAGs was very important, since it was directly going to affect everything else that followed. After a long discussion and some benchmarks we finally decided to have NetworkX as a dependency. This helped a lot, since I had a lot of graph algorithms already implemented for me. The file rag.py implements the RAG class and the RAG construction methods. I also implemented threshold_cut, a function which segments images by simply thresholding edge weights. To know more, you can visit, RAG Introduction.

Normalized Cut

The function cut_normazlied, implements the Normalized Cut algorithm for RAGs. You can visit Normalized Cut on RAGs to know more. See the videos at the end to get a quick idea of how NCut works. Also see, A closer look at NCut, where I have benchmarked the function and indicated bottlenecks.

Drawing Regions Adjacency Graphs

In my posts, I had been using a small piece of code I had written to display RAGs. This Pull Request implements the same functionality for scikit-image. This would be immensely useful for anyone who is experimenting with RAGs. For a more detailed explanation, check out Drawing RAGs.

Hierarchical Merging of Region Adjacency Graphs

This Pull Request implements a simple form of Hierarchical Merging. For more details, see Hierarchical Merging of Region Adjacency Graphs. This post also contains videos at the end, do check them out. This can also be easily extended to a boundary map based approach, which I plan to do post-GSoC

The most important thing for me is that I am a better Python programmer as compared to what I was before GSoC began this year. I was able to see how some graph based segmentation methods work at their most basic level. Although GSoC has come to an end, I don’t think my contributions to scikit-image have. Contributing to it has been a tremendous learning experience and plan to continue doing so. I have been been fascinated with Image Processing since me and my friends wrote an unholy piece of Matlab code about 3 years ago to achieve this. And as far as I can see its a fascination I will have for the rest of my life.

Finally, I would like to thank my mentors Juan, Johannes Schönberger and Guillaume Gay. I would also like to thank Stefan for reviewing my Pull Requests.

# Hierarchical Merging of Region Adjacency Graphs

Region Adjacency Graphs model regions in an image as nodes of a graph with edges between adjacent regions. Superpixel methods tend to over segment images, ie, divide into more regions than necessary. Performing a Normalized Cut and Thresholding Edge Weights are two ways of extracting a better segmentation out of this. What if we could combine two small regions into a bigger one ? If we keep combining small similar regions into bigger ones, we will end up with bigger regions which are significantly different from its adjacent ones. Hierarchical Merging explores this possibility. The current working code can be found at this Pull Request

## Code Example

The merge_hierarchical function performs hierarchical merging on a RAG. It picks up the smallest weighing edge and combines the regions connected by it. The new region is adjacent to all previous neighbors of the two combined regions. The weights are updated accordingly. It continues doing so till the minimum edge weight in the graph in more than the supplied thresh value. The function takes a RAG as input where smaller edge weight imply similar regions. Therefore, we use the rag_mean_color function with the default "distance" mode for RAG construction. Here is a minimal code snippet.

from skimage import graph, data, io, segmentation, color

img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
labels2 = graph.merge_hierarchical(labels, g, 40)
g2 = graph.rag_mean_color(img, labels2)

out = color.label2rgb(labels2, img, kind='avg')
out = segmentation.mark_boundaries(out, labels2, (0, 0, 0))
io.imsave('out.png',out)


I arrived at the threshold 40 after some trial and error. Here is the output.

The drawback here is that the thresh argument can vary significantly depending on image to image.

## Comparison with Normalized Cut

Loosely speaking the normalized cut follows a top-down approach where as the hierarchical merging follow a bottom-up approach. Normalized Cut starts with the graph as a whole and breaks it down into smaller parts. On the other hand hierarchical merging, starts with individual regions and merges them into bigger ones till a criteria is reached. The Normalized Cut however, is much more robust and requires little tuning of its parameters as images change. Hierarchical merging is a lot faster, even though most of its computation logic is written in Python.

## Effect of change in threshold

Setting a very low threshold, will not merge any regions and will give us back the original image. A very large threshold on the other hand would merge all regions and give return the image as just one big blob. The effect is illustrated below.

## Hierarchical Merging in Action

With this modification the following code can output the effect of all the intermediate segmentation during each iteration.

from skimage import graph, data, io, segmentation, color
import time
from matplotlib import pyplot as plt

img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
labels2 = graph.merge_hierarchical(labels, g, 60)

c = 0

out = color.label2rgb(graph.graph_merge.seg_list[-10], img, kind='avg')
for label in graph.graph_merge.seg_list:
out = color.label2rgb(label, img, kind='avg')
out = segmentation.mark_boundaries(out, label, (0, 0, 0))
io.imsave('/home/vighnesh/Desktop/agg/' + str(c) + '.png', out)
c += 1


I then used avconv -f image2 -r 3 -i %d.png -r 20 car.mp4 to output a video. Below are a few examples.

In each of these videos, at every frame, a boundary dissapears. This means that the two regions separated by that boundary are merged. The frame rate is 5 FPS, so more than one region might be merged at a time.

# 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))
plt.imshow(img)


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')
show_image(image)


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))
show_image(border_image)


## 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)
show_image(out)


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)
show_image(out)


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,
colormap=blue_red)
show_image(out)


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.

out = graph.draw_rag(labels, rag, border_image, desaturate=True,
colormap=blue_red, thresh=10)
show_image(out)


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,
colormap=cyan_red)
show_image(out)


Ahhh, magnificent.

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

image = data.coffee()
labels = segmentation.slic(image, compactness=30, n_segments=400)
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,
colormap=cmap)
show_image(out)


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.

## Examples

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 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.

## 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.

# Normalized Cuts on Region Adjacency Graphs

In my last post I demonstrated how removing edges with high weights can leave us with a set of disconnected graphs, each of which represents a region in the image. The main drawback however was that the user had to supply a threshold. This value varied significantly depending on the context of the image. For a fully automated approach, we need an algorithm that can remove edges automatically.

The first thing that I can think of which does something useful in the above mention situation is the Minimum Cut Algorithm. It divides a graph into two parts, A and B such that the weight of the edges going from nodes in Set A to the nodes in Set B is minimum.

For the Minimum Cut algorithm to work, we need to define the weights of our Region Adjacency Graph (RAG) in such a way that similar regions have more weight. This way, removing lesser edges would leave us with the similar regions.

## Getting Started

For all the examples below to work, you will need to pull from this Pull Request. The tests fail due to outdated NumPy and SciPy versions on Travis. I have also submitted a Pull Request to fix that. Just like the last post, I have a show_img function.

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

def show_img(img):

width = img.shape[1]/75.0
height = img.shape[0]*width/img.shape[1]
f = plt.figure(figsize=(width, height))
plt.imshow(img)


I have modified the display_edges function for this demo. It draws nodes in yellow. Edges with low edge weights are greener and edges with high edge weight are more red.

def display_edges(image, g):
"""Draw edges of a RAG on its image

Returns a modified image with the edges drawn. Edges with high weight are
drawn in red and edges with a low weight are drawn in green. Nodes are drawn
in yellow.

Parameters
----------
image : ndarray
The image to be drawn on.
g : RAG
The Region Adjacency Graph.
threshold : float
Only edges in g below threshold are drawn.

Returns:
out: ndarray
Image with the edges drawn.
"""

image = image.copy()
max_weight = max([d['weight'] for x, y, d in g.edges_iter(data=True)])
min_weight = min([d['weight'] for x, y, d in g.edges_iter(data=True)])

for edge in g.edges_iter():
n1, n2 = edge

r1, c1 = map(int, rag.node[n1]['centroid'])
r2, c2 = map(int, rag.node[n2]['centroid'])

green = 0,1,0
red = 1,0,0

line  = draw.line(r1, c1, r2, c2)
circle = draw.circle(r1,c1,2)
norm_weight = ( g[n1][n2]['weight'] - min_weight ) / ( max_weight - min_weight )

image[line] = norm_weight*red + (1 - norm_weight)*green
image[circle] = 1,1,0

return image


To see demonstrate the display_edges function, I will load an image, which just has two regions of black and white.

demo_image = io.imread('bw.png')
show_img(demo_image)


Let’s compute the pre-segmenetation using the SLIC method. In addition to that, we will also use regionprops to give us the centroid of each region to aid the display_edges function.

labels = segmentation.slic(demo_image, compactness=30, n_segments=100)
labels = labels + 1  # So that no labelled region is 0 and ignored by regionprops
regions = regionprops(labels)


We will use label2rgb to replace each region with its average color. Since the image is so monotonous, the difference is hardly noticeable.

label_rgb = color.label2rgb(labels, demo_image, kind='avg')
show_img(label_rgb)


We can use mark_boundaries to display region boundaries.

label_rgb = segmentation.mark_boundaries(label_rgb, labels, (0, 1, 1))
show_img(label_rgb)


As mentioned earlier we need to construct a graph with similar regions having more weights between them. For this we supply the "similarity" option to rag_mean_color.

rag = graph.rag_mean_color(demo_image, labels, mode="similarity")

for region in regions:
rag.node[region['label']]['centroid'] = region['centroid']

label_rgb = display_edges(label_rgb, rag)
show_img(label_rgb)


If you notice above the black and white regions have red edges between them, i.e. they are very similar. However the edges between the black and white regions are green, indicating they are less similar.

## Problems with the min cut

Consider the following graph

The minimum cut approach would partition the graph as {A, B, C, D} and {E}. It has a tendency to separate out small isolated regions of the graph. This is undesirable for image segmentation as this would separate out small, relatively disconnected regions of the image. A more reasonable partition would be {A, C} and {B, D, E}. To counter this aspect of the minimum cut, we used the Normalized Cut.

## The Normalized Cut

It is defined as follows
Let $V$ be the set of all nodes and $w(u,v)$ for $u, v \in V$ be the edge weight between $u$ and $v$

$NCut(A,B) = \frac{cut(A,B)}{Assoc(A,V)} + \frac{cut(A,B)}{Assoc(B,V)}$
where
$cut(A,B) = \sum_{a \in A ,b \in B}{w(a,b)}$

$Assoc(X,V) = cut(X,V) = \sum_{x \in X ,v \in V}{w(x,v)}$

With the above equation, NCut won’t be low is any of A or B is not well-connected with the rest of the graph. Consider the same graph as the last one.

We can see that minimizing the NCut gives us the expected partition, that is, {A, C} and {B, D, E}.

## Normalized Cuts for Image Segmentation

The idea of using Normalized Cut for segmenting images was first suggested by Jianbo Shi and Jitendra Malik in their paper Normalized Cuts and Image Segmentation. Instead of pixels, we are considering RAGs as nodes.

The problem of finding NCut is NP-Complete. Appendix A of the paper has a proof for it. It is made tractable by an approximation explained in Section 2.1 of the paper. The function _ncut_relabel is responsible for actually carrying out the NCut. It divides the graph into two parts, such that the NCut is minimized. Then for each of the two parts, it recursively carries out the same procedure until the NCut is unstable, i.e. it evaluates to a value greater than the specified threshold. Here is a small snippet to illustrate.

img = data.coffee()

labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')

g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg')

show_img(out2)


## NCut in Action

To observe how the NCut works, I wrote a small hack. This shows us the regions as divides by the method at every stage of recursion. The code relies on a modification in the original code, which can be seen here.

from skimage import graph, data, io, segmentation, color
from matplotlib import pyplot as plt
import os

#img = data.coffee()
os.system('rm *.png')
img = data.coffee()
#img = color.gray2rgb(img)

labels1 = segmentation.slic(img, compactness=30, n_segments=400)
out1 = color.label2rgb(labels1, img, kind='avg')

g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)

offset = 1000
count = 1
tmp_labels = labels1.copy()
for g1,g2 in graph.graph_cut.sub_graph_list:
for n,d in g1.nodes_iter(data=True):
for l in d['labels']:
tmp_labels[labels1 == l] = offset
offset += 1
for n,d in g2.nodes_iter(data=True):
for l in d['labels']:
tmp_labels[labels1 == l] = offset
offset += 1
tmp_img = color.label2rgb(tmp_labels, img, kind='avg')
io.imsave(str(count) + '.png',tmp_img)
count += 1


The two components at each stage are stored in the form of tuples in sub_graph_list. Let’s say, the Graph was divided into A and B initially, and later A was divided into A1 and A2. The first iteration of the loop will label A and B. The second iteration will label A1, A2 and B, and so on. I used the PNGs saved and converted them into a video with avconv using the command avconv -f image2 -r 1 -i %d.png -r 20 demo.webm. GIFs would result in a loss of color, so I made webm videos. Below are a few images and their respective successive NCuts. Use Full Screen for better viewing.

Note that although there is a user supplied threshold, it does not have to vary significantly. For all the demos below, the default value is used.

### Colors Image

During each iteration, one region (area of the image with the same color) is split into two. A region is represented by its average color. Here’s what happens in the video

• The image is divided into red, and the rest of the regions (gray at this point)
• The grey is divided into a dark pink region (pink, maroon and yellow) and a
dark green ( cyan, green and blue region ).
• The dark green region is divided into light blue ( cyan and blue ) and the
green region.
• The light blue region is divided into cyan and blue
• The dark pink region is divided into yellow and a darker pink (pink and marron
region.
• The darker pink region is divided into pink and maroon regions.

# scikit-image RAG Introduction

Humans possess an incredible ability to identify objects in an image. Image processing algorithms are still far behind this ability. Segmentation is the process of dividing an image into meaningful regions. All pixels belonging to a region should get a unique label in an ideal segmentation.

The current segmentation functions in scikit-image are too fine grained and fall closer to superpixel methods, providing a starting point for segmentation. Region Adjacency Graphs (RAGs) are a common data structure for many segmentation algorithms. As part of GSoC this year I am implementing RAGs for scikit-image. The current HEAD of scikit-image’s master branch contains my RAG implementation based on Networkx from my recent Pull Request. In the example below, we will see how Region Adjacency Graphs (RAGs) attempt to solve the segmentation problem.Please note that you need the latest master branch of scikit-image to run the following code.

# Getting Started

We define the function show_img in preference to the standard call to imshow to set nice default size parameters.
We start with coffee, a nice fresh image of a coffee cup.

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

def show_img(img):
width = 10.0
height = img.shape[0]*width/img.shape[1]
f = plt.figure(figsize=(width, height))
plt.imshow(img)

img = data.coffee()
show_img(img)



# Over Segmentation

We segment the image using SLIC algorithm. The SLIC algorithm will
assign a unique label to each region. This is a
localized cluster of pixels sharing some similar property, in this case their
color. The label of each pixel is stored in the labels array.

regionprops helps us compute various features of these regions. We will be
sing the centroid, purely for visualization.

labels = segmentation.slic(img, compactness=30, n_segments=400)
labels = labels + 1  # So that no labelled region is 0 and ignored by regionprops
regions = regionprops(labels)


The label2rgb function assigns a specific color to all pixels belonging to one
region (having the same label). In this case, in label_rgb each pixel is
replaces with the average RGB color of its region.

label_rgb = color.label2rgb(labels, img, kind='avg')
show_img(label_rgb)


Just for clarity, we use mark_boundaries to highlight the region boundaries.
You will notice the the image is divided into more regions than required. This
phenomenon is called over-segmentation.

label_rgb = segmentation.mark_boundaries(label_rgb, labels, (0, 0, 0))
show_img(label_rgb)


# Enter, RAGs

Region Adjacency Graphs, as the name suggests represent adjacency of regions
with a graph. Each region in the image is a node in a graph. There is an edge
between every pair of adjacent regions (regions whose pixels are adjacent). The
weight of between every two nodes can be defined in a variety of ways. For this
example, we will use the difference of average color between two regions as
their edge weight. The more similar the regions, the lesser the weight between
them. Because we are using difference in mean color to compute the edge weight,
the method has been named rag_mean_color.

rag = graph.rag_mean_color(img, labels)


For our visualization, we are also adding an additional property to a node, the
coordinated of its centroid.

for region in regions:
rag.node[region['label']]['centroid'] = region['centroid']


display_edges is a function to draw the edges of a RAG on its corresponding
image. It draws edges as green lines and centroids as yellow dots.
It also accepts an argument, thresh. We only draw edges with weight below this threshold.

def display_edges(image, g, threshold):
"""Draw edges of a RAG on its image

Returns a modified image with the edges drawn.Edges are drawn in green
and nodes are drawn in yellow.

Parameters
----------
image : ndarray
The image to be drawn on.
g : RAG
The Region Adjacency Graph.
threshold : float
Only edges in g below threshold are drawn.

Returns:
out: ndarray
Image with the edges drawn.
"""
image = image.copy()
for edge in g.edges_iter():
n1, n2 = edge

r1, c1 = map(int, rag.node[n1]['centroid'])
r2, c2 = map(int, rag.node[n2]['centroid'])

line  = draw.line(r1, c1, r2, c2)
circle = draw.circle(r1,c1,2)

if g[n1][n2]['weight'] < threshold :
image[line] = 0,1,0
image[circle] = 1,1,0

return image


We call the function with thresh = infinity so that all edges are drawn. I
myself was surprised with the beauty of the following output.

edges_drawn_all = display_edges(label_rgb, rag, np.inf )
show_img(edges_drawn_all)


Let’s see what happens by setting thresh to 29, a value I arrived at with
some trial and error.

edges_drawn_29 = display_edges(label_rgb, rag, 29 )
show_img(edges_drawn_29)


### Alas, the graph is cut

As you can see above, the RAG is now divided into disconnected regions. If you
notice, the table above and to the right of the dish is one big connected
component.

# Threshold Cut

The function cut_threshold removes edges below a specified threshold and then
labels a connected component as one region. Once the RAG is constructed, many similar
and more sophisticated strategies can improve the initial segmentation.

final_labels = graph.cut_threshold(labels, rag, 29)
final_label_rgb = color.label2rgb(final_labels, img, kind='avg')
show_img(final_label_rgb)


Not perfect, but not that bad I’d say. My next steps will be to implement better algorithms to process the RAG after the initial segmentation.These include the merging predicates mention here and N-cut.

# So far

For the story so far see –  the Introduction, 2nd week update, Graph Data Structures Comparison .

After much debate on the mailing list with many scikit-image developers we finally decided to use the NetworkX Graph Class for our Region Adjacency Graph ( RAG ). It comes with a lot of well-tested functionality and would speed up the GSoC progress. It is also pure Python, and shares a lot of its dependencies with scikit-image.

# Constructing the RAG

To construct a RAG, we need to iterate over the entire labeled image, looking for adjacent pixels with distinct labels. Initially I wrote a special case Cython loop for 2D and 3D, much like this one. But to scikit-image developers suggested, and rightly so, a more genaral n-dimensional approach. I looked for a function, which would iterate over an entire array and call a given function. As it turns out generic_filter does exactly that. I have used it here  with the callable defined here.

The footprint is created such that only the elements which are 2nd and 3rd along all axes are set according to the connectivity. Rest all are forced to zero. In the 2D case with connectivity 2 it would be the bottom right elements ( a[1,1], a[1,2] , a[2,1], a[2,2] ) which are set . This ensures that one pair of adjacent pixels is not processed again when the filter window moves ahead.

The footprint ensures that the first element in the array passed to the callable is the central element in the footprint. All other elements are adjacent to the central element and an edge is created between them and the central element.

# Pruning the RAG

I implemented a skeletal RAG algorithm following Juan’s suggestion. It takes the labeled image as input. This is typically an over-segmented image obtained by algorithms like SLIC or watershed. Each region in the labeled image is represented by a node in the graph. Nodes of adjacent regions are joined by an edge. The weight of this edge is the magnitude of difference in mean color. Once the graph is constructed, nodes joined by edges with weights lesser than a given threshold are combined into one region.

You can view the Pull Request here.

# Results

Below are the test results of executing the example code on two scikit-image sample images. The threshold value for both the results is different and is found by trial and error. Typically, a higher threshold value, gives fewer regions in the final image.

# Conclusion

The mechanism for RAGs is in place now. The segmentation is pretty satisfactory, considering the simple logic. The major drawback however is that it’s not fully automatic. Over the next few weeks I will implement more sophisticated merging predicates, including N-cut.