In this assignment, you are going to implement an edge detector for grayscale PNG images. Edge detection is an image processing technique to find boundaries of objects in images. Here are an example image and the detected edges:
There are lots of edge detection algorithms but in this assignment, you will implement an algorithm with the following three steps:
(if you are familiar with edge detection algorithms, you will that notice these are the first three steps of the Canny algorithm.)
The following sections will give you more details about each of the steps.
Before starting the actual edge detection, we first smooth the image to reduce the amount of edges detected from the noise. Smoothing is typically done by applying a lowpass filter to the image (as the noise is often high frequency term in the image). In this assignment, we will use a Gaussian blur. The following example shows the effect of the Gaussian smoothing:
Gaussian blur is defined as a convolution between the image and the following 5x5 filter, a discrete Gaussian function in 2D space truncated after two tabs:
$$
\mathbf{Img}_{\mathit{smooth}} =
\frac{1}{159} \ \left( \begin{array}{ccccc}
2 & 4 & 5 & 4 & 2
\\
4 & 9 & 12 & 9 & 4
\\
5 & 12 & 15 & 12 & 5
\\
4 & 9 & 12 & 9 & 4
\\
2 & 4 & 5 & 4 & 2
\end{array}
\right) * \mathbf{Img}
$$
As a starting point, this Gaussian blur is already implemented in the code (task smooth
). Understanding how the task implements convolution in Regent will be helpful to do this assignment.
$$ G_x = \left( \begin{array}{ccc} 1 & 0 & 1 \\ 2 & 0 & 2 \\ 1 & 0 & 1 \end{array} \right) * \mathbf{Img}_{\mathit{smooth}} $$ 
$$ G_y = \left( \begin{array}{ccc} 1 & 2 & 1 \\ 0 & 0 & 0 \\ 1 & 2 & 1 \end{array} \right) * \mathbf{Img}_{\mathit{smooth}} $$ 
Once x and y components of the gradient is computed, the magnitude can be computed by:
$$\big\nabla \mathbf{Img}_{\mathit{smooth}}\big = \sqrt{G_x^2 + G_y^2}$$
In the code, tasks sobelX
and sobelY
are already declared, but have some parts missing. Your first task in this assignment is to complete tasks sobelX
and sobelY
.
If you implement them correctly, you should be able to get this image after running the code:
You might notice that the edges in this image look thicker than those in the edge image above. In the next step, we are going to make them thinner.
One of the artifacts from smoothing is that it also makes the object boundaries blurry. As a result, the gradients calculated from the previous step might make the final edges span multiple pixels. In order to get sharper edges, we find local maxima in the gradient image and remove the others. Local maxima can be found in various ways, but in this assignment we will implement a simple one: a gradient is considered locally maximal if it is either greater than or equal to its neighbors in the positive and negative gradient direction. The gradient direction, which is calculated from arctangent of the gradient vector
$$
\theta_G = \mathbf{arctan}(\frac{G_y}{G_x}),
$$
is rounded to the nearest 45° in order to conform to one of 8 discrete directions. This diagram shows with which neighbors a pixel needs to be compared, based on its gradient direction:
j  1  j  j + 1  xaxis  

i  1 
45° or 225° 
90° or 270° 
135° or 315° 

i 
0° or 180° 
Center 
0° or 180° 

i + 1 
135° or 315° 
90° or 270° 
45° or 225° 

yaxis 
51 ↖ 
143 ↖ 
276 ↖ 
383 ↖ 
363 ↖ 
176 ↖ 
309 ↖ 
398 ↖ 
408 ↖ 
388 ↖ 
343 ↖ 
408 ↖ 
401 ↖ 
388 ↖ 
404 ↖ 
307 ↖ 
394 ↖ 
391 ↖ 
353 ↖ 
319 ↖ 
Each cell shows its gradient magnitude and direction. For this image, nonmaximum suppression will find the following local maxima (boxed in bold lines):
51 ↖ 
143 ↖ 
276 ↖ 
383 ↖ 
363 ↖ 
176 ↖ 
309 ↖ 
398 ↖ 
408 ↖ 
388 ↖ 
343 ↖ 
408 ↖ 
401 ↖ 
388 ↖ 
404 ↖ 
307 ↖ 
394 ↖ 
391 ↖ 
353 ↖ 
319 ↖ 
Your second task is to implement this nonmaximum suppression step. Each pixel has boolean field local_maximum
, which tells whether the pixel is a local maximum or not. The field is initialized to true
in the task initialize
and you should implement the suppressNonmax
task so that it sets false
to the pixels that are not local maxima.
You will notice that the pixels near the image boundary do not have enough neighbors to run some of the three steps above. The Gaussian smoothing requires two neighbor pixels on each direction and the gradient calculation and nonmaximum suppression require one. Therefore, none of the three steps can be applied to the pixels right on the image boundary and the smoothing cannot be applied to those that are onepixel inside. We made two simplifications in this assignment to handle these pixels:
smooth
field and {0, 0} for the gradient
field.In the code, tasks use the region r_interior
to update only the interior image, while they also read the region r_image
, which contains the whole image, in order to access the neighbor pixels.
The code we gave you takes the gradients from nonmaximum suppression and filters them by a threshold (80 by default) to determine the final edge image. However, the original Canny algorithm does two more steps from here: double thresholding and edge tracking by hysteresis. Double thresholding categorizes edges into two groups using two threshold values: strong edges, whose gradient values are greater than high threshold, and weak edges, whose gradient values are in between two thresholds. Then, the edge tracking step finds all connected components and select the pixels as final edges that are included in a component with at least one strong edge pixel. These two steps can effectively distinguish between the true edges that accidentally have small gradients and spurious ones from the noise.
Implement these two steps to get the complete Canny algorithm.
File assignment1.tar.gz has several source files and some images (under directory images
) that you can test. All you have to change will be file edge.rg
unless you try the bonus problem. The parts that you need to modify are marked with TODO
s. Here is the complete list of options you can pass (the same list will appear with option h
):
Usage: regent.py edge.rg [OPTIONS]
OPTIONS
h : Print the usage and exit.
i {file} : Use {file} as input.
o {file} : Save the final edge to {file}. Will use 'edge.png' by default.
s {file} : Save the image after Gaussian smoothing to {file}.
t {value} : Set {value} to the threshold.
nosmooth : Skip Gaussian smoothing.
nosuppress : Skip nonmaximum suppression.
You need libpng
to run this code. Most of your systems have already installed this package, but here are some commands to install the package:
brew install libpng
(if you use Homebrew) or port install libpng
(if you use MacPorts)aptget install libpng
You can find more information about the package at the libpng webpage.
Finally, you do not need to use Certainty for this assignment, although getting used to running jobs on Certainty is highly recommended.
Send your edge.rg
file to cs315baut1617staff@lists.stanford.edu. If you made any changes in the other files, send an archive of the whole directory.