Due: At the start of class, Tuesday, January 24, 2017.
The purpose of this assignment is to get some initial experience with Python and to learn the basics of constructing and using linear filters.
There are different ways to import libraries/modules into Python. Styles and practices vary. For consistency (and to make life easier for the markers) you are required to import modules for this assignment exactly as follows:
from PIL import Image import numpy as np import math from scipy import signal
HINT: Review Assignment 1 for the basics of reading/writing images, converting colour to greyscale, and converting PIL images to/from Numpy arrays.
Hand in all parts of this assignment in hardcopy form. To get full marks, your functions (i.e., *.py files) must not only work correctly, but also must be clearly documented with sufficient comments for others to easily use and understand the code. You will lose marks for insufficient or unclear comments. In this assignment, you also need to hand in scripts showing tests of your functions on all the cases specified as well as the images and other answers requested.
In CPSC 425, we follow the convention that 2D filters always have an odd number of rows and columns (so that the center row/column of the filter is well-defined).
As a simple warm-up exercise, write a Python function, ‘boxfilter(n)’, that returns a box filter of size n by n. You should check that n is odd, checking and signaling an error with an ‘assert’ statement. The filter should be a Numpy array. For example, your function should work as follows:
>>> boxfilter(5) array([[ 0.04, 0.04, 0.04, 0.04, 0.04], [ 0.04, 0.04, 0.04, 0.04, 0.04], [ 0.04, 0.04, 0.04, 0.04, 0.04], [ 0.04, 0.04, 0.04, 0.04, 0.04], [ 0.04, 0.04, 0.04, 0.04, 0.04]]) >>> boxfilter(4) Traceback (most recent call last): ... AssertionError: Dimension must be odd
HINT: The generation of the filter can be done as a simple one-line expression. Of course, checking that n is odd requires a bit more work.
Show the results of your boxfilter(n) function for the cases n=3, n=4, and n=5.
Write a Python function, ‘gauss1d(sigma)’, that returns a 1D Gaussian filter for a given value of sigma. The filter should be a 1D array with length 6 times sigma rounded up to the next odd integer. Each value of the filter can be computed from the Gaussian function, exp(- x^2 / (2*sigma^2)), where x is the distance of an array value from the center. This formula for the Gaussian ignores the constant factor. Therefore, you should normalize the values in the filter so that they sum to 1.
HINTS: For efficiency and compactness, it is best to avoid ‘for’ loops in Python. One way to do this is to first generate a 1D array of values for x, for example [-3 -2 -1 0 1 2 3] for a sigma of 1.0. These can then be used in a single Numpy expression to calculate the Gaussian value corresponding to each element.
Show the filter values produced for sigma values of 0.3, 0.5, 1, and 2.
Create a Python function ‘gauss2d(sigma)’ that returns a 2D Gaussian filter for a given value of sigma. The filter should be a 2D array. Remember that a 2D Gaussian can be formed by convolution of a 1D Gaussian with its transpose. You can use the function ‘convolve2d’ in the Scipy Signal Processing toolbox to do the convolution. You will need to provide signal.convolve2d with a 2D array. To convert a 1D array, f, to a 2D array f, of the same size you use ‘f = f[np.newaxis]’
Show the 2D Gaussian filter for sigma values of 0.5 and 1.
Write a function ‘gaussconvolve2d(array,sigma)’ that applies Gaussian convolution to a 2D array for the given value of sigma. The result should be a 2D array. Do this by first generating a filter with your ‘gauss2d’, and then applying it to the array with signal.convolve2d(array,filter,'same'). The ‘same’ option makes the result the same size as the image.
The Scipy Signal Processing toolbox also has a function ‘signal.correlate2d’. Applying the filter ‘gauss2d’ to the array with signal.correlate2d(array,filter,'same') produces the same result as with signal.convolve2d(array,filter,'same'). Why does Scipy have separate functions ‘signal.convolve2d’ and ‘signal.correlate2d’? HINT: Think of a situation in which ‘signal.convolve2d’ and ‘signal.correlate2d’ (with identical arguments) produce different results.
Try downloading an image of your choice from the web (right-click on an image in your browser and choose “save as”). Load this image into Python, convert it to a greyscale, Numpy array and run your ‘gaussconvolve2d’ on it with a sigma of 3.
Use PIL to show both the original and filtered images.
Convolution with a 2D Gaussian filter is not the most efficient way to perform Gaussian convolution on an image. In a few sentences, explain how this could be implemented more efficiently taking advantage of separability and why, indeed, this would be faster. NOTE: It is not necessary to implement this. Just the explanation is required. Your answer will be graded for clarity.
Assignments are to be handed in at the start of the lecture on their due date. A course TA will collect the assignments before the lecture begins, and then leave.
For this assignment, we are offering the option of electronic handin for the images requested,
Note: All other deliverables must be submitted in printed form.
Refer to the department's Handin instructions for information about submitting assignments electronically. The course account is cs425 and the assignment name is hw2.