EECS 442: Computer Vision (Fall 2019)

The goal of this assignment is to incentivize learning to write reasonably good python/numpy code. This is so that:

• You don't learn it on your own and discover some super useful function at the end of the semester

• If you're doing something like calculating eigenvectors wrong, you find out in a low-stakes way

• You get credit for spending time doing this

A great gentle introduction to python and Numpy from Justin Johnson is here Numpy Tutorial.

The Mastery Assignment

See homework here: resources/mastery_assignment.zip.

Each assignment requires you to fill in the blank in a function (in tests.py and warmup.py) and return the value described in the comment for the function.

def sample1(L):
#Given:
#   a list L
#Return:
#   the 1st entry of L (counting like humans, not computers)
#Hint: No hints for you

return None

You can then fill in:

def sample1(L):
#Given:
#   a list L
#Return:
#   the 1st entry of L (counting like humans, not computers)
#Hint: No hints for you

return L

You can test your implementation by running the test script.

python run.py --test b1             #Test problem b1
python run.py --allwarmups          #Test all the warmup problems
python run.py --alltests            #Test all the test problems
python run.py --alltests --pdb      #Test all the test problems, and launch the
#pdb debugger if things don't match so you
#can find the differences

This will show:

\$ python run.py --allwarmups
Running b1
Running b2
...
Running b20
Ran warmup tests
20/20 = 100.0

Warmup Problems

You need to solve all 20 warmup problems (in warmup.py). These are all solvable in one line.

Tests

You need to solve any 15 of the 20 problems (in tests.py). Many are not solvable in one line. Only one (p10) should be done with a for loop. You are free to choose any 15 that you want but you are highly encouraged to do all 20. It may pay off to know how to do: p2, p11, p12, p14, and one of (p18, p19, p20)

Here is one example:

def p4(t):
#Given:
#   a tuple of 3x3 rotation matrix R
#   Nx3 matrix M
#Return:
#   a Nx3 matrix of the rotated vectors
#Par: 3 lines
#Instructor: 1 line
#Hint:
#   1) Applying a rotation to a vector is right-multiplying the rotation
#      matrix with the vector
#   2) .T transposes; this may make your life easier
#   3) np.dot matrix-multiplies
R, M = t #unpack

return None

For each, we provide:

• Given: The arguments that are given as input

• Return: What you have to return

• Par: How many lines it might take. If it takes more than this, there's a better way. Unless the hints suggest a for loop, you should not use one.

• Instructor: How many lines I (David) took to write it the first time around. See if you can beat me (no semicolons though)!

• Hint: functions you're likely to find useful and tips for writing the function.

Submit a pdf of: