Fast methods to convert list to set? python 2.7

Question

so in my current code I'm using

im_set=set(map(tuple, im_list[0]))

to convert a list into a set. However it's very slow. Is there a faster/ none map method to speed things up? This is my current code

while True:
    cam = cv2.VideoCapture(0)
    start=time.time()
    while(cam.isOpened()):                  #Opens camera
        ret, im = cam.read()                #Takes screenshot
    #im=cv2.imread('RGB.png')
        im=cv2.resize(im,(325,240))         #Resize to make it faster
        im= im.reshape(1,-1,3)
    #im=OneNumber(im)               #Converts the pixels rgb to a singe number
        im_list=im.tolist() 
        im_set=set(map(tuple, im_list[0]))
        ColourCount= set(im_set) & set(om_set)
        print len(ColourCount)

    print N
    end=time.time()-start
    print end

Note that om_set is from a different program.

Anyway, basically I have to convert an np.array to a set, and compare that set to see what overlaps with them. But, its very slow. Is there a method I can use to speed up this conversion?

Answer 1

If you were using Python 3, that would be fast; but in Python 2, you should use a set comprehension:

im_set = {tuple(item) for item in im_list[0]}

map() is slower in Python 2 because it returns a list. That means that each tuple is stored in memory before you even start creating a set. This way, however, you are adding each item to the set as it is converted to a tuple.

Answer 2

Using itertools.imap is going to be faster:

In [18]: from itertools import imap

In [19]: timeit set(imap(tuple, l))
100 loops, best of 3: 2.27 ms per loop

In [20]: timeit {tuple(item) for item in l}
100 loops, best of 3: 2.69 ms per loop

You also don't need to create extra sets, you use the ones you have created and use set.intersection :

im_set.intersection(om_set)

Unless you specifically need lists this can also all be done using numpy .