CODEWITHSUNDEEP
pythondictionarypython-2.7numpy
Mala
Mala

Reputation: 33

Perform a Standard Deviation on the values in a dictionary

I'm using Numpy and Python2.7, and I'm writing a function that counts the amount of times a color appears per column of pixels as I read-in an image (Using PIL):

for i in range(wbmp.size[0]):
    bcount = 0
    for j in range(wbmp.size[1]):
        if wbmp.getpixel((i,j)) == 1:
            bcount = bcount + 1
    bdict[i] = bcount

The dictionary returns as {Column#: # of times color appears}, and I'd like to be able to perform a standard deviation on all of the values in the dictionary. Would I need to put them all into a list first? Or is there away to just pull it from the dictionary?

Upvotes: 3

Views: 5512

Answers (2)

jorgeca
jorgeca

Reputation: 5522

The list of all values in the dictionary can be obtained with bdict.values(), so you could use this:

std = np.std(bdict.values())

A faster way to do this would use more numpy:

img = np.array(img)
colour_mask = img == 1  # or whichever colour you want
per_col_count = colour_mask.sum(axis=0)
std = np.std(per_col_count)

colour_mask is a boolean mask, and summing it along axis 0 adds up all True values for every column. This is bound to be much faster, and the difference will increase with the size of the image.

Upvotes: 4

askewchan
askewchan

Reputation: 46578

Your dictionary already has the list you want, 

bdict.values()

So you can call std on this:

np.std(bdict.values())

But I would recommend converting your image into a numpy array immediately, and doing a histogram along one axis, instead of using your version of counting.

from PIL import Image
i = Image.open('imfile.png')
a = np.array(i)
c = 1   # or whatever color you want
b = 256 # bit depth of image, so histogram bins are 1 color / bin

hists = np.array([ np.histogram(row, bins=b)[0] for row in a ])
s = hists[:,c].std()

Upvotes: 2

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