How can I return the indexes of RBG?
Say p is my Numeric Python array.

>>>x = 58
>>>y = 72
>>>p[y][x]
array([255, 255, 255], dtype=uint8)

I don't know what a Numeric Python array is or how to handle them but I found how to convert it to ['255', '255', '255'], which I think I am pretty sure is a list, by using map(str, p[y][x]) and I can deal with those simple enough.

When I try p.index(p[y][x]) it throws AttributeError: 'numpy.ndarray' object has no attribute 'index'.

And for p[y][x] in p: throws ValueError: output operand requires a reduction, but reduction is not enabled

I want to return the y and x of all the same RBG and also count how many occurances there are of them.
gtk.gdk.Pixbuf.get_pixels_array This is where I got the Numeric Python array from.

np.where(p == np.array([255,255,255])) doesn't work because it also returns things like [0,0,255], basically anything with 255 and the output is confusing
(array([ 35, 35, 35, ..., 70, 71, 71]), array([ 35, 35, 35, ..., 190, 188, 189]), array([0, 1, 2, ..., 2, 2, 2]))

So I guess I cross reference check for
x = np.where(p == np.array([255,255,255]))[0]
and
y = np.where(p == np.array([255,255,255]))[1]
unless I have them mixed up and x is y and vice versa, actually infact I think I am pretty sure that it is the other way around.
Then I can use np.count_nonzero(x) and np.count_nonzero(y).

But is there a way to omit the third array? Otherwise I have to check if x[i] and y[i] are equal three sequential times in the loop some how and count them that way.

This doesn't work.
np.where(np.where(im == np.array([255,255,255]))[0] == np.where(im == np.array([255,255,255]))[1])

Edited 2 Years Ago by nouth

Actually in the case of
y = np.where(p == np.array([255,255,255]))[0]
and
x = np.where(p == np.array([255,255,255]))[1]
when I count them I want to count the zeroes so map(str, x) and map(str, y) can come in handy there so I can get the real truth of about their capacities.

Actually this post is wrong because there is no need for map(str, x) or map(str, y) because I can just use len(x) and len(y) instead of np.count_nonzero(x) and np.count_nonzero(y), I think?

Edited 2 Years Ago by nouth

You say lists are inferior to arrays, how so? I cannot find this fact anywhere else.

Edited 2 Years Ago by nouth

Numpy.array is specifically designed to work with collections of numbers, which seems to be what you want also.

I think you have array as member of array, when it is easier with matrix of three columns, R,G,B

Edited 2 Years Ago by pyTony: shape comment

I am somewhat confused, are you talking about a list of (r, g, b) tuples?
If you don't need the high speed of numpy, go with those.

Well speed is always more for the proficient. :)

How can I change it to a matrix of three columns, R,G,B?
Is there a way to return the x and y position of the RGBs from a matrix of three columns?

My goal is just to get the count and position of RGB in a picture, like RGB = np.array([255,255,255]) and iterate through all of them all.
np.where(p == RGB) was close but no cigar, because of it also return other RGBs if they contain 255 in them atleast once. And it return x and y three times in a row if it matches all three of the 255 as np.array([255,255,255]), I want it to only return x and y once, and only if all three of it's values match RGB

This is my p.

>>> p
array([[[ 28,  32,  33],
        [ 43,  46,  47],
        [ 45,  47,  48],
        ..., 
        [ 27,  31,  31],
        [ 30,  33,  34],
        [ 30,  33,  34]],

       [[ 29,  33,  34],
        [ 42,  45,  46],
        [ 42,  45,  46],
        ..., 
        [ 27,  31,  31],
        [ 30,  33,  34],
        [ 30,  33,  34]],

       [[ 30,  33,  34],
        [ 45,  47,  48],
        [ 45,  47,  48],
        ..., 
        [ 27,  31,  32],
        [ 30,  33,  34],
        [ 28,  32,  33]],

       ..., 
       [[206, 206, 206],
        [206, 206, 206],
        [206, 206, 206],
        ..., 
        [206, 206, 206],
        [183, 180, 178],
        [151, 149, 147]],

       [[206, 206, 206],
        [206, 206, 206],
        [206, 206, 206],
        ..., 
        [206, 206, 206],
        [183, 180, 178],
        [153, 150, 149]],

       [[206, 206, 206],
        [206, 206, 206],
        [206, 206, 206],
        ..., 
        [183, 180, 178],
        [183, 180, 178],
        [155, 152, 151]]], dtype=uint8)

I just found out
np.where(p == np.array([255,255,255]))
is the same as
np.where(p == np.array([255]))
is there another way?
I want to be able to match all three RGBs at once instead of sectionally.

In fact np.where(p == 255) is also the same thing as both of those two above.

Edited 2 Years Ago by nouth

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