My apologies.
It is clear now that the issue was not in the idea for the implementation,
or the code, but clearly my incomplete understanding of graycomatrix and
graycoprops.
Since I am sliding along a 16x5 array, with 9 levels, I should get a
16x9x9x1x1 5D array. Now that I understand this better, it occurs to me
that I just need to slide over this array also, passing each of the 16
9x9x1x1 4D partial array to greycopros, to get 16 values of dissimilarity.
Does that make sense?
diss = []
for i in np.arange(np.shape(out)[0]):
ds = greycoprops(out[i], 'dissimilarity')
diss.append(ds)
diss
will give:
[array([[ 3.75]]),
array([[ 3.5]]),
array([[ 1.5]]),
array([[ 2.]]),
array([[ 2.]]),
array([[ 2.25]]),
array([[ 2.25]]),
array([[ 2.]]),
array([[ 1.75]]),
array([[ 1.5]]),
array([[ 3.25]]),
array([[ 4.75]]),
array([[ 5.]]),
array([[ 5.25]]),
array([[ 5.5]]),
array([[ 3.75]])]
Thank you
On Friday, December 16, 2016 at 11:15:00 AM UTC-7, Matteo wrote:
>
> Sorry, below I meant to write:"Say I have an 16x1 1D array. I make 5
> copies of it to get a 16x5 2D array..."
>
> On Friday, December 16, 2016 at 11:13:20 AM UTC-7, Matteo wrote:
>>
>> I am very new to GLCM.
>>
>> I have some 1D array I would like to calculate dissimilarity in the
>> vertical direction, so I am trying to trick scikit-image by converting my
>> 1D array into 2D.
>>
>> Say I have an 16x5 2D array. I make 5 copies of it to get a 16x5 array. I
>> want to pass rows 0-4, then rows 5-9 and so on to run greycomatrix, by
>> just lookign at the vertical direction, append to output, and ultimately
>> calculate dissimilarity.
>>
>> Example array:
>>
>> x =np.array([7, 2, 0, 8, 8, 3, 2, 2, 0, 5, 3, 3, 4, 8, 7, 0])
>> w=5
>> s = np.array([x,]*w,dtype=np.uint8).transpose()
>>
>>
>> array([
>> [7, 7, 7, 7, 7],
>> [2, 2, 2, 2, 2],
>> [0, 0, 0, 0, 0],
>> [8, 8, 8, 8, 8],
>> [8, 8, 8, 8, 8],
>> [3, 3, 3, 3, 3],
>> [2, 2, 2, 2, 2],
>> [2, 2, 2, 2, 2],
>> [0, 0, 0, 0, 0],
>> [5, 5, 5, 5, 5],
>> [3, 3, 3, 3, 3],
>> [3, 3, 3, 3, 3],
>> [4, 4, 4, 4, 4],
>> [8, 8, 8, 8, 8],
>> [7, 7, 7, 7, 7],
>> [0, 0, 0, 0, 0]], dtype=uint8)
>>
>>
>> Below I test rolling:
>>
>>
>> test = np.roll(s[:, :], -5)
>> test
>>
>> array([[2, 2, 2, 2, 2],
>> [0, 0, 0, 0, 0],
>> [8, 8, 8, 8, 8],
>> [8, 8, 8, 8, 8],
>> [3, 3, 3, 3, 3],
>> [2, 2, 2, 2, 2],
>> [2, 2, 2, 2, 2],
>> [0, 0, 0, 0, 0],
>> [5, 5, 5, 5, 5],
>> [3, 3, 3, 3, 3],
>> [3, 3, 3, 3, 3],
>> [4, 4, 4, 4, 4],
>> [8, 8, 8, 8, 8],
>> [7, 7, 7, 7, 7],
>> [0, 0, 0, 0, 0],
>> [7, 7, 7, 7, 7]], dtype=uint8)
>>
>>
>>
>> Now I roll and pass top 5 rows to greycomatrix:
>>
>>
>> levels = 9# this rolls the 2D array up one row at a time, each time
>> calculating GLCM on the top 5x5 elements
>> out = []
>> counter = 0
>> while counter < np.shape(s)[0]:
>> s = np.roll(s[:, :], -5)
>> glcm = greycomatrix(s[:5,:], [1], [np.pi/2], levels = levels, symmetric
>> = True, normed = True)
>> out.append(glcm)
>> counter +=1
>>
>>
>>
>> But unfortunately output is a 5D array:
>>
>>
>> print np.shape(out)
>> (16, 9, 9, 1, 1)
>>
>>
>>
>> I tried to recast into a 4D array to pass to greycoprops in different ways,
>> but unsuccessfully.
>>
>>
>> With:
>>
>> out1 = np.array(out).reshape((16, 9, 9, 1))
>> np.shape(out1)
>> (16, 9, 9, 1)
>>
>> diss = greycoprops(out1, 'dissimilarity')
>>
>>
>> I get this error:
>>
>>
>>
>> ---------------------------------------------------------------------------
>> AssertionError Traceback (most recent call last)
>> <ipython-input-15-a88f05b0e3e2> in <module>()
>> ----> 1 diss = greycoprops(out1, 'dissimilarity')
>> 2 diss
>>
>> /Users/matteoniccoli/anaconda2/lib/python2.7/site-packages/skimage/
>> feature/texture.pyc in greycoprops(P, prop)
>> 187
>> 188 (num_level, num_level2, num_dist, num_angle) = P.shape
>> --> 189 assert num_level == num_level2
>> 190 assert num_dist > 0
>> 191 assert num_angle > 0
>>
>> AssertionError:
>>
>>
>> It may be that I do not really understand the shape of the output, and the
>> requirements of greycoprops.
>>
>>
>> print np.shape(np.array(out))
>> print np.shape(np.array(out[0][:][:][:][:]))
>> print np.shape(np.array(out[:][0][:][:][:]))
>> print np.shape(np.array(out[:][:][0][:][:]))
>> print np.shape(np.array(out[:][:][:][0][:]))
>> print np.shape(np.array(out[:][:][:][:][0]))
>>
>>
>> (16, 9, 9, 1, 1)
>> (9, 9, 1, 1)
>> (9, 9, 1, 1)
>> (9, 9, 1, 1)
>> (9, 9, 1, 1)
>> (9, 9, 1, 1)
>>
>>
>> Can anybody suggest a way to make this work?
>>
>>
>> Thanks
>>
>>
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