GLCM calculation using scikit-learn. Error when using greycomatrix

[ioannisgkan259 at gmail.com]

Thank you very much Juan for your quick reply.
That was helpful :)

Ioannis

On Monday, 19 September 2016 01:03:45 UTC+1, Juan Nunez-Iglesias wrote:
>
> Hi Ioannis,
>
> Unfortunately the levels keyword is used as a hint to the function about 
> the number of levels when the image is uint16, because the possible number 
> of levels is huge. But if you want to convert the image to those levels, 
> you have to do it manually. I suggest you look at the "rescale_intensity" 
> function:
>
>
> http://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.rescale_intensity
>
> and process your image before passing it to the glcm function.
>
> I hope this helps! Keep pinging if you have more questions. =)
>
> Juan.
>
> On Sun, Sep 18, 2016 at 4:57 AM, <ioannis... at gmail.com <javascript:>> 
> wrote:
>
>> Hello everyone,
>>
>> I am using a SAR image (16-bit) and trying to implement GLCM algorithm 
>> using sciki-learn. When trying to calculate the GLCM using greycomatrix i 
>> get the following error: 
>>
>> assert image.max() < levels. It says that the maximum value of the image intensity must be less than the number of grey levels.
>> Because the SAR image is really big, i want to reduce the calculation time by reducing the levels to 8. 
>> Even if i remove the parameter 'level=8' when using greycomatrix, still gives me the same error
>>
>> My code is the following:
>>
>> from skimage.feature import greycomatrix, greycoprops
>> import numpy as np
>> from skimage import data
>> import rasterio
>>
>> path = 'C:\Users\GLCM_implementation\glasgow.tif'
>>
>> with rasterio.open(path, 'r') as src:
>>     import_file = src.read()
>>     img = import_file[0,:,:] #i need only the two dimentions (height, width)
>>     print img.shape
>>     
>>
>> #calculate the GLCM specifying the distance, direction(4 directions) and number of grey levels
>> GLCM = greycomatrix(img, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4],levels=8, symmetric=False, normed=True)
>> #list(GLCM[:,:,0,2])
>>
>>
>> #Calculate texture statistics
>> contrast = greycoprops(GLCM, 'contrast')
>>
>> dissimilarity = greycoprops(GLCM, 'dissimilarity')
>>
>> homogeneity = greycoprops(GLCM, 'homogeneity')
>>
>> energy = greycoprops(GLCM, 'energy')
>>
>> correlation = greycoprops(GLCM, 'correlation')
>>
>> ASM = greycoprops(GLCM, 'ASM')
>>
>>
>>
>> Error message:
>>
>>  101     image = np.ascontiguousarray(image)    102     assert image.min() >= 0--> 103     assert image.max() < levels    104     image = image.astype(np.uint8)    105     distances = np.ascontiguousarray(distances, dtype=np.float64)
>> AssertionError: 
>>
>>
>> I would appreciate any help.
>> Thank you in advance
>>
>> Ioannis 
>>
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