Thank you very much for your quick reply, i appreciate it. It seems you always have a good answer to give me.
Ioannis On Sunday, 2 October 2016 03:41:10 UTC+1, Juan Nunez-Iglesias wrote: > > Hi Ioannis, > > So, the size of the GLCM for a 32-bit image that uses all possible > intensity levels is 2**64, way too big to fit in memory. The key is: > > - rescale the image to be in [0, max_value-1] (for some integer max_value > that you determine) > - call greycomatrix on this rescaled image with the keyword argument > `levels=max_value`. > > That should work. However, note that the size of the matrix will be `4 * > max_value ** 2` or `8 * max_value ** 2`, and multiple temporary matrices of > the same size might be created for processing. So, you need to make sure > that your computer has enough memory for that. Typically, this will require > your image to have much fewer levels than even 16-bit. > > Additionally, I think this feature requires the development version of > scikit-image. You can install that with "pip install git+ > https://github.com/scikit-image/scikit-image.git";. (I think that's the > command anyway...) > > Juan. > > On Sun, Oct 2, 2016 at 6:33 AM, <ioannis...@gmail.com <javascript:>> > wrote: > >> Hello Juan, >> >> I want to thank you for your quick reply to my question the last time and >> i do not want to bother you with my questions. I always try to solve my >> problems before posting my question here. Sometime, i just cannot figure >> out how to fix the error. >> >> What i want to do is to create a GLCM image using different grey levels >> (8-bit, 16-bit and 32-bit) and compare the results. The type of the image i >> am using is a float32. I used the '' rescale intensity'' as you suggested, >> i converted the image to 8-bit and it worked. When i try to rescale the >> image in 16-bit and 32-bit for some reason it doesn't work. >> >> i get the error: >> >> >> AssertionError Traceback (most recent call >> last)<ipython-input-16-ca616e8222fb> in <module>() 22 23 for j >> in xrange(img.shape[1] ):---> 24 glcm = greycomatrix(rescale, [1], >> [0], symmetric = True, normed = True ) 25 26 >> testraster1[i,j] = greycoprops(glcm, 'contrast') >> C:\Anaconda2\lib\site-packages\skimage\feature\texture.pyc in >> greycomatrix(image, distances, angles, levels, symmetric, normed) 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: >> >> It seems that i am allowed only to use an 8-bit image for the GLCM >> calculation. >> So, does this code in scikit-image work only for 8-bit images? is there >> another way to solve my problem? >> I will keep trying to find a solution but if you have an idea on how to >> solve this problem, please tell me >> >> Thank you in advance >> Ioannis >> >> >> >> On Monday, 19 September 2016 15:41:07 UTC+1, ioannis...@gmail.com wrote: >>> >>> 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...@gmail.com> 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 >>>>> >>>>> -- >>>>> You received this message because you are subscribed to the Google >>>>> Groups "scikit-image" group. >>>>> To unsubscribe from this group and stop receiving emails from it, send >>>>> an email to scikit-image...@googlegroups.com. >>>>> To post to this group, send email to scikit...@googlegroups.com. >>>>> To view this discussion on the web, visit >>>>> https://groups.google.com/d/msgid/scikit-image/520f5f2b-4750-4b56-a40b-28b938b750d8%40googlegroups.com >>>>> >>>>> <https://groups.google.com/d/msgid/scikit-image/520f5f2b-4750-4b56-a40b-28b938b750d8%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>> . >>>>> For more options, visit https://groups.google.com/d/optout. >>>>> >>>> >>>> -- >> You received this message because you are subscribed to the Google Groups >> "scikit-image" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to scikit-image...@googlegroups.com <javascript:>. >> To post to this group, send email to scikit...@googlegroups.com >> <javascript:>. >> To view this discussion on the web, visit >> https://groups.google.com/d/msgid/scikit-image/978af8fd-0dfa-47ca-9e16-4a4ccccb4bc3%40googlegroups.com >> >> <https://groups.google.com/d/msgid/scikit-image/978af8fd-0dfa-47ca-9e16-4a4ccccb4bc3%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > > -- You received this message because you are subscribed to the Google Groups "scikit-image" group. 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