scikit-image

I am trying to do texture analysis in a satellite imagery using GLCM algorithm. The scikit-image documentation is very helpful on that but for GLCM calculation we need a window size looping over the image. This is too slow in Python. I found many posts on stackoverflow about sliding windows but the computation takes for ever. I have an example shown below, it works but takes forever. I guess this must be a a naive way of doing it image = np.pad(image, int(win/2), mode='reflect') row, cols = image.shape feature_map = np.zeros((M, N)) for m in xrange(0, row): for n in xrange(0, cols): window =

I'm working in python with many images. While I'm analyzing this when I use: from skimage import color from skimage import io img = color.rgb2gray(io.imread(path)) I get this error: Traceback (most recent call last): File "C:\wamp\www\NewIESP\FUNZIONApy\up1feature.py", line 180, in <module> updatefeatures(infile) File "C:\wamp\www\NewIESP\FUNZIONApy\up1feature.py", line 43, in updatefeatures temp = compareup1features.comparison(path) File "C:\wamp\www\NewIESP\FUNZIONApy\compareup1features.py", line 496, in comparison skellist = moduloSkeleton.skelfeatures(path1img) File "C:\wamp\www\NewIESP

I've installed python packages using the synaptic package manager on Raspberry Pi-2. However, the skimage module version 0.6 is the latest available version in synaptic. Can someone guide me how to upgrade it to 0.11, as certain functions are missing in the older version. I tried pip install scikit-image , but it gives the output Running setup.py install for scikit image and then gets stuck there. I don't think there is anything wrong there. It's just that there is a number of C extensions to compile in scikit-image and it is slow on Raspberry Pi. For instance, on my laptop with i7 dual core

when using skimage I get the following error: win = skimage.util.view_as_windows(x, windowSize, windowShift) C:\Program Files\Anaconda2\lib\site-packages\skimage\util\shape.py:247: RuntimeWarning: Cannot provide views on a non-contiguous input array without copying. warn(RuntimeWarning("Cannot provide views on a non-contiguous input " as far I understood this is because x is a non contiguous array. I think I solved the problem adding in my code np.ascontiguousarray as below: win = skimage.util.view_as_windows(np.ascontiguousarray(x), windowSize, windowShift) Is this the right thing to do? Note

When running: from skimage import data in jupyter notebook I always get the error : "The kernel appears to have died. It will restart automatically" I use: Anaconda 4.2.0 (64-bit) Python 3.5.2 scikit-image 0.12.3 np111py35_1 When I run a notebook with python 2.7 kernel it goes fine with no error. So I guess there is a compatibility issue? But I need to use python 3.5, any suggestion? Problem solved with: conda update mkl working versions: mkl 2017.0.1 0 mkl-service 1.1.2 py35_2 来源： https://stackoverflow.com/questions/42912076/the-kernel-appears-to-have-died-it-will-restart-automatically

Currently, I use skimage in python to extract the skeleton of open space from some a binarized map as following pictures, With following python codes: from skimage.morphology import skeletonize from skimage import draw from skimage.io import imread, imshow from skimage.color import rgb2gray # load image from file img_fname=os.path.join('images','mall1_2F_schema.png') image=imread(img_fname) # Change RGB color to gray image=rgb2gray(image) # Change gray image to binary image=np.where(image>np.mean(image),1.0,0.0) # perform skeletonization skeleton = skeletonize(image) Now I would like to

I am wondering how to smart sharpen an image using python or any related image library like ndimage , skimage or even PIL .I could find methods that actually sharpen my image but with a lot of noise and pixelating when zooming in .So since I know Photoshop I tried to get that smart sharpen effect which sharpens the image with a less noising and with a nice sweet contrast through python but I failed. Notes:- (1) methods has been tested:- >>> # The 1st Method: >>> import Image >>> import ImageFilter >>> image.filter(ImageFilter.SHARPEN) >>> Image.filter(ImageFilter.EDGE_ENHANCE_MORE) #Look down