scikit-image

I'm trying to install the scikit-image package on my Windows 7 64-bit machine with python 3.5. While installing scikit, the requirements are met: Requirement already satisfied: six>=1.7.3 in c:\users\x\appdata\local\programs\python\python35\lib\site-packages (from scikit-image) Requirement already satisfied: networkx>=1.8 in c:\users\x\appdata\local\programs\python\python35\lib\site-packages (from scikit-image) Requirement already satisfied: pillow>=2.1.0 in c:\users\x\appdata\local\programs\python\python35\lib\site-packages (from scikit-image) Requirement already satisfied: PyWavelets>=0.4.0

Outline: I need to use scikit-image inside some AWS lambda functions, so I'm looking to build a custom AWS lambda layer containing scikit-image . My questions in general should apply to any python module, notably scikit-learn, or any custom layer in general I think. Background: After much googling and reading it seems the best way to do that is to use docker to run the AWS lambda runtime locally, and then inside there install/compile scikit-image (or whichever module you're looking for). After that's done, you can upload/install it to AWS as a custom layer. This is conceptually pretty simple,

I'm using skimage to crop a rectangle in a given image, now I have (x1,y1,x2,y2) as the rectangle coordinates, then I had loaded the image image = skimage.io.imread(filename) cropped = image(x1,y1,x2,y2) However this is the wrong way to crop the image, how would I do it in the right way in skimage This seems a simple error on syntax. Well, in Matlab you can use _'parentheses'_ to extract a pixel or an image region. But in Python, and numpy.ndarray you should use the brackets to slice a region of your image, besides in this code you is using the wrong way to cut a rectangle. The right way to

I have a 2-d numpy array as follows: a = np.array([[1,5,9,13], [2,6,10,14], [3,7,11,15], [4,8,12,16]] I want to extract it into patches of 2 by 2 sizes with out repeating the elements. The answer should exactly be the same. This can be 3-d array or list with the same order of elements as below: [[[1,5], [2,6]], [[3,7], [4,8]], [[9,13], [10,14]], [[11,15], [12,16]]] How can do it easily? In my real problem the size of a is (36, 72). I can not do it one by one. I want programmatic way of doing it. Here's a rather cryptic numpy one-liner to generate your 3-d array, called result1 here: In [60]: x

I have many skeletonized images like this: How can i detect a cycle, a loop in the skeleton? Are there "special" functions that do this or should I implement it as a graph? In case there is only the graph option, can the python graph library NetworkX can help me? You can exploit the topology of the skeleton. A cycle will have no holes, so we can use scipy.ndimage to find any holes and compare. This isn't the fastest method, but it's extremely easy to code. import scipy.misc, scipy.ndimage # Read the image img = scipy.misc.imread("Skel.png") # Retain only the skeleton img[img!=255] = 0 img =

I am building code on python using skimage. But I am getting import errors while using skimage.segmentation. Traceback (most recent call last): File "superpixel.py", line 5, in from skimage.segmentation import slic ImportError: No module named skimage.segmentation Joseph You can use pip install scikit-image . Also see the recommended procedure . As per the official installation page of skimage ( skimage Installation ) : python-skimage package depends on matplotlib, scipy, pil, numpy and six. So install them first using sudo apt-get install python-matplotlib python-numpy python-pil python-scipy

I am using skimage library for most of image analysis work. I have an RGB image and I intend to extract texture features like entropy , energy , homogeneity and contrast from the image. Below are the steps that I am performing: from skimage import io, color, feature from skimage.filters import rank rgbImg = io.imread(imgFlNm) grayImg = color.rgb2gray(rgbImg) print(grayImg.shape) # (667,1000), a 2 dimensional grayscale image glcm = feature.greycomatrix(grayImg, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4]) print(glcm.shape) # (256, 256, 1, 4) rank.entropy(glcm, disk(5)) # throws an error since entropy

I am trying to use skimage.restoration.wiener , but I always end up with an image with a bunch of 1 (or -1), what am I doing wrong? The original image comes from Uni of Waterloo . import numpy as np from scipy.misc import imread from skimage import color, data, restoration from scipy.signal import convolve2d as conv2 def main(): image = imread("/Users/gsamaras/Downloads/boat.tif") psf = np.ones((5, 5)) / 25 image = conv2(image, psf, 'same') image += 0.1 * image.std() * np.random.standard_normal(image.shape) deconvolved = restoration.wiener(image, psf, 0.00001) print deconvolved print image if