scikit-image

I'm currently working with image processing in python using the scikit-image library. I'm trying to make a binary image using sauvola thresholding with the following code: from PIL import Image import numpy from skimage.color import rgb2gray from skimage.filters import threshold_sauvola im = Image.open("test.jpg") pix = numpy.array(im) img = rgb2gray(pix) window_size = 25 thresh_sauvola = threshold_sauvola(img, window_size=window_size) binary_sauvola = img > thresh_sauvola Which gives the following result: the output is a numpy array with data type of this image is a bool [[ True True True ...

I'm using the local_binary_pattern from skimage.feature with uniform mode like this: >>> from skimage.feature import local_binary_pattern >>> lbp_image=local_binary_pattern(some_grayscale_image,8,2,method='uniform') >>> histogram=scipy.stats.itemfreq(lbp_image) >>> print histogram [[ 0.00000000e+00 1.57210000e+04] [ 1.00000000e+00 1.86520000e+04] [ 2.00000000e+00 2.38530000e+04] [ 3.00000000e+00 3.23200000e+04] [ 4.00000000e+00 3.93960000e+04] [ 5.00000000e+00 3.13570000e+04] [ 6.00000000e+00 2.19800000e+04] [ 7.00000000e+00 2.46530000e+04] [ 8.00000000e+00 2.76230000e+04] [ 9.00000000e+00 4

I have an image stored as a 2d numpy array (possibly multi-d). I can make a view onto that array that reflects a 2d sliding window, but when I reshape it so that each row is a flattened window (rows are windows, column is a pixel in that window) python makes a full copy. It does this because I'm using the typical stride trick, and the new shape isn't contiguous in memory. I need this because I'm passing entire large images to an sklearn classifier, which accepts 2d matrices, where there's no batch/partial fit procedure, and the full expanded copy is far too large for memory. My Question: Is

I would like to robustly estimate a polynomial geometric transform with scikit-image skimage.transform and skimage.measure.ransac The ransack documentation gives a very nice example of how to do exactly that but with a Similarity Transform. Here is how it goes: from skimage.transform import SimilarityTransform from skimage.measure import ransac model, inliers = ransac((src, dst), SimilarityTransform, 2, 10) I need to use skimage.transform.PolynomialTransform instead of SimilarityTransform, and I need to be able to specify the polynomial order. But the RANSAC call takes as input the

I have an image and I want to get the pixels that cross through its medial axis. I tried to use skeletonize and medial axis methods in order to get them but both methods return one dimensional line which is shorter than the corresponding object. Here's the code with a sample image:- >>> import skimage.filter >>> import skimage.morphology >>> import numpy as np >>> import scipy.misc >>> im=scipy.misc.imread('img.jpg') >>> thr=skimage.filter.threshold_otsu(im) >>> im=im > thr # Threshold the image >>> im_sk=skimage.morphology.skeletonize(im) >>> mask=np.where(im_sk==1) # pixels of skeleton >>>