scikit-image

问题 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

问题 I have experienced very strange problem related to scikit-image. Namely, I'm not able to run "started example": from skimage import data, io, filter image = data.coins() # or any NumPy array! edges = filter.sobel(image) io.imshow(edges) After that Python 2.7 reports Error: ValueError: can not convert object to float64. Moreover, I am not able to open any "tif, png" images, because Python reports similar error. Interestingly, this problem appeared suddenly, because at the beginning everything

问题 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

问题 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,

问题 I would like to get the sub-image that results from cutting out a polygon within an image. I have an image in skimage, and I have a polygon in matplotlib.patches. How to do it? Below is what I have tried. I am not necessarily looking for a way similar to below, I am looking for the cleanest, most efficient implementation. With this code, the polygon correctly overlays the part of the image I want to extract (but doesn't extract the segment of interest): import numpy as np import skimage.io as

问题 I need to shear and skew some images using python. I've come across this skimage module but I don't seem able to understand exactly how I'm supposed to use this. I've tried a few things, which obviously gave me errors, because as I suddenly realized later, I'm not passing in my image to the function. I then noticed that the function doesn't take my image as an input parameter in the first place. So how should the transformation be applied? Or is this even the right function to be looking at

问题 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

问题 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

问题 I have a NumPy array consisting of only the 0 and 1 elements as follows: import numpy as np data = np.array([[1, 1 , 0 , 0 , 0 , 0 , 1 , 0], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [0, 0 , 1 , 1 , **1** , 1 , 1 , 0], [0, 0 , 1 , 1 , 1 , 1 , 1 , 1], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [1, 1 , 0 , 0 , 0 , 0 , 0 , 0]]) I have to find out the indices of the element 1 which is surrounded by 1 in 2 by 2 pixels in every directions. The location of the expected answer is shown in

问题 I have a NumPy array consisting of only the 0 and 1 elements as follows: import numpy as np data = np.array([[1, 1 , 0 , 0 , 0 , 0 , 1 , 0], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [0, 0 , 1 , 1 , **1** , 1 , 1 , 0], [0, 0 , 1 , 1 , 1 , 1 , 1 , 1], [1, 1 , 1 , 1 , 1 , 1 , 1 , 0], [1, 1 , 0 , 0 , 0 , 0 , 0 , 0]]) I have to find out the indices of the element 1 which is surrounded by 1 in 2 by 2 pixels in every directions. The location of the expected answer is shown in