python - Create mask from skimage contour -

i have image found contours on skimage.measure.find_contours() want create mask pixels outside largest closed contour. idea how this?

modifying example in documentation:

import numpy np import matplotlib.pyplot plt skimage import measure  # construct test data x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] r = np.sin(np.exp((np.sin(x)**2 + np.cos(y)**2)))  # find contours @ constant value of 0.8 contours = measure.find_contours(r, 0.8)  # select largest contiguous contour contour = sorted(contours, key=lambda x: len(x))[-1]  # display image , plot contour fig, ax = plt.subplots() ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray) x, y = ax.get_xlim(), ax.get_ylim() ax.step(contour.t[1], contour.t[0], linewidth=2, c='r') ax.set_xlim(x), ax.set_ylim(y) plt.show() 

here contour in red:

but if zoom in, notice contour not @ resolution of pixels.

how can create image of same dimensions original pixels outside (i.e. not crossed contour line) masked? e.g.

from numpy import ma masked_image = ma.array(r.copy(), mask=false) masked_image.mask[pixels_outside_contour] = true 

thanks!

ok, able make work converting contour path , selecting pixels inside:

# convert contour closed path matplotlib import path closed_path = path.path(contour.t)  # points lie within closed path idx = np.array([[(i,j) in range(r.shape[0])] j in range(r.shape[1])]).reshape(np.prod(r.shape),2) mask = closed_path.contains_points(idx).reshape(r.shape)  # invert mask , apply image mask = np.invert(mask) masked_data = ma.array(r.copy(), mask=mask) 

however, kind of slow testing n = r.shape[0]*r.shape[1] pixels containment. have faster algorithm? thanks!