How to go from outline to image mask using Matplotlib

Question

If I build a 2D array and contour, I can access the segmentation map through cs = plt.contour(...); cs.allsegs cs = plt.contour(...); cs.allsegs , but it is parameterized as a string. I would like segmap to be the logical mask of what interval for the line, so I can, say, quickly summarize everything within this outline.

Thank you very much!

Answer 1

I don’t think there is a very simple way, mainly because you want to mix raster and vector data. Fortunately, Matplotlib paths have the ability to check if a point is in the path by doing this for all pixels, creating a mask, but I think this method can be very slow for large datasets.

 import matplotlib.patches as patches from matplotlib.nxutils import points_inside_poly import matplotlib.pyplot as plt import numpy as np # generate some data X, Y = np.meshgrid(np.arange(-3.0, 3.0, 0.025), np.arange(-3.0, 3.0, 0.025)) Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0) Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1) # difference of Gaussians Z = 10.0 * (Z2 - Z1) fig, axs = plt.subplots(1,2, figsize=(12,6), subplot_kw={'xticks': [], 'yticks': [], 'frameon': False}) # create a normal contour plot axs[0].set_title('Standard contour plot') im = axs[0].imshow(Z, cmap=plt.cm.Greys_r) cs = axs[0].contour(Z, np.arange(-3, 4, .5), linewidths=2, colors='red', linestyles='solid') # get the path from 1 of the contour lines verts = cs.collections[7].get_paths()[0] # highlight the selected contour with yellow axs[0].add_patch(patches.PathPatch(verts, facecolor='none', ec='yellow', lw=2, zorder=50)) # make a mask from it with the dimensions of Z mask = verts.contains_points(list(np.ndindex(Z.shape))) mask = mask.reshape(Z.shape).T axs[1].set_title('Mask of everything within one contour line') axs[1].imshow(mask, cmap=plt.cm.Greys_r, interpolation='none') # get the sum of everything within the contour # the mask is inverted because everything within the contour should not be masked print np.ma.MaskedArray(Z, mask=~mask).sum() 

Please note that the contour lines, which by default "leave" the graph on different edges, do not make the path following these edges. These lines will require additional processing.

Answer 2

Another way, perhaps more intuitive, is the binary_fill_holes function from scipy.ndimage .

 import numpy as np import scipy image = np.zeros((512, 512)) image[contour1[:, 0], contour1[:, 1]] = 1 masked_image = scipy.ndimage.morphology.binary_fill_holes(image) ```