As a slightly more direct alternative to @Anil's answer, matplotlib has matplotlib.nxutils.points_inside_poly
that can be used to quickly rasterize an arbitrary polygon. E.g.
import numpy as np
from matplotlib.nxutils import points_inside_poly
nx, ny = 10, 10
poly_verts = [(1,1), (5,1), (5,9),(3,2),(1,1)]
# Create vertex coordinates for each grid cell...
# (<0,0> is at the top left of the grid in this system)
x, y = np.meshgrid(np.arange(nx), np.arange(ny))
x, y = x.flatten(), y.flatten()
points = np.vstack((x,y)).T
grid = points_inside_poly(points, poly_verts)
grid = grid.reshape((ny,nx))
print grid
Which yields (a boolean numpy array):
[[False False False False False False False False False False]
[False True True True True False False False False False]
[False False False True True False False False False False]
[False False False False True False False False False False]
[False False False False True False False False False False]
[False False False False True False False False False False]
[False False False False False False False False False False]
[False False False False False False False False False False]
[False False False False False False False False False False]
[False False False False False False False False False False]]
You should be able to pass grid
to any of the scipy.ndimage.morphology functions quite nicely.