Hi John
How about this for a numpy implementation? Runs nearly as fast as the C version
on my mac (1.58 vs 1.16 secs).
def hillShadeNumPy(filenameIn, filenameOut, scale=1.0, azdeg=315.0,
altdeg=45.0):
''' Create a hill shade version of the given image using numpy '''
from numpy import sin, cos, hypot, arctan, arctan2, pi
dScale = (1.0 * scale)
# convert alt, az to radians
az = azdeg*pi/180.0
alt = altdeg*pi/180.0
(img, data, imgS, dataS) = initialiseImages(filenameIn)
# get the image data as a numpy array of floats
adata = N.asarray(img).astype('float')
# gradient in x and y directions
dx, dy = N.gradient(adata/dScale)
slope = 0.5*pi - arctan(hypot(dx, dy))
aspect = arctan2(dx, dy)
c = sin(alt)*sin(slope) + cos(alt)*cos(slope)*cos(-az - aspect - 0.5*pi)
c = N.where(c > 0.0, c*255.0, 0.0)
imgS = Image.fromarray(c.astype('uint8'))
imgS.save(filenameOut,'PNG')
Cheers
Will
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