Reputation: 20307
I'd like fill in an image outside a circular area with the nearest value within the circle. The effect is something like skimage's mode='edge' but applying to a circular area of an image instead of a rectangular area.
Simple code which does the right thing - extremely slowly:
def circle_pad(img, xc, yc, r):
img_out = img.copy()
for i in range(img.shape[0]):
for j in range(img.shape[1]):
d = math.sqrt( (i-yc)**2 + (j-xc)**2 )
if d > r:
i1, j1 = int( yc + (i-yc)*(r/d) ), int( xc + (j-xc)*(r/d) )
img_out[i,j] = img[i1,j1]
return img_out
How to speed this up with numpy? (probably avoid looping over each pixel in python code; typical images are tens of millions pixels)
I thought of using something along the lines of meshgrid as a starting point to calculate the coordinates of the value to fill in at each point, but the way to do it isn't clear.
Upvotes: 0
Views: 713
Reputation: 20307
Solved using mgrid - not pretty but blazing fast. Just in case it's useful as an example for other folks with similar image processing problesm:
def circle_pad(img, xc, yc, r):
mg = np.mgrid[:img.shape[0],0:img.shape[1]]
yi, xi = mg[0,:,:], mg[1,:,:]
mask = ((yi-yc)**2 + (xi-xc)**2) < r**2
d = np.sqrt( (yi-yc)**2 + (xi-xc)**2 )
d = np.clip(d, r, None)
ye = yc + (yi-yc)*(r/d)
xe = xc + (xi-xc)*(r/d)
ye = np.clip(ye.astype(int), 0, img.shape[0])
xe = np.clip(xe.astype(int), 0, img.shape[1])
img_out = img * mask + img[ye,xe] * (~mask)
return img_out
The key parts are:
xi, yi
with np.mgrid
- each has the same size as the imagexe, ye
of the nearest edge pixel by doing math of xi, yiimg[ye,xe]
Upvotes: 2