def sharpen(img, k=11, lo_pass=True, min_diff=0.05, alpha=1.0):
"""
Sharpens the input image with unsharp masking. See Wikipedia
(http://en.wikipedia.org/wiki/Unsharp_masking) for details. Note that
this function only changes pixel values by a maximum of max_difference
at each iteration.
Optionally applies a low-pass Gaussian filter at the end,
to reduce the effect of high frequency noise.
"""
# sharpen each color channel separately
out = np.zeros(img.shape)
sharp_mask = bf.gauss_mask(k)
blur_mask = bf.gauss_mask(2 * int(1.0 + (k - 1) / 4.0) + 1)
for i in range(0, img.shape[2]):
blurred = convolve2d(img[:, :, i], sharp_mask,
mode="same", boundary="symm")
diff = img[:, :, i] - blurred
scaled = alpha * diff
if lo_pass:
# if necessary, blur each color channel separately
diff = convolve2d(diff, blur_mask,
mode="same", boundary="symm")
diff[np.abs(diff) < min_diff] = 0.0
out[:, :, i] = img[:, :, i] + scaled
# truncate to [0, 1]
out = bf.truncate(out)
return out
def blur(img, mode="gaussian", k=11):
"""
Blurs image with a kernel mask of the given type. Supports the following
modes, each of which can have varying size k:
(1) gaussian: can also provide variance var
(2) box: no additional parameters needed
"""
if mode == "gaussian":
mask = bf.gauss_mask(k)
elif mode == "box":
mask = bf.box_mask(k)
else:
raise Exception("Mode %s not supported." % mode)
# blur each color channel separately
out = np.zeros(img.shape)
for i in range(0, img.shape[2]):
out[:, :, i] = convolve2d(img[:, :, i], mask,
mode="same", boundary="symm")
return out
