wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-18 12:40:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fixes

Browse Source
This commit is contained in:
Pieter Holtzhausen
2011-08-22 14:40:13 +02:00
parent 3e8cc62e67
commit 72fc24fc90
1 changed files with 7 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scikits/image/transform/radon_transform.py
+7 -16
View File
@@ -41,12 +41,12 @@ def radon(image, theta=None):
if theta == None:
theta = np.arange(180)
height, width = image.shape
diagonal = np.sqrt(height**2 + width**2)
diagonal = np.sqrt(height ** 2 + width ** 2)
heightpad = np.ceil(diagonal - height) + 2
widthpad = np.ceil(diagonal - width) + 2
padded_image = np.zeros((int(height+heightpad), int(width+widthpad)))
y0, y1 = int(np.ceil(heightpad/2)), int((np.ceil(heightpad/2)+height))
x0, x1 = int((np.ceil(widthpad/2))), int((np.ceil(widthpad/2)+width))
padded_image = np.zeros((int(height + heightpad), int(width + widthpad)))
y0, y1 = int(np.ceil(heightpad / 2)), int((np.ceil(heightpad / 2) + height))
x0, x1 = int((np.ceil(widthpad / 2))), int((np.ceil(widthpad / 2) + width))
padded_image[y0:y1, x0:x1] = image
out = np.zeros((max(padded_image.shape), len(theta)))
for i in range(len(theta)):
@@ -140,24 +140,15 @@ def iradon(radon_image, theta=None, output_size=None, filter="ramp", interpolati
if interpolation == "nearest":
for i in range(len(theta)):
k = np.round(mid_index + xpr*np.sin(th[i]) - ypr*np.cos(th[i]))
reconstructed += radon_filtered[((((k > 0) & (k < n))*k) - 1).astype(np.int), i]
reconstructed += radon_filtered[((((k > 0) & (k < n)) * k) - 1).astype(np.int), i]
elif interpolation == "linear":
for i in range(len(theta)):
t = xpr*np.sin(th[i]) - ypr*np.cos(th[i])
a = np.floor(t)
b = mid_index + a
b0 = ((((b + 1 > 0) & (b + 1 < n))*(b + 1)) - 1).astype(np.int)
b1 = ((((b > 0) & (b < n))*b) - 1).astype(np.int)
b0 = ((((b + 1 > 0) & (b + 1 < n)) * (b + 1)) - 1).astype(np.int)
b1 = ((((b > 0) & (b < n)) * b) - 1).astype(np.int)
reconstructed += (t - a) * radon_filtered[b0, i] + (a - t + 1) * radon_filtered[b1, i]
# XXX slow with some artifacts
# elif interpolation == "spline":
# axis = np.arange(0, radon_filtered.shape[0]) - mid_index
# for i in range(len(theta)):
# print i
# t = xpr*np.sin(th[i]) - ypr*np.cos(th[i])
# #f = interp1d(axis, radon_filtered[:, i], kind="cubic", bounds_error=False, fill_value=0)
# f = interp1d(axis, radon_filtered[:, i], kind="linear", bounds_error=False, fill_value=0)
# reconstructed += f(t).reshape(output_size, output_size)
else:
raise ValueError("Unknown interpolation: %s" % interpolation)