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Return 1D array for single channel images

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Juan Nunez-Iglesias
2014-01-27 15:36:46 +11:00
parent 1170d87ba4
commit ecb50571b1
1 changed files with 6 additions and 16 deletions
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skimage/measure/profile.py
+6 -16
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@@ -5,7 +5,7 @@ def _calc_vert(img, col, src_row, dst_row, linewidth):
# Quick calculation if perfectly vertical
pixels = img[src_row:dst_row:np.sign(dst_row - src_row),
col - linewidth / 2: col + linewidth / 2 + 1]
return pixels.mean(axis=1)[:, np.newaxis]
return pixels.mean(axis=1)[..., np.newaxis]
def profile_line(img, src, dst, linewidth=1, mode='constant', cval=0.0):
@@ -43,28 +43,21 @@ def profile_line(img, src, dst, linewidth=1, mode='constant', cval=0.0):
[1, 1, 1, 2, 2, 2],
[0, 0, 0, 0, 0, 0]])
>>> profile_line(img, (2, 1), (2, 5))
array([[ 1.],
[ 1.],
[ 2.],
[ 2.]])
array([ 1., 1., 2., 2.])
"""
src_row, src_col = src = np.asarray(src, dtype=float)
dst_row, dst_col = dst = np.asarray(dst, dtype=float)
d_col, d_row = dst - src
channels = 1
if img.ndim == 3:
channels = 3
d_row, d_col = dst - src
# Quick calculation if perfectly vertical; shortcuts div0 error
if src_col == dst_col:
if channels == 1:
if img.ndim == 2:
img = img[:, :, np.newaxis]
img = np.rollaxis(img, -1)
intensities = np.hstack([_calc_vert(im, src_col,
src_row, dst_row, linewidth)
for im in img])
return intensities
return np.squeeze(intensities)
theta = np.arctan2(d_row, d_col)
a = d_row / d_col
@@ -89,8 +82,5 @@ def profile_line(img, src, dst, linewidth=1, mode='constant', cval=0.0):
intensities = pixels.mean(axis=1)
if intensities.ndim == 1:
return intensities[..., np.newaxis]
else:
return intensities
return intensities