[python-dtcwt] 465/497: registration: add customisation of level selection

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/sid
in repository python-dtcwt.

commit 722961fa126e9de5bf56b29df0984b906a824d0c
Author: Rich Wareham <rjw57 at cam.ac.uk>
Date:   Thu Oct 16 09:30:35 2014 +0100

    registration: add customisation of level selection
---
 dtcwt/registration.py | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/dtcwt/registration.py b/dtcwt/registration.py
index 49d5ddd..7dcaf97 100644
--- a/dtcwt/registration.py
+++ b/dtcwt/registration.py
@@ -301,7 +301,7 @@ def warptransform(t, avecs, levels, method=None):
     # Clone the transform
     return dtcwt.numpy.Pyramid(t.lowpass, tuple(warped_highpasses), t.scales)
 
-def estimatereg(source, reference, regshape=None):
+def estimatereg(source, reference, regshape=None, levels=None):
     """
     Estimate registration from which will map *source* to *reference*.
 
@@ -318,6 +318,10 @@ def estimatereg(source, reference, regshape=None):
     Use the :py:func:`velocityfield` function to convert the return value from
     this function into a velocity field.
 
+    If not-`None`, *levels* is a sequence of sequences of 0-based level indices
+    to use when calculating the registration. If `None` then a default set of
+    levels are used.
+
     """
     # Extract number of levels and shape of level 4 (i.e. index 3) subband
     nlevels = len(source.highpasses)
@@ -329,11 +333,19 @@ def estimatereg(source, reference, regshape=None):
     # Initialise matrix of 'a' vectors
     avecs = np.zeros(avecs_shape)
 
+    if levels is None:
+        levels = []
+        levels.append(list(x for x in xrange(nlevels-1, nlevels-3, -1) if x>=0))
+        for s in np.arange(nlevels-1, 0, -0.5):
+            refine_levels = list(int(np.floor(s))-x for x in range(2) if s-x >= 2)
+            if len(refine_levels) < 2:
+                continue
+            levels.append(refine_levels)
+
     # Compute initial global transform
-    levels = list(x for x in xrange(nlevels-1, nlevels-3, -1) if x>=0)
     Qt_mats = list(
             np.sum(np.sum(x, axis=0), axis=0)
-            for x in qtildematrices(source, reference, levels)
+            for x in qtildematrices(source, reference, levels[0])
     )
     Qt = np.sum(Qt_mats, axis=0)
 
@@ -342,16 +354,12 @@ def estimatereg(source, reference, regshape=None):
         avecs[:,:,idx] = a[idx]
 
     # Refine estimate
-    for s in np.arange(nlevels-1, 0, -0.5):
-        levels = list(int(np.floor(s))-x for x in range(2) if s-x >= 2)
-        if len(levels) < 2:
-            continue
-
+    for est_levels in levels[1:]:
         # Warp the levels we'll be looking at with the current best-guess transform
-        warped = warptransform(source, avecs, levels, method='bilinear')
+        warped = warptransform(source, avecs, est_levels, method='bilinear')
 
         # Rescale and sample all the Qtilde matrix results
-        all_qts = qtildematrices(warped, reference, levels)
+        all_qts = qtildematrices(warped, reference, est_levels)
         if all_qts is None or len(all_qts) < 1:
             continue
 

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