def run_refine(ctx,
dataset,
zero,
a,
b,
corr_params,
match_params,
indices=None):
'''
Refine the given lattice for each frame by calculating approximate peak positions and refining
them for each frame by using the blobcorrelation and gridmatching.fastmatch().
indices:
Indices to refine. This is trimmed down to positions within the frame.
As a convenience, for the indices parameter this function accepts both shape
(n, 2) and (2, n, m) so that numpy.mgrid[h:k, i:j] works directly to specify indices.
This saves boilerplate code when using this function. Default: numpy.mgrid[-10:10, -10:10].
match_params['affine']: If True, use affine transformation matching. This is very fast and
robust against a distorted field of view, but doesn't exclude outliers.
returns:
(result, used_indices) where result is
{
'centers': BufferWrapper(
kind="nav", extra_shape=(num_disks, 2), dtype="u2"
),
'refineds': BufferWrapper(
kind="nav", extra_shape=(num_disks, 2), dtype="float32"
),
'peak_values': BufferWrapper(
kind="nav", extra_shape=(num_disks,), dtype="float32"
),
'peak_elevations': BufferWrapper(
kind="nav", extra_shape=(num_disks,), dtype="float32"
),
'zero': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
'a': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
'b': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
'selector': BufferWrapper(
kind="nav", extra_shape=(num_disks,), dtype="bool"
),
}
and used_indices are the indices that were within the frame.
'''
if indices is None:
indices = np.mgrid[-10:10, -10:10]
s = indices.shape
# Output of mgrid
if (len(s) == 3) and (s[0] == 2):
indices = np.concatenate(indices.T)
# List of (i, j) pairs
elif (len(s) == 2) and (s[1] == 2):
pass
else:
raise ValueError(
"Shape of indices is %s, expected (n, 2) or (2, n, m)" %
str(indices.shape))
(fy, fx) = tuple(dataset.shape.sig)
peaks = grm.calc_coords(zero, a, b, indices).astype('int')
selector = grm.within_frame(peaks, corr_params['radius'], fy, fx)
peaks = peaks[selector]
indices = indices[selector]
result = ctx.run_udf(
dataset=dataset,
fn=functools.partial(
refine,
start_zero=zero,
start_a=a,
start_b=b,
match_params=match_params,
indices=indices,
),
init=functools.partial(init_pass_2,
peaks=peaks,
parameters=corr_params),
make_buffers=functools.partial(
get_result_buffers_refine,
num_disks=len(peaks),
),
)
return (result, indices)
def test_within_frame():
points = np.array([(0, 0), (1, 1), (9, 19), (19, 9), (8, 19), (8, 18),
(9, 18), (18, 8)])
expected = np.array([False, True, False, False, False, True, False, False])
result = grm.within_frame(points, r=1, fy=10, fx=20)
assert (np.all(result == expected))
def run_refine(ctx,
dataset,
zero,
a,
b,
parameters,
indices=None,
bounds=None):
'''
Refine the given lattice for each frame by optimizing the correlation
with full rendered frames.
Full frame matching inspired by Christoph Mahr, Knut Müller-Caspary
and the Bremen group in general
indices:
Indices to refine. This is trimmed down to positions within the frame.
As a convenience, for the indices parameter this function accepts both shape
(n, 2) and (2, n, m) so that numpy.mgrid[h:k, i:j] works directly to specify indices.
This saves boilerplate code when using this function. Default: numpy.mgrid[-10:10, -10:10].
returns:
(result, used_indices) where result is
{
'intensity': BufferWrapper(
kind="nav", dtype="float32"
),
'zero': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
'a': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
'b': BufferWrapper(
kind="nav", extra_shape=(2,), dtype="float32"
),
}
and used_indices are the indices that were within the frame.
'''
if indices is None:
indices = np.mgrid[-10:10, -10:10]
s = indices.shape
# Output of mgrid
if (len(s) == 3) and (s[0] == 2):
indices = np.concatenate(indices.T)
# List of (i, j) pairs
elif (len(s) == 2) and (s[1] == 2):
pass
else:
raise ValueError(
"Shape of indices is %s, expected (n, 2) or (2, n, m)" %
str(indices.shape))
(fy, fx) = tuple(dataset.shape.sig)
peaks = grm.calc_coords(zero, a, b, indices).astype('int')
selector = grm.within_frame(peaks, parameters['radius'], fy, fx)
indices = indices[selector]
result = ctx.run_udf(
dataset=dataset,
fn=functools.partial(refine,
start_zero=zero,
start_a=a,
start_b=b,
indices=indices,
parameters=parameters,
bounds=bounds),
make_buffers=get_result_buffers_refine,
)
return (result, indices)