Commit beb4a8a3 authored by Paul McCarthy's avatar Paul McCarthy 🚵
Browse files

RF: Sample along line tool supports 2D and multi-channel images (currently

just taking mean intensity for latter)
parent 2af711d7
......@@ -36,6 +36,60 @@ import fsleyes.plotting.plotcanvas as plotcanvas
import fsleyes.plugins.profiles.samplelineprofile as samplelineprofile
def sampleAlongLine(data, start, end, resolution, order):
"""Samples from ``data``, along a line between ``start`` and ``end``.
:arg data: 3D array
:arg start: Start coordinate
:arg end: End coordinate
:arg resolution: Number of points to sample
:arg order: Interpolation (see ``scipy.ndimage.map_coordinates``)
:returns: Tuple containing:
- 1D Numpy array containing the sampled values
- ``(3, N)`` numpy array containing the coordinates for
each sample
start = list(start)
end = list(end)
shape = data.shape
coords = np.linspace(start, end, resolution).T
# map_coordinates doesn't take
# kindly to dims of length 1
data = data.squeeze()
if any(s == 1 for s in shape):
drop = [i for i, s in enumerate(shape) if s == 1]
mapcoords = np.delete(coords, drop, axis=0)
mapcoords = coords
# multi-channel data?
if len(data.dtype) == 1:
data = [data]
channels = []
for chan in data.dtype.fields.keys():
data = channels
ys = []
for arr in data:
# For multi channel data, we currently
# just take the mean across all
# channels, but this might change in
# the future (if there is any need).
if len(ys) > 1: y = np.mean(ys, axis=0)
else: y = ys[0]
return y, coords
class SampleLineAction(actions.ToggleControlPanelAction):
"""The ``SampleLineAction`` simply shows/hides a :class:`SampleLinePanel`.
......@@ -164,22 +218,17 @@ class SampleLineDataSeries(plotting.DataSeries):
change. Re-samples the data from the image.
overlay = self.overlay
opts = self.displayCtx.getOpts(overlay)
data = overlay[self.__index]
resolution = self.resolution
order = self.interp
normalisex = 'x' in self.normalise
normalisey = 'y' in self.normalise
opts = self.displayCtx.getOpts(self.overlay)
data = self.overlay[self.__index]
start = self.__start
end = self.__end
coords = np.zeros((3, resolution))
coords[0, :] = np.linspace(start[0], end[0], resolution)
coords[1, :] = np.linspace(start[1], end[1], resolution)
coords[2, :] = np.linspace(start[2], end[2], resolution)
y = ndimage.map_coordinates(data, coords, order=order,
y, coords = sampleAlongLine(data, start, end, resolution, order)
if normalisey:
y = (y - y.min()) / (y.max() - y.min())
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