def overlayPoints(dataSource,
pointSource,
sink=None,
pointColor=[1, 0, 0],
x=all,
y=all,
z=all):
"""Overlay points on 3D data and return as color image
Arguments:
dataSouce (str or array): volumetric image data
pointSource (str or array): point data to be overlayed on the image data
pointColor (array): RGB color for the overlayed points
x, y, z (all or tuple): sub-range specification
Returns:
(str or array): image overlayed with points
See Also:
:func:`overlayLabel`
"""
data = io.readData(dataSource, x=x, y=y, z=z)
points = io.readPoints(pointSource, x=x, y=y, z=z, shift=True)
#print data.shape
if not pointColor is None:
dmax = data.max()
dmin = data.min()
if dmin == dmax:
dmax = dmin + 1
cimage = np.repeat((data - dmin) / (dmax - dmin), 3)
cimage = cimage.reshape(data.shape + (3, ))
if data.ndim == 2:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], :] = pointColor
elif data.ndim == 3:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], p[2], :] = pointColor
else:
raise RuntimeError(
'overlayPoints: data dimension %d not suported' % data.ndim)
else:
cimage = vox.voxelize(points, data.shape, method='Pixel')
cimage = cimage.astype(data.dtype) * data.max()
data.shape = data.shape + (1, )
cimage.shape = cimage.shape + (1, )
cimage = np.concatenate((data, cimage), axis=3)
#print cimage.shape
return io.writeData(sink, cimage)
def resamplePointsInverse(pointSource,
pointSink=None,
dataSizeSource=None,
dataSizeSink=None,
orientation=None,
resolutionSource=(4.0625, 4.0625, 3),
resolutionSink=(25, 25, 25),
**args):
"""Resample points from the coordinates of the resampled image to the original data
The resampling of points here corresponds to he resampling of an image in :func:`resampleDataInverse`
Arguments:
pointSource (str or array): image to be resampled
pointSink (str or None): destination of resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
dataSizeSource (str, tuple or None): size of the data source
dataSizeSink (str, tuple or None): target size of the resampled image
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
Returns:
(array or str): data or file name of inversely resampled points
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
orientation = fixOrientation(orientation)
#datasize of data source
if isinstance(dataSizeSource, basestring):
dataSizeSource = io.dataSize(dataSizeSource)
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(
dataSizeSource=dataSizeSource,
dataSizeSink=dataSizeSink,
resolutionSource=resolutionSource,
resolutionSink=resolutionSink,
orientation=orientation)
points = io.readPoints(pointSource)
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation)
#resolutionSinkI = orientResolutionInverse(resolutionSink, orientation);
#scaling factors
scale = [
float(dataSizeSource[i]) / float(dataSizeSinkI[i]) for i in range(3)
]
#print scale
rpoints = points.copy()
#invert axis inversion and permutations
if not orientation is None:
#invert permuation
iorientation = inverseOrientation(orientation)
per = orientationToPermuation(iorientation)
rpoints = rpoints[:, per]
for i in range(3):
if iorientation[i] < 0:
rpoints[:, i] = dataSizeSinkI[i] - rpoints[:, i]
#scale points
for i in range(3):
rpoints[:, i] = rpoints[:, i] * scale[i]
return io.writePoints(pointSink, rpoints)
import ClearMap.IO.IO as io
import os
import ClearMap.Visualization.Plot as plt
import ClearMap.Analysis.Label as lbl
import numpy as np
sampleName = 'LowNIC'
#execfile('/d2/studies/ClearMap/IA_iDISCO/' + sampleName + '/parameter_file_' + sampleName + '.py')
execfile(
'/d2/studies/ClearMap/Alex_Acute_iDISCO/3RB_HighNIC/parameter_file_template_3RB.py'
)
baseDirectory = '/d2/studies/ClearMap/Alex_Acute_iDISCO/NIC_HeatMaps/FiguresForPaper/'
region = 'MSC'
points = io.readPoints(TransformedCellsFile)
data = plt.overlayPoints(AnnotationFile, points.astype(int), pointColor=None)
io.writeData(
os.path.join(BaseDirectory,
sampleName + '_Annotations_Points_Overlay_newAtlas2.tif'),
data)
data = data[:, :, :, 1:]
io.writeData(
os.path.join(BaseDirectory,
sampleName + '_Points_Transformed_newAtlas2.tif'), data)
label = io.readData(AnnotationFile)
label = label.astype('int32')
labelids = np.unique(label)
outside = np.zeros(label.shape, dtype=bool)
def resamplePointsInverse(pointSource, pointSink = None, dataSizeSource = None, dataSizeSink = None, orientation = None, resolutionSource = (4.0625, 4.0625, 3), resolutionSink = (25, 25, 25), **args):
"""Resample points from the coordinates of the resampled image to the original data
The resampling of points here corresponds to he resampling of an image in :func:`resampleDataInverse`
Arguments:
pointSource (str or array): image to be resampled
pointSink (str or None): destination of resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
dataSizeSource (str, tuple or None): size of the data source
dataSizeSink (str, tuple or None): target size of the resampled image
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
Returns:
(array or str): data or file name of inversely resampled points
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
orientation = fixOrientation(orientation);
#datasize of data source
if isinstance(dataSizeSource, basestring):
dataSizeSource = io.dataSize(dataSizeSource);
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(dataSizeSource = dataSizeSource, dataSizeSink = dataSizeSink,
resolutionSource = resolutionSource, resolutionSink = resolutionSink, orientation = orientation);
points = io.readPoints(pointSource);
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation);
#resolutionSinkI = orientResolutionInverse(resolutionSink, orientation);
#scaling factors
scale = [float(dataSizeSource[i]) / float(dataSizeSinkI[i]) for i in range(3)];
#print scale
rpoints = points.copy();
#invert axis inversion and permutations
if not orientation is None:
#invert permuation
iorientation = inverseOrientation(orientation);
per = orientationToPermuation(iorientation);
rpoints = rpoints[:,per];
for i in range(3):
if iorientation[i] < 0:
rpoints[:,i] = dataSizeSinkI[i] - rpoints[:,i];
#scale points
for i in range(3):
rpoints[:,i] = rpoints[:,i] * scale[i];
return io.writePoints(pointSink, rpoints);
