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)
"""
#row = (0,0) : peak intensity from the raw data
#row = (1,1) : peak intensity from the DoG filtered data
#row = (2,2) : peak intensity from the background subtracted data
#row = (3,3) : voxel size from the watershed
points, intensities = thresholdPoints(points,
intensities,
threshold=3000,
row=(0, 0))
io.writePoints(FilteredCellsFile, (points, intensities))
## Check Cell detection (For the testing phase only, remove when running on the full size dataset)
#######################
import ClearMap.Visualization.Plot as plt
pointSource = os.path.join(BaseDirectory, FilteredCellsFile[0])
data = plt.overlayPoints(cFosFile,
pointSource,
pointColor=None,
**cFosFileRange)
io.writeData(os.path.join(BaseDirectory, 'cells_check.tif'), data)
# Transform point coordinates
#############################
points = io.readPoints(CorrectionResamplingPointsParameter["pointSource"])
points = resamplePoints(**CorrectionResamplingPointsParameter)
points = transformPoints(
points,
transformDirectory=CorrectionAlignmentParameter["resultDirectory"],
indices=False,
resultDirectory=None)
CorrectionResamplingPointsInverseParameter["pointSource"] = points
points = resamplePointsInverse(**CorrectionResamplingPointsInverseParameter)
RegistrationResamplingPointParameter["pointSource"] = points
#points, intensities = thresholdPoints(points,cintensity, threshold = (5,20), row = (1,1));
points, cellSizesPost = thresholdPoints(points,
cellSizesPre,
threshold=pointsThresh,
row=threshType)
#pdb.set_trace()
#io.writeData(os.path.join(homeDir, 'Results/OverlayWatershed.tif'), overlay_Img);
overlay_Img = plt.fredOverlayPoints(cfos_fn,
points,
pointColor=[200, 0, 0])
io.writeData(os.path.join(resultDir, fName + '_PointsOriginalImg.tif'),
overlay_Img)
overlay_Img = plt.overlayPoints(imgD, points, pointColor=[200, 0, 0])
io.writeData(os.path.join(resultDir, fName + '_PointsFilterDoG.tif'),
overlay_Img)
#Convert points from 3d to 2d
points = numpy.delete(points, 2, axis=1)
io.writePoints(points_fn, points)
##############################################################333
imgR = io.readData(auto_fn)
imgR = imgR[..., numpy.newaxis]
imgR = imgR.astype('int16')
imgR, scaleFactor = resampleData(imgR,
auto_R_fn,
orientation=(1, 2, 3),
""" note that the Z coordinate in fileRange variable are relative to the planes imported in the data variable.
So in this case, you would be imaging planes 675-684.
"""
plt.plotTiling(data, inverse=True, x=(1250, 1350), y=(550, 650), z=(25, 34)) #
# background subtraction
dataBGR = bgr.removeBackground(data.astype('float'),
size=(5, 5),
verbose=False,
save=None)
dataBGR_write = plt.plotTiling(dataBGR,
inverse=True,
x=(1250, 1350),
y=(550, 650),
z=(25, 34))
dataBGR_write = plt.overlayPoints(dataBGR, fileRange)
mplt.pyplot.savefig(os.path.join(BaseDirectory, 'dataBGR_write.tif'))
io.writeData(os.path.join(BaseDirectory, 'background_8.tif'), dataBGR_write)
io.writeData(os.path.join(BaseDirectory, 'cells_check.tif'), data)
pointSource = os.path.join(BaseDirectory, FilteredCellsFile[0])
data_write = plt.overlayPoints(filename,
dataBGR_write,
fileRange,
pointColor=None)
io.writeData(os.path.join(BaseDirectory, 'cells_check.tif'), data)
#DoG Filter
from ClearMap.ImageProcessing.Filter.DoGFilter import filterDoG
dataDoG = filterDoG(dataBGR, size=(7, 7, 9), verbose=False)
