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main_fullScaleTransform.py
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main_fullScaleTransform.py
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'''
This script takes all the modifications that were performed on the baseline image
and applies them to the full-scale tif images
'''
from HelperFunctions.nonRigidAlign import nonRigidDeform
from HelperFunctions.Utilities import getSampleName, exactBound
from HelperFunctions.SP_AlignSamples import getSpecShift, transformSamples
from HelperFunctions.SP_SpecimenID import imgStandardiser
from multiprocessing import Pool
from glob import glob
import cv2
from itertools import repeat
if __name__ == "__main__":
size = 1.25
res = 0.4
scale = 1/res
cpuNo = 20
dataHomes = [
# '/Volumes/USB/H653A_11.3/',
# '/Volumes/USB/H671A_18.5/',
# '/Volumes/USB/H671B_18.5/',
# '/Volumes/USB/H673A_7.6/',
# '/Volumes/USB/H710B_6.1/',
'/Volumes/USB/H710C_6.1/',
# '/Volumes/USB/H750A_7.0/',
# '/Volumes/USB/H1029A_8.4/'
]
dataHomes = [
# '/eresearch/uterine/jres129/BoydCollection/H653A_11.3/',
# '/eresearch/uterine/jres129/BoydCollection/H671A_18.5/',
# '/eresearch/uterine/jres129/BoydCollection/H671B_18.5/',
# '/eresearch/uterine/jres129/BoydCollection/H710B_6.1/',
# '/eresearch/uterine/jres129/BoydCollection/H710C_6.1/',
# '/eresearch/uterine/jres129/BoydCollection/H673A_7.6/',
# '/eresearch/uterine/jres129/BoydCollection/H750A_7.0/',
# '/eresearch/uterine/jres129/BoydCollection/H1029A_8.4/'
'/eresearch/uterine/jres129/BoydCollection/test/'
]
for d in dataHomes:
# define all the directories where the necessary information is stored
datasrc = d + str(size) + "/"
imgsrc = datasrc + "tifFiles/"
baselineAlignedSample = datasrc + "alignedSamples/"
imgMaskedSamples = datasrc + "fullScaleMaskedSamples/"
featureInfoPath = datasrc + "info/"
baselineReAlignedSample = datasrc + "RealignedSamples/"
alignedSamples = datasrc + "fullScaleAlignedSamples/"
NLfeatureInfoPath = datasrc + "infoNL/"
RealignedSamples = datasrc + "fullScaleReAlignedSamples/"
featureStore = datasrc + "FeatureSections/"
NLAlignedSamples = datasrc + "fullScaleNLAlignedSamples/"
NLAlignedSamplesBound = datasrc + "/fullScaleNLAlignedSamplesBound/"
# apply the masks onto the tif images and normalise the colours
print("------ Masking ------")
imgref = cv2.imread(getSampleName(d, "refimg.png"))
imgMasked = datasrc + "maskedSamples/masks/"
masks = sorted(glob(imgMasked + "*.pbm"))
with Pool(processes = cpuNo) as pool:
pool.starmap(imgStandardiser, zip(repeat(imgMaskedSamples), masks, repeat(imgsrc), repeat(imgref), repeat(scale)))
print("------ Linear regisration ------")
# perform the first iteration of the aligned samples
maxShape, minShift = getSpecShift(featureInfoPath)
samples = sorted(glob(imgMaskedSamples + "*.tif"))
with Pool(processes = cpuNo) as pool:
pool.starmap(transformSamples, zip(samples, repeat(maxShape), repeat(minShift), repeat(featureInfoPath), repeat(alignedSamples), repeat(False), repeat(2.5)))
# perform the re-linear alignment with the NL features
print("------ Linear re-registration ------")
maxShape, minShift = getSpecShift(NLfeatureInfoPath)
samples = sorted(glob(alignedSamples + "*.tif"))
with Pool(processes = cpuNo) as pool:
pool.starmap(transformSamples, zip(samples, repeat(maxShape), repeat(minShift), repeat(NLfeatureInfoPath), repeat(RealignedSamples), repeat(False), repeat(2.5)))
# perform NL warping
print("------ NL registration ------")
nonRigidDeform(RealignedSamples, NLAlignedSamples, featureStore, scl = scale, prefix = "tif")
exactBound(NLAlignedSamples, "tif", dest = NLAlignedSamplesBound)