def evaluate(gt, auto):
"""Run the main Fiji-based functionality.
"""
from jython_imports import IJ, RandError
impgt = IJ.openImage(gt)
impauto = IJ.openImage(auto)
fscore = RandError.adaptedRandIndexFScore3D(impgt, impauto)
print(1.0 - fscore)
def main(fn, verbose):
"""Run the main Fiji-based functionality.
"""
from jython_imports import IJ
impin = IJ.openImage(fn)
fout = fn + '.out.tif'
if verbose:
print(fout)
IJ.saveAs(impin, 'tif', fout)
def main(imageFile,outputFile, verbose):
"""Run the main Fiji-based functionality.
"""
from jython_imports import IJ,SIFT,FloatArray2DSIFT,ArrayList
if verbose:
"Extracting features from %s" % imageFile
#open the image
imp= IJ.openImage(imageFile)
#enhance the contrast
percent_saturated=.5
ip = enhance_contrast(imp,percent_saturated)
#get features
maxsize = min(imp.getHeight(),imp.getWidth())
p = make_SIFT_parameters(maxOctaveSize=maxsize)
features = extract_features(ip,p)
#write object to file
writeObjectToFile(features,outputFile)
#print some results
if verbose:
"Extracting features from %s" % imageFile
print "found %d features in image at %s"%(len(features),imageFile)
print "used contrast enhance with %3.2f " % percent_saturated
maxHeight=int(maxy-miny)
for file in transform_files:
print file
(base,ext)=os.path.splitext(file)
image_file=os.path.join(ImageDir,base+'.tif')
#read the transform for this file
transform=readObjectFromFile(os.path.join(linearAlignmentDir,file))
transform.translate(-minx,-miny)
model=AffineModel2D()
model.set(transform)
mapping =InverseTransformMapping(model)
#read in this file
imp=IJ.openImage(image_file)
in_ip = imp.getProcessor()
in_ip.setInterpolationMethod(ImageProcessor.BILINEAR)
out_ip=in_ip.createProcessor(maxWidth,maxHeight)
out_ip.setMinAndMax(in_ip.getMin(),in_ip.getMax())
mapping.mapInterpolated(in_ip,out_ip)
imp.setProcessor(out_ip)
fs = FileSaver( imp )
fs.saveAsTiff(os.path.join(outputDir,base+'.tif'))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Find Final Alignment Transformations Given Pairwise Point Matches")
parser.add_argument('--AlignmentDir',
def main(targetImageFile,sourceImageFile,blockMatchFile,verbose=False):
"""Run the main Fiji-based functionality.
"""
from jython_imports import IJ,ArrayList,RigidModel2D,AffineModel2D,SimilarityModel2D,Float,Vector,FloatArray2DSIFT
from mpicbg.trakem2.align.Util import imageToFloatAndMask
from mpicbg.models import ErrorStatistic
from mpicbg.trakem2.util import Triple
from ij.process import FloatProcessor
from mpicbg.models import SpringMesh
from mpicbg.ij.blockmatching import BlockMatching
#from mpicbg.trakem2.align.concurrent import BlockMatchPairCallable
#read in the features
pm12 = ArrayList()
pm21 = ArrayList()
#open the images
img1 = IJ.openImage(targetImageFile).getImage()
img2 = IJ.openImage(sourceImageFile).getImage()
#get the image height and width
width = img1.getWidth()
height = img1.getHeight()
#get the second image height and width
width2= img2.getWidth()
height2 = img2.getHeight()
#the image sizes should match
assert (width==width2),"image widths do not match"
assert (height==height2),"image heights do not match"
ip1 = FloatProcessor( width, height )
ip2 = FloatProcessor( width, height )
ip1Mask = FloatProcessor( width, height )
ip2Mask = FloatProcessor( width, height )
#elastic alignment parameters
blockRadius = 150 #half-width of block to cut out (pixels)
searchRadius = 50 #size of search neighborhood (pixels)
minR = .4 #minimum correlation coefficent to find match
rodR = .9 #maximal_second_best_r/best_r
maxCurvatureR = 10.0 #maximal_curvature_ratio
scalefactor=0.5
imageToFloatAndMask( img1, ip1, ip1Mask )
imageToFloatAndMask( img2, ip2, ip2Mask )
#variables to save the error statistics
error1=ErrorStatistic(1)
error2=ErrorStatistic(1)
#i don't think these are relevant because this is just setting up the vertices
#not actually setting up the springs
spring_stiff=0.1
max_stretch=2000.0
spring_damp=0.9
interVertexDistance=blockRadius*2.2
numX=int(width/interVertexDistance)
numY=int(height/interVertexDistance)
m1=SpringMesh(numX,numY,width,height,spring_stiff,max_stretch,spring_damp)
m2=SpringMesh(numX,numY,width,height,spring_stiff,max_stretch,spring_damp)
#the only reason to setup the springmesh is to get these vertices
v1 = m1.getVertices()
v2 = m2.getVertices()
print "matching 1"
print numX,numY
print width,height
print v1.size()
BlockMatching.matchByMaximalPMCC(
ip1,
ip2,
ip1Mask,
ip2Mask,
scalefactor,
RigidModel2D().createInverse(),
blockRadius,
blockRadius,
searchRadius,
searchRadius,
minR,
rodR,
maxCurvatureR,
v1,
pm12,
error1)
print pm12.size()
#for each block it cuts out, it find the best fit, this defines a vector field between the two images
#it then excludes outliers by systematically going through each element in the vector field,
#for each element it takes creates a local average
#use a rigid model to calculate local weighted average
localSmoothnessFilterModel = RigidModel2D()
localRegionSigma = 1.75 * blockRadius #local smoothness neighborhood
maxLocalEpsilon = 10 #local smoothness error (pixels)
maxLocalTrust = 3.0 #factor by which average error of localized fit must be less
print "smoothing 1"
#filter out the results that don't match localSmoothness filter
localSmoothnessFilterModel.localSmoothnessFilter( pm12, pm12, localRegionSigma, maxLocalEpsilon,maxLocalTrust )
print pm12.size()
print "matching 2"
BlockMatching.matchByMaximalPMCC(
ip2,
ip1,
ip2Mask,
ip1Mask,
scalefactor,
RigidModel2D(),
blockRadius,
blockRadius,
searchRadius,
searchRadius,
minR,
rodR,
maxCurvatureR,
v2,
pm21,
error2)
print "smoothing 2"
localSmoothnessFilterModel.localSmoothnessFilter( pm21, pm21, localRegionSigma, maxLocalEpsilon, maxLocalTrust )
#i'm packaging up the results here into a more compact format.. original results package resulted in a 50MB file per comparison where this is 1.6 MB. could be further reduce.
pm12comp=[]
for i in range(pm12.size()):
p1=(pm12.get(i).getP1().getL(),pm12.get(i).getP1().getW())
p2=(pm12.get(i).getP2().getL(),pm12.get(i).getP2().getW())
pm12comp.append((p1,p2))
pm21comp=[]
for i in range(pm21.size()):
p1=(pm21.get(i).getP1().getL(),pm21.get(i).getP1().getW())
p2=(pm21.get(i).getP2().getL(),pm21.get(i).getP2().getW())
pm21comp.append((p1,p2))
results=(pm12comp, pm21comp)
#save the results
writeObjectToFile(results,blockMatchFile)
