def extractBlockMatches(filepath1, filepath2, params, paramsSIFT, properties,
csvDir, exeload, load):
"""
filepath1: the file path to an image of a section.
filepath2: the file path to an image of another section.
params: dictionary of parameters necessary for BlockMatching.
exeload: an ExecutorService for parallel loading of image files.
load: a function that knows how to load the image from the filepath.
return False if the CSV file already exists, True if it has to be computed.
"""
# Skip if pointmatches CSV file exists already:
csvpath = os.path.join(
csvDir,
basename(filepath1) + '.' + basename(filepath2) + ".pointmatches.csv")
if os.path.exists(csvpath):
return False
try:
# Load files in parallel
futures = [
exeload.submit(Task(load, filepath1)),
exeload.submit(Task(load, filepath2))
]
fp1 = futures[0].get(
) # FloatProcessor, already Gaussian-blurred, contrast-corrected and scaled!
fp2 = futures[1].get() # FloatProcessor, idem
# Define points from the mesh
sourcePoints = ArrayList()
# List to fill
sourceMatches = ArrayList(
) # of PointMatch from filepath1 to filepath2
# Don't use blockmatching if the dimensions are different
use_blockmatching = fp1.getWidth() == fp2.getWidth() and fp1.getHeight(
) == fp2.getHeight()
# Fill the sourcePoints
mesh = TransformMesh(params["meshResolution"], fp1.width, fp1.height)
PointMatch.sourcePoints(mesh.getVA().keySet(), sourcePoints)
syncPrintQ("Extracting block matches for \n S: " + filepath1 +
"\n T: " + filepath2 + "\n with " +
str(sourcePoints.size()) + " mesh sourcePoints.")
# Run
BlockMatching.matchByMaximalPMCCFromPreScaledImages(
fp1,
fp2,
params["scale"], # float
params["blockRadius"], # X
params["blockRadius"], # Y
params["searchRadius"], # X
params["searchRadius"], # Y
params["minR"], # float
params["rod"], # float
params["maxCurvature"], # float
sourcePoints,
sourceMatches)
# At least some should match to accept the translation
if len(sourceMatches) < max(20, len(sourcePoints) / 5) / 2:
syncPrintQ(
"Found only %i blockmatching pointmatches (from %i source points)"
% (len(sourceMatches), len(sourcePoints)))
syncPrintQ(
"... therefore invoking SIFT pointmatching for:\n S: " +
basename(filepath1) + "\n T: " + basename(filepath2))
# Can fail if there is a shift larger than the searchRadius
# Try SIFT features, which are location independent
#
# Images are now scaled: load originals
futures = [
exeload.submit(
Task(loadFloatProcessor,
filepath1,
params,
paramsSIFT,
scale=False)),
exeload.submit(
Task(loadFloatProcessor,
filepath2,
params,
paramsSIFT,
scale=False))
]
fp1 = futures[0].get() # FloatProcessor, original
fp2 = futures[1].get() # FloatProcessor, original
# Images can be of different size: scale them the same way
area1 = fp1.width * fp1.height
area2 = fp2.width * fp2.height
if area1 == area2:
paramsSIFT1 = paramsSIFT.clone()
paramsSIFT1.maxOctaveSize = int(
max(properties.get("SIFT_max_size", 2048),
fp1.width * params["scale"]))
paramsSIFT1.minOctaveSize = int(paramsSIFT1.maxOctaveSize /
pow(2, paramsSIFT1.steps))
paramsSIFT2 = paramsSIFT1
else:
bigger, smaller = (fp1, fp2) if area1 > area2 else (fp2, fp1)
target_width_bigger = int(
max(1024, bigger.width * params["scale"]))
if 1024 == target_width_bigger:
target_width_smaller = int(1024 * float(smaller.width) /
bigger.width)
else:
target_width_smaller = smaller.width * params["scale"]
#
paramsSIFT1 = paramsSIFT.clone()
paramsSIFT1.maxOctaveSize = target_width_bigger
paramsSIFT1.minOctaveSize = int(paramsSIFT1.maxOctaveSize /
pow(2, paramsSIFT1.steps))
paramsSIFT2 = paramsSIFT.clone()
paramsSIFT2.maxOctaveSize = target_width_smaller
paramsSIFT2.minOctaveSize = int(paramsSIFT2.maxOctaveSize /
pow(2, paramsSIFT2.steps))
ijSIFT1 = SIFT(FloatArray2DSIFT(paramsSIFT1))
features1 = ArrayList() # of Point instances
ijSIFT1.extractFeatures(fp1, features1)
ijSIFT2 = SIFT(FloatArray2DSIFT(paramsSIFT2))
features2 = ArrayList() # of Point instances
ijSIFT2.extractFeatures(fp2, features2)
# Vector of PointMatch instances
sourceMatches = FloatArray2DSIFT.createMatches(
features1,
features2,
params.get(
"max_sd",
1.5), # max_sd: maximal difference in size (ratio max/min)
TranslationModel2D(),
params.get("max_id", Double.MAX_VALUE
), # max_id: maximal distance in image space
params.get("rod", 0.9)) # rod: ratio of best vs second best
# Store pointmatches
savePointMatches(os.path.basename(filepath1),
os.path.basename(filepath2), sourceMatches, csvDir,
params)
return True
except:
printException()
def call(self, save_images=False):
self.thread_used = threading.currentThread().getName()
self.started = time.time()
try:
IJ.log(time.asctime())
IJ.log(str(self.patchA))
IJ.log(str(self.patchB))
print str(self.patchA)
print str(self.patchB)
# Adapted from ElasticMontage.java
# https://github.com/trakem2/TrakEM2/blob/master/TrakEM2_/src/main/java/mpicbg/trakem2/align/ElasticMontage.java
pi1 = self.patchA.createTransformedImage()
pi2 = self.patchB.createTransformedImage()
fp1 = pi1.target.convertToFloat()
mask1 = pi1.getMask()
if mask1 is None:
fpMask1 = None
else:
fpMask1 = scaleByte(mask1)
fp2 = pi2.target.convertToFloat()
mask2 = pi2.getMask()
if mask2 is None:
fpMask2 = None
else:
fpMask2 = scaleByte(mask1)
w = self.tileA.getWidth()
h = self.tileA.getHeight()
num_x = Math.max(2, int(Math.ceil(w / self.params.spring_length) + 1))
num_y = Math.max(2, int(Math.ceil(h / self.params.spring_triangle_height_twice) + 1))
w_mesh = (num_x - 1) * self.params.spring_length
h_mesh = (num_y - 1) * self.params.spring_triangle_height_twice
mesh = SpringMesh(num_x,
num_y,
w_mesh,
h_mesh,
self.params.stiffness,
self.params.max_stretch * self.params.scale,
self.params.damp)
vertices = mesh.getVertices()
maskSamples = RealPointSampleList(2)
for vertex in vertices:
maskSamples.add(RealPoint(vertex.getL()), ARGBType(-1)) # equivalent of 0xffffffff
pm12 = ArrayList()
v1 = mesh.getVertices()
t = self.tileB.getModel().createInverse()
t.concatenate(self.tileA.getModel())
BlockMatching.matchByMaximalPMCC(
fp1,
fp2,
fpMask1,
fpMask2,
self.params.scale,
t,
self.params.block_radius,
self.params.block_radius,
self.params.search_radius,
self.params.search_radius,
self.params.min_R,
self.params.rod_R,
self.params.max_curvature_R,
v1,
pm12,
ErrorStatistic(1))
pre_smooth_block_matches = len(pm12)
if self.params.save_data:
pre_smooth_filename = self.params.output_folder + str(self.patchB) + "_" + str(self.patchA) + "_pre_smooth_pts.txt"
self.writePointsFile(pm12, pre_smooth_filename)
if self.params.use_local_smoothness_filter:
model = Util.createModel(self.params.local_model_index)
try:
model.localSmoothnessFilter(pm12, pm12, self.params.local_region_sigma, self.params.max_local_epsilon, self.params.max_local_trust)
if self.params.save_data:
post_smooth_filename = self.params.output_folder + str(self.patchB) + "_" + str(self.patchA) + "_post_smooth_pts.txt"
self.writePointsFile(pm12, post_smooth_filename)
except:
pm12.clear()
color_samples, max_displacement = self.matches2ColorSamples(pm12)
post_smooth_block_matches = len(pm12)
print time.asctime()
print str(self.patchB) + "_" + str(self.patchA) + "\tblock_matches\t" + str(pre_smooth_block_matches) + "\tsmooth_filtered\t" + str(pre_smooth_block_matches - post_smooth_block_matches) + "\tmax_displacement\t" + str(max_displacement) + "\trelaxed_length\t" + str(self.params.spring_length) + "\tsigma\t" + str(self.params.local_region_sigma)
IJ.log(time.asctime())
IJ.log(str(self.patchB) + "_" + str(self.patchA) + ": block_matches " + str(pre_smooth_block_matches) + ", smooth_filtered " + str(pre_smooth_block_matches - post_smooth_block_matches) + ", max_displacement " + str(max_displacement) + ", relaxed_length " + str(self.params.spring_length) + ", sigma " + str(self.params.local_region_sigma))
if self.params.save_data and self.wf:
self.wf.write(str(self.patchB) +
"\t" + str(self.patchA) +
"\t" + str(pre_smooth_block_matches) +
"\t" + str(pre_smooth_block_matches - post_smooth_block_matches) +
"\t" + str(max_displacement) +
"\t" + str(self.params.spring_length) +
"\t" + str(self.params.local_region_sigma) +
"\t" + str(num_x) + "\n")
if self.params.export_point_roi:
pm12Sources = ArrayList()
pm12Targets = ArrayList()
PointMatch.sourcePoints(pm12, pm12Sources)
PointMatch.targetPoints(pm12, pm12Targets)
roi1 = pointsToPointRoi(pm12Sources)
roi2 = pointsToPointRoi(pm12Targets)
# # Adapted from BlockMatching.java
# # https://github.com/axtimwalde/mpicbg/blob/master/mpicbg/src/main/java/mpicbg/ij/blockmatching/BlockMatching.java
# tTarget = TranslationModel2D()
# sTarget = SimilarityModel2D()
# tTarget.set(-self.params.search_radius, -self.params.search_radius)
# sTarget.set(1.0/self.params.scale, 0, 0, 0)
# lTarget = CoordinateTransformList()
# lTarget.add(sTarget)
# lTarget.add(tTarget)
# lTarget.add(t)
# targetMapping = TransformMapping(lTarget)
# mappedScaledTarget = FloatProcessor(fp1.getWidth() + 2*search_radius, fp1.getHeight() + 2*search_radius)
# targetMapping.mapInverseInterpolated(fp2, mappedScaledTarget)
# imp1 = tileImagePlus("imp1", fp1)
# imp1.show()
# imp2 = ImagePlus("imp2", mappedScaledTarget)
# imp2.show()
imp1 = ImagePlus("imp1", fp1)
imp1.show()
imp2 = ImagePlus("imp2", fp2)
imp2.show()
imp1.setRoi(roi1)
imp2.setRoi(roi2)
if self.params.export_displacement_vectors:
pm12Targets = ArrayList()
PointMatch.targetPoints(pm12, pm12Targets)
maskSamples2 = RealPointSampleList(2)
for point in pm12Targets:
maskSamples2.add(RealPoint(point.getW()), ARGBType(-1))
factory = ImagePlusImgFactory()
kdtreeMatches = KDTree(color_samples)
kdtreeMask = KDTree(maskSamples)
img = factory.create([fp1.getWidth(), fp1.getHeight()], ARGBType())
self.drawNearestNeighbor(
img,
NearestNeighborSearchOnKDTree(kdtreeMatches),
NearestNeighborSearchOnKDTree(kdtreeMask))
scaled_img = self.scaleIntImagePlus(img, 0.03)
if self.params.save_data:
fs = FileSaver(scaled_img)
fs.saveAsTiff(self.params.output_folder + str(self.patchB) + "_" + str(self.patchA) + ".tif")
else:
scaled_img.show()
print time.asctime()
print str(self.patchB) + "_" + str(self.patchA) + "\tsaved"
IJ.log(time.asctime())
IJ.log(str(self.patchB) + "_" + str(self.patchA) + ": saved")
except Exception, ex:
self.exception = ex
print str(ex)
IJ.log(str(ex))
if self.params.save_data and self.wf:
self.wf.write(str(ex) + "\n")
def call(self, save_images=False): self.thread_used = threading.currentThread().getName() self.started = time.time() try: filenames = os.listdir(self.params.input_folder) filenames.sort() imp1 = IJ.openImage(os.path.join(self.params.input_folder, filenames[self.imgA])) imp2 = IJ.openImage(os.path.join(self.params.input_folder, filenames[self.imgB])) IJ.log(time.asctime()) IJ.log(str(self.imgA) + ": " + str(imp1)) IJ.log(str(self.imgB) + ": " + str(imp2)) print str(self.imgA) + ": " + str(imp1) print str(self.imgB) + ": " + str(imp2) mesh_resolution = int(imp1.getWidth() / self.params.point_distance) effective_point_distance = imp1.getWidth() / mesh_resolution mesh = SpringMesh(mesh_resolution, imp1.getWidth(), imp1.getHeight(), 1, 1000, 0.9) vertices = mesh.getVertices() maskSamples = RealPointSampleList(2) for vertex in vertices: maskSamples.add(RealPoint(vertex.getL()), ARGBType(-1)) # equivalent of 0xffffffff pm12 = ArrayList() v1 = mesh.getVertices() ip1 = imp1.getProcessor().convertToFloat().duplicate() ip2 = imp2.getProcessor().convertToFloat().duplicate() ip1Mask = self.createMask(imp1.getProcessor().convertToRGB()) ip2Mask = self.createMask(imp2.getProcessor().convertToRGB()) ct = TranslationModel2D() BlockMatching.matchByMaximalPMCC( ip1, ip2, ip1Mask, ip2Mask, self.params.scale, ct, self.params.block_radius, self.params.block_radius, self.params.search_radius, self.params.search_radius, self.params.min_R, self.params.rod_R, self.params.max_curvature_R, v1, pm12, ErrorStatistic(1)) pre_smooth_block_matches = len(pm12) if self.params.save_data: pre_smooth_filename = self.params.output_folder + str(imp2.getTitle())[:-4] + "_" + str(imp1.getTitle())[:-4] + "_pre_smooth_pts.txt" self.writePointsFile(pm12, pre_smooth_filename) if self.params.use_local_smoothness_filter: model = Util.createModel(self.params.local_model_index) try: model.localSmoothnessFilter(pm12, pm12, self.params.local_region_sigma, self.params.max_local_epsilon, self.params.max_local_trust) if self.params.save_data: post_smooth_filename = self.params.output_folder + str(imp2.getTitle())[:-4] + "_" + str(imp1.getTitle())[:-4] + "_post_smooth_pts.txt" self.writePointsFile(pm12, post_smooth_filename) except: pm12.clear() color_samples, max_displacement = self.matches2ColorSamples(pm12) post_smooth_block_matches = len(pm12) print time.asctime() print str(self.imgB) + "_" + str(self.imgA) + "\tblock_matches\t" + str(pre_smooth_block_matches) + "\tsmooth_filtered\t" + str(pre_smooth_block_matches - post_smooth_block_matches) + "\tmax_displacement\t" + str(max_displacement) + "\trelaxed_length\t" + str(effective_point_distance) + "\tsigma\t" + str(self.params.local_region_sigma) IJ.log(time.asctime()) IJ.log(str(self.imgB) + "_" + str(self.imgA) + ": block_matches " + str(pre_smooth_block_matches) + ", smooth_filtered " + str(pre_smooth_block_matches - post_smooth_block_matches) + ", max_displacement " + str(max_displacement) + ", relaxed_length " + str(effective_point_distance) + ", sigma " + str(self.params.local_region_sigma)) if self.params.save_data and self.wf: self.wf.write(str(self.imgB) + "\t" + str(self.imgA) + "\t" + str(imp2.getTitle())[:-4] + "\t" + str(imp1.getTitle())[:-4] + "\t" + str(pre_smooth_block_matches) + "\t" + str(pre_smooth_block_matches - post_smooth_block_matches) + "\t" + str(max_displacement) + "\t" + str(effective_point_distance) + "\t" + str(self.params.local_region_sigma) + "\t" + str(mesh_resolution) + "\n") if self.params.export_point_roi: pm12Sources = ArrayList() pm12Targets = ArrayList() PointMatch.sourcePoints(pm12, pm12Sources) PointMatch.targetPoints(pm12, pm12Targets) roi1 = pointsToPointRoi(pm12Sources) roi2 = pointsToPointRoi(pm12Targets) imp1.setRoi(roi1) imp2.setRoi(roi2) if self.params.export_displacement_vectors: pm12Targets = ArrayList() PointMatch.targetPoints(pm12, pm12Targets) maskSamples2 = RealPointSampleList(2) for point in pm12Targets: maskSamples2.add(RealPoint(point.getW()), ARGBType(-1)) factory = ImagePlusImgFactory() kdtreeMatches = KDTree(color_samples) kdtreeMask = KDTree(maskSamples) img = factory.create([imp1.getWidth(), imp1.getHeight()], ARGBType()) self.drawNearestNeighbor( img, NearestNeighborSearchOnKDTree(kdtreeMatches), NearestNeighborSearchOnKDTree(kdtreeMask)) scaled_img = self.scaleIntImagePlus(img, 0.1) if self.params.save_data: fs = FileSaver(scaled_img) fs.saveAsTiff(self.params.output_folder + str(imp2.getTitle())[:-4] + "_" + str(imp1.getTitle())) else: scaled_img.show() print time.asctime() print str(self.imgB) + "_" + str(self.imgA) + "\tsaved\t" + filenames[self.imgB] IJ.log(time.asctime()) IJ.log(str(self.imgB) + "_" + str(self.imgA) + ": saved " + filenames[self.imgB]) except Exception, ex: self.exception = ex print str(ex) IJ.log(str(ex)) if self.params.save_data and self.wf: self.wf.write(str(ex) + "\n")
def extractBlockMatches(filepath1,
filepath2,
params,
csvDir,
exeload,
load=loadFPMem):
"""
filepath1: the file path to an image of a section.
filepath2: the file path to an image of another section.
params: dictionary of parameters necessary for BlockMatching.
exeload: an ExecutorService for parallel loading of image files.
load: a function that knows how to load the image from the filepath.
return False if the CSV file already exists, True if it has to be computed.
"""
# Skip if pointmatches CSV file exists already:
csvpath = os.path.join(
csvDir,
basename(filepath1) + '.' + basename(filepath2) + ".pointmatches.csv")
if os.path.exists(csvpath):
return False
try:
# Load files in parallel
futures = [
exeload.submit(Task(load, filepath1)),
exeload.submit(Task(load, filepath2))
]
# Define points from the mesh
sourcePoints = ArrayList()
mesh = TransformMesh(params["meshResolution"], dimensions[0],
dimensions[1])
PointMatch.sourcePoints(mesh.getVA().keySet(), sourcePoints)
# List to fill
sourceMatches = ArrayList(
) # of PointMatch from filepath1 to filepath2
syncPrint("Extracting block matches for \n S: " + filepath1 +
"\n T: " + filepath2 + "\n with " +
str(sourcePoints.size()) + " mesh sourcePoints.")
BlockMatching.matchByMaximalPMCCFromPreScaledImages(
futures[0].get(), # FloatProcessor
futures[1].get(), # FloatProcessor
params["scale"], # float
params["blockRadius"], # X
params["blockRadius"], # Y
params["searchRadius"], # X
params["searchRadius"], # Y
params["minR"], # float
params["rod"], # float
params["maxCurvature"], # float
sourcePoints,
sourceMatches)
# At least some should match to accept the translation
if len(sourceMatches) < max(20, len(sourcePoints) / 5) / 2:
syncPrint(
"Found only %i blockmatching pointmatches (from %i source points)"
% (len(sourceMatches), len(sourcePoints)))
syncPrint("... therefore invoking SIFT pointmatching for:\n S: " +
basename(filepath1) + "\n T: " + basename(filepath2))
# Can fail if there is a shift larger than the searchRadius
# Try SIFT features, which are location independent
#
# Images are now scaled: load originals
futures = [
exeload.submit(Task(loadFloatProcessor, filepath1,
scale=False)),
exeload.submit(Task(loadFloatProcessor, filepath2,
scale=False))
]
ijSIFT = SIFT(FloatArray2DSIFT(paramsSIFT))
features1 = ArrayList() # of Point instances
ijSIFT.extractFeatures(futures[0].get(), features1)
features2 = ArrayList() # of Point instances
ijSIFT.extractFeatures(futures[1].get(), features2)
# Vector of PointMatch instances
sourceMatches = FloatArray2DSIFT.createMatches(
features1,
features2,
1.5, # max_sd
TranslationModel2D(),
Double.MAX_VALUE,
params["rod"]) # rod: ratio of best vs second best
# Store pointmatches
savePointMatches(os.path.basename(filepath1),
os.path.basename(filepath2), sourceMatches, csvDir,
params)
return True
except:
syncPrint(sys.exc_info())
syncPrint("".join(traceback.format_exception()), out="stderr")
