# Create a copy of the image.
input_image_plus_copy = input_image_plus.duplicate()
image_processor_copy = input_image_plus_copy.getProcessor()
try:
# Set binary options.
options = jython_utils.get_binary_options(
black_background=black_background)
IJ.run(input_image_plus_copy, "Options...", options)
# Convert image to binary if necessary.
if not image_processor_copy.isBinary():
IJ.run(input_image_plus_copy, "Make Binary", "")
# Run AnalyzeSkeleton
analyze_skeleton = AnalyzeSkeleton_()
analyze_skeleton.setup("", input_image_plus_copy)
if prune_cycle_method == 'none':
prune_index = analyze_skeleton.NONE
elif prune_cycle_method == 'shortest_branch':
prune_index = analyze_skeleton.SHORTEST_BRANCH
elif prune_cycle_method == 'lowest_intensity_voxel':
prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL
elif prune_cycle_method == 'lowest_intensity_branch':
prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH
result = analyze_skeleton.run(prune_index, prune_ends,
calculate_largest_shortest_path,
input_image_plus_copy, True, True)
# Save the results.
save(result, output, show_detailed_info, calculate_largest_shortest_path)
except Exception, e:
# Open the input image file.
input_image_plus = IJ.openImage( input )
bit_depth = input_image_plus.getBitDepth()
image_type = input_image_plus.getType()
# Create a copy of the image.
input_image_plus_copy = input_image_plus.createImagePlus()
image_processor_copy = input_image_plus.getProcessor().duplicate()
input_image_plus_copy.setProcessor( "iCopy", image_processor_copy )
try:
if image_type not in VALID_IMAGE_TYPES:
# Convert the image to binary grayscale.
IJ.run( input_image_plus_copy, "Make Binary","" )
# Run AnalyzeSkeleton
analyze_skeleton = AnalyzeSkeleton_()
analyze_skeleton.setup( "", input_image_plus_copy )
if prune_cycle_method == 'none':
prune_index = analyze_skeleton.NONE
elif prune_cycle_method == 'shortest_branch':
prune_index = analyze_skeleton.SHORTEST_BRANCH
elif prune_cycle_method == 'lowest_intensity_voxel':
prune_index = analyze_skeleton.LOWEST_INTENSITY_VOXEL
elif prune_cycle_method == 'lowest_intensity_branch':
prune_index = analyze_skeleton.LOWEST_INTENSITY_BRANCH
result = analyze_skeleton.run( prune_index,
prune_ends,
calculate_largest_shortest_path,
input_image_plus_copy,
True,
True )
def processOneImage(inputDir):
tmp = glob.glob(os.path.join(inputDir, "fibrone*"))
fibronectin = tmp[0]
tmp = glob.glob(os.path.join(inputDir, "nucleus*"))
nucleus = tmp[0]
tmp = glob.glob(os.path.join(inputDir, "actin*"))
actin = tmp[0]
# read sample name
head,tail = os.path.split(inputDir)
sample = tail.replace(".tif_Files","")
# original images
imp_fn_orig = IJ.openImage(fibronectin)
imp_nuc_orig = IJ.openImage(nucleus)
# work copies
imp_fn = imp_fn_orig.duplicate()
imp_nuc = imp_nuc_orig.duplicate()
IJ.run(imp_fn,"Set Scale...", "distance=1 known=1 pixel=1 unit=pixels")
IJ.run(imp_fn,"Gaussian Blur...","sigma=5")
IJ.run(imp_fn,"Make Binary","")
IJ.run(imp_nuc,"Set Scale...", "distance=1 known=1 pixel=1 unit=pixels")
IJ.run(imp_nuc,"Gaussian Blur...","sigma=5")
IJ.run(imp_nuc,"Make Binary","")
# get moments of the fibronectin image
moments_file = os.path.join(OUTPUT, sample + " moments.txt")
printMoments(fibronectin, moments_file)
moments = readMoments(moments_file)
print moments.m00
sys.exit()
# centroid of fibronectin anchor
centers = getParticleCenters(imp_fn)
cxfn = int(round(centers[0][0]))
cyfn = int(round(centers[1][0]))
fn_centroid_roi = PointRoi(cxfn,cyfn)
fn_centroid_roi.setDefaultMarkerSize("Large")
fn_centroid_roi.setStrokeColor(Color.CYAN)
# center of mass of nucleus
centers = getParticleCenters(imp_nuc)
cxnuc = int(round(centers[2][0]))
cynuc = int(round(centers[3][0]))
nuc_com_roi = PointRoi(cxnuc,cynuc)
nuc_com_roi.setDefaultMarkerSize("Large")
# skeletonize fibronectin anchor to find its orientation
IJ.run(imp_fn,"Skeletonize","")
skel = AnalyzeSkeleton_()
skel.setup("",imp_fn)
skelResult = skel.run(skel.NONE, False, True, None, True, True)
graph = skelResult.getGraph()
print len(graph)
print skelResult.getNumOfTrees()
# find the longest graph
graph = sorted(graph, key=lambda g: getGraphLength(g), reverse=True)
graph = graph[0]
edges = graph.getEdges()
# find longest edge, the main axis of the anchor
edges = sorted(edges, key=lambda edge: edge.getLength(), reverse=True)
#for e in edges:
# print e.getLength()
v1long = edges[0].getV1()
v2long = edges[0].getV2()
x1 = v1long.getPoints()[0].x
y1 = v1long.getPoints()[0].y
x2 = v2long.getPoints()[0].x
y2 = v2long.getPoints()[0].y
anchor_roi = PointRoi(x1,y1)
anchor_roi = anchor_roi.addPoint(x2,y2)
# find top and bottom vertices of the graph
vertices = graph.getVertices()
vertices = sorted(vertices, key=lambda vertex: vertex.getPoints()[0].y)
v1short = vertices[len(vertices)-1]
v2short = vertices[0]
x3 = v1short.getPoints()[0].x
y3 = v1short.getPoints()[0].y
x4 = v2short.getPoints()[0].x
y4 = v2short.getPoints()[0].y
anchor_roi = anchor_roi.addPoint(x3,y3)
anchor_roi = anchor_roi.addPoint(x4,y4)
# calculate angles
a1 = math.atan(abs(float(y2-y1)/float(x2-x1))) / math.pi * 360
a2 = math.atan(abs(float(x4-x3)/float(y4-y3))) / math.pi * 360
amean = float((a1+a2)/2)
dx = cxfn-cxnuc
print sample,cxfn,cyfn,cxnuc,cynuc,dx,math.cos(amean)*dx,x1,y1,x2,y2,x3,y3,x4,y4,a1,a2
# create composite
comp = ImagePlus("composite",imp_nuc_orig.getProcessor().convertToColorProcessor())
comp.getProcessor().setChannel(2,imp_fn_orig.getProcessor())
comp.getProcessor().setChannel(3,imp_fn.getProcessor())
comp.show()
comp.getProcessor().drawRoi(fn_centroid_roi)
comp.getProcessor().drawRoi(nuc_com_roi)
comp.getProcessor().drawRoi(anchor_roi)
comp.repaintWindow()
IJ.saveAsTiff(comp, os.path.join(OUTPUT,sample + ".tif"))