def getSpots(imp, channel, detector_type, radius, threshold, overlay,
roi_type="large", roi_color=ColorRGB("blue")):
""" Performs the detection, adding spots to the image overlay
:imp: The image (ImagePlus) being analyzed
:channel: The target channel
:detector_type: A string describing the detector: "LoG" or "DoG"
:radius: Spot radius (NB: trackmate GUI accepts diameter)
:threshold: Quality cutoff value
:overlay: The image overlay to store spot (MultiPoint) ROIs
:roi_type: A string describing how spot ROIs should be displayed
:returns: The n. of detected spots
"""
settings = Settings()
settings.setFrom(imp)
settings.detectorFactory = (LogDetectorFactory() if "LoG" in detector_type
else DogDetectorFactory())
settings.detectorSettings = {
DK.KEY_DO_SUBPIXEL_LOCALIZATION: False,
DK.KEY_DO_MEDIAN_FILTERING: True,
DK.KEY_TARGET_CHANNEL: channel,
DK.KEY_RADIUS: radius,
DK.KEY_THRESHOLD: threshold,
}
trackmate = TrackMate(settings)
if not trackmate.execDetection():
lservice.error(str(trackmate.getErrorMessage()))
return 0
model = trackmate.model
spots = model.getSpots()
count = spots.getNSpots(False)
ch_id = "Spots Ch%d" % channel
if count > 0:
roi = None
cal = imp.getCalibration()
t_pos = imp.getT()
if (t_pos > 1):
lservice.warn("Only frame %d was considered..." % t_pos)
for spot in spots.iterable(False):
x = cal.getRawX(spot.getFeature(spot.POSITION_X))
y = cal.getRawY(spot.getFeature(spot.POSITION_Y))
z = spot.getFeature(spot.POSITION_Z)
if z == 0 or not cal.pixelDepth or cal.pixelDepth == 0:
z = 1
else:
z = int(z // cal.pixelDepth)
imp.setPosition(channel, z, t_pos)
if roi is None:
roi = PointRoi(int(x), int(y), imp)
else:
roi.addPoint(imp, x, y)
roi.setStrokeColor(colorRGBtoColor(roi_color))
if "large" in roi_type:
roi.setPointType(3)
roi.setSize(4)
else:
roi.setPointType(2)
roi.setSize(1)
overlay.add(roi, ch_id)
return count
def draw_centers(data):
pathname = data[1]
filename = data[2]
platename = data[3]
print data[4]
nx = int(data[4])
ny = int(data[5])
nx_com = int(data[6])
ny_com = int(data[7])
ax = int(data[8])
ay = int(data[9])
orientation = 2*math.pi * float(data[10]) / 360.0
line_dy = int(round(math.atan(orientation) * LINE_DX))
ax2 = ax - LINE_DX
ay2 = ay - line_dy
orientation_avg = 2*math.pi * float(data[11]) / 360.0
line_dy = int(round(math.atan(orientation_avg) * LINE_DX))
ax3 = ax - LINE_DX
ay3 = ay - line_dy
imp = IJ.openImage(os.path.join(pathname,filename))
roi_nucleus = PointRoi(nx,ny)
roi_nucleus.setDefaultMarkerSize("Large")
roi_nucleus.setStrokeColor(Color.CYAN)
roi_nucleus_com = PointRoi(nx_com,ny_com)
roi_nucleus_com.setDefaultMarkerSize("Large")
roi_nucleus_com.setStrokeColor(Color.GREEN)
roi_anchor = PointRoi(ax,ay)
roi_anchor.setDefaultMarkerSize("Large")
imp.getProcessor().drawRoi(roi_nucleus)
imp.getProcessor().drawRoi(roi_nucleus_com)
imp.getProcessor().drawRoi(roi_anchor)
imp.setColor(Color.RED)
imp.getProcessor().drawLine(ax,ay,ax2,ay2)
imp.setColor(Color.WHITE)
imp.getProcessor().drawLine(ax,ay,ax3,ay3)
IJ.saveAsTiff(imp,os.path.join(pathname,filename))
def draw_classes(inputfile,data):
pathname,filename = os.path.split(inputfile)
# last subdir is for well
## TODO ## support multiple plates, read plate and well from csv
well = os.path.basename(pathname)
# create output directory
outputdir = os.path.join(OUTPUTROOT,well)
if not os.path.isdir(outputdir):
os.makedirs(outputdir)
# create output filename
head,tail = os.path.splitext(filename)
outputfilename = head + "_classes" + tail
outputfilename = os.path.join(outputdir,outputfilename)
print outputfilename
imp = IJ.openImage(inputfile).duplicate()
IJ.run(imp,"RGB Color","")
for d in data:
x = int(float(d[0]))
y = int(float(d[1]))
c = int(float(d[2]))
classname = classes[str(c)]
colorname = colors[classname]
color = colormap[colorname]
#print colorname
roi = PointRoi(x,y)
roi.setDefaultMarkerSize("Large")
roi.setStrokeColor(colormap[colorname])
imp.getProcessor().drawRoi(roi)
IJ.saveAs(imp,"png",outputfilename)
def updatePointRoi(self):
# Surround with try/except to prevent blocking
# ImageJ's stack slice updater thread in case of error.
try:
# Update PointRoi
self.imp.killRoi()
point = self.nuclei[self.imp.getFrame()] # map 1-based slices
# to 0-based nuclei Z coords
if len(point) == 0:
return
IJ.log("Cell found in frame " + str(self.imp.getFrame()))
# New empty PointRoi for the current slice
roi = PointRoi(point[0], point[1])
# Style: large, red dots
roi.setSize(4) # ranges 1-4
roi.setPointType(2) # 2 is a dot (filled circle)
roi.setFillColor(Color.red)
roi.setStrokeColor(Color.red)
self.imp.setRoi(roi)
except:
IJ.error(sys.exc_info())
def findEndPoints(imageID, maskID):
endPoints1 = []
endPoints2 = []
imp = IJ.getImage()
imp.setSlice(1);
roiManager = RoiManager.getInstance2()
rois = roiManager.getRoisAsArray()
roisToBeRemoved = []
index = 0
for roi in rois:
outerBounds = roi.getBounds()
impMT, innerBounds = duplicateMaskInRoi(imp, roi)
nr, endPoint1, endPoint2 = findEndPointsInSkeleton(impMT)
if (nr==2):
endPoint1.x = endPoint1.x + outerBounds.x + innerBounds.x - 1
endPoint1.y = endPoint1.y + outerBounds.y + innerBounds.y - 1
endPoint2.x = endPoint2.x + outerBounds.x + innerBounds.x - 1
endPoint2.y = endPoint2.y + outerBounds.y + innerBounds.y - 1
endPoints1.append(endPoint1)
endPoints2.append(endPoint2)
else:
roisToBeRemoved.append(index)
impMT.close()
index = index + 1
if (len(roisToBeRemoved)>0):
roiManager.setSelectedIndexes(roisToBeRemoved)
roiManager.runCommand("Delete")
roiManager.moveRoisToOverlay(WindowManager.getImage(maskID))
inputIMP = WindowManager.getImage(imageID)
inputIMP.setOverlay(PointRoi([seq.x for seq in endPoints1], [seq.y for seq in endPoints1]), Color.magenta, 1, Color.magenta)
otherPoints = PointRoi([seq.x for seq in endPoints2], [seq.y for seq in endPoints2])
otherPoints.setStrokeColor(Color.cyan)
otherPoints.setFillColor(Color.cyan)
inputIMP.getOverlay().add(otherPoints)
def updatePointRoi(self):
# Surround with try/except to prevent blocking
# ImageJ's stack slice updater thread in case of error.
try:
# Update PointRoi
self.imp.killRoi()
points = self.nuclei[
self.imp.getSlice() -
1] # map 1-based slices to 0-based nuclei Z coords
if 0 == len(points):
IJ.log("No points for slice " + str(self.imp.getSlice()))
return
roi = PointRoi()
# Style: large, red dots
roi.setSize(4) # ranges 1-4
roi.setPointType(2) # 2 is a dot (filled circle)
roi.setFillColor(Color.red)
roi.setStrokeColor(Color.red)
# Add points
for point in points: # points are floats
roi.addPoint(self.imp, int(point[0]), int(point[1]))
self.imp.setRoi(roi)
except:
IJ.error(sys.exc_info())
def run():
mask_ip = impSkel.getProcessor()
part_ip = impPart.getProcessor()
if not mask_ip.isBinary():
error(impSkel.getTitle() + " is not a binary mask.")
return
# Mask grayscale image and skeletonize mask
try:
mask_pixels = mask_ip.getPixels()
part_pixels = part_ip.getPixels()
for i in xrange(len(part_pixels)):
if mask_pixels[i] == 0:
part_pixels[i] = 0
part_ip.setPixels(part_pixels)
except IndexError:
error("Chosen images are not the same size.")
skeletonize(impSkel)
# Get skeleton features
end_points, junctions, junction_voxels, total_len = skeleton_properties(impSkel)
if not end_points and not junction_voxels:
error(impSkel.getTitle() + " does not seem a valid skeleton.")
return
# Retrieve centroids from IJ1
threshold_lower = get_threshold(impPart, thres_method)
cx, cy, n_particles = get_centroids(impPart, threshold_lower)
if None in (cx, cy):
error("Verify parameters: No particles detected.")
return
# Loop through each centroids and categorize its position
# according to its distance to skeleton features
n_bp = n_tip = n_none = n_both = 0
overlay = cleanse_overlay(impPart.getOverlay())
for i in range(n_particles):
j_dist = ep_dist = sys.maxint
# Retrieve the distance between this particle and the closest junction voxel
for jvoxel in junction_voxels:
dist = distance(cx[i], cy[i], jvoxel.x, jvoxel.y)
if (dist <= cutoff_dist and dist < j_dist):
j_dist = dist
# Retrieve the distance between this particle and the closest end-point
for end_point in end_points:
dist = distance(cx[i], cy[i], end_point.x, end_point.y)
if (dist <= cutoff_dist and dist < ep_dist):
ep_dist = dist
roi_id = str(i).zfill(len(str(n_particles)))
roi_name = "Unknown:" + roi_id
roi_color = Color.ORANGE
roi_type = 2 # dot
# Is particle associated with neither junctions nor end-points?
if j_dist > cutoff_dist and ep_dist > cutoff_dist:
roi_name = "Unc:" + roi_id
#n_none += 1
# Is particle associated with both?
elif abs(j_dist - ep_dist) <= pixel_size(impPart) / 2:
roi_name = "J+T:" + roi_id
roi_color = Color.CYAN
#roi_type = 1 # crosshair
n_both += 1
# Is particle associated with an end-point?
elif ep_dist < j_dist:
roi_name = "Tip:" + roi_id
roi_color = Color.GREEN
#roi_type = 0 # hybrid
n_tip += 1
# Is particle associated with a junction?
elif ep_dist > j_dist:
roi_name = "Junction:" + roi_id
roi_color = Color.MAGENTA
#roi_type = 3 # circle
n_bp += 1
roi = PointRoi(cx[i], cy[i])
roi.setName(roi_name)
roi.setStrokeColor(roi_color)
roi.setPointType(roi_type)
roi.setSize(2) # medium
overlay.add(roi)
# Display result
impSkel.setOverlay(overlay)
impPart.setOverlay(overlay)
# Output some measurements
if "table" in output:
t = ResultsTable.getResultsTable() if "IJ1" in output else DefaultGenericTable()
addToTable(t, "Part. image", "%s (%s)" % (impPart.getTitle(), impPart.getCalibration().getUnits()))
addToTable(t, "Skel. image", "%s (%s)" % (impSkel.getTitle(), impSkel.getCalibration().getUnits()))
addToTable(t, "Junction particles", n_bp)
addToTable(t, "Tip particles", n_tip)
addToTable(t, "J+T particles", n_both)
addToTable(t, "Unc. particles", n_none)
addToTable(t, "Junctions w/ particles", n_bp + n_both)
addToTable(t, "Tips w/ particles", n_tip + n_both)
addToTable(t, "Total skel. lenght", total_len)
addToTable(t, "Total end points", len(end_points))
addToTable(t, "Total junctions", sum(junctions))
addToTable(t, "Unc. particles / Total skel. lenght)", n_none/total_len)
addToTable(t, "Snap-to dist.", str(cutoff_dist) + impPart.getCalibration().getUnits())
addToTable(t, "Threshold", "%d (%s)" % (threshold_lower, thres_method))
showTable(t, "Results")
def getSpots(imp,
channel,
detector_type,
radius,
threshold,
overlay,
roi_type="large",
roi_color=ColorRGB("blue")):
""" Performs the detection, adding spots to the image overlay
:imp: The image (ImagePlus) being analyzed
:channel: The target channel
:detector_type: A string describing the detector: "LoG" or "DoG"
:radius: Spot radius (NB: trackmate GUI accepts diameter)
:threshold: Quality cutoff value
:overlay: The image overlay to store spot (MultiPoint) ROIs
:roi_type: A string describing how spot ROIs should be displayed
:returns: The n. of detected spots
"""
settings = Settings()
settings.setFrom(imp)
settings.detectorFactory = (LogDetectorFactory() if "LoG" in detector_type
else DogDetectorFactory())
settings.detectorSettings = {
DK.KEY_DO_SUBPIXEL_LOCALIZATION: False,
DK.KEY_DO_MEDIAN_FILTERING: True,
DK.KEY_TARGET_CHANNEL: channel,
DK.KEY_RADIUS: radius,
DK.KEY_THRESHOLD: threshold,
}
trackmate = TrackMate(settings)
if not trackmate.execDetection():
lservice.error(str(trackmate.getErrorMessage()))
return 0
model = trackmate.model
spots = model.getSpots()
count = spots.getNSpots(False)
ch_id = "Spots Ch%d" % channel
if count > 0:
roi = None
cal = imp.getCalibration()
t_pos = imp.getT()
if (t_pos > 1):
lservice.warn("Only frame %d was considered..." % t_pos)
for spot in spots.iterable(False):
x = cal.getRawX(spot.getFeature(spot.POSITION_X))
y = cal.getRawY(spot.getFeature(spot.POSITION_Y))
z = spot.getFeature(spot.POSITION_Z)
if z == 0 or not cal.pixelDepth or cal.pixelDepth == 0:
z = 1
else:
z = int(z // cal.pixelDepth)
imp.setPosition(channel, z, t_pos)
if roi is None:
roi = PointRoi(int(x), int(y), imp)
else:
roi.addPoint(imp, x, y)
roi.setStrokeColor(colorRGBtoColor(roi_color))
if "large" in roi_type:
roi.setPointType(3)
roi.setSize(4)
else:
roi.setPointType(2)
roi.setSize(1)
overlay.add(roi, ch_id)
return count
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"))
