try:
modelFound = model.filterRansac(candidates, inliers, 1000,
maxEpsilon, minInlierRatio, minNumInliers)
if modelFound:
# Apply the transformation defined by the model to the first point
# of each pair (PointMatch) of points. That is, to the point from
# the first image.
PointMatch.apply(inliers, model)
except NotEnoughDataPointsException, e:
print e
if modelFound:
# Store inlier pointmatches: the spatially coherent subset
roi1pm = PointRoi()
roi1pm.setName("matches in cut1")
roi2pm = PointRoi()
roi2pm.setName("matches in cut2")
for pm in inliers:
p1 = pm.getP1()
roi1pm.addPoint(p1.getL()[0], p1.getL()[1])
p2 = pm.getP2()
roi2pm.addPoint(p2.getL()[0], p2.getL()[1])
roi_manager.addRoi(roi1pm)
roi_manager.addRoi(roi2pm)
# Register images
# Transform the top-left and bottom-right corner of imp2
# (use applyInverse: the model describes imp1 -> imp2)
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")
