def find_initial_worm(small_image, well_mask):
# plan here is to find known good worm edges with Canny using a stringent threshold, then
# relax the threshold in the vicinity of the good edges.
# back off another pixel from the well edge to avoid gradient from the edge
shrunk_mask = ndimage.binary_erosion(well_mask, structure=S)
smoothed, gradient, sobel = canny.prepare_canny(small_image, 2, shrunk_mask)
local_maxima = canny.canny_local_maxima(gradient, sobel)
# Calculate stringent and medium-stringent thresholds. The stringent threshold
# is the 200th-brightest edge pixel, and the medium is the 450th-brightest pixel
highp = 100 * (1-200/local_maxima.sum())
highp = max(highp, 94)
mediump = 100 * (1-450/local_maxima.sum())
mediump = max(mediump, 94)
low_worm, medium_worm, high_worm = numpy.percentile(gradient[local_maxima], [94, mediump, highp])
stringent_worm = canny.canny_hysteresis(local_maxima, gradient, low_worm, high_worm)
# Expand out 20 pixels from the stringent worm edges to make our search space
stringent_area = ndimage.binary_dilation(stringent_worm, mask=well_mask, iterations=20)
# now use the relaxed threshold but only in the stringent area
relaxed_worm = canny.canny_hysteresis(local_maxima, gradient, low_worm, medium_worm) & stringent_area
# join very close-by objects, and remove remaining small objects
candidate_worm = ndimage.binary_dilation(relaxed_worm, structure=S)
candidate_worm = ndimage.binary_erosion(candidate_worm)
candidate_worm = mask.remove_small_area_objects(candidate_worm, 30, structure=S)
# Now figure out the biggest blob of nearby edges, and call that the worm region
glommed_candidate = ndimage.binary_dilation(candidate_worm, structure=S, iterations=2)
glommed_candidate = ndimage.binary_erosion(glommed_candidate, iterations=2)
# get just outline, not any regions filled-in due to closing
glommed_candidate ^= ndimage.binary_erosion(glommed_candidate)
glommed_candidate = mask.get_largest_object(glommed_candidate, structure=S)
worm_area = ndimage.binary_dilation(glommed_candidate, mask=well_mask, structure=S, iterations=12)
worm_area = mask.fill_small_radius_holes(worm_area, max_radius=15)
candidate_edges = relaxed_worm & candidate_worm & worm_area
return candidate_edges, worm_area
def refine_worm(image, initial_area, candidate_edges):
# find strong worm edges (roughly equivalent to the edges found by find_initial_worm,
# which are in candidate_edges): smooth the image, do canny edge-finding, and
# then keep only those edges near candidate_edges
smooth_image = restoration.denoise_tv_bregman(image, 140).astype(numpy.float32)
smoothed, gradient, sobel = canny.prepare_canny(smooth_image, 8, initial_area)
local_maxima = canny.canny_local_maxima(gradient, sobel)
candidate_edge_region = ndimage.binary_dilation(candidate_edges, iterations=4)
strong_edges = local_maxima & candidate_edge_region
# Now threshold the image to find dark blobs as our initial worm region
# First, find areas in the initial region unlikely to be worm pixels
mean, std = mcd.robust_mean_std(smooth_image[initial_area][::4], 0.85)
non_worm = (smooth_image > mean - std) & initial_area
# now fit a smoothly varying polynomial to the non-worm pixels in the initial
# region of interest, and subtract that from the actual image to generate
# an image with a flat illumination field
background = polyfit.fit_polynomial(smooth_image, mask=non_worm, degree=2)
minus_bg = smooth_image - background
# now recalculate a threshold from the background-subtracted pixels
mean, std = mcd.robust_mean_std(minus_bg[initial_area][::4], 0.85)
initial_worm = (minus_bg < mean - std) & initial_area
# Add any pixels near the strong edges to our candidate worm position
initial_worm |= ndimage.binary_dilation(strong_edges, iterations=3)
initial_worm = mask.fill_small_radius_holes(initial_worm, 5)
# Now grow/shrink the initial_worm region so that as many of the strong
# edges from the canny filter are in contact with the region edges as possible.
ac = active_contour.EdgeClaimingAdvection(initial_worm, strong_edges,
max_region_mask=initial_area)
stopper = active_contour.StoppingCondition(ac, max_iterations=100)
while stopper.should_continue():
ac.advect(iters=1)
ac.smooth(iters=1, depth=2)
worm_mask = mask.fill_small_radius_holes(ac.mask, 7)
# Now, get edges from the image at a finer scale
smoothed, gradient, sobel = canny.prepare_canny(smooth_image, 0.3, initial_area)
local_maxima = canny.canny_local_maxima(gradient, sobel)
strong_sum = strong_edges.sum()
highp = 100 * (1 - 1.5*strong_sum/local_maxima.sum())
lowp = max(100 * (1 - 3*strong_sum/local_maxima.sum()), 0)
low_worm, high_worm = numpy.percentile(gradient[local_maxima], [lowp, highp])
fine_edges = canny.canny_hysteresis(local_maxima, gradient, low_worm, high_worm)
# Expand out the identified worm area to include any of these finer edges
closed_edges = ndimage.binary_closing(fine_edges, structure=S)
worm = ndimage.binary_propagation(worm_mask, mask=worm_mask|closed_edges, structure=S)
worm = ndimage.binary_closing(worm, structure=S, iterations=2)
worm = mask.fill_small_radius_holes(worm, 5)
worm = ndimage.binary_opening(worm)
worm = mask.get_largest_object(worm)
# Last, smooth the shape a bit to reduce sharp corners, but not too much to
# sand off the tail
ac = active_contour.CurvatureMorphology(worm, max_region_mask=initial_area)
ac.smooth(depth=2, iters=2)
return strong_edges, ac.mask
def get_well_mask(image):
small_image = pyramid.pyr_down(image, 4)
smoothed, gradient, sobel = canny.prepare_canny(small_image, 2)
local_maxima = canny.canny_local_maxima(gradient, sobel)
# well outline has ~6000 px full-size = 1500 px at 4x downsampled
# So find the intensity value of the 2000th-brightest pixel, via percentile:
highp = 100 * (1-2000/local_maxima.sum())
highp = max(highp, 90)
low_edge, high_edge = numpy.percentile(gradient[local_maxima], [90, highp])
# Do canny edge-finding starting with gradient pixels as bright or brighter
# than the 2000th-brightest pixel, and spread out to the 90th percentile
# intensity:
well_edge = canny.canny_hysteresis(local_maxima, gradient, low_edge, high_edge)
# connect nearby edges and remove small unconnected bits
well_edge = ndimage.binary_closing(well_edge, structure=S)
well_edge = mask.remove_small_area_objects(well_edge, 300, structure=S)
# Get map of distances and directions to nearest edge to use for contour-fitting
distances, nearest_edge = active_contour.edge_direction(well_edge)
# initial curve is the whole image less one pixel on the outside
initial = numpy.ones(well_edge.shape, dtype=bool)
initial[:,[0,-1]] = 0
initial[[0,-1],:] = 0
# Now evolve the curve inward until it contacts the canny well edges.
gac = active_contour.GAC(initial, nearest_edge, advection_mask=(distances < 10), balloon_direction=-1)
stopper = active_contour.StoppingCondition(gac, max_iterations=200)
while stopper.should_continue():
# otherwise evolve the curve by shrinking, advecting toward edges, and smoothing
gac.balloon_force(iters=3)
gac.advect(iters=2)
gac.smooth()
gac.smooth(depth=3)
# now erode everywhere the contour edge is right on a canny edge:
gac.move_to_outside(well_edge[tuple(gac.inside_border_indices.T)])
gac.smooth()
well_mask = gac.mask
if well_mask.sum() / well_mask.size < 0.25:
# if the well mask is too small, something went very wrong
well_mask = None
return small_image, well_mask
