def setSegmentation(self, segmentation, cseg=0):
"""Set the line segmentation."""
# reorder the labels by the x center of bounding box
segmentation = common.renumber_labels_by_boxes(
segmentation, key=lambda x: mean((x[1].start, x[1].stop)))
# compute the bounding boxes in order
boxes = [None] + measurements.find_objects(segmentation)
n = len(boxes)
# now consider groups of boxes
groups = []
for i in range(1, n):
for r in range(1, self.maxrange + 1):
box = None
gap = 0
labels = []
for j in range(i, min(n, i + r)):
if box is not None:
gap = max(gap, boxes[j][1].start - box[1].stop)
box = sl.union(box, boxes[j])
labels.append(j)
# skip if two constituent boxes have too large a gap between them
if gap > self.maxdist: continue
a = sl.aspect(box)
# skip if the aspect ratio is wrong
if 1.0 / a > self.maxaspect: continue
groups.append((box, labels))
# compute some statistics
mw = median([sl.dim0(g[0]) for g in groups])
# now select based on statistics
groups = [g for g in groups if sl.dim1(g[0]) < self.maxwidth * mw]
# now we have a list of candidate groups
self.segmentation = segmentation
self.groups = groups
self.clearLattice()
return len(self.groups)
def compute_lines(segmentation, scale, min_width):
"""Given a line segmentation map, computes a list
of tuples consisting of 2D slices and masked images."""
lobjects = morph.find_objects(segmentation)
lines = []
for i, o in enumerate(lobjects):
if o is None: continue
if sl.dim1(o) < min_width * scale or sl.dim0(o) < 0.5 * scale:
print(sl.dim1(o), sl.dim0(o))
continue
mask = (segmentation[o] == i + 1)
if amax(mask) == 0: continue
result = record()
result.label = i + 1
result.bounds = o
result.mask = mask
lines.append(result)
return lines
def compute_lines(segmentation,scale):
"""Given a line segmentation map, computes a list
of tuples consisting of 2D slices and masked images."""
lobjects = morph.find_objects(segmentation)
lines = []
for i,o in enumerate(lobjects):
if o is None: continue
if sl.dim1(o)<2*scale or sl.dim0(o)<scale: continue
mask = (segmentation[o]==i+1)
if amax(mask)==0: continue
result = record()
result.label = i+1
result.bounds = o
result.mask = mask
lines.append(result)
return lines
def show_lines(image, lines, lsort):
"""Overlays the computed lines on top of the image, for debugging purposes."""
ys,xs = [],[]
plt.clf()
plt.cla()
plt.imshow(image)
for i in range(len(lines)):
l = lines[lsort[i]]
y,x = sl.center(l.bounds)
xs.append(x)
ys.append(y)
o = l.bounds
r = mpatches.Rectangle((o[1].start,o[0].start),edgecolor='r',fill=0,width=sl.dim1(o),height=sl.dim0(o))
plt.gca().add_patch(r)
h,w = image.shape
plt.ylim(h,0)
plt.xlim(0,w)
plt.plot(xs,ys)
def show_lines(image,lines,lsort):
"""Overlays the computed lines on top of the image, for debugging
purposes."""
ys,xs = [],[]
plt.clf()
plt.cla()
plt.imshow(image)
for i in range(len(lines)):
l = lines[lsort[i]]
y,x = sl.center(l.bounds)
xs.append(x)
ys.append(y)
o = l.bounds
r = mpatches.Rectangle((o[1].start,o[0].start),edgecolor='r',fill=0,width=sl.dim1(o),height=sl.dim0(o))
plt.gca().add_patch(r)
h,w = image.shape
plt.ylim(h,0)
plt.xlim(0,w)
plt.plot(xs,ys)
