def __init__(self, track_x, track_y, tracks_Z, gEpG, oracle):
self.track_x = track_x
self.track_y = track_y
self.tracks_Z = tracks_Z
self.xO = Counter(soft_fetch(oracle, track_x))
self.yO = Counter(soft_fetch(oracle, track_y))
"""
Errors in the xO/yO track operand
if len(_O)==0 we don' t kow much about the track because supervised
information is not available
if len(_O)==1 track_y is a correct track as far as we know !!!
if len(_O)>1 track_y is already wrong before the computation !!!
"""
self.ZO = [
Counter(fetch(oracle, track_z_it))
for track_z_it in tracks_z]
"""
Errors in the results Z:
tracks_z and ZO may contain 0,1 or 2 elements
0:if the computation discarded both points
(should it happen? maybe, maybe not,
probably should be a rare event)
1:if the computation merged the tracks
2:if the computation splitted the tracks
for zO in ZO:
if len(zO)==0 we don' t kow much about the track because supervised
information is not available
if len(zO)==1 the resulting track is correct as far as we know
if len(zO)>1 the resulting track is wrong
completeness:
missing keypoints:
keypoints that
are in the operands
are not in the results
spilled keypoints:
missing keypoints that
should be in the results
have the same oracle of some result
"""
kps2views = lambda some_keypoint_key_sequence: {
view_key: v
for v in track_x.allViews() + track_y.allViews()
if view_key == v.key()
for view_key in some_keypoint_key_sequence}
operand_kp = set([
v.key() for v in track_x.allViews() + track_y.allViews()])
result_kp = reduce(
lambda a, b: a + b,
[[v.key() for v in track_z] for track_z in tracks_Z],
[])
result_kp = set(result_kp)
missing = operand_kp.difference(result_kp)
missing_views = kps2views(missing)
missing_oracles = soft_fetch_views(oracle, missing_views.values())
dominant_oracles_of_results = set([dominant(zO) for zO in ZO])
self.spilled_oracles = Counter([o
for o in missing_oracles
if o in dominant_oracles_of_results])
self.spilled = sum(self.spilled_oracles.values())
pass
def print_merge_operation(track_x, track_y, result, gEpG, oracle):
"""
arguments:
track_x,track_y : track operands
result : list resulting from merging operands
gEpG : epipolar geometries
oracle : oracle partition
print to a string a table with columns:
Im : image identifier
- : header identifier or keypoint oracle
P : point P
Q : point Q
Z1,Z2 : results
for each image in the merge problem an header made of 3 lines is printed
O : oracle header, for each track the dominant oracle
X/Y : coordinates for the each track on the image
for each keypoint observed in the image a line will display
keypoint oracle identifier
keypoint identifier in the column of every track that referece it
"""
tracks = [track_x, track_y] + results
labels = ['Im', '', 'P', 'Q', 'Z1', 'Z2']
s = ""
Tc = map(compactPoint, tracks)
# {image_id:(image_id,homogeneus_keypoint,keypoint_radius)}
# Tc is a list of compacted tracks
images = set(track_x.views.keys() + track_y.views.keys())
keypoints = {
im_id:
set(sum(
[t.getKeypointsIds(im_id) for t in tracks],
[] # sum with lists as argument
#catenate the lists, [] is initial
))
for im_id in images}
s += line(labels)
for im_id in sorted(images):
keypoints_im = [
set([
v.id_keypoint
for v in t.views[im_id]]
) for t in tracks]
# image header:
# 1-oracle header
image_oracles = [
dominant(soft_fetch_views(t.views[im]))
for t in tracks]
s += line([im_id, 'O'] + image_oracles)
# 2-X header
xs = [tc[im_id][1][0] for tc in Tc]
s += line([im_id, 'X'] + xs)
# 3-Y header
ys = [tc[im_id][1][1] for tc in Tc]
s += line([im_id, 'Y'] + ys)
for kp_id in keypoints[im_id]:
o = oracle.views[im_id].get(kp_id)
track_has_kp = [
kp_id if (kp_id in kp_set) else ' '
for kp_set in keypoints_im]
if o:
s += line([im_id, o] + track_has_kp)
else:
s += line([im_id, o] + track_has_kp)
s += '\n'
s += print_list_epipolar_constraints(images, gEpG)
return s