else:
angle_deg = compute_angle(ned1, ned2,
match[0]) * r2d
if angle_deg < args.min_angle:
mark_list.append([k, i])
if (k + 1) % step == 0:
bar.next()
bar.finish()
# Pairs with very small average angles between each feature and camera
# location indicate closely located camera poses and these cause
# problems because very small changes in camera pose lead to very
# large changes in feature location.
# mark selection
cull.mark_using_list(mark_list, matches)
mark_sum = len(mark_list)
def delete_marked_features(matches):
print(" deleting marked items...")
for i in reversed(range(len(matches))):
match = matches[i]
has_bad_elem = False
for j in reversed(range(1, len(match))):
p = match[j]
if p == [-1, -1]:
has_bad_elem = True
match.pop(j)
if len(match) < 3:
print("deleting match that is now in less than 2 images:", match)
mark_count += 1
return mark_count
# load the group connections within the image set
groups = Groups.load(args.project)
error_list = compute_feature_depths(proj.image_list, groups[0], matches_sba)
if args.interactive:
# interactively pick outliers
mark_list = cull.show_outliers(error_list, matches_sba, proj.image_list)
# mark both direct and/or sba match lists as requested
cull.mark_using_list(mark_list, matches_direct)
cull.mark_using_list(mark_list, matches_sba)
mark_sum = len(mark_list)
else:
# trim outliers by some # of standard deviations high
mark_sum = mark_outliers(error_list, args.std)
# after marking the bad matches, now count how many remaining features
# show up in each image
for i in proj.image_list:
i.feature_count = 0
for i, match in enumerate(matches_direct):
for j, p in enumerate(match[1:]):
if p[1] != [-1, -1]:
image = proj.image_list[p[0]]
image.feature_count += 1
if line[0] > mean + stddev * trim_stddev:
cull.mark_feature(matches_orig, line[1], line[2], line[0])
cull.mark_feature(matches_opt, line[1], line[2], line[0])
mark_count += 1
return mark_count
error_list = compute_feature_depths(proj.image_list, groups[0], matches_opt)
if args.interactive:
# interactively pick outliers
mark_list = cull.show_outliers(error_list, matches_opt, proj.image_list)
# mark both direct and optimized match lists as requested
cull.mark_using_list(mark_list, matches_orig)
cull.mark_using_list(mark_list, matches_opt)
mark_sum = len(mark_list)
else:
# trim outliers by some # of standard deviations high
mark_sum = mark_outliers(error_list, args.stddev)
# after marking the bad matches, now count how many remaining features
# show up in each image
for image in proj.image_list:
image.feature_count = 0
for i, match in enumerate(matches_orig):
for j, p in enumerate(match[1:]):
if p[1] != [-1, -1]:
image = proj.image_list[p[0]]
image.feature_count += 1
mark_list = []
for line in by_feature:
if line[0] < 0.175: # 10 degrees
mark_list.append([line[1], line[2]])
elif mode == 'by_pair':
mark_list = []
for line in by_pair:
# 0.087 = 5 degrees
# 0.175 = 10 degrees
# 0.262 = 15 degrees
if line[2] < 0.087:
print(line)
mark_list += find_image_pairs(line[0], line[1])
# mark selection
cull.mark_using_list(mark_list, matches_grouped)
cull.mark_using_list(mark_list, matches_sba)
mark_sum = len(mark_list)
# after marking the bad matches, now count how many remaining features
# show up in each image
for i in proj.image_list:
i.feature_count = 0
for i, match in enumerate(matches_sba):
for j, p in enumerate(match[1:]):
if p[1] != [-1, -1]:
image = proj.image_list[p[0]]
image.feature_count += 1
purge_weak_images = False
if purge_weak_images:
