mark_count = 0
for line in error_list:
# print "line:", line
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:]):
matches.pop(i)
elif len(match) < 3:
print("deleting match that is now in less than 2 images:", match)
matches.pop(i)
by_feature, by_pair = compute_shakers(matches_sba)
for line in by_pair:
print(line[0], line[1], 'avg:', line[2], 'std:', line[3], 'min:', line[4])
#mode = 'by_feature'
mode = 'by_pair'
if args.interactive:
mark_list = cull.show_outliers(by_feature, matches_sba, proj.image_list)
elif mode == 'by_feature':
# trim outliers by some number of standard deviations high
# (for movers) mark_sum = mark_outliers(error_list, args.stddev)
# construct a 'mark list' from the most colocated image pairs (note,
# 3+ way matches are less likely to show up on this bad list.)
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