def mark_outliers(error_list, trim_stddev):
print("Marking outliers...")
sum = 0.0
count = len(error_list)
# numerically it is better to sum up a list of floatting point
# numbers from smallest to biggest (error_list is sorted from
# biggest to smallest)
for line in reversed(error_list):
sum += line[0]
# stats on error values
print(" computing stats...")
mre = sum / count
stddev_sum = 0.0
for line in error_list:
error = line[0]
stddev_sum += (mre-error)*(mre-error)
stddev = math.sqrt(stddev_sum / count)
print("mre = %.4f stddev = %.4f" % (mre, stddev))
# mark match items to delete
print(" marking outliers...")
mark_count = 0
for line in error_list:
# print "line:", line
if line[0] > mre + stddev * trim_stddev:
cull.mark_feature(matches, line[1], line[2], line[0])
mark_count += 1
return mark_count
def mark_image_features(index, matches):
# iterate through the match dictionary and mark any matches for
# the specified image for deletion
print("Marking feature matches for image:", index)
count = 0
new_matches = []
for i, match in enumerate(matches):
for j, p in enumerate(match[2:]):
if p[0] == index:
cull.mark_feature(matches, i, j, 0)
count += 1
return count
