weak_dict = {}
for i, img in enumerate(proj.image_list):
# print img.name, img.feature_count
if img.feature_count > 0 and img.feature_count < 25:
weak_dict[i] = True
print('weak images:', weak_dict)
# mark any features in the weak images list
for i, match in enumerate(matches_orig):
#print 'before:', match
for j, p in enumerate(match[1:]):
if p[0] in weak_dict:
match[j + 1] = [-1, -1]
mark_sum += 1
#print 'after:', match
if mark_sum > 0:
print('Outliers removed from match lists:', mark_sum)
result = input('Save these changes? (y/n):')
if result == 'y' or result == 'Y':
cull.delete_marked_features(matches_orig)
cull.delete_marked_features(matches_opt)
# write out the updated match dictionaries
print("Writing original matches...")
pickle.dump(matches_orig, open(os.path.join(args.project, source),
"wb"))
print("Writing optimized matches...")
pickle.dump(matches_opt,
open(os.path.join(args.project, "matches_opt"), "wb"))
if m[0] == i and m[1] == p0:
pos1 = pos
if m[0] == j and m[1] == p1:
pos2 = pos
if pos1 >= 0 and pos2 >= 0:
print("found in match: ", k)
mark_list.append([k, pos1])
mark_list.append([k, pos2])
if len(mark_list):
print('Outliers marked:', len(mark_list))
result = input('Save these changes? (y/n):')
if result == 'y' or result == 'Y':
# mark and delete the outliers
cull.mark_using_list(mark_list, matches)
cull.delete_marked_features(matches)
# write out the updated match dictionaries
print("Writing matches (direct) ...")
pickle.dump(matches,
open(os.path.join(args.project, "matches_direct"), "wb"))
print('Number of features:', len(matches))
print('Hard coded exit in mid script...')
quit()
# run through the match list and compute the homography error (in image space)
error_list = []
for k, match in enumerate(matches):
max_dist = 0.0
