# quick hack angle approximation
avg = (np.array(ned1) + np.array(ned2)) * 0.5
y = np.linalg.norm(np.array(ned2) - np.array(ned1))
x = np.linalg.norm(avg - np.array(match[0]))
angle_deg = math.atan2(y, x) * r2d
else:
angle_deg = compute_angle(ned1, ned2,
match[0]) * r2d
if angle_deg < args.min_angle:
mark_list.append([k, i])
# 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)
mark_sum = len(mark_list)
if mark_sum > 0:
print('Outliers to remove from match lists:', mark_sum)
result = input('Save these changes? (y/n):')
if result == 'y' or result == 'Y':
cull.delete_marked_features(matches, min_chain_len)
# write out the updated match dictionaries
print("Writing original matches:", source)
pickle.dump(matches, open(os.path.join(proj.analysis_dir, source),
"wb"))
def mre(project_dir, mre_options):
group_id = mre_options[0]
stddev = mre_options[1]
initial_pose = mre_options[2]
strong = mre_options[3]
interactive = mre_options[4]
proj = ProjectMgr.ProjectMgr(project_dir)
proj.load_images_info()
# a value of 2 let's pairs exist which can be trouble ...
matcher_node = getNode('/config/matcher', True)
min_chain_len = matcher_node.getInt("min_chain_len")
if min_chain_len == 0:
min_chain_len = 3
print("Notice: min_chain_len is:", min_chain_len)
source = 'matches_grouped'
print("Loading matches:", source)
matches = pickle.load(open(os.path.join(proj.analysis_dir, source), "rb"))
print('Number of original features:', len(matches))
# load the group connections within the image set
groups = Groups.load(proj.analysis_dir)
print('Group sizes:', end=" ")
for group in groups:
print(len(group), end=" ")
print()
opt = Optimizer.Optimizer(project_dir)
if initial_pose:
opt.setup(proj, groups, group_id, matches, optimized=False)
else:
opt.setup(proj, groups, group_id, matches, optimized=True)
x0 = np.hstack((opt.camera_params.ravel(), opt.points_3d.ravel(),
opt.K[0, 0], opt.K[0, 2], opt.K[1, 2], opt.distCoeffs))
error = opt.fun(x0, opt.n_cameras, opt.n_points,
opt.by_camera_point_indices, opt.by_camera_points_2d)
print('cameras:', opt.n_cameras)
print(len(error))
mre = np.mean(np.abs(error))
std = np.std(error)
max = np.amax(np.abs(error))
print('mre: %.3f std: %.3f max: %.2f' % (mre, std, max))
print('Tabulating results...')
results = []
results_by_cam = []
count = 0
for i, cam in enumerate(opt.camera_params.reshape(
(opt.n_cameras, opt.ncp))):
# print(i, opt.camera_map_fwd[i])
orig_cam_index = opt.camera_map_fwd[i]
cam_errors = []
# print(count, opt.by_camera_point_indices[i])
for j in opt.by_camera_point_indices[i]:
match = matches[opt.feat_map_rev[j]]
match_index = 0
#print(orig_cam_index, match)
for k, p in enumerate(match[2:]):
if p[0] == orig_cam_index:
match_index = k
# print(match[0], opt.points_3d[j*3:j*3+3])
e = error[count * 2:count * 2 + 2]
#print(count, e, np.linalg.norm(e))
#if abs(e[0]) > 5*std or abs(e[1]) > 5*std:
# print("big")
cam_errors.append(np.linalg.norm(e))
results.append(
[np.linalg.norm(e), opt.feat_map_rev[j], match_index])
count += 1
if len(cam_errors):
results_by_cam.append([
np.mean(np.abs(np.array(cam_errors))),
np.amax(np.abs(np.array(cam_errors))),
proj.image_list[orig_cam_index].name
])
else:
results_by_cam.append(
[9999.0, 9999.0, proj.image_list[orig_cam_index].name])
#print(proj.image_list[orig_cam_index].name, ':',
# np.mean(np.abs(np.array(cam_errors))))
print("Report of images that aren't fitting well:")
results_by_cam = sorted(results_by_cam,
key=lambda fields: fields[0],
reverse=True)
for line in results_by_cam:
if line[0] > mre + 3 * std:
print("%s - mean: %.3f max: %.3f" % (line[2], line[0], line[1]))
for line in results_by_cam:
if line[0] > mre + 3 * std:
print(line[2], end=" ")
print()
error_list = sorted(results, key=lambda fields: fields[0], reverse=True)
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
if interactive:
# interactively pick outliers
mark_list = cull.show_outliers(error_list, matches, proj.image_list)
# mark selection
cull.mark_using_list(mark_list, matches)
mark_sum = len(mark_list)
else:
# trim outliers by some # of standard deviations high
mark_sum = mark_outliers(error_list, stddev)
# 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):
for j, p in enumerate(match[2:]):
if p[1] != [-1, -1]:
image = proj.image_list[p[0]]
image.feature_count += 1
purge_weak_images = False
if purge_weak_images:
# make a dict of all images with less than 25 feature matches
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):
#print 'before:', match
for j, p in enumerate(match[2:]):
if p[0] in weak_dict:
match[j + 1] = [-1, -1]
mark_sum += 1
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, min_chain_len, strong=strong)
# write out the updated match dictionaries
print("Writing:", source)
pickle.dump(matches,
open(os.path.join(proj.analysis_dir, source), "wb"))
image = proj.image_list[ p[0] ]
image.feature_count += 1
purge_weak_images = False
if purge_weak_images:
# make a dict of all images with less than 25 feature matches
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):
#print 'before:', match
for j, p in enumerate(match[2:]):
if p[0] in weak_dict:
match[j+1] = [-1, -1]
mark_sum += 1
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, min_chain_len, strong=args.strong)
# write out the updated match dictionaries
print("Writing:", source)
pickle.dump(matches, open(os.path.join(proj.analysis_dir, source), "wb"))
def colocated(project_dir, colocated_options):
group_id = colocated_options[0]
min_angle = colocated_options[1]
r2d = 180.0 / math.pi
proj = ProjectMgr.ProjectMgr(project_dir)
proj.load_images_info()
# a value of 2 let's pairs exist which can be trouble ...
matcher_node = getNode('/config/matcher', True)
min_chain_len = matcher_node.getInt("min_chain_len")
if min_chain_len == 0:
min_chain_len = 3
print("Notice: min_chain_len is:", min_chain_len)
#source = 'matches_direct'
source = 'matches_grouped'
print("Loading matches:", source)
matches = pickle.load(open(os.path.join(proj.analysis_dir, source), "rb"))
print('Number of original features:', len(matches))
# load the group connections within the image set
groups = Groups.load(proj.analysis_dir)
print('Group sizes:', end=" ")
for group in groups:
print(len(group), end=" ")
print()
def compute_angle(ned1, ned2, ned3):
vec1 = np.array(ned3) - np.array(ned1)
vec2 = np.array(ned3) - np.array(ned2)
n1 = np.linalg.norm(vec1)
n2 = np.linalg.norm(vec2)
denom = n1 * n2
if abs(denom - 0.000001) > 0:
try:
tmp = np.dot(vec1, vec2) / denom
if tmp > 1.0: tmp = 1.0
return math.acos(tmp)
except:
print('vec1:', vec1, 'vec2', vec2, 'dot:', np.dot(vec1, vec2))
print('denom:', denom)
return 0
else:
return 0
print("Scanning match pair angles:")
mark_list = []
for k, match in enumerate(tqdm(matches)):
if match[1] == group_id: # used by current group
for i, m1 in enumerate(match[2:]):
for j, m2 in enumerate(match[2:]):
if i < j:
i1 = proj.image_list[m1[0]]
i2 = proj.image_list[m2[0]]
if i1.name in groups[group_id] and i2.name in groups[
group_id]:
ned1, ypr1, q1 = i1.get_camera_pose(opt=True)
ned2, ypr2, q2 = i2.get_camera_pose(opt=True)
quick_approx = False
if quick_approx:
# quick hack angle approximation
avg = (np.array(ned1) + np.array(ned2)) * 0.5
y = np.linalg.norm(
np.array(ned2) - np.array(ned1))
x = np.linalg.norm(avg - np.array(match[0]))
angle_deg = math.atan2(y, x) * r2d
else:
angle_deg = compute_angle(
ned1, ned2, match[0]) * r2d
if angle_deg < min_angle:
mark_list.append([k, i])
# 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)
mark_sum = len(mark_list)
if mark_sum > 0:
print('Outliers to remove from match lists:', mark_sum)
result = input('Save these changes? (y/n):')
if result == 'y' or result == 'Y':
cull.delete_marked_features(matches, min_chain_len)
# write out the updated match dictionaries
print("Writing original matches:", source)
pickle.dump(matches,
open(os.path.join(proj.analysis_dir, source), "wb"))
