def bootstrap_reconstruction(data, graph, im1, im2):
'''Starts a reconstruction using two shots.
'''
print 'Initial reconstruction with', im1, 'and', im2
d1 = data.load_exif(im1)
d2 = data.load_exif(im2)
cameras = data.load_camera_models()
tracks, p1, p2 = dataset.common_tracks(graph, im1, im2)
print 'Number of common tracks', len(tracks)
f1 = d1['focal_prior']
f2 = d2['focal_prior']
threshold = data.config.get('five_point_algo_threshold', 0.006)
ret = csfm.two_view_reconstruction(p1, p2, f1, f2, threshold)
if ret is not None:
R, t, cov, inliers = ret
else:
return None
if len(inliers):
print 'Number of inliers', len(inliers)
reconstruction = {
"cameras": cameras,
"shots" : {
im1: {
"camera": str(d1['camera']),
"rotation": [0.0, 0.0, 0.0],
"translation": [0.0, 0.0, 0.0],
},
im2: {
"camera": str(d2['camera']),
"rotation": list(R),
"translation": list(t),
},
},
"points" : {
},
}
add_gps_position(data, reconstruction['shots'][im1], im1)
add_gps_position(data, reconstruction['shots'][im2], im2)
triangulate_shot_features(
graph, reconstruction, im1,
data.config.get('triangulation_threshold', 0.004),
data.config.get('triangulation_min_ray_angle', 2.0))
print 'Number of reconstructed 3D points :{}'.format(len(reconstruction['points']))
if len(reconstruction['points']) > data.config.get('five_point_algo_min_inliers', 50):
print 'Found initialize good pair', im1 , 'and', im2
return reconstruction
print 'Pair', im1, ':', im2, 'fails'
return None
def compute_image_pairs(graph, image_graph, config):
'''All matched image pairs sorted by reconstructability.
'''
pairs = []
score = []
for im1, im2, d in image_graph.edges(data=True):
tracks, p1, p2 = dataset.common_tracks(graph, im1, im2)
if len(tracks) >= 50:
H, inliers = cv2.findHomography(p1, p2, cv2.RANSAC, config.get('homography_threshold', 0.004))
r = pairwise_reconstructability(len(tracks), inliers.sum())
if r > 0:
pairs.append((im1,im2))
score.append(r)
order = np.argsort(-np.array(score))
return [pairs[o] for o in order]
def retriangulate(graph, reconstruction, image_graph, config):
'''Re-triangulate 3D points
'''
P_by_id = {}
KR1_by_id = {}
Kinv_by_id = {}
shots = reconstruction['shots']
points = reconstruction['points']
points_added = 0
tracks_added = []
points_before = len(points)
for im1, im2, d in image_graph.edges(data=True):
if (im1 in shots) and (im2 in shots):
tracks, p1, p2 = dataset.common_tracks(graph, im1, im2)
# find already reconstructed tracks
diff = np.setdiff1d(tracks, points.keys())
reconstruct_ratio = 1 - len(diff) / float(len(tracks))
if reconstruct_ratio < 0.3:
for track in diff:
if track not in tracks_added:
triangulate_track(track,
graph,
reconstruction,
P_by_id,
KR1_by_id,
Kinv_by_id,
reproj_threshold=0.006)
points_added += 1
tracks_added.append(track)
# bundle adjustment
bundle(graph, reconstruction, config)
# filter points with large reprojection errors
track_to_delete = []
for track in tracks_added:
error = reprojection_error_track(track, graph, reconstruction)
if error > config.get('triangulation_threshold', 0.004):
track_to_delete.append(track)
print 'Add {0} points after retriangulation.'.format(
len(reconstruction['points']) - points_before)
for t in track_to_delete:
if t in reconstruction['points']:
del reconstruction['points'][t]
# bundle adjustment
bundle(graph, reconstruction, config)
def bootstrap_reconstruction(data, graph, im1, im2):
"""Starts a reconstruction using two shots.
"""
print "Initial reconstruction with", im1, "and", im2
d1 = data.load_exif(im1)
d2 = data.load_exif(im2)
cameras = data.load_camera_models()
camera1 = cameras[d1["camera"]]
camera2 = cameras[d2["camera"]]
tracks, p1, p2 = dataset.common_tracks(graph, im1, im2)
print "Number of common tracks", len(tracks)
threshold = data.config.get("five_point_algo_threshold", 0.006)
R, t, inliers = two_view_reconstruction(p1, p2, camera1, camera2, threshold)
if len(inliers) > 5:
print "Number of inliers", len(inliers)
reconstruction = {
"cameras": cameras,
"shots": {
im1: {"camera": str(d1["camera"]), "rotation": [0.0, 0.0, 0.0], "translation": [0.0, 0.0, 0.0]},
im2: {"camera": str(d2["camera"]), "rotation": list(R), "translation": list(t)},
},
"points": {},
}
add_gps_position(data, reconstruction["shots"][im1], im1)
add_gps_position(data, reconstruction["shots"][im2], im2)
triangulate_shot_features(
graph,
reconstruction,
im1,
data.config.get("triangulation_threshold", 0.004),
data.config.get("triangulation_min_ray_angle", 2.0),
)
print "Number of reconstructed 3D points :{}".format(len(reconstruction["points"]))
if len(reconstruction["points"]) > data.config.get("five_point_algo_min_inliers", 50):
print "Found initialize good pair", im1, "and", im2
bundle_single_view(graph, reconstruction, im2, data.config)
retriangulate(graph, reconstruction, data.config)
bundle_single_view(graph, reconstruction, im2, data.config)
return reconstruction
print "Pair", im1, ":", im2, "fails"
return None
def retriangulate(graph, reconstruction, image_graph, config):
'''Re-triangulate 3D points
'''
P_by_id = {}
KR1_by_id = {}
Kinv_by_id = {}
shots = reconstruction['shots']
points = reconstruction['points']
points_added = 0
tracks_added = []
points_before = len(points)
for im1, im2, d in image_graph.edges(data=True):
if (im1 in shots) and (im2 in shots):
tracks, p1, p2 = dataset.common_tracks(graph, im1, im2)
# find already reconstructed tracks
diff = np.setdiff1d(tracks, points.keys())
reconstruct_ratio = 1 - len(diff)/float(len(tracks))
if reconstruct_ratio < 0.3:
for track in diff:
if track not in tracks_added:
triangulate_track(track, graph, reconstruction, P_by_id, KR1_by_id, Kinv_by_id, reproj_threshold=0.006)
points_added += 1
tracks_added.append(track)
# bundle adjustment
bundle(graph, reconstruction, config)
# filter points with large reprojection errors
track_to_delete = []
for track in tracks_added:
error = reprojection_error_track(track, graph, reconstruction)
if error > config.get('triangulation_threshold', 0.004):
track_to_delete.append(track)
print 'Add {0} points after retriangulation.'.format(len(reconstruction['points']) - points_before)
for t in track_to_delete:
if t in reconstruction['points']:
del reconstruction['points'][t]
# bundle adjustment
bundle(graph, reconstruction, config)