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 = matching.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 = types.Reconstruction()
reconstruction.cameras = cameras
shot1 = types.Shot()
shot1.id = im1
shot1.camera = cameras[str(d1['camera'])]
shot1.pose = types.Pose()
shot1.metadata = get_image_metadata(data, im1)
reconstruction.add_shot(shot1)
shot2 = types.Shot()
shot2.id = im2
shot2.camera = cameras[str(d2['camera'])]
shot2.pose = types.Pose()
shot2.pose.rotation = R
shot2.pose.translation = t
shot2.metadata = get_image_metadata(data, im2)
reconstruction.add_shot(shot2)
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 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 = matching.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]
