def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
# TODO: implement
frame_count = len(corner_storage)
view_mats = [pose_to_view_mat3x4(known_view_1[1])] * frame_count
corners_0 = corner_storage[0]
point_cloud_builder = PointCloudBuilder(corners_0.ids[:1],
np.zeros((1, 3)))
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
for mode in tracking_modes:
try:
print(f"trying \"{mode.name}\" mode...")
view_mats, point_cloud_builder = _track_camera(
corner_storage,
intrinsic_mat,
mode
)
print(f"trying \"{mode.name}\" mode... Success!")
break
except ValueError as error:
print(f"trying \"{mode.name}\" mode... Failed with {error.args}!")
else:
raise ValueError("all tracking modes failed")
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
view_mats, point_cloud_builder = [eye3x4()] * len(corner_storage), PointCloudBuilder()
try:
known_view_1, known_view_2 = _find_pos_pair(intrinsic_mat, corner_storage)
tracker = CameraTracker(intrinsic_mat, corner_storage, known_view_1, known_view_2)
view_mats, point_cloud_builder = tracker.track()
except TrackingError as error:
print(error)
print('Poses and point cloud are not calculated')
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
view_mats, point_ids, points3d, corner_storage = track(intrinsic_mat, corner_storage,
known_view_1, known_view_2)
point_cloud_builder = PointCloudBuilder(point_ids, points3d)
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
parameters = TriangulationParameters(max_reprojection_error=1.,
min_triangulation_angle_deg=2.,
min_depth=0.1)
view_mats, point_cloud_builder = track_camera(corner_storage,
intrinsic_mat, parameters,
known_view_1, known_view_2)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
frames_n = len(rgb_sequence)
if known_view_1 is None or known_view_2 is None:
(pose1_idx, pose2_idx), pose1, pose2 = find_best_start_poses(
frames_n, corner_storage, intrinsic_mat)
known_view_1, known_view_2 = (pose1_idx, pose1), (pose2_idx, pose2)
view_mats, point_cloud_builder = CameraTracker(intrinsic_mat,
corner_storage,
known_view_1, known_view_2,
frames_n).track()
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
view_mats, point_cloud_builder = _track_camera(corner_storage,
intrinsic_mat)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def __init__(self,
camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]],
known_view_2: Optional[Tuple[int, Pose]]
):
self.rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
self.frame_count = len(self.rgb_sequence)
self.camera_parameters = camera_parameters
self.corner_storage = without_short_tracks(corner_storage, CORNER_MIN_TRACK_LENGTH)
self.intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters, self.rgb_sequence[0].shape[0])
self.point_cloud_builder = PointCloudBuilder()
self.known_view_mats = {}
self.unknown_view_ids = set(range(self.frame_count))
if known_view_1 is None or known_view_2 is None:
self.initialize_known_views()
else:
for (frame_idx, pose) in (known_view_1, known_view_2):
self.update_view(frame_idx, pose_to_view_mat3x4(pose))
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
np.random.seed(1337)
camera_tracker = CameraTracker(corner_storage, intrinsic_mat, known_view_1, known_view_2)
# try:
camera_tracker.track()
view_mats = [camera_tracker.view_mats[key] for key in sorted(camera_tracker.view_mats.keys())]
# except Exception as e:
# print("Exception ocurred, can\'t restore more positions")
# view_mats_ = {key: camera_tracker.view_mats[key] for key in sorted(camera_tracker.view_mats.keys())}
# for key in sorted(camera_tracker.view_nones):
# view_mats_[key] = eye3x4()
# view_mats = [view_mats_[key] for key in sorted(view_mats_)]
point_cloud_builder = camera_tracker.point_cloud_builder
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
known_view_1 = None
known_view_2 = None
if known_view_1 is None or known_view_2 is None:
track = [None] * len(corner_storage)
i, j = initialize(corner_storage, track, intrinsic_mat)
poses = add_points(corner_storage, track, intrinsic_mat,
[None] * (corner_storage.max_corner_id() + 1), i, j)
else:
track = init_track_with_known_views(corner_storage, known_view_1,
known_view_2)
poses = add_points(corner_storage, track, intrinsic_mat,
[None] * (corner_storage.max_corner_id() + 1),
known_view_1[0], known_view_2[0])
track, poses = track_(len(corner_storage), track, poses, intrinsic_mat,
corner_storage)
point_cloud_builder = PointCloudBuilder(
ids=np.array([i for i, point in enumerate(poses)
if point is not None]),
points=np.array([point for point in poses if point is not None]))
view_mats = np.array(track)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
np.random.seed(197)
triangulation_parameters = TriangulationParameters(
max_reprojection_error=8.0,
min_triangulation_angle_deg=0.8,
min_depth=0.1)
RETRIANGULATION_FRAME_COUNT = 10
RETRIANGULATION_MIN_INLIERS = 6
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
known_view_1, known_view_2 = detect_motion(intrinsic_mat, corner_storage,
known_view_1, known_view_2)
if known_view_1 is None or known_view_2 is None:
print("\rFailed to solve scene")
exit(0)
frame_count = len(corner_storage)
frame_1, camera_pose_1 = known_view_1
frame_2, camera_pose_2 = known_view_2
view_mat_1 = pose_to_view_mat3x4(camera_pose_1)
view_mat_2 = pose_to_view_mat3x4(camera_pose_2)
# определение структуры сцены
initial_correspondences = build_correspondences(corner_storage[frame_1],
corner_storage[frame_2])
points3d, ids, _ = triangulate_correspondences(initial_correspondences,
view_mat_1, view_mat_2,
intrinsic_mat,
triangulation_parameters)
point_cloud_builder = MyPointCloudBuilder(frame_count, intrinsic_mat, ids,
points3d)
point_cloud_builder.add_frame(corner_storage[frame_1], frame_1)
point_cloud_builder.add_frame(corner_storage[frame_2], frame_2)
point_cloud_builder.set_view_mat(frame_1, view_mat_1)
point_cloud_builder.set_view_mat(frame_2, view_mat_2)
# определение движения относительно точек сцены
is_changing = True
first_time = True
last_processed_frames = deque(
[]) # последние RETRIANGULATION_FRAME_COUNT обработанных кадров
while is_changing:
is_changing = False
processing_range = range(known_view_1[0],
frame_count) if first_time else range(
frame_count - 1, -1, -1)
for frame_1 in processing_range:
if point_cloud_builder.processed_view_mats[frame_1]:
continue
corners = corner_storage[frame_1]
intersection, (ids_3d, ids_2d) = snp.intersect(
point_cloud_builder.ids.flatten(),
corners.ids.flatten(),
indices=True)
if len(intersection) < 4:
continue
succeeded, r_vec, t_vec, inliers = solvePnP(
point_cloud_builder.points[ids_3d], corners.points[ids_2d],
intrinsic_mat, triangulation_parameters.max_reprojection_error)
if succeeded:
share_of_inliers = len(inliers) / len(intersection)
if share_of_inliers < 0.1 and point_cloud_builder.times_passed[
frame_1] < 3:
point_cloud_builder.times_passed[frame_1] += 1
is_changing = True
continue
if share_of_inliers > 0.1:
last_processed_frames.append(frame_1)
view_mat = rodrigues_and_translation_to_view_mat3x4(
r_vec, t_vec)
point_cloud_builder.set_view_mat(frame_1, view_mat)
point_cloud_builder.add_frame(corner_storage[frame_1], frame_1)
print(
f"\rProcessing frame {frame_1}, inliers: {len(inliers)},"
f" processed {point_cloud_builder.processed_frames} out"
f" of {frame_count} frames, {len(point_cloud_builder.ids)} points in cloud",
end="")
# дополнение структуры сцены
corners_1 = filter_frame_corners(corner_storage[frame_1],
inliers)
for frame_2 in range(frame_count):
if not point_cloud_builder.processed_view_mats[frame_2]:
continue
corners_2 = corner_storage[frame_2]
correspondences = build_correspondences(
corners_1, corners_2, point_cloud_builder.ids)
if len(correspondences.ids) == 0:
continue
points3d, ids, _ = triangulate_correspondences(
correspondences, view_mat,
point_cloud_builder.view_mats[frame_2], intrinsic_mat,
triangulation_parameters)
is_changing = True
point_cloud_builder.add_points(ids, points3d)
# ретриангуляция
if len(last_processed_frames) < RETRIANGULATION_FRAME_COUNT:
continue
view_mat_list = [
point_cloud_builder.view_mats[frame]
for frame in last_processed_frames
]
ids_retriangulation = corner_storage[
last_processed_frames[0]].ids.flatten()
for frame in last_processed_frames:
ids_retriangulation = snp.intersect(
ids_retriangulation,
corner_storage[frame].ids.flatten())
if len(ids_retriangulation) > 0:
points2d_list = []
for frame in last_processed_frames:
_, (_, ids) = snp.intersect(
ids_retriangulation,
corner_storage[frame].ids.flatten(),
indices=True)
points2d_list.append(corner_storage[frame].points[ids])
points3d, st = retriangulate_points_ransac(
np.array(points2d_list), np.array(view_mat_list),
intrinsic_mat, RETRIANGULATION_MIN_INLIERS,
triangulation_parameters)
st = check_points(
point_cloud_builder, points3d, ids_retriangulation,
triangulation_parameters.max_reprojection_error, st)
point_cloud_builder.add_points(ids_retriangulation[st],
points3d[st])
for i in range(RETRIANGULATION_FRAME_COUNT // 3):
last_processed_frames.popleft()
first_time = False
print(
f"\rProcessed {point_cloud_builder.processed_frames} out of {frame_count} frames,"
f" {len(point_cloud_builder.ids)} points in cloud")
# adjust(point_cloud_builder, corner_storage, triangulation_parameters.max_reprojection_error)
# если какие-то позиции вычислить не удалось, возьмем ближайшие вычисленные
view_mats_processed_inds = np.argwhere(
point_cloud_builder.processed_view_mats)
if len(view_mats_processed_inds) == 0:
print("\rFailed to solve scene")
exit(0)
point_cloud_builder.set_view_mat(
0, point_cloud_builder.view_mats[view_mats_processed_inds[0]])
for i in range(1, frame_count):
if not point_cloud_builder.processed_view_mats[i]:
point_cloud_builder.set_view_mat(
i, point_cloud_builder.view_mats[i - 1])
inds, view_mats_slerp = [], []
for i in range(1, frame_count - 1):
vm_0, vm_1, vm_2 = point_cloud_builder.view_mats[i - 1:i + 2]
diff_0_1 = np.degrees(calc_rotation_error_rad(vm_0[:, :3],
vm_1[:, :3]))
diff_1_2 = np.degrees(calc_rotation_error_rad(vm_1[:, :3],
vm_2[:, :3]))
diff_0_2 = np.degrees(calc_rotation_error_rad(vm_0[:, :3],
vm_2[:, :3]))
if diff_0_1 > diff_0_2 + 6 and diff_1_2 > diff_0_2 + 6:
inds.append(i)
view_mats_slerp.append(interpolate_slerp(vm_0, vm_2))
point_cloud_builder.view_mats[np.array(
inds, dtype=np.int)] = np.array(view_mats_slerp).reshape((-1, 3, 4))
calc_point_cloud_colors(point_cloud_builder, rgb_sequence,
point_cloud_builder.view_mats, intrinsic_mat,
corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, point_cloud_builder.view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
triang_params = TriangulationParameters(
max_reprojection_error=MAX_PROJ_ERROR,
min_triangulation_angle_deg=MIN_TRIANG_ANG,
min_depth=MIN_DEPTH)
correspondences = build_correspondences(corner_storage[known_view_1[0]],
corner_storage[known_view_2[0]])
view_mat_1 = pose_to_view_mat3x4(known_view_1[1])
view_mat_2 = pose_to_view_mat3x4(known_view_2[1])
points, ids, median_cos = triangulate_correspondences(
correspondences, view_mat_1, view_mat_2, intrinsic_mat, triang_params)
if len(points) < 10:
print("\rPlease, try other frames")
exit(0)
view_mats = [None] * len(rgb_sequence)
view_mats[known_view_1[0]] = view_mat_1
view_mats[known_view_2[0]] = view_mat_2
point_cloud_builder = PointCloudBuilder(ids, points)
was_update = True
while was_update:
was_update = False
for i, (frame, corners) in enumerate(zip(rgb_sequence,
corner_storage)):
if view_mats[i] is not None:
continue
_, (idx_1,
idx_2) = snp.intersect(point_cloud_builder.ids.flatten(),
corners.ids.flatten(),
indices=True)
try:
retval, rvec, tvec, inliers = cv2.solvePnPRansac(
point_cloud_builder.points[idx_1],
corners.points[idx_2],
intrinsic_mat,
distCoeffs=None)
inliers = np.array(inliers, dtype=int)
if len(inliers) > 0:
view_mats[i] = rodrigues_and_translation_to_view_mat3x4(
rvec, tvec)
was_update = True
print(
f"\rFrame {i} out of {len(rgb_sequence)}, inliners: {len(inliers)}",
end="")
except Exception:
print(f"\rFrame {i} out of {len(rgb_sequence)}, inliners: {0}",
end="")
if view_mats[i] is None:
continue
cur_corner = filter_frame_corners(corner_storage[i],
inliers.flatten())
for j in range(len(rgb_sequence)):
if view_mats[j] is None:
continue
correspondences = build_correspondences(
corner_storage[j], cur_corner)
if len(correspondences.ids) == 0:
continue
points, ids, median_cos = triangulate_correspondences(
correspondences, view_mats[j], view_mats[i], intrinsic_mat,
triang_params)
point_cloud_builder.add_points(ids, points)
print()
first_mat = next((mat for mat in view_mats if mat is not None), None)
if first_mat is None:
print("\rFail")
exit(0)
view_mats[0] = first_mat
for i in range(1, len(view_mats)):
if view_mats[i] is None:
view_mats[i] = view_mats[i - 1]
view_mats = np.array(view_mats)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
frame_count = len(corner_storage)
view_mats = [None] * frame_count
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = init_first_camera_positions(
intrinsic_mat, corner_storage)
id_1, view_mat_1 = known_view_1
id_2, view_mat_2 = known_view_2
view_mats[id_1], view_mats[id_2] = view_mat_1, view_mat_2
else:
id_1, pose_1 = known_view_1
id_2, pose_2 = known_view_2
view_mats[id_1] = pose_to_view_mat3x4(pose_1)
view_mats[id_2] = pose_to_view_mat3x4(pose_2)
params = TriangulationParameters(max_reprojection_error=2,
min_triangulation_angle_deg=1,
min_depth=0.5)
correspondences = build_correspondences(corner_storage[id_1],
corner_storage[id_2])
points, ids, _ = triangulate_correspondences(correspondences,
view_mats[id_1],
view_mats[id_2],
intrinsic_mat, params)
point_cloud_builder = PointCloudBuilder(ids.reshape(-1), points)
while True:
update = False
for i in range(frame_count):
if view_mats[i] is not None:
continue
corners = corner_storage[i]
ids, ids1, ids2 = np.intersect1d(corners.ids,
point_cloud_builder.ids,
return_indices=True)
points_2d, points_3d = corners.points[
ids1], point_cloud_builder.points[ids2]
if len(ids) < 6:
continue
retval, rvec, tvec, inliers = solvePnPRansac(points_3d,
points_2d,
intrinsic_mat,
distCoeffs=None)
if not retval:
continue
retval, rvec, tvec = solvePnP(points_3d[inliers],
points_2d[inliers],
intrinsic_mat,
distCoeffs=None,
rvec=rvec,
tvec=tvec,
useExtrinsicGuess=True)
if not retval:
continue
view_mats[i] = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
update = True
outliers = np.delete(ids, inliers)
for j in range(i):
if view_mats[j] is None:
continue
correspondences = build_correspondences(corners,
corner_storage[j],
ids_to_remove=outliers)
points, ids, _ = triangulate_correspondences(
correspondences, view_mats[i], view_mats[j], intrinsic_mat,
params)
point_cloud_builder.add_points(ids.reshape(-1), points)
click.echo("Process frame %d/%d. %d 3D points found. inliners=%d" %
(i + 1, frame_count, len(
point_cloud_builder.points), len(inliers)))
if not update:
break
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
i_1 = known_view_1[0]
i_2 = known_view_2[0]
print('Known frames are', i_1, 'and', i_2)
global INLIERS_MIN_SIZE, DELTA, MIN_SIZE, TRIANG_PARAMS
view_mat3x4_1 = pose_to_view_mat3x4(known_view_1[1])
view_mat3x4_2 = pose_to_view_mat3x4(known_view_2[1])
correspondences = build_correspondences(corner_storage[i_1],
corner_storage[i_2])
print(len(correspondences.ids))
points3d, corr_ids, median_cos = triangulate_correspondences(
correspondences, view_mat3x4_1, view_mat3x4_2, intrinsic_mat,
TRIANG_PARAMS)
view_mats, point_cloud_builder = [view_mat3x4_1 for _ in corner_storage
], PointCloudBuilder(
corr_ids.astype(np.int64), points3d)
err_indexes = set(range(len(corner_storage)))
err_indexes.remove(i_1)
err_indexes.remove(i_2)
res_code, rodrig, inliers_1, cloud_points = get_ransac(
point_cloud_builder, corner_storage[i_1], intrinsic_mat)
view_mats[i_1] = rodrig
res_code, rodrig, inliers_2, cloud_points = get_ransac(
point_cloud_builder, corner_storage[i_2], intrinsic_mat)
view_mats[i_2] = rodrig
INLIERS_MIN_SIZE = min(len(inliers_1), len(inliers_2)) - 20 * max(
len(corner_storage) // 100, 1)
descr_template = 'Point cloud calc - {} inliers, {} points found, cloud size is {}'
tqdm_iterator = tqdm(range(len(corner_storage) - 2),
total=len(corner_storage),
desc=descr_template.format('?', '?', '?'))
params = [
corner_storage, view_mats, point_cloud_builder, intrinsic_mat,
err_indexes, descr_template, tqdm_iterator
]
DELTA = 5 * max(len(corner_storage) // 100, 1)
MIN_SIZE = 20 * max(len(corner_storage) // 100, 1)
TRIANG_PARAMS = TriangulationParameters(2, 1e-2, 1e-2)
for i in range(i_1 + 1, i_2):
update(i, *params)
print('Points between handled')
DELTA = 20 * max(len(corner_storage) // 100, 1)
MIN_SIZE = 20 * max(len(corner_storage) // 100, 1)
TRIANG_PARAMS = TriangulationParameters(4, 1e-2, 1e-2)
for i in range(i_1, -1, -1):
if i in err_indexes:
update(i, *params)
for i in range(i_2, len(corner_storage)):
if i in err_indexes:
update(i, *params)
for i in range(i_1, -1, -1):
if i in err_indexes:
view_mats[i] = view_mats[i + 1]
for i in range(i_2, len(corner_storage)):
if i in err_indexes:
view_mats[i] = view_mats[i - 1]
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
view_mats = np.zeros((len(corner_storage), 3, 4), dtype=np.float64)
processed_frames = np.zeros(len(corner_storage), dtype=np.bool)
points3d = np.zeros((corner_storage.max_corner_id() + 1, 3),
dtype=np.float64)
added_points = np.zeros(corner_storage.max_corner_id() + 1, dtype=np.bool)
print('Trying to extract 3d points from known frames...')
view_mats[known_view_1[0]] = pose_to_view_mat3x4(known_view_1[1])
view_mats[known_view_2[0]] = pose_to_view_mat3x4(known_view_2[1])
processed_frames[known_view_1[0]] = processed_frames[
known_view_2[0]] = True
extracted_points = extract_points(corner_storage[known_view_1[0]],
corner_storage[known_view_2[0]],
view_mats[known_view_1[0]],
view_mats[known_view_2[0]],
intrinsic_mat)
if not extracted_points:
print(
'Extracting 3d points from known frames failed: '
'either there are no common points, or triangulation angle between frames is too small.\n'
'Try to choose another initial frames.',
file=sys.stderr)
exit(0)
print('Succeeded! Trying to build point cloud...')
added_points[extracted_points[0]] = True
points3d[extracted_points[0]] = extracted_points[1]
was_updated = True
while was_updated:
was_updated = False
unprocessed_frames = np.arange(len(corner_storage),
dtype=np.int32)[~processed_frames]
for frame in unprocessed_frames:
points3d_ids = np.arange(corner_storage.max_corner_id() + 1,
dtype=np.int32)[added_points]
common, (indices1, indices2) = snp.intersect(
points3d_ids,
corner_storage[frame].ids.flatten(),
indices=True)
if len(common) <= 5:
continue
try:
print(f'Processing frame {frame}: ', end='')
retval, rvec, tvec, inliers = cv2.solvePnPRansac(
objectPoints=points3d[common],
imagePoints=corner_storage[frame].points[indices2],
cameraMatrix=intrinsic_mat,
distCoeffs=None)
except:
retval = False
if not retval:
print(f'failed to solve PnP RANSAC, trying another frame')
continue
print(f'succeeded, found {len(inliers)} inliers')
was_updated = True
view_mats[frame] = rodrigues_and_translation_to_view_mat3x4(
rvec, tvec)
for processed_frame in np.arange(len(corner_storage),
dtype=np.int32)[processed_frames]:
extracted_points = extract_points(
corner_storage[frame], corner_storage[processed_frame],
view_mats[frame], view_mats[processed_frame],
intrinsic_mat, points3d_ids)
if extracted_points:
added_points[extracted_points[0]] = True
points3d[extracted_points[0]] = extracted_points[1]
processed_frames[frame] = True
print(f'Current point cloud contains {sum(added_points)} points')
for _ in range(2):
for i in range(1, len(corner_storage)):
if not processed_frames[i]:
processed_frames[i] = True
view_mats[i] = view_mats[i - 1]
processed_frames = processed_frames[::-1]
view_mats = view_mats[::-1]
print(
f'Finished building point cloud, now it contains {sum(added_points)} points'
)
point_cloud_builder = PointCloudBuilder(ids=np.arange(
corner_storage.max_corner_id() + 1, dtype=np.int32)[added_points],
points=points3d[added_points])
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(
camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None
) -> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = initial_camera_views(
corner_storage, intrinsic_mat)
print(known_view_1[1])
print(known_view_2[1])
print(known_view_1[0], known_view_2[0])
print(intrinsic_mat)
points_3d, points_ids = calc_starting_points(intrinsic_mat, corner_storage,
known_view_1, known_view_2)
#initial_points_3d, initial_points_ids = points_3d.copy(), points_ids.copy()
n_points = corner_storage.max_corner_id() + 1
res_points_3d = np.full((n_points, 3), None)
#res_points_3d[points_ids] = points_3d
view_mats = [np.full((3, 4), None) for _ in range(len(corner_storage))]
view_mats[known_view_1[0]], view_mats[
known_view_2[0]] = pose_to_view_mat3x4(
known_view_1[1]), pose_to_view_mat3x4(known_view_2[1])
print(f'len(corner_storage):{len(corner_storage)}')
id1 = known_view_1[0]
for n_frame, corners in enumerate(corner_storage[id1 + 1:], id1 + 1):
common_obj, common_img, common_ids = get_common_points(
corners, points_ids, points_3d)
mat, points_3d, points_ids, reprojection_error = build_view_mat(
common_obj, common_img, common_ids, intrinsic_mat)
points_ids = np.array(points_ids)
view_mats[n_frame] = mat
is_null = np.array([(x[0] is None) for x in res_points_3d[points_ids]])
#res_points_3d[points_ids[is_null]] = points_3d[is_null]
n_inliers = len(points_3d)
points_3d, points_ids = calc_new_points(n_frame, intrinsic_mat,
corner_storage, view_mats,
points_3d, points_ids)
print(
f"Processing frame #{n_frame}. Number of inliers: {n_inliers}. "
f"Reprojection error: {reprojection_error}. Tracking points: {len(common_img)}"
)
for n_frame, corners in list(enumerate(corner_storage))[::-1]:
common_obj, common_img, common_ids = get_common_points(
corners, points_ids, points_3d)
mat, points_3d, points_ids, reprojection_error = build_view_mat(
common_obj, common_img, common_ids, intrinsic_mat)
view_mats[n_frame] = mat
is_null = np.array([(x[0] is None) for x in res_points_3d[points_ids]])
#res_points_3d[points_ids[is_null]] = points_3d[is_null]
n_inliers = len(points_3d)
points_3d, points_ids = calc_new_points(n_frame, intrinsic_mat,
corner_storage, view_mats,
points_3d, points_ids)
print(
f"Processing frame #{n_frame}. Number of inliers: {n_inliers}. "
f"Reprojection error: {reprojection_error}. Tracking points: {len(common_img)}"
)
# Approximating
n_iter = 1
for iter in range(n_iter):
for n_frame, corners in enumerate(corner_storage):
common_obj, common_img, common_ids = get_common_points(
corners, points_ids, points_3d)
mat, points_3d, points_ids, reprojection_error = build_view_mat(
common_obj, common_img, common_ids, intrinsic_mat)
view_mats[n_frame] = mat
if iter == n_iter - 1:
res_points_3d[points_ids] = points_3d
n_inliers = len(points_3d)
points_3d, points_ids = calc_new_points(n_frame, intrinsic_mat,
corner_storage, view_mats,
points_3d, points_ids)
print(
f"Processing frame #{n_frame}. Number of inliers: {n_inliers}. "
f"Reprojection error: {reprojection_error}. Tracking points: {len(common_img)}"
)
for n_frame, corners in list(enumerate(corner_storage))[::-1]:
common_obj, common_img, common_ids = get_common_points(
corners, points_ids, points_3d)
mat, points_3d, points_ids, reprojection_error = build_view_mat(
common_obj, common_img, common_ids, intrinsic_mat)
view_mats[n_frame] = mat
if iter == n_iter - 1:
res_points_3d[points_ids] = points_3d
n_inliers = len(points_3d)
points_3d, points_ids = calc_new_points(n_frame, intrinsic_mat,
corner_storage, view_mats,
points_3d, points_ids)
print(
f"Processing frame #{n_frame}. Number of inliers: {n_inliers}. "
f"Reprojection error: {reprojection_error}. Tracking points: {len(common_img)}"
)
res_points_ids = np.array(
[i for i, x in enumerate(res_points_3d) if x[0] is not None])
res_points_3d = np.array(res_points_3d[res_points_ids], dtype=float)
point_cloud_builder = PointCloudBuilder(ids=res_points_ids,
points=res_points_3d)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
frames_num = len(rgb_sequence)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = select_frames(rgb_sequence,
corner_storage,
intrinsic_mat)
if known_view_2[0] == -1:
print("Failed to find good starting frames")
exit(0)
correspondences = build_correspondences(corner_storage[known_view_1[0]],
corner_storage[known_view_2[0]])
view_mat1 = pose_to_view_mat3x4(known_view_1[1])
view_mat2 = pose_to_view_mat3x4(known_view_2[1])
points, ids, _ = triangulate_correspondences(correspondences, view_mat1,
view_mat2, intrinsic_mat,
TRIANG_PARAMS)
if len(ids) < 8:
print("Bad frames: too few correspondences!")
exit(0)
view_mats, point_cloud_builder = [None] * frames_num, PointCloudBuilder(
ids, points)
view_mats[known_view_1[0]] = view_mat1
view_mats[known_view_2[0]] = view_mat2
updated = True
pass_num = 0
while updated:
updated = False
pass_num += 1
for i in range(frames_num):
if view_mats[i] is not None:
continue
corners = corner_storage[i]
_, ind1, ind2 = np.intersect1d(point_cloud_builder.ids,
corners.ids.flatten(),
return_indices=True)
try:
_, rvec, tvec, inliers = cv2.solvePnPRansac(
point_cloud_builder.points[ind1],
corners.points[ind2],
intrinsic_mat,
distCoeffs=None)
if inliers is None:
print(f"Pass {pass_num}, frame {i}. No inliers!")
continue
print(
f"Pass {pass_num}, frame {i}. Number of inliers == {len(inliers)}"
)
view_mats[i] = rodrigues_and_translation_to_view_mat3x4(
rvec, tvec)
except Exception:
continue
updated = True
cloud_changed = add_points(point_cloud_builder, corner_storage, i,
view_mats, intrinsic_mat)
if cloud_changed:
print(f"Size of point cloud == {len(point_cloud_builder.ids)}")
first_not_none = next(item for item in view_mats if item is not None)
if view_mats[0] is None:
view_mats[0] = first_not_none
for i in range(frames_num):
if view_mats[i] is None:
view_mats[i] = view_mats[i - 1]
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
view_mats_tmp = [None for _ in range(len(corner_storage))]
all_points = {}
not_none_mats = [known_view_1[0], known_view_2[0]]
none_mats = set(
[ind for ind in range(len(view_mats_tmp)) if ind not in not_none_mats])
view_mats_tmp[known_view_1[0]] = pose_to_view_mat3x4(known_view_1[1])
view_mats_tmp[known_view_2[0]] = pose_to_view_mat3x4(known_view_2[1])
add_new_points(corner_storage, view_mats_tmp, known_view_1[0],
known_view_2[0], all_points, intrinsic_mat)
old_len = 2
for _ in range(len(view_mats_tmp)):
for ind in none_mats:
corner_ids = list(corner_storage[ind].ids.reshape(-1))
intersection_inds = [
point_ind for point_ind in corner_ids if
point_ind in all_points and all_points[point_ind] is not None
]
# There is an assert, so points number has to be at least 5
if len(intersection_inds) < 5:
continue
# print(ind, len(corner_storage), len(corner_storage[ind].points), np.max(intersection_inds))
corner_points = {
i: p
for i, p in zip(corner_ids, corner_storage[ind].points)
}
intersection_points_c = np.array(
[corner_points[i] for i in intersection_inds])
intersection_points_f = np.array(
[all_points[i] for i in intersection_inds])
# print(intersection_points_f)
# print(intersection_points_f.shape, intersection_points_c.shape, intrinsic_mat)
retval, rvec, tvec, inliers = cv2.solvePnPRansac(
intersection_points_f, intersection_points_c, intrinsic_mat,
None)
if not retval:
continue
print(f'Processing frame: {ind}')
newly_none_number = 0
for i in intersection_inds:
if i not in inliers:
newly_none_number += 1
all_points[i] = None
print(
f'{newly_none_number} points filled as None, len of inliers: {inliers.shape[0]}'
)
print(
f'Number of not points: {len([p for p in all_points.keys() if all_points[p] is not None])}'
)
view_mats_tmp[ind] = rodrigues_and_translation_to_view_mat3x4(
rvec, tvec)
for not_none_ind in not_none_mats:
add_new_points(corner_storage, view_mats_tmp, ind,
not_none_ind, all_points, intrinsic_mat)
not_none_mats.append(ind)
none_mats.remove(ind)
break
if len(not_none_mats) == old_len:
break
old_len = len(not_none_mats)
view_mats = [None for _ in range(len(corner_storage))]
for view_mat_ind in not_none_mats:
view_mats[view_mat_ind] = view_mats_tmp[view_mat_ind]
last_ind = 0
for i in range(len(view_mats)):
if view_mats[i] is None:
view_mats[i] = view_mats[last_ind]
else:
last_ind = i
all_points = {k: v for k, v in all_points.items() if v is not None}
point_cloud_builder = PointCloudBuilder(
np.array(list(all_points.keys()), dtype=np.int),
np.array(list(all_points.values())))
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(
camera_parameters,
rgb_sequence[0].shape[0]
)
# TODO: implement
frame_count = len(corner_storage)
view_mats = [None] * frame_count
view_1_id, view_1 = known_view_1[0], pose_to_view_mat3x4(known_view_1[1])
view_2_id, view_2 = known_view_2[0], pose_to_view_mat3x4(known_view_2[1])
view_mats[view_1_id] = view_1
view_mats[view_2_id] = view_2
correspondences = build_correspondences(corner_storage[view_1_id], corner_storage[view_2_id], None)
triangulation_parameters = TriangulationParameters(1, 5, 0)
points, ids, _ = triangulate_correspondences(correspondences, view_1, view_2,
intrinsic_mat, TriangulationParameters(0.1, 0.1, 0))
#corners_0 = corner_storage[0]
point_cloud_builder = PointCloudBuilder(ids, points)
unknown_frames = frame_count - 2
known_frames = {view_1_id, view_2_id}
while unknown_frames > 0:
for i in range(frame_count):
if view_mats[i] is None:
corners = corner_storage[i]
interesting_ids, in_corners, in_cloud = np.intersect1d(corners.ids.flatten(), point_cloud_builder.ids.flatten(), return_indices=True)
points_2d = corners.points[in_corners]
points_3d = point_cloud_builder.points[in_cloud]
if len(ids) < 3:
continue
method = cv2.SOLVEPNP_EPNP
if len(ids) == 3:
method = cv2.SOLVEPNP_P3P
retval, rvec, tvec, inliers = cv2.solvePnPRansac(points_3d, points_2d, intrinsic_mat, None,
flags=method)
if not retval:
continue
retval, rvec, tvec = cv2.solvePnP(points_3d[inliers], points_2d[inliers], intrinsic_mat, None,
rvec=rvec, tvec=tvec, useExtrinsicGuess=True)
if not retval:
continue
view_mats[i] = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
unknown_frames -= 1
known_frames.add(i)
outliers = np.delete(interesting_ids, inliers)
for frame in known_frames:
correspondences = build_correspondences(corners, corner_storage[frame], outliers)
points_3d, corner_ids, _ = triangulate_correspondences(
correspondences, view_mats[i], view_mats[frame], intrinsic_mat, triangulation_parameters)
point_cloud_builder.add_points(corner_ids, points_3d)
calc_point_cloud_colors(
point_cloud_builder,
rgb_sequence,
view_mats,
intrinsic_mat,
corner_storage,
5.0
)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(
camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None
) -> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = calculate_known_views(
intrinsic_mat, corner_storage)
print('Calculated known views: {} and {}'.format(
known_view_1[0], known_view_2[0]))
random.seed(239)
view_mats = np.full((len(rgb_sequence), ), None)
view_mats[known_view_1[0]] = pose_to_view_mat3x4(known_view_1[1])
view_mats[known_view_2[0]] = pose_to_view_mat3x4(known_view_2[1])
point_cloud_builder = PointCloudBuilder()
def triangulate_and_add_points(corners1, corners2, view_mat1, view_mat2):
points3d, ids, median_cos = triangulate_correspondences(
build_correspondences(corners1, corners2), view_mat1, view_mat2,
intrinsic_mat, triang_params)
point_cloud_builder.add_points(ids, points3d)
triangulate_and_add_points(corner_storage[known_view_1[0]],
corner_storage[known_view_2[0]],
view_mats[known_view_1[0]],
view_mats[known_view_2[0]])
inliers_points3d, inliers_points2d = None, None
def find_errs(rtvecs):
rvecs, tvecs = rtvecs[:3], rtvecs[3:]
rmat, _ = cv2.Rodrigues(np.expand_dims(rvecs, axis=1))
rmat = np.concatenate((rmat, np.expand_dims(tvecs, axis=1)), axis=1)
return (project_points(inliers_points3d, intrinsic_mat @ rmat) -
inliers_points2d).flatten()
while True:
random_ids = list(range(len(view_mats)))
random.shuffle(random_ids)
found_new_view_mat = False
for i in random_ids:
if view_mats[i] is not None:
continue
common, (ids1,
ids2) = snp.intersect(point_cloud_builder.ids.flatten(),
corner_storage[i].ids.flatten(),
indices=True)
if len(common) <= 10:
continue
points3d = point_cloud_builder.points[ids1]
points2d = corner_storage[i].points[ids2]
retval, rvecs, tvecs, inliers = cv2.solvePnPRansac(
points3d,
points2d,
intrinsic_mat,
iterationsCount=108,
reprojectionError=triang_params.max_reprojection_error,
distCoeffs=None,
confidence=0.999,
flags=cv2.SOLVEPNP_EPNP)
if retval:
# M-оценки
inliers_points3d = points3d[inliers.flatten()]
inliers_points2d = points2d[inliers.flatten()]
rtvecs = least_squares(find_errs,
x0=np.concatenate(
(rvecs, tvecs)).flatten(),
loss='huber',
method='trf').x
rvecs = rtvecs[:3].reshape((-1, 1))
tvecs = rtvecs[3:].reshape((-1, 1))
if not retval:
continue
print(
'Iteration {}/{}, processing {}th frame: {} inliers, {} points in point cloud'
.format(
len([v for v in view_mats if v is not None]) - 1,
len(rgb_sequence) - 2, i, len(inliers),
len(point_cloud_builder.points)))
view_mats[i] = rodrigues_and_translation_to_view_mat3x4(
rvecs, tvecs)
found_new_view_mat = True
# Add new points
inliers = np.array(inliers).astype(int).flatten()
inlier_corners = FrameCorners(
*[c[inliers] for c in corner_storage[i]])
for j in range(len(view_mats)):
if view_mats[j] is not None:
triangulate_and_add_points(corner_storage[j],
inlier_corners, view_mats[j],
view_mats[i])
sys.stdout.flush()
if not found_new_view_mat:
break
for i in range(0, len(view_mats)):
if view_mats[i] is None:
print('Я сдох: не все кадры обработаны :(')
exit(1)
if len(view_mats) < 100: # Иначе долго работает
view_mats = bundle_adjustment(view_mats, point_cloud_builder,
intrinsic_mat, corner_storage)
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = calc_known_views(corner_storage,
intrinsic_mat)
# TODO: implement
num_frames_retr = 10
triang_params = TriangulationParameters(8.0, 1.0, .1)
num_frames = len(corner_storage)
frame_1 = known_view_1[0]
frame_2 = known_view_2[0]
print(f'{frame_1}, {frame_2}')
tvecs = [None] * num_frames
tvecs[frame_1] = known_view_1[1].t_vec
tvecs[frame_2] = known_view_2[1].t_vec
view_mat_1 = pose_to_view_mat3x4(known_view_1[1])
view_mat_2 = pose_to_view_mat3x4(known_view_2[1])
view_mats = [None] * num_frames
view_mats[frame_1] = view_mat_1
view_mats[frame_2] = view_mat_2
cloud = {}
corrs = build_correspondences(corner_storage[frame_1],
corner_storage[frame_2])
points3d, ids, _ = triangulate_correspondences(corrs, view_mat_1,
view_mat_2, intrinsic_mat,
triang_params)
for point3d, id in zip(points3d, ids):
cloud[id] = point3d
current_corners_occurences = {}
corner_to_frames = {}
for i, corners in enumerate(corner_storage):
for j, id in enumerate(corners.ids.flatten()):
current_corners_occurences[id] = 0
if id not in corner_to_frames.keys():
corner_to_frames[id] = [[i, j]]
else:
corner_to_frames[id].append([i, j])
while len(untracked_frames(view_mats)) > 0:
untr_frames = untracked_frames(view_mats)
max_num_inl = -1
best_frame = -1
best_rvec = None
best_tvec = None
for frame in untr_frames:
rvec, tvec, num_inl = calc_camera_pose(frame, corner_storage,
cloud, intrinsic_mat)
if rvec is not None:
if num_inl > max_num_inl:
best_frame = frame
max_num_inl = num_inl
best_rvec = rvec
best_tvec = tvec
if max_num_inl == -1:
break
corners_to_add = []
for id in corner_storage[best_frame].ids.flatten():
if id in current_corners_occurences.keys():
current_corners_occurences[id] += 1
if current_corners_occurences[id] >= 2:
corners_to_add.append(id)
for corner in corners_to_add:
add_new_point(corner, corner_to_frames, view_mats, tvecs,
corner_storage, cloud, current_corners_occurences,
intrinsic_mat)
print(f"Frame: {best_frame}, Inliers: {max_num_inl}")
print(f"Cloud size: {len(cloud)}")
view_mats[best_frame] = rodrigues_and_translation_to_view_mat3x4(
best_rvec, best_tvec)
tvecs[best_frame] = best_tvec
last_mat = None
for frame in range(num_frames):
if view_mats[frame] is None:
view_mats[frame] = last_mat
else:
last_mat = view_mats[frame]
ids = []
points = []
for id in cloud.keys():
ids.append(id)
points.append(cloud[id])
point_cloud_builder = PointCloudBuilder(np.array(ids, dtype=np.int),
np.array(points))
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
if known_view_1 is None or known_view_2 is None:
raise NotImplementedError()
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
frames_cnt = len(rgb_sequence)
unused = np.array([
i for i in range(0, frames_cnt)
if i not in [known_view_1[0], known_view_2[0]]
])
used = np.array([known_view_1[0], known_view_2[0]])
used_pose = [known_view_1[1], known_view_2[1]]
points, ids, cos = triangulate(known_view_1[0], known_view_2[0],
known_view_1[1], known_view_2[1],
corner_storage, intrinsic_mat, None)
point_cloud_builder = PointCloudBuilder(ids, points)
# print("Add frames", *known_ids)
while unused.shape[0] > 0:
added = []
for i in range(len(unused)):
pose_i, ids_to_remove = camera_pose(unused[i], corner_storage,
point_cloud_builder,
intrinsic_mat)
if pose_i is None:
continue
for j in range(len(used)):
points, ids, cos = triangulate(unused[i], used[j], pose_i,
used_pose[j], corner_storage,
intrinsic_mat, ids_to_remove)
point_cloud_builder.add_points(ids, points)
used = np.append(used, [unused[i]])
used_pose.append(pose_i)
added.append(unused[i])
# print("Frame", unused[i], "done!!")
if len(added) == 0:
break
unused = np.setdiff1d(unused, added)
used_pose = recalculate_poses(used, point_cloud_builder,
corner_storage, intrinsic_mat)
view_mats = [None for i in range(frames_cnt)]
for i in range(len(used)):
view_mats[used[i]] = pose_to_view_mat3x4(used_pose[i])
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
if known_view_1 is None or known_view_2 is None:
known_view_1, known_view_2 = get_matrix_poses(corner_storage,
intrinsic_mat)
video_size = len(rgb_sequence)
frame_trackers = [
FrameTrack(i, corner_storage[i]) for i in range(video_size)
]
known_tracker_creator = lambda x: FrameTrack(x[0], corner_storage[x[0]],
pose_to_view_mat3x4(x[1]))
frame_trackers[known_view_1[0]] = known_tracker_creator(known_view_1)
frame_trackers[known_view_2[0]] = known_tracker_creator(known_view_2)
init_params = triang_params
for angle in range(90, 0, -2):
params = TriangulationParameters(
max_reprojection_error=MAX_REPROJECTION_ERROR,
min_triangulation_angle_deg=angle,
min_depth=0.001)
_, points = triangulate_trackers(frame_trackers[known_view_1[0]],
frame_trackers[known_view_2[0]],
intrinsic_mat, params)
if len(points) > 100:
print(f"Chosen init angle: {angle}")
init_params = params
break
ids, points = triangulate_trackers(frame_trackers[known_view_1[0]],
frame_trackers[known_view_2[0]],
intrinsic_mat, init_params)
point_cloud_builder = PointCloudBuilder(ids, points)
if len(points) < MIN_STARTING_POINTS:
print(
f"Not enough starting points ({len(points)}), please choose another initial frames pair"
f"\n0, 20 is a good pair for short fox, ")
return [], PointCloudBuilder()
frame_trackers[known_view_1[0]].update_reproj_error(
point_cloud_builder, intrinsic_mat)
frame_trackers[known_view_2[0]].update_reproj_error(
point_cloud_builder, intrinsic_mat)
point_cloud_builder = track(ITERATIONS, frame_trackers,
point_cloud_builder, intrinsic_mat)
view_mats = [x.mtx for x in frame_trackers]
for i in range(1, len(view_mats)):
if view_mats[i] is None:
view_mats[i] = view_mats[i - 1]
for i in range(len(view_mats) - 2, -1, -1):
if view_mats[i] is None:
view_mats[i] = view_mats[i + 1]
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
def track_and_calc_colors(camera_parameters: CameraParameters,
corner_storage: CornerStorage,
frame_sequence_path: str,
known_view_1: Optional[Tuple[int, Pose]] = None,
known_view_2: Optional[Tuple[int, Pose]] = None) \
-> Tuple[List[Pose], PointCloud]:
# функция для проверки что матрица поворота дейтсвительно матрица поворота
def check_if_mat_is_rot_mat(mat):
mat = mat[:, :3]
det = np.linalg.det(mat)
# определитель должен быть = 1
diff1 = abs(1 - det)
x = mat @ mat.T
# обратная матрица должна получаться транспонированием
diff2 = abs(np.sum(np.eye(3) - x))
eps = 0.001
return diff1 < eps and diff2 < eps
MAX_REPROJECTION_ERROR = 1.6
MIN_DIST_TRIANGULATE = 0.01
rgb_sequence = frameseq.read_rgb_f32(frame_sequence_path)
intrinsic_mat = to_opencv_camera_mat3x3(camera_parameters,
rgb_sequence[0].shape[0])
frame_count = len(corner_storage)
t_vecs = [None] * frame_count
view_mats = [None] * frame_count # позиции камеры на всех кадрах
# для каждого уголка находим все кадры на котором он есть и его порядковый номер в каждом из них
cur_corners_occurencies = {}
frames_of_corner = {}
for i, corners in enumerate(corner_storage):
for id_in_list, j in enumerate(corners.ids.flatten()):
cur_corners_occurencies[j] = 0
if j not in frames_of_corner.keys():
frames_of_corner[j] = [[i, id_in_list]]
else:
frames_of_corner[j].append([i, id_in_list])
#по двум кадрам находим общие точки и восстанавливаем их позиции в 3D
def triangulate(frame_0, frame_1, params=TriangulationParameters(2, 1e-3, 1e-4), ids_to_remove=None) \
-> Tuple[np.ndarray, np.ndarray, float]:
corrs = build_correspondences(corner_storage[frame_0],
corner_storage[frame_1])
return triangulate_correspondences(corrs, view_mats[frame_0],
view_mats[frame_1], intrinsic_mat,
params)
#константы для инициализации
BASELINE_THRESHOLD = 0.9
REPROJECTION_ERROR_THRESHOLD = 1.4
MIN_3D_POINTS = 10
frame_steps = [9, 30, 40]
#нахождение относительного движения между двумя данными кадрами
def get_first_cam_pose(frame_0, frame_1):
corrs = build_correspondences(corner_storage[frame_0],
corner_storage[frame_1])
e_mat, _ = cv2.findEssentialMat(corrs.points_1, corrs.points_2,
intrinsic_mat, cv2.RANSAC, 0.999, 2.0)
def essential_mat_to_camera_view_matrix(e_mat):
U, S, VH = np.linalg.svd(e_mat, full_matrices=True)
W = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]], dtype=np.float)
u3 = U[:, 2]
possible_R = [U @ W @ VH, U @ W.T @ VH]
possible_t = [u3, -u3]
best_R = None
best_t = None
max_positive_z = 0
for R in possible_R:
for t in possible_t:
view_mat0 = np.hstack((np.eye(3), np.zeros((3, 1))))
view_mat1 = np.hstack((R, t.reshape((3, 1))))
view_mats[frame_0] = view_mat0
view_mats[frame_1] = view_mat1
points3d, ids, median_cos = triangulate(frame_0, frame_1)
cur_positive_z = 0
for pt in points3d:
pt = np.append(pt, np.zeros((1))).reshape((4, 1))
pt_transformed0 = (view_mat0 @ pt).flatten()
z0 = pt_transformed0[2]
pt_transformed1 = (view_mat1 @ pt).flatten()
z1 = pt_transformed1[2]
cur_positive_z += (z0 > 0.1) + (z1 > 0.1)
if cur_positive_z > max_positive_z:
max_positive_z = cur_positive_z
best_R = R
best_t = t
return best_R, best_t
R, t = essential_mat_to_camera_view_matrix(e_mat)
baseline = np.linalg.norm(t)
view_mat0 = np.hstack((np.eye(3), np.zeros((3, 1))))
view_mat1 = np.hstack((R, t.reshape((3, 1))))
view_mats[frame_0] = view_mat0
view_mats[frame_1] = view_mat1
points3d, ids, median_cos = triangulate(frame_0, frame_1)
points0 = []
points1 = []
for i in ids:
for j in frames_of_corner[i]:
if j[0] == frame_0:
points0.append(j[1])
if j[0] == frame_1:
points1.append(j[1])
points0 = np.array(points0)
points1 = np.array(points1)
sum_error = compute_reprojection_errors(
points3d, corner_storage[frame_0].points[points0],
intrinsic_mat @ view_mat0) + compute_reprojection_errors(
points3d, corner_storage[frame_1].points[points1],
intrinsic_mat @ view_mat1)
reprojection_error = np.mean(sum_error)
points3d_count = len(points3d)
view_mats[frame_0] = None
view_mats[frame_1] = None
#проверяем насколько хороша данная пара кадров, и заодно проверяем что матрица R - реально поворот
if baseline < BASELINE_THRESHOLD or reprojection_error > REPROJECTION_ERROR_THRESHOLD or points3d_count < MIN_3D_POINTS or not check_if_mat_is_rot_mat(
np.hstack((R, t.reshape((3, 1))))):
return False, baseline, reprojection_error, points3d_count, None, None
else:
return True, baseline, reprojection_error, points3d_count, R, t
#делаем перебор пар кадров для инициализации
INF = 1e9
best_frame_step = 5
best = [-INF, INF, -INF]
ind = None
for frame_step in frame_steps:
for i in range(frame_count // 2):
if i + frame_step < frame_count * 0.85:
ok, baseline, reproection_error, points3d_count, Rx, tx = get_first_cam_pose(
i, i + frame_step)
if ok:
best = [baseline, reproection_error, points3d_count]
ind = i
best_frame_step = frame_step
if frame_count > 100:
break
# крашаемся, если не удалось инициализироваться ни по какой паре кадров
if ind is None:
raise NotImplementedError()
#инициализируемся по лучшей найденной паре
frame_0 = ind
frame_1 = ind + best_frame_step
ok, baseline, reproection_error, points3d_count, R, t = get_first_cam_pose(
frame_0, frame_1)
print('INITIALIZATION RESULTS:')
print('FRAMES: frame_0={}, frame_1={}, total_frames={}'.format(
frame_0, frame_1, frame_count))
print(baseline, reproection_error, points3d_count, R, t)
view_mat0 = np.hstack((np.eye(3), np.zeros((3, 1))))
view_mat1 = np.hstack((R, t.reshape((3, 1))))
view_mats[frame_0] = view_mat0
view_mats[frame_1] = view_mat1
t_vecs[frame_0] = np.zeros((3), dtype=np.float64)
t_vecs[frame_1] = t
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# зачем хранить инфу о пендинг точках вперемешку с остальными точками облака???
# 'id': coords [., ., .] 3d coordinates, rays [(frame, coords)] - for new points addition
points_cloud = {}
# инициализируем облако точек по 2 положениям
points3d, ids, median_cos = triangulate(frame_0, frame_1)
for id, point3d in zip(ids, points3d):
points_cloud[id] = point3d
def find_camera_pose(frame_id):
# сначала находим инлаеры ранзаком
# потом на инлаерах делаем пнп с итеративной мин кв
corners = corner_storage[frame_id]
points3d = []
points2d = []
for id, point2d in zip(corners.ids.flatten(), corners.points):
if id in points_cloud.keys():
points3d.append(points_cloud[id])
points2d.append(point2d)
# чтобы епнп работал
points3d = np.array(points3d, dtype=np.float64)
points2d = np.array(points2d, dtype=np.float64)
if len(points3d) < 4:
return None
# выбираем эталонную 4 точек, строим по ней R и t
# затем смотрим, на какой выборке эталонных точек больше всего других точек
# спроецируются корректно
success, R, t, inliers = cv2.solvePnPRansac(
objectPoints=points3d,
imagePoints=points2d,
cameraMatrix=intrinsic_mat,
distCoeffs=None,
flags=cv2.SOLVEPNP_EPNP,
confidence=0.9995,
reprojectionError=MAX_REPROJECTION_ERROR)
if not success:
return None
inliers = np.asarray(inliers, dtype=np.int32).flatten()
points3d = np.array(points3d)
points2d = np.array(points2d)
points3d = points3d[inliers]
points2d = points2d[inliers]
retval, R, t = cv2.solvePnP(objectPoints=points3d,
imagePoints=points2d,
cameraMatrix=intrinsic_mat,
distCoeffs=None,
flags=cv2.SOLVEPNP_ITERATIVE,
useExtrinsicGuess=True,
rvec=R,
tvec=t)
return R, t, len(inliers)
# можно ускорить
def frames_without_pose():
frames = []
for ind, mat in enumerate(view_mats):
if mat is None:
frames.append(ind)
return frames
def try_add_new_point_to_cloud(corner_id):
# все фреймы на которых есть данный уголок, и у которых уже найдена view_mat
# а можно для уголка хранить не все фреймы где он есть, а только те, где он инлаер!
frames = []
for frame in frames_of_corner[corner_id]:
if view_mats[frame[0]] is not None:
frames.append(frame)
if len(frames) < 2:
return
max_dist = 0
best_frames = [None, None]
best_ids = [None, None]
# можно ускорить хотя бы вдвое
for frame_1 in frames:
for frame_2 in frames:
if frame_1 == frame_2:
continue
# эм, по идее это не возможно, когда есть view_mat есть и t_vec
if t_vecs[frame_1[0]] is None or t_vecs[frame_2[0]] is None:
continue
t_vec_1 = t_vecs[frame_1[0]]
t_vec_2 = t_vecs[frame_2[0]]
dist = np.linalg.norm(t_vec_1 - t_vec_2)
if max_dist < dist:
max_dist = dist
best_frames = [frame_1[0], frame_2[0]]
best_ids = [frame_1[1], frame_2[1]]
if max_dist > MIN_DIST_TRIANGULATE:
ids = np.array([corner_id])
points1 = corner_storage[best_frames[0]].points[np.array(
[best_ids[0]])]
points2 = corner_storage[best_frames[1]].points[np.array(
[best_ids[1]])]
# что за ужасный синтаксис??????
corrs = corrs = Correspondences(ids, points1, points2)
points3d, ids, med_cos = triangulate_correspondences(
corrs,
view_mats[best_frames[0]],
view_mats[best_frames[1]],
intrinsic_mat,
parameters=TriangulationParameters(2, 1e-3, 1e-4))
# зачем проверка??? зачем делаем поп?????
if len(points3d) > 0:
points_cloud[ids[0]] = points3d[0]
cur_corners_occurencies.pop(ids[0], None)
# в этой функции мы еще бонусом возвращаем те точки, которые встречаются хотя бы 2 раза
# а также мы добавляем оккуранси тем точкам которые уже в облаке))))))))))))))))))))))))))))
def add_corners_occurencies(frame_id):
two_and_greater = []
for id in corner_storage[frame_id].ids.flatten():
if id in cur_corners_occurencies.keys():
cur_corners_occurencies[id] += 1
if cur_corners_occurencies[id] >= 2:
two_and_greater.append(id)
return two_and_greater
# очень эффективно))))))))))
while len(frames_without_pose()) > 0:
# пока есть не найденные положения камеры будем искать лучшее для восстановления
# т.е. такое, где больше всего инлаеров после применения пнп ранзака
wanted_frames = frames_without_pose()
# inliers_amount = [] не понял зачем этот лист
max_col = -1
best_frame_id = -1
best_R = None
best_t = None
for frame_id in wanted_frames:
result = find_camera_pose(frame_id)
if result is not None:
R, t, col = result
if col > max_col:
max_col = col
best_frame_id = frame_id
best_R = R
best_t = t
if max_col == -1:
# больше позиции камер не находятся
break
view_mats[best_frame_id] = rodrigues_and_translation_to_view_mat3x4(
best_R, best_t)
t_vecs[best_frame_id] = best_t
try_add = add_corners_occurencies(best_frame_id)
for corner in try_add:
try_add_new_point_to_cloud(corner)
print(
'Now we add camera pose on {}-th frame, camera pose was calculated by {} inliers'
.format(best_frame_id, max_col))
ttl_poses_calculated = np.sum([mat is not None for mat in view_mats])
percent = "{:.0f}".format(ttl_poses_calculated / frame_count * 100.0)
print('{}% poses calculated'.format(percent))
print('{} points in 3D points cloud'.format(len(points_cloud)))
last_view_mat = None
for i in range(frame_count):
if view_mats[i] is None:
view_mats[i] = last_view_mat
else:
last_view_mat = view_mats[i]
#check that all matrices are rotation matrices
for ind, i in enumerate(view_mats):
if not check_if_mat_is_rot_mat(i):
print(
'In frame {}/{} we have matrix wich is not rotation matrix:('.
format(ind + 1, frame_count))
raise NotImplementedError()
ids = []
points = []
for id in points_cloud.keys():
ids.append(id)
points.append(points_cloud[id])
point_cloud_builder = PointCloudBuilder(np.array(ids, dtype=np.int32),
np.array(points))
calc_point_cloud_colors(point_cloud_builder, rgb_sequence, view_mats,
intrinsic_mat, corner_storage, 5.0)
point_cloud = point_cloud_builder.build_point_cloud()
poses = list(map(view_mat3x4_to_pose, view_mats))
return poses, point_cloud
