def build_view_mat(
common_obj: np.ndarray, common_img: np.ndarray, common_ids: np.ndarray,
intrinsic_mat: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]:
_, rvec, tvec, inliers = cv2.solvePnPRansac(
common_obj,
common_img,
intrinsic_mat,
None,
#flags=cv2.SOLVEPNP_EPNP,
reprojectionError=RANSAC_REPROJECTION_ERROR,
iterationsCount=100)
mat = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
inliers = np.array(inliers).reshape(-1)
inlier_points = common_obj[inliers]
inlier_img = common_img[inliers]
inlier_ids = common_ids[inliers]
reprojection_errors = compute_reprojection_errors(inlier_points,
inlier_img,
intrinsic_mat @ mat)
#to_take = reprojection_errors < RANSAC_REPROJECTION_ERROR
#reprojection_error = compute_reprojection_errors(inlier_points, inlier_img, intrinsic_mat @ mat).mean()
#reprojection_error = np.linalg.norm(common_img - projections, axis=-1).mean()
#return mat, inlier_points[to_take], inlier_ids[to_take], reprojection_errors[to_take].mean()
return mat, inlier_points, inlier_ids, reprojection_errors.mean()
def pnp(self, cloud: PointCloudBuilder, camera):
ids1, ids2 = build_index_intersection(cloud.ids, self.corners.ids)
try:
ret, rvec, tvec, inliers = cv2.solvePnPRansac(
cloud.points[ids1],
self.corners.points[ids2],
camera,
None,
reprojectionError=MAX_REPROJECTION_ERROR)
except:
return self.last_inliners
if not ret or len(inliers) < FrameTrack.MIN_INLIERS or np.linalg.norm(
tvec) > MAX_TRANSLATION:
return self.last_inliners
potential_new_mtx = rodrigues_and_translation_to_view_mat3x4(
rvec, tvec)
potential_reprojection_error = compute_reprojection_errors(
cloud.points[ids1], self.corners.points[ids2],
camera @ potential_new_mtx)
if self.current_reproject_error is None or potential_reprojection_error.mean(
) < self.current_reproject_error:
self.mtx = potential_new_mtx
self.current_reproject_error = potential_reprojection_error.mean()
self.last_inliners = cloud.ids[ids1][np.array(inliers)]
return self.last_inliners
def try_solve_pnp(self, points_3d: np.ndarray, points_2d: np.ndarray) -> Optional[Tuple[np.ndarray, np.ndarray]]:
if len(points_3d) < 6:
return None
ok, r_vec, t_vec, inliers = cv2.solvePnPRansac(
objectPoints=points_3d,
imagePoints=points_2d,
cameraMatrix=self.intrinsic_mat,
distCoeffs=None,
confidence=PNP_RANSAC_CONFIDENCE,
reprojectionError=PNP_RANSAC_INLIERS_MAX_REPROJECTION_ERROR,
flags=cv2.SOLVEPNP_EPNP
)
if not ok:
return None
inliers = inliers.flatten()
if len(inliers) < 6:
return None
_, r_vec, t_vec = cv2.solvePnP(
points_3d,
points_2d,
self.intrinsic_mat,
distCoeffs=None,
rvec=r_vec,
tvec=t_vec,
useExtrinsicGuess=True,
flags=cv2.SOLVEPNP_ITERATIVE
)
return rodrigues_and_translation_to_view_mat3x4(r_vec, t_vec), inliers
def calc_vec_errors(vec, point2d):
r, t = vec[0:3], vec[3:6]
view_mat, point3d = rodrigues_and_translation_to_view_mat3x4(
r.reshape(3, 1), t.reshape(3, 1)), vec[6:9]
return compute_reprojection_errors(point3d.reshape(1, -1),
point2d.reshape(1, -1),
intrinsic_mat @ view_mat)[0]
def solve_pnp_ransac_one(self, frame_id):
ids_3d = self.point_cloud_builder.ids
ids_2d = self.corner_storage[frame_id].ids
ids_intersection, _ = snp.intersect(ids_3d.flatten(), ids_2d.flatten(), indices=True)
idx3d = [i for i, j in enumerate(ids_3d) if j in ids_intersection]
idx2d = [i for i, j in enumerate(ids_2d) if j in ids_intersection]
points3d = self.point_cloud_builder.points[idx3d]
points2d = self.corner_storage[frame_id].points[idx2d]
if len(points3d) < 6:
return None, []
_, _, _, inliers = cv2.solvePnPRansac(
objectPoints=points3d,
imagePoints=points2d,
cameraMatrix=self.intrinsic_mat,
distCoeffs=np.array([]),
iterationsCount=250,
flags=cv2.SOLVEPNP_EPNP
)
if len(points3d[inliers]) < 6:
return None, []
_, r_vec, t_vec = cv2.solvePnP(
objectPoints=points3d[inliers],
imagePoints=points2d[inliers],
cameraMatrix=self.intrinsic_mat,
distCoeffs=np.array([]),
flags=cv2.SOLVEPNP_ITERATIVE
)
view_mat = rodrigues_and_translation_to_view_mat3x4(r_vec, t_vec)
return view_mat, inliers
def _get_pos(self, frame_number):
corners = self.corner_storage[frame_number]
cur_corners, cur_cloud_pts = [], []
for i, corner in zip(corners.ids.flatten(), corners.points):
if i in self.point_cloud.keys():
cur_corners.append(corner)
cur_cloud_pts.append(self.point_cloud[i].pos)
cur_corners, cur_cloud_pts = np.array(cur_corners), np.array(
cur_cloud_pts)
if len(cur_cloud_pts) < 4:
return None # Not enough points for ransac
is_success, r_vec, t_vec, inliers = cv2.solvePnPRansac(
cur_cloud_pts,
cur_corners,
self.intrinsic_mat,
None,
flags=cv2.SOLVEPNP_EPNP)
if not is_success:
return None
_, r_vec, t_vec = cv2.solvePnP(cur_cloud_pts[inliers],
cur_corners[inliers],
self.intrinsic_mat,
distCoeffs=None,
flags=cv2.SOLVEPNP_ITERATIVE,
useExtrinsicGuess=True,
rvec=r_vec,
tvec=t_vec)
pos = rodrigues_and_translation_to_view_mat3x4(r_vec, t_vec)
inliers_n = len(np.array(inliers).flatten())
return CameraInfo(pos, inliers_n)
def camera_pose(id,
corner_storage,
point_cloud_builder,
intrinsic_mat,
dist_coef=None):
frame_corners = corner_storage[id]
points = frame_corners.points
frame_ids = frame_corners.ids
cloud_ids = point_cloud_builder.ids
ids, frame_indexes, cloud_indexes = np.intersect1d(frame_ids,
cloud_ids,
return_indices=True)
if ids.shape[0] < 4:
return None
cloud_points = point_cloud_builder.points[cloud_indexes]
frame_points = points[frame_indexes]
retval, rvec, tvec, inliers = cv2.solvePnPRansac(cloud_points,
frame_points,
intrinsic_mat, dist_coef)
if retval is None:
return None
retval, rvec, tvec = cv2.solvePnP(cloud_points[inliers],
frame_points[inliers], intrinsic_mat,
dist_coef)
if retval is None:
return None
return view_mat3x4_to_pose(
rodrigues_and_translation_to_view_mat3x4(rvec, tvec)), np.setdiff1d(
ids, inliers)
def _get_pos(self, frame_number) -> np.ndarray:
corners = self.corner_storage[frame_number]
_, pts_ids, corners_ids = np.intersect1d(self.pc_builder.ids, corners.ids, assume_unique=True,
return_indices=True)
common_pts_3d = self.pc_builder.points[pts_ids]
common_ids = self.pc_builder.ids[pts_ids]
common_corners = corners.points[corners_ids]
inlier_mask = np.zeros_like(common_pts_3d, dtype=np.bool)
inlier_counter = 0
for common_id, mask_elem in zip(common_ids[:, 0], inlier_mask):
if common_id not in self.outliers:
mask_elem[:] = True
inlier_counter += 1
if inlier_counter > 7:
common_pts_3d = common_pts_3d[inlier_mask].reshape(-1, 3)
common_ids = common_ids[inlier_mask[:, :1]].reshape(-1, 1)
common_corners = common_corners[inlier_mask[:, :2]].reshape(-1, 2)
max_reproj_error = 3.0
if len(common_pts_3d) < 4:
raise TrackingError('Not enough points to solve RANSAC on frame ' + str(frame_number))
_, rot_vec, tr_vec, inliers = cv2.solvePnPRansac(common_pts_3d,
common_corners,
self.intrinsic_mat,
None,
reprojectionError=max_reproj_error,
flags=cv2.SOLVEPNP_EPNP)
extrinsic_mat = rodrigues_and_translation_to_view_mat3x4(rot_vec, tr_vec)
proj_matr = self.intrinsic_mat @ extrinsic_mat
if inliers is None:
raise TrackingError('Failed to solve PnP on frame' + str(frame_number))
while len(inliers) < 8 and max_reproj_error < 50:
max_reproj_error *= 1.2
inliers = calc_inlier_indices(common_pts_3d, common_corners, proj_matr, max_reproj_error)
inlier_pts = common_pts_3d[inliers]
inlier_corners = common_corners[inliers]
outlier_ids = np.setdiff1d(common_ids, common_ids[inliers], assume_unique=True)
self.outliers.update(outlier_ids)
if len(inliers) < 4:
inlier_pts = common_pts_3d
inlier_corners = common_corners
print('Found position on', len(inlier_corners), 'inliers')
_, rot_vec, tr_vec, inliers = cv2.solvePnPRansac(inlier_pts, inlier_corners, self.intrinsic_mat, None,
rot_vec, tr_vec, useExtrinsicGuess=True)
return rodrigues_and_translation_to_view_mat3x4(rot_vec, tr_vec)
def f(rtp):
r_vec = rtp[:3].reshape(3, 1)
t_vec = rtp[3:6].reshape(3, 1)
p = rtp[6:]
proj = intrinsic_mat @ rodrigues_and_translation_to_view_mat3x4(
r_vec, t_vec) @ p
proj /= proj[2]
return np.square(proj - point2d).sum()
def apply_res(res_):
for i in range(len(intersected)):
p_ = res_[6 * len(frames) + i * 3:6 * len(frames) + i * 3 + 3]
_idx = np.argwhere(self.point_cloud_builder.ids == intersected[i])[0][0]
_p = self.point_cloud_builder.points[_idx]
check_point(_p, p_, intersected[i])
for i in range(len(frames)):
r_vec_ = np.array([res_[i * 6], res_[i * 6 + 1], res_[i * 6 + 2]])
t_vec_ = np.array([[res_[i * 6 + 3]], [res_[i * 6 + 4]], [res_[i * 6 + 5]]])
v_mat = rodrigues_and_translation_to_view_mat3x4(r_vec_, t_vec_)
self.view_mats[frames[i]] = v_mat
def solve_pnp(points2d: np.ndarray,
points3d: np.ndarray,
intrinsic_mat: np.ndarray) -> np.ndarray:
_, rvev, tvec = cv2.solvePnP(
objectPoints=points3d.astype(np.float64),
imagePoints=points2d,
cameraMatrix=intrinsic_mat,
distCoeffs=None
)
return rodrigues_and_translation_to_view_mat3x4(rvev, tvec)
def solve_frame(frame_num, corner_storage, point_cloud_builder, view_mat,
intrinsic_mat, direction):
print("Frame ", frame_num)
corners = corner_storage[frame_num]
_, corner_indexes, points_indexes = np.intersect1d(corners.ids,
point_cloud_builder.ids,
assume_unique=True,
return_indices=True)
corners = corners.points[corner_indexes]
points = point_cloud_builder.points[points_indexes]
x, r, t, inliers = cv2.solvePnPRansac(points,
corners,
intrinsic_mat,
None,
reprojectionError=1.0,
flags=cv2.SOLVEPNP_EPNP)
good_corners = corners[inliers]
good_points = points[inliers]
x, r, t = cv2.solvePnP(good_points,
good_corners,
intrinsic_mat,
None,
r,
t,
useExtrinsicGuess=True,
flags=cv2.SOLVEPNP_ITERATIVE)
print("Points in cloud ", len(point_cloud_builder.ids))
new_views = rodrigues_and_translation_to_view_mat3x4(r, t)
if new_views is None:
view_mat[frame_num] = view_mat[frame_num + direction]
else:
view_mat[frame_num] = new_views
for i in range(40):
other_frame_num = frame_num + i * direction
if check_baseline(view_mat[other_frame_num], view_mat[frame_num], 0.1):
correspondences = build_correspondences(
corner_storage[other_frame_num],
corner_storage[frame_num],
ids_to_remove=point_cloud_builder.ids)
if len(correspondences) != 0:
new_points, ids, _ = triangulate_correspondences(
correspondences, view_mat[other_frame_num],
view_mat[frame_num], intrinsic_mat,
TriangulationParameters(1.0, 1.0, 0.1))
point_cloud_builder.add_points(ids, new_points)
def get_ransac(point_cloud_builder, corners_i, intrinsic_mat):
intersection, (indexes_cloud, indexes_corners) = snp.intersect(
point_cloud_builder.ids.flatten(),
corners_i.ids.flatten(),
indices=True)
if len(intersection) < 6:
return False, None, None, None
try:
res_code, rvec, tvec, inliers = cv2.solvePnPRansac(
point_cloud_builder.points[indexes_cloud],
corners_i.points[indexes_corners],
intrinsic_mat,
distCoeffs=None,
reprojectionError=1.5)
except Exception:
print('Exception happened in solving PnP, continuing')
return False, None, None, None
rodrig = None
cloud_points = None
if res_code:
rodrig = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
cloud_points = point_cloud_builder.points[indexes_cloud][
inliers.flatten()]
return res_code, rodrig, inliers, cloud_points
def track(self):
for cur_index in range(self._length):
print(f'Processing frame {cur_index}/{self._length}')
corners = self._corner_storage[cur_index]
ids = []
object_points = []
image_points = []
for id, point in zip(corners.ids, corners.points):
indices_x, _ = np.where(self._builder.ids == id)
if len(indices_x) == 0:
continue
ids.append(id)
object_points.append(self._builder.points[indices_x[0]])
image_points.append(point)
if len(object_points) < 5:
continue
retval, rvec, tvec, inliers = cv2.solvePnPRansac(
np.array(object_points),
np.array(image_points),
self._intrinsic_mat,
None,
flags=cv2.SOLVEPNP_EPNP)
if not retval:
continue
view_mat = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
print(f'Position based on {len(inliers)} inliers')
self._track[cur_index] = view_mat
updatesCount = sum([self._update_cloud(cur_index, next_index) for next_index in range(cur_index) \
if self._track[next_index] is not None])
print(
f'Points updated:{updatesCount} Current cloud size:{len(self._builder.ids)}'
)
for i in range(len(self._track)):
self._track[i] = self._track[i] if self._track[
i] is not None else self._track[i - 1]
return np.array(self._track), self._builder
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_find_point_cloud(intrinsic_mat, corner_storage, known_view_1, known_view_2):
num_frames = len(corner_storage)
view_mats = [None] * num_frames
point_cloud_builder = PointCloudBuilder()
print(f'Frames with known views: {known_view_1[0]} and {known_view_2[0]}')
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])
triang_params = TriangulationParameters(max_reprojection_error=MAX_REPR_ERR,
min_triangulation_angle_deg=MIN_TRIANG_ANGLE_DEG,
min_depth=MIN_DEPTH)
add_points_to_cloud(point_cloud_builder,
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,
triang_params)
while True:
was_updated = False
for i in range(num_frames):
if view_mats[i] is not None:
continue
print(f"\nCurrent frame: {i}")
# find intersection of current point cloud and current frame
corners = corner_storage[i]
corner_ids = []
points_3d = []
points_2d = []
for id, corner in zip(corners.ids, corners.points):
if id not in point_cloud_builder.ids:
continue
ind_in_builder, _ = np.nonzero(point_cloud_builder.ids == id)
corner_ids.append(id)
points_3d.append(point_cloud_builder.points[ind_in_builder[0]])
points_2d.append(corner)
print(f"Size of intersection of current point cloud and current frame: {len(corner_ids)}")
if len(corner_ids) < 5:
continue
points_3d = np.array(points_3d)
points_2d = np.array(points_2d)
retval, rvec, tvec, inliers = cv2.solvePnPRansac(points_3d.reshape((-1, 1, 3)),
points_2d.reshape((-1, 1, 2)),
cameraMatrix=intrinsic_mat,
distCoeffs=None,
reprojectionError=PNP_REPROJ_ERROR)
if not retval:
print("Unsuccessful solution of PnP")
continue
print(f"Position found by {len(inliers)} inliers")
view_mat = rodrigues_and_translation_to_view_mat3x4(rvec, tvec)
view_mats[i] = view_mat
for j in range(num_frames):
if i != j and view_mats[j] is not None:
points_added = add_points_to_cloud(point_cloud_builder,
corner_storage[i],
corner_storage[j],
view_mats[i],
view_mats[j],
intrinsic_mat,
triang_params)
if points_added > 0:
was_updated = True
print(f"Current size of point cloud: {point_cloud_builder.points.size}")
if was_updated is False:
break
for i in range(num_frames):
if view_mats[i] is None:
view_mats[i] = view_mats[i - 1]
return view_mats, point_cloud_builder
def bundle_adjustment(view_mats, point_cloud_builder, intrinsic_mat,
corner_storage):
ids2d, ids3d, frameids = [], [], []
for frame in range(len(view_mats)):
_, (id1, id2) = snp.intersect(point_cloud_builder.ids.flatten(),
corner_storage[frame].ids.flatten(),
indices=True)
inliers = calc_inlier_indices(point_cloud_builder.points[id1],
corner_storage[frame].points[id2],
intrinsic_mat @ view_mats[frame], 1.0)
for i in inliers:
ids2d.append(id2[i])
ids3d.append(id1[i])
frameids.append(frame)
view_mats_vec = []
for view_mat in view_mats:
tvec = view_mat[:, 3]
rvec, _ = cv2.Rodrigues(view_mat[:, :3])
view_mats_vec.append(np.concatenate([rvec.squeeze(), tvec]))
view_mats_vec = np.array(view_mats_vec)
used_3ds = list(set(ids3d))
points3d_vec = np.array(point_cloud_builder.points[used_3ds])
for i in range(len(ids3d)):
ids3d[i] = used_3ds.index(ids3d[i])
n, m = len(view_mats_vec.reshape(-1)), len(points3d_vec.reshape(-1))
def calc_vec_errors(vec, point2d):
r, t = vec[0:3], vec[3:6]
view_mat, point3d = rodrigues_and_translation_to_view_mat3x4(
r.reshape(3, 1), t.reshape(3, 1)), vec[6:9]
return compute_reprojection_errors(point3d.reshape(1, -1),
point2d.reshape(1, -1),
intrinsic_mat @ view_mat)[0]
def calc_jacobian():
J = np.zeros((len(frameids), n + m))
for i in range(len(frameids)):
frameid, id3d, id2d = frameids[i], ids3d[i], ids2d[i]
point3d = points3d_vec[id3d]
point2d = corner_storage[frameid].points[id2d]
vec = np.concatenate([view_mats_vec[frameid], point3d])
der = scipy.optimize.approx_fprime(
vec, lambda vec: calc_vec_errors(vec, point2d),
np.full(vec.shape, 1e-7))
J[i, n + 3 * id3d:n + 3 * id3d + 3] = der[6:]
J[i, 6 * frameid:6 * frameid + 6] = der[:6]
return J
def calc_errors():
errors = []
for i in range(len(frameids)):
frameid, id3d, id2d = frameids[i], ids3d[i], ids2d[i]
errors.append(
calc_vec_errors(
np.concatenate(
[view_mats_vec[frameid], points3d_vec[id3d]]),
corner_storage[frameid].points[id2d]))
return errors
error_before_bundle_adjustment = np.mean(calc_errors())
for iter in range(5):
J = calc_jacobian()
u = np.array(calc_errors())
g = J.T @ u
gx, gc = g[n:], g[:n]
Q = J.T @ J + 10 * np.diag(np.diag(J.T @ J))
U, W, V = Q[:n, :n], Q[:n, n:], Q[n:, n:]
try:
V_inv = np.linalg.inv(V)
deltac = np.linalg.solve(U - W @ V_inv @ W.T, W @ V_inv @ gx - gc)
deltax = -V_inv @ (gx + W.T @ deltac)
view_mats_vec = (view_mats_vec.reshape(-1) + deltac).reshape(
(-1, 6))
points3d_vec = (points3d_vec.reshape(-1) + deltax).reshape((-1, 3))
except:
pass
print('Bundle-adjustment: iteration {}, error {}'.format(
iter, np.mean(calc_errors())))
sys.stdout.flush()
error_after_bundle_adjustment = np.mean(calc_errors())
if error_after_bundle_adjustment > error_before_bundle_adjustment:
print('Better without bundle adjustment')
return view_mats
point_cloud_builder.update_points(point_cloud_builder.ids[used_3ds],
points3d_vec)
new_view_mats = []
for vec in view_mats_vec:
rvec, tvec = vec[0:3], vec[3:6]
view_mat = rodrigues_and_translation_to_view_mat3x4(
rvec.reshape(3, 1), tvec.reshape(3, 1))
new_view_mats.append(view_mat)
return np.array(new_view_mats)
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)
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 run_bundle_adjustment(intrinsic_mat: np.ndarray,
list_of_corners: List[FrameCorners],
max_inlier_reprojection_error: float,
view_mats: List[np.ndarray],
pc_builder: PointCloudBuilder) -> List[np.ndarray]:
n_frames = len(view_mats)
inliers = get_inliers(intrinsic_mat, list_of_corners,
max_inlier_reprojection_error, view_mats, pc_builder)
rs = [cv2.Rodrigues(i[:, :3])[0].flatten() for i in view_mats]
ts = [i[:, 3] for i in view_mats]
point_id_to_cloud = -np.ones(pc_builder.ids.max() + 1, dtype=np.int32)
point_id_to_cloud[pc_builder.ids.flatten()] = np.arange(len(
pc_builder.ids))
re = calc_reprojection_error(inliers, pc_builder, point_id_to_cloud,
list_of_corners, intrinsic_mat, view_mats)
print('Reprojection error before bundle adjustment:', re)
for start in range(0, n_frames, FRAMES_STEP):
end = min(start + FRAMES_STEP, n_frames)
cameras_params_dim = (end - start) * 6
matches = get_matches(inliers[start:end], len(list_of_corners), start)
relevant_point_ids = np.array(
list(
sorted(
{point_id
for point_id, frame_id, point_index in matches})))
point_id_to_cur = {p: i for i, p in enumerate(relevant_point_ids)}
_, (_, cloud_indices) = snp.intersect(relevant_point_ids,
pc_builder.ids.flatten(),
indices=True)
relevant_points = pc_builder.points[cloud_indices]
alpha = ALPHA_INITIAL
us_mean_prev = -float('inf')
for i in range(ITERATIONS):
us = get_residuals(pc_builder, point_id_to_cloud, list_of_corners,
intrinsic_mat, matches, view_mats)
jacobian = calc_jacobian(matches, cameras_params_dim, rs, ts,
pc_builder, list_of_corners,
point_id_to_cur, point_id_to_cloud,
intrinsic_mat, start)
jtj = jacobian.T @ jacobian
us_mean = us.mean()
alpha = alpha / ALPHA_DEC_STEP if us_mean < us_mean_prev else alpha * ALPHA_INC_STEP
us_mean_prev = us_mean
alpha_multiplier = 1 + alpha
jtj[np.arange(jtj.shape[0]),
np.arange(jtj.shape[0])] *= alpha_multiplier
u = jtj[:cameras_params_dim, :cameras_params_dim]
v = jtj[cameras_params_dim:, cameras_params_dim:]
w = jtj[:cameras_params_dim, cameras_params_dim:]
wt = jtj[cameras_params_dim:, :cameras_params_dim]
v_inv = block_inv(v)
g = jacobian.T @ us
b = w @ v_inv @ g[cameras_params_dim:] - g[:cameras_params_dim]
a = (u - w @ v_inv @ wt).toarray()
delta_c = np.linalg.solve(a, b)
delta_x = v_inv @ (-g[cameras_params_dim:] - wt @ delta_c)
for k, j in enumerate(range(start, end)):
rs[j] += delta_c[k * 6:k * 6 + 3]
ts[j] += delta_c[k * 6 + 3:k * 6 + 6]
view_mats[j] = rodrigues_and_translation_to_view_mat3x4(
rs[j], ts[j].reshape(3, 1))
relevant_points += delta_x.reshape((-1, 3))
pc_builder.update_points(relevant_point_ids, relevant_points)
re = calc_reprojection_error(inliers, pc_builder, point_id_to_cloud,
list_of_corners, intrinsic_mat, view_mats)
print('Reprojection error after bundle adjustment:', re)
return view_mats
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
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]:
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]:
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])
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]:
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
