def run(self, file_path, opensfm_config, self_compute=False, self_path=''):
#data = dataset.DataSet(args.dataset)
reconstructions = opensfm_interface.load_reconstruction(file_path)
no_cameras = True
no_points = False
if reconstructions:
self.save_ply(file_path, reconstructions[0], None, no_cameras,
no_points)
if False and reconstructions:
udata = opensfm_interface.UndistortedDataSet(
file_path, opensfm_config, 'undistorted', self_compute,
self_path)
for id, shot in reconstructions[0].shots.items():
rgb = udata.load_undistorted_image(id)
for t in ('clean', 'raw'):
path_depth = udata._depthmap_file(id, t + '.npz')
if not os.path.exists(path_depth):
continue
depth = np.load(path_depth)['depth']
rgb = scale_down_image(rgb, depth.shape[1], depth.shape[0])
ply = depthmap_to_ply(shot, depth, rgb)
with io.open_wt(udata._depthmap_file(id,
t + '.ply')) as fout:
fout.write(ply)
def run(self, args):
data = dataset.DataSet(args.dataset)
reconstructions = data.load_reconstruction()
no_cameras = args.no_cameras
no_points = args.no_points
_all = args.all
path_all = args.dataset + "/reconstruction_files/"
if reconstructions and not _all:
data.save_ply(reconstructions[0], None, no_cameras, no_points)
elif reconstructions and _all:
if not os.path.isdir(path_all):
os.mkdir(path_all)
for r in range(len(reconstructions)):
data.save_ply(
reconstructions[r],
"reconstruction_files/reconstruction_" + str(r) + ".ply",
no_cameras, no_points)
if args.depthmaps and reconstructions:
udata = dataset.UndistortedDataSet(data, 'undistorted')
for id, shot in reconstructions[0].shots.items():
rgb = udata.load_undistorted_image(id)
for t in ('clean', 'raw'):
path_depth = udata._depthmap_file(id, t + '.npz')
if not os.path.exists(path_depth):
continue
depth = np.load(path_depth)['depth']
rgb = scale_down_image(rgb, depth.shape[1], depth.shape[0])
ply = depthmap_to_ply(shot, depth, rgb)
with io.open_wt(udata._depthmap_file(id,
t + '.ply')) as fout:
fout.write(ply)
def run_dataset(data, no_cameras, no_points, depthmaps):
""" Export reconstruction to PLY format
Args:
no_cameras: do not save camera positions
no_points: do not save points
depthmaps: export per-image depthmaps as pointclouds
"""
reconstructions = data.load_reconstruction()
no_cameras = no_cameras
no_points = no_points
if reconstructions:
data.save_ply(reconstructions[0], None, no_cameras, no_points)
if depthmaps and reconstructions:
udata = dataset.UndistortedDataSet(data, 'undistorted')
for id, shot in reconstructions[0].shots.items():
rgb = udata.load_undistorted_image(id)
for t in ('clean', 'raw'):
path_depth = udata._depthmap_file(id, t + '.npz')
if not os.path.exists(path_depth):
continue
depth = np.load(path_depth)['depth']
rgb = scale_down_image(rgb, depth.shape[1], depth.shape[0])
ply = depthmap_to_ply(shot, depth, rgb)
with io.open_wt(udata._depthmap_file(id, t + '.ply')) as fout:
fout.write(ply)
def test_depthmap_to_ply():
height, width = 2, 3
camera = pygeometry.Camera.create_perspective(0.8, 0.0, 0.0)
camera.id = "cam1"
camera.height = height
camera.width = width
r = types.Reconstruction()
r.add_camera(camera)
shot = r.create_shot("shot1", camera.id,
pygeometry.Pose([0.0, 0.0, 0.0], [0.0, 0.0, 0.0]))
image = np.zeros((height, width, 3))
depth = np.ones((height, width))
ply = dense.depthmap_to_ply(shot, depth, image)
assert len(ply.splitlines()) == 16
def test_depthmap_to_ply():
height, width = 2, 3
camera = pygeometry.Camera.create_perspective(0.8, 0.0, 0.0)
camera.id = 'cam1'
camera.height = height
camera.width = width
shot = types.Shot()
shot.id = 'shot1'
shot.camera = camera
shot.pose = types.Pose([0.0, 0.0, 0.0], [0.0, 0.0, 0.0])
image = np.zeros((height, width, 3))
depth = np.ones((height, width))
ply = dense.depthmap_to_ply(shot, depth, image)
assert len(ply.splitlines()) == 16
def run(self, args):
data = dataset.DataSet(args.dataset)
reconstructions = data.load_reconstruction()
no_cameras = args.no_cameras
no_points = args.no_points
if reconstructions:
data.save_ply(reconstructions[0], None, no_cameras, no_points)
if args.depthmaps and reconstructions:
for id, shot in reconstructions[0].shots.items():
rgb = data.load_undistorted_image(id)
for t in ('clean', 'raw'):
path_depth = data._depthmap_file(id, t + '.npz')
if not os.path.exists(path_depth):
continue
depth = np.load(path_depth)['depth']
rgb = scale_down_image(rgb, depth.shape[1], depth.shape[0])
ply = depthmap_to_ply(shot, depth, rgb)
with io.open_wt(data._depthmap_file(id, t + '.ply')) as fout:
fout.write(ply)
def test_depthmap_to_ply():
height, width = 2, 3
camera = types.PerspectiveCamera()
camera.id = 'cam1'
camera.focal = 0.8
camera.k1 = 0.0
camera.k2 = 0.0
camera.height = height
camera.width = width
shot = types.Shot()
shot.id = 'shot1'
shot.camera = camera
shot.pose = types.Pose([0.0, 0.0, 0.0], [0.0, 0.0, 0.0])
image = np.zeros((height, width, 3))
depth = np.ones((height, width))
ply = dense.depthmap_to_ply(shot, depth, image)
assert len(ply.splitlines()) == 16
def test_depthmap_to_ply() -> None:
height, width = 2, 3
camera = pygeometry.Camera.create_perspective(0.8, 0.0, 0.0)
camera.id = "cam1"
camera.height = height
camera.width = width
r = types.Reconstruction()
r.add_camera(camera)
shot = r.create_shot(
# pyre-fixme[6]: For 1st param expected `ndarray` but got `List[float]`.
# pyre-fixme[6]: For 2nd param expected `ndarray` but got `List[float]`.
"shot1",
camera.id,
pygeometry.Pose([0.0, 0.0, 0.0], [0.0, 0.0, 0.0]))
image = np.zeros((height, width, 3))
depth = np.ones((height, width))
ply = dense.depthmap_to_ply(shot, depth, image)
assert len(ply.splitlines()) == 16
def run_dataset(data: DataSet, no_cameras, no_points, depthmaps,
point_num_views):
"""Export reconstruction to PLY format
Args:
no_cameras: do not save camera positions
no_points: do not save points
depthmaps: export per-image depthmaps as pointclouds
point_num_views: Export the number of views associated with each point
"""
reconstructions = data.load_reconstruction()
tracks_manager = data.load_tracks_manager()
no_cameras = no_cameras
no_points = no_points
point_num_views = point_num_views
if reconstructions:
data.save_ply(reconstructions[0], tracks_manager, None, no_cameras,
no_points, point_num_views)
if depthmaps:
udata = dataset.UndistortedDataSet(data)
urec = udata.load_undistorted_reconstruction()[0]
for shot in urec.shots.values():
rgb = udata.load_undistorted_image(shot.id)
for t in ("clean", "raw"):
path_depth = udata.depthmap_file(shot.id, t + ".npz")
if not os.path.exists(path_depth):
continue
depth = np.load(path_depth)["depth"]
rgb = scale_down_image(rgb, depth.shape[1], depth.shape[0])
ply = depthmap_to_ply(shot, depth, rgb)
with io.open_wt(udata.depthmap_file(shot.id,
t + ".ply")) as fout:
fout.write(ply)
