def connections(self, _p):
train = self.train
orb = self.orb
graph = [
self.gray_image[:] >> orb['image'],
self.points3d[:] >> orb['points3d'],
self.depth_mask[:] >> orb['points3d_mask']
]
matcher = Matcher()
# if self.use_lsh:
# matcher = self.lsh
graph += [
orb['descriptors'] >> matcher['test'],
train['descriptors'] >> matcher['train'],
]
#3d estimation
pose_estimation = self.pose_estimation
graph += [
matcher['matches'] >> pose_estimation['matches'],
orb['points', 'points3d'] >> pose_estimation['test_2d', 'test_3d'],
train['points', 'points3d'] >> pose_estimation['train_2d',
'train_3d']
]
if self.show_matches:
#display matches
match_drawer = DrawMatches()
graph += [
pose_estimation['matches'] >> match_drawer['matches'],
pose_estimation['matches_mask'] >>
match_drawer['matches_mask'],
orb['points'] >> match_drawer['test'],
train['points'] >> match_drawer['train'],
self.rgb_image[:] >> match_drawer['test_image'],
train['image'] >> match_drawer['train_image'],
match_drawer['output'] >> imshow(name='Matches')['']
]
tr = self.tr
fps = self.fps
pose_draw = PoseDrawer()
graph += [
train['R', 'T'] >> tr['R1', 'T1'],
pose_estimation['R', 'T'] >> tr['R2', 'T2'],
tr['R', 'T'] >> pose_draw['R', 'T'],
pose_estimation['found'] >> pose_draw['trigger'],
self.K_image[:] >> pose_draw['K'],
self.rgb_image[:] >> pose_draw['image'],
pose_draw['output'] >> fps[:],
]
return graph
def connections(self):
n_features = 4000
train = self.train
orb = FeatureFinder('ORB test', n_features=n_features, n_levels=3, scale_factor=1.1, thresh=100, use_fast=False)
graph = [ self.gray_image[:] >> orb['image'],
self.points3d[:] >> orb['points3d'],
self.depth_mask[:] >> orb['mask']
]
matcher = LSHMatcher('LSH', n_tables=4, multi_probe_level=1, key_size=10, radius=70)
#matcher = Matcher()
graph += [ orb['descriptors'] >> matcher['test'],
train['descriptors'] >> matcher['train'],
]
#3d estimation
pose_estimation = self.pose_estimation
graph += [matcher['matches'] >> pose_estimation['matches'],
orb['points'] >> pose_estimation['test_2d'],
train['points'] >> pose_estimation['train_2d'],
orb['points3d'] >> pose_estimation['test_3d'],
train['points3d'] >> pose_estimation['train_3d'],
]
if self.show_matches:
#display matches
match_drawer = DrawMatches()
graph += [pose_estimation['matches'] >> match_drawer['matches'],
pose_estimation['matches_mask'] >> match_drawer['matches_mask'],
orb['points'] >> match_drawer['test'],
train['points'] >> match_drawer['train'],
self.rgb_image[:] >> match_drawer['test_image'],
train['image'] >> match_drawer['train_image'],
match_drawer['output'] >> imshow(name='Matches')['']
]
tr = self.tr
fps = self.fps
pose_draw = PoseDrawer()
graph += [train['R', 'T'] >> tr['R1', 'T1'],
pose_estimation['R', 'T'] >> tr['R2', 'T2'],
tr['R', 'T'] >> pose_draw['R', 'T'],
pose_estimation['found'] >> pose_draw['trigger'],
self.K[:] >> pose_draw['K'],
self.rgb_image[:] >> pose_draw['image'],
pose_draw['output'] >> fps[:],
]
return graph
def connect_debug(self):
self.pose_drawer = PoseDrawer()
self.graph += [
self.observation_renderer['R'] >> MatPrinter(name='R')['mat'],
self.observation_renderer['T'] >> MatPrinter(name='T')['mat'],
self.observation_renderer['K'] >> MatPrinter(name='K')['mat'],
self.observation_renderer['R', 'T',
'K'] >> self.pose_drawer['R', 'T', 'K'],
self.observation_renderer['debug_image'] >> self.pose_drawer['image'],
]
self.graph += [
self.pose_drawer['output'] >> self.display['image'],
# self.observation_renderer['depth'] >> self.display['image'],
# self.observation_renderer['mask'] >> self.display['image'],
]
rows = 4
cols = 5
square_size = 0.025 #2.5 cm
pattern_type = CHESSBOARD
rgb2gray = cvtColor(flag=Conversion.RGB2GRAY)
detector = PatternDetector(rows=rows,
cols=cols,
pattern_type=pattern_type,
square_size=square_size)
pattern_drawer = PatternDrawer(rows=rows, cols=cols)
camera_intrinsics = CameraIntrinsics(camera_file=args.camera)
poser = FiducialPoseFinder()
pose_drawer = PoseDrawer()
plasm = ecto.Plasm()
plasm.connect(
image_reader['image'] >> (pattern_drawer['input'], rgb2gray['image']),
rgb2gray['image'] >> detector['input'],
detector['ideal', 'out', 'found'] >> poser['ideal', 'points', 'found'],
camera_intrinsics['K'] >> poser['K'],
detector['out', 'found'] >> pattern_drawer['points', 'found'],
poser['R', 'T'] >> pose_drawer['R', 'T'],
poser['R'] >> MatPrinter(name='R')['mat'],
poser['T'] >> MatPrinter(name='T')['mat'],
pattern_drawer['out'] >> pose_drawer['image'],
camera_intrinsics['K'] >> pose_drawer['K'],
pose_drawer['output'] >> imshow(name='Pose', waitKey=0)['image'],
)