Different smoothing and filtering before parsing data
This commit is contained in:
Ruben van de Ven
parent
0f96611771
commit
d6eac14898
7 changed files with 238 additions and 71 deletions
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@ -77,16 +77,17 @@ class CameraAction(argparse.Action):
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if values is None:
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setattr(namespace, self.dest, None)
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else:
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values = Path(values)
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with values.open('r') as fp:
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data = json.load(fp)
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# print(data)
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# print(data['camera_matrix'])
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# camera = {
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# 'camera_matrix': np.array(data['camera_matrix']),
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# 'dist_coeff': np.array(data['dist_coeff']),
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# }
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camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H, namespace.camera_fps)
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camera = Camera.from_calibfile(Path(values), namespace.H, namespace.camera_fps)
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# values = Path(values)
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# with values.open('r') as fp:
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# data = json.load(fp)
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# # print(data)
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# # print(data['camera_matrix'])
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# # camera = {
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# # 'camera_matrix': np.array(data['camera_matrix']),
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# # 'dist_coeff': np.array(data['dist_coeff']),
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# # }
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# camera = Camera(np.array(data['camera_matrix']), np.array(data['dist_coeff']), data['dim']['width'], data['dim']['height'], namespace.H, namespace.camera_fps)
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setattr(namespace, 'camera', camera)
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@ -338,6 +338,9 @@ class CvRenderer:
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i=0
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first_time = None
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cv2.namedWindow("frame", cv2.WND_PROP_FULLSCREEN)
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cv2.setWindowProperty("frame",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
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while self.is_running.is_set():
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i+=1
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@ -385,7 +388,7 @@ class CvRenderer:
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if self.streaming_process:
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self.streaming_process.stdin.write(img.tobytes())
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if self.config.render_window:
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cv2.imshow('frame',img)
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cv2.imshow('frame',cv2.resize(img, (1920, 1080)))
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cv2.waitKey(1)
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logger.info('Stopping')
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@ -57,10 +57,17 @@ class UrlOrPath():
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return Path(self.url.path)
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return Path(self.url.geturl()) # can include scheme, such as C:/
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class Space(IntFlag):
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Image = 1 # As detected in the image
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Undistorted = 2 # After applying lense undistortiion
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World = 4 # After lens undistort and homography
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Render = 8 # View space of renderer
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class DetectionState(IntFlag):
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Tentative = 1 # state before n_init (see DeepsortTrack)
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Confirmed = 2 # after tentative
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Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
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Interpolated = 8 # A position estimated through interpolation of adjecent detections
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@classmethod
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def from_deepsort_track(cls, track: DeepsortTrack):
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@ -83,6 +90,14 @@ class DetectionState(IntFlag):
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return cls.Confirmed
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raise RuntimeError("Should not run into Deleted entries here")
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def H_from_path(path: Path):
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if path.suffix == '.json':
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with path.open('r') as fp:
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H = np.array(json.load(fp))
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else:
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H = np.loadtxt(path, delimiter=',')
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return H
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@dataclass
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class Camera:
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mtx: cv2.Mat
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@ -98,7 +113,28 @@ class Camera:
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def __post_init__(self):
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self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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# print(data)
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# print(data['camera_matrix'])
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# camera = {
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# 'camera_matrix': np.array(data['camera_matrix']),
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# 'dist_coeff': np.array(data['dist_coeff']),
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# }
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return cls(
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np.array(data['camera_matrix']),
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np.array(data['dist_coeff']),
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data['dim']['width'],
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data['dim']['height'],
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H, fps)
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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return cls.from_calibfile(calibration_path, H, fps)
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# def __init__(self, mtx, dist, w, h, H):
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# self.mtx = mtx
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# self.dist = dist
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@ -107,6 +143,14 @@ class Camera:
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# self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
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# self.H = H # homography
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@dataclass
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class Position:
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x: float
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y: float
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conf: float
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state: DetectionState
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frame_nr: int
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det_class: str
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@dataclass
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class Detection:
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@ -120,7 +164,7 @@ class Detection:
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frame_nr: int
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det_class: str
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def get_foot_coords(self) -> list[tuple[float, float]]:
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def get_foot_coords(self) -> list[float, float]:
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return [self.l + 0.5 * self.w, self.t+self.h]
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@classmethod
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@ -153,7 +197,17 @@ class Detection:
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def to_ltrb(self):
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return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
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@dataclass
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class Trajectory:
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# TODO)) Replace history and predictions in Track with Trajectory
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space: Space
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fps: int = 12
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points: List[Detection] = field(default_factory=list)
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def __iter__(self):
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for d in self.points:
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yield d
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@dataclass
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class Track:
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@ -162,7 +216,7 @@ class Track:
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and acceleration.
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"""
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track_id: str = None
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history: List[Detection] = field(default_factory=lambda: [])
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history: List[Detection] = field(default_factory=list)
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predictor_history: Optional[list] = None # in image space
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predictions: Optional[list] = None
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fps: int = 12
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@ -235,6 +289,50 @@ class Track:
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t.predictor_history,
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t.predictions,
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t.fps/step_size)
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def get_binned(self, bin_size=.5, remove_overlap=True):
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"""
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For an experiment: what if we predict using only concrete positions, by mapping
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dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
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see ~/notes/attachments example svg
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"""
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new_history: List[Detection] = []
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for i, (det0, det1) in enumerate(zip(self.history[:-1], self.history[1:]):
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if i == 0:
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new_history.append(det0)
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continue
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if abs(det1.x - new_history[-1].x) < bin_size or abs(det1.y - new_history[-1].y) < bin_size:
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continue
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# det1 falls outside of the box [-bin_size:+bin_size] around last detection
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# 1. Interpolate exact point between det0 and det1 that this happens
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if abs(det1.x - new_history[-1].x) >= bin_size:
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if det1.x - new_history[-1].x >= bin_size:
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# det1 left of last
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x = new_history[-1].x + bin_size
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f = inv_lerp(det0.x, det1.x, x)
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elif new_history[-1].x - det1.x >= bin_size:
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# det1 left of last
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x = new_history[-1].x - bin_size
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f = inv_lerp(det0.x, det1.x, x)
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y = lerp(det0.y, det1.y, f)
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if abs(det1.y - new_history[-1].y) >= bin_size:
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if det1.y - new_history[-1].y >= bin_size:
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# det1 left of last
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y = new_history[-1].y + bin_size
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f = inv_lerp(det0.y, det1.y, x)
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elif new_history[-1].y - det1.y >= bin_size:
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# det1 left of last
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y = new_history[-1].y - bin_size
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f = inv_lerp(det0.y, det1.y, x)
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x = lerp(det0.x, det1.x, f)
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# 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
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points = [[bin_size, 0], [bin_size, bin_size], [0, bin_size], [-bin_size, bin_size], [-bin_size, 0], [-bin_size, -bin_size], [0, -bin_size], [bin_size, -bin_size]]
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# todo Offsets to points:[ history for in points]
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def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:
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@ -171,7 +171,7 @@ class PredictionServer:
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self.prediction_socket.send_pyobj(frame)
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def run(self):
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print(self.config)
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if self.config.seed is not None:
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random.seed(self.config.seed)
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np.random.seed(self.config.seed)
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@ -208,18 +208,9 @@ class PredictionServer:
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logger.info(f"Use hyperparams: {hyperparams=}")
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output_save_dir = os.path.join(self.config.output_dir, 'pred_figs')
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pathlib.Path(output_save_dir).mkdir(parents=True, exist_ok=True)
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with open(self.config.eval_data_dict, 'rb') as f:
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eval_env = dill.load(f, encoding='latin1')
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if eval_env.robot_type is None and hyperparams['incl_robot_node']:
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eval_env.robot_type = eval_env.NodeType[0] # TODO: Make more general, allow the user to specify?
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for scene in eval_env.scenes:
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scene.add_robot_from_nodes(eval_env.robot_type)
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logger.info('Loaded data from %s' % (self.config.eval_data_dict,))
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# Creating a dummy environment with a single scene that contains information about the world.
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@ -237,6 +228,7 @@ class PredictionServer:
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model_registrar = ModelRegistrar(self.config.model_dir, self.config.eval_device)
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model_iterations = pathlib.Path(self.config.model_dir).glob('model_registrar-*.pt')
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highest_iter = max([int(p.stem.split('-')[-1]) for p in model_iterations])
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logger.info(f"Loading model {highest_iter}")
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model_registrar.load_models(iter_num=highest_iter)
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@ -429,8 +421,8 @@ class PredictionServer:
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# if self.config.center_data:
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# prediction_dict, histories_dict, futures_dict = offset_trajectron_dict(prediction_dict, cx, cy), offset_trajectron_dict(histories_dict, cx, cy), offset_trajectron_dict(futures_dict, cx, cy)
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print('pred timesteps', list(prediction_dict.keys()))
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print('histories', [n.data.data.shape[0] for n in prediction_dict[frame.index].keys()])
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# print('pred timesteps', list(prediction_dict.keys()))
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# print('histories', [n.data.data.shape[0] for n in prediction_dict[frame.index].keys()])
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if self.config.cm_to_m:
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# convert back to fit homography
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prediction_dict, histories_dict, futures_dict = prediction_m_to_cm(prediction_dict), prediction_m_to_cm(histories_dict), prediction_m_to_cm(futures_dict)
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@ -1,6 +1,7 @@
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from collections import defaultdict
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import datetime
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from pathlib import Path
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from random import shuffle
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import sys
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import os
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import time
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@ -14,7 +15,7 @@ from typing import List
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from trap.config import CameraAction, HomographyAction
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from trap.frame_emitter import Camera
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from trap.tracker import Smoother, TrackReader
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from trap.tracker import FinalDisplacementFilter, Smoother, TrackReader
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#sys.path.append("../../")
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from trajectron.environment import Environment, Scene, Node
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@ -28,7 +29,7 @@ state_dim = 6
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frame_diff = 10
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desired_frame_diff = 1
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dt = 1/FPS # dt per frame (e.g. 1/FPS)
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smooth_window = FPS * 1.5 # see also tracker.py
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smooth_window = FPS # see also tracker.py
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min_track_length = 20
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standardization = {
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@ -80,7 +81,7 @@ class TrackIteration:
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# maybe_makedirs('trajectron-data')
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# for desired_source in [ 'hof2', ]:# ,'hof-maskrcnn', 'hof-yolov8', 'VIRAT-0102-parsed', 'virat-resnet-keypoints-full']:
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def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, cm_to_m: bool, center_data: bool, bin_positions: bool, camera: Camera, step_size: int):
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def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, cm_to_m: bool, center_data: bool, bin_positions: bool, camera: Camera, step_size: int, filter_displacement:float):
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name += f"-{datetime.date.today()}"
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print(f"Process data in {src_dir}, to {dst_dir}, identified by {name}")
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@ -90,11 +91,15 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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skipped_for_error = 0
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created = 0
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smoother = Smoother(window_len=smooth_window, convolution=False) if smooth_tracks else None
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smoother = Smoother(window_len=smooth_window, convolution=True) if smooth_tracks else None
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reader = TrackReader(src_dir, camera.fps)
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tracks = [t for t in reader]
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if filter_displacement > 0:
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filter = FinalDisplacementFilter(filter_displacement)
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tracks = filter.apply(tracks, camera)
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total = len(reader)
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total = len(tracks)
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bar = tqdm.tqdm(total=total)
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destinations = {
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@ -108,13 +113,21 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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print(max_frame_nr)
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# separate call so cursor is kept during multiple loops
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track_iterator = iter(reader)
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shuffle(tracks)
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dt1 = RollingAverage()
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dt2 = RollingAverage()
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dt3 = RollingAverage()
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dt4 = RollingAverage()
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sets = {}
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offset = 0
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for data_class, nr in destinations.items():
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# TODO)) think of a way to shuffle while keeping scenes
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sets[data_class] = tracks[offset : offset+nr]
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offset += nr
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print(f"Camera FPS: {camera.fps}, actual fps: {camera.fps/step_size} (or {(1/camera.fps)*step_size})")
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for data_class, nr_of_items in destinations.items():
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@ -135,7 +148,7 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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scene_nodes = defaultdict(lambda: [])
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iterations = TrackIteration.iteration_variations(smooth_tracks, False, step_size)
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for i, track in zip(range(nr_of_items), track_iterator):
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for i, track in enumerate(sets[data_class]):
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bar.update()
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track_source = track.source
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@ -179,7 +192,7 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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# track.get_projected_history(H=None, camera=self.config.camera)
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node = track.to_trajectron_node(camera, env)
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d = time.time()
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data_class = time.time()
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# if center_data:
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# data['pos_x'] -= cx
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@ -198,13 +211,22 @@ def process_data(src_dir: Path, dst_dir: Path, name: str, smooth_tracks: bool, c
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dt1.add(b-a)
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dt2.add(c-b)
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dt3.add(d-c)
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dt4.add(e-d)
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dt3.add(data_class-c)
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dt4.add(e-data_class)
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for scene_nr, nodes in scene_nodes.items():
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scene = Scene(timesteps=nodes[-1].last_timestep, dt=(1/camera.fps)*step_size, name=f'{split_id}_{scene_nr}', aug_func=None)
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first_ts = min([n.first_timestep for n in nodes])
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for node in nodes:
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node.first_timestep -= (first_ts - 1)
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last_ts = max([n.last_timestep for n in nodes])
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# print(sorted([n.first_timestep for n in nodes]))
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scene = Scene(timesteps=last_ts, dt=(1/camera.fps)*step_size, name=f'{split_id}_{scene_nr}', aug_func=None)
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scene.nodes.extend(nodes)
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scenes.append(scene)
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# print(scene)
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# print(scene.nodes[0].first_timestep)
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print(f'Processed {len(scenes):.2f} scene for data class {data_class}')
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@ -244,6 +266,11 @@ def main():
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# type=Path,
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default=None,
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action=CameraAction)
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parser.add_argument("--filter-displacement",
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help="Filter tracks with a final displacement less then the given value",
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# type=Path,
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default=0,
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type=float)
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args = parser.parse_args()
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@ -257,6 +284,7 @@ def main():
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args.center_data,
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args.bin_positions,
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args.camera,
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args.step_size
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args.step_size,
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filter_displacement=args.filter_displacement
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)
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@ -25,7 +25,7 @@ from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
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from ultralytics import YOLO
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from ultralytics.engine.results import Results as YOLOResult
|
||||
|
||||
from trap.frame_emitter import DetectionState, Frame, Detection, Track
|
||||
from trap.frame_emitter import Camera, DataclassJSONEncoder, DetectionState, Frame, Detection, Track
|
||||
from bytetracker import BYTETracker
|
||||
|
||||
from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother
|
||||
|
|
@ -93,14 +93,33 @@ class Multifile():
|
|||
|
||||
FIELDNAMES = ['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state', 'source']
|
||||
|
||||
class TrackFilter:
|
||||
pass
|
||||
|
||||
def apply(self, tracks: List[Track], camera: Camera):
|
||||
return [t for t in tracks if self.filter(t, camera)]
|
||||
|
||||
class FinalDisplacementFilter(TrackFilter):
|
||||
def __init__(self, min_displacement):
|
||||
self.min_displacement = min_displacement
|
||||
|
||||
def filter(self, track: Track, camera: Camera):
|
||||
history = track.get_projected_history(H=None, camera=camera)
|
||||
displacement = np.linalg.norm(history[0]-history[-1])
|
||||
return displacement > self.min_displacement
|
||||
|
||||
class TrackReader:
|
||||
def __init__(self, path: Path, fps: int, include_blacklisted = False, exclude_whitelisted = False):
|
||||
self.blacklist_file = path / "blacklist.jsonl"
|
||||
self.whitelist_file = path / "whitelist.jsonl" # for skipping
|
||||
self.tracks_file = path / "tracks.json"
|
||||
self.tracks_file = path / "tracks.pkl"
|
||||
|
||||
# with self.tracks_file.open('r') as fp:
|
||||
# tracks_dict: dict = json.load(fp)
|
||||
|
||||
with self.tracks_file.open('rb') as fp:
|
||||
tracks: dict = pickle.load(fp)
|
||||
|
||||
with self.tracks_file.open('r') as fp:
|
||||
tracks_dict: dict = json.load(fp)
|
||||
|
||||
if self.blacklist_file.exists():
|
||||
with jsonlines.open(self.blacklist_file, 'r') as reader:
|
||||
|
|
@ -117,7 +136,7 @@ class TrackReader:
|
|||
|
||||
|
||||
self._tracks = { track_id: detection_values
|
||||
for track_id, detection_values in tracks_dict.items()
|
||||
for track_id, detection_values in tracks.items()
|
||||
if (include_blacklisted or track_id not in blacklist) and
|
||||
(not exclude_whitelisted or track_id not in whitelist)
|
||||
}
|
||||
|
|
@ -127,26 +146,27 @@ class TrackReader:
|
|||
return len(self._tracks)
|
||||
|
||||
def get(self, track_id):
|
||||
detection_values = self._tracks[track_id]
|
||||
history = []
|
||||
# for detection_values in
|
||||
source = None
|
||||
for detection_items in detection_values:
|
||||
d = dict(zip(FIELDNAMES, detection_items))
|
||||
history.append(Detection(
|
||||
d['track_id'],
|
||||
d['l'],
|
||||
d['t'],
|
||||
d['w'],
|
||||
d['h'],
|
||||
nan,
|
||||
d['state'],
|
||||
d['frame_id'],
|
||||
1,
|
||||
))
|
||||
source = int(d['source'])
|
||||
return self._tracks[track_id]
|
||||
# detection_values = self._tracks[track_id]
|
||||
# history = []
|
||||
# # for detection_values in
|
||||
# source = None
|
||||
# for detection_items in detection_values:
|
||||
# d = dict(zip(FIELDNAMES, detection_items))
|
||||
# history.append(Detection(
|
||||
# d['track_id'],
|
||||
# d['l'],
|
||||
# d['t'],
|
||||
# d['w'],
|
||||
# d['h'],
|
||||
# nan,
|
||||
# d['state'],
|
||||
# d['frame_id'],
|
||||
# 1,
|
||||
# ))
|
||||
# source = int(d['source'])
|
||||
|
||||
return Track(track_id, history, fps=self.fps, source=source)
|
||||
# return Track(track_id, history, fps=self.fps, source=source)
|
||||
|
||||
def __iter__(self):
|
||||
for track_id in self._tracks:
|
||||
|
|
@ -239,7 +259,8 @@ def rewrite_raw_track_files(path: Path):
|
|||
# for source_file in source_files:
|
||||
|
||||
tracks_file = path / 'tracks.json'
|
||||
tracks = defaultdict(lambda: [])
|
||||
tracks_pkl = path / 'tracks.pkl'
|
||||
tracks = defaultdict(lambda: Track())
|
||||
|
||||
offset = 0
|
||||
max_track_id = 0
|
||||
|
|
@ -285,18 +306,31 @@ def rewrite_raw_track_files(path: Path):
|
|||
if track_id > max_track_id:
|
||||
max_track_id = track_id
|
||||
|
||||
parts[1] = str(track_id)
|
||||
track_id = str(track_id)
|
||||
target_fp.write("\t".join(parts))
|
||||
|
||||
parts = [float(p) for p in parts]
|
||||
tracks[track_id].append([
|
||||
int(parts[0] / 10),
|
||||
track_id,
|
||||
] + parts[2:8] + [int(parts[8]), src_file_nr])
|
||||
# ['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state', 'source']
|
||||
|
||||
point = Detection(track_id, parts[2], parts[3], parts[4], parts[5], 1, DetectionState(int(parts[8])), int(parts[0]/10), 1)
|
||||
# history = [
|
||||
|
||||
# for d in parts]
|
||||
tracks[track_id].track_id = track_id
|
||||
tracks[track_id].source = src_file_nr
|
||||
tracks[track_id].history.append(point)
|
||||
# tracks[track_id].append([
|
||||
# int(parts[0] / 10),
|
||||
# track_id,
|
||||
# ] + parts[2:8] + [int(parts[8]), src_file_nr])
|
||||
|
||||
with tracks_file.open('w') as fp:
|
||||
logger.info(f"Write {len(tracks)} tracks to {str(tracks_file)}")
|
||||
json.dump(tracks, fp)
|
||||
json.dump(tracks, fp, cls=DataclassJSONEncoder, indent=2)
|
||||
with tracks_pkl.open('wb') as fp:
|
||||
logger.info(f"Write {len(tracks)} tracks to {str(tracks_pkl)}")
|
||||
pickle.dump(dict(tracks), fp)
|
||||
|
||||
|
||||
|
||||
class TrackerWrapper():
|
||||
|
|
@ -641,7 +675,7 @@ class Smoother:
|
|||
def __init__(self, window_len=6, convolution=False):
|
||||
# for some reason this smoother messes the predictions. Probably skews the points too much??
|
||||
if convolution:
|
||||
self.smoother = ConvolutionSmoother(window_len=window_len, window_type='ones', copy=None)
|
||||
self.smoother = ConvolutionSmoother(window_len=window_len, window_type='hanning', copy=None)
|
||||
else:
|
||||
# "Unlike Kalman filtering, which focuses on predicting and updating the current state using historical measurements, Kalman smoothing enhances the accuracy of past state values"
|
||||
# see https://medium.com/@shahalkp1/kalman-smoothing-using-tsmoothie-0175260464e5
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
# lerp & inverse lerp from https://gist.github.com/laundmo/b224b1f4c8ef6ca5fe47e132c8deab56
|
||||
def lerp(a: float, b: float, t: float) -> float:
|
||||
"""Linear interpolate on the scale given by a to b, using t as the point on that scale.
|
||||
Examples
|
||||
|
|
@ -5,4 +6,14 @@ def lerp(a: float, b: float, t: float) -> float:
|
|||
50 == lerp(0, 100, 0.5)
|
||||
4.2 == lerp(1, 5, 0.8)
|
||||
"""
|
||||
return (1 - t) * a + t * b
|
||||
return (1 - t) * a + t * b
|
||||
|
||||
|
||||
def inv_lerp(a: float, b: float, v: float) -> float:
|
||||
"""Inverse Linar Interpolation, get the fraction between a and b on which v resides.
|
||||
Examples
|
||||
--------
|
||||
0.5 == inv_lerp(0, 100, 50)
|
||||
0.8 == inv_lerp(1, 5, 4.2)
|
||||
"""
|
||||
return (v - a) / (b - a)
|
||||
Loading…
Reference in a new issue