EKF-Diagnostic/sensor-anomalies
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small rework on type annotations

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Florian Förster 2025-10-09 08:29:10 +02:00
parent 67e49a47d8
commit 85effb25b0
1 changed files with 15 additions and 12 deletions
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27
src/dopt_sensor_anomalies/detection.py
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@ -31,14 +31,17 @@ pixels_per_metric_Y: float = 0.251
# measuring # measuring
def midpoint(pt_A: npt.NDArray, pt_B: npt.NDArray) -> tuple[float, float]: def midpoint(
pt_A: npt.NDArray[np.floating],
pt_B: npt.NDArray[np.floating],
) -> tuple[float, float]:
"""to identify the midpoint of a 2D area """to identify the midpoint of a 2D area
Parameters Parameters
---------- ----------
pt_A : npt.NDArray pt_A : npt.NDArray[np.floating]
tuple of coordinates x, y; shape (2, ) tuple of coordinates x, y; shape (2, )
pt_B : npt.NDArray pt_B : npt.NDArray[np.floating]
tuple of coordinates x, y; shape (2, ) tuple of coordinates x, y; shape (2, )
Returns Returns
@ -50,17 +53,17 @@ def midpoint(pt_A: npt.NDArray, pt_B: npt.NDArray) -> tuple[float, float]:
def check_box_redundancy( def check_box_redundancy(
box1: t.Box, box_1: t.Box,
box2: t.Box, box_2: t.Box,
tolerance: float = 5.0, tolerance: float = 5.0,
) -> bool: ) -> bool:
"""to check if bounding box has already been identified and is just a redundant one """to check if bounding box has already been identified and is just a redundant one
Parameters Parameters
---------- ----------
box1 : t.Box box_1 : t.Box
tuple of box values: ((center_x, center_y), (width, height), angle) tuple of box values: ((center_x, center_y), (width, height), angle)
box2 : t.Box box_2 : t.Box
tuple of box values: ((center_x, center_y), (width, height), angle) tuple of box values: ((center_x, center_y), (width, height), angle)
tolerance : float, optional tolerance : float, optional
distance threshold for width and height, by default 5.0 distance threshold for width and height, by default 5.0
@ -71,8 +74,8 @@ def check_box_redundancy(
redundancy evaluation redundancy evaluation
""" """
# unpack the boxes # unpack the boxes
c1, s1, _ = box1 c1, s1, _ = box_1
c2, s2, _ = box2 c2, s2, _ = box_2
# sort width and height such that (w, h) == (h, w) is treated the same # sort width and height such that (w, h) == (h, w) is treated the same
# (might have been recognized in different orders) # (might have been recognized in different orders)
s1 = sorted(s1) s1 = sorted(s1)
@ -81,7 +84,7 @@ def check_box_redundancy(
center_dist = cast(float, np.linalg.norm(np.array(c1) - np.array(c2))) center_dist = cast(float, np.linalg.norm(np.array(c1) - np.array(c2)))
size_diff = cast(float, np.linalg.norm(np.array(s1) - np.array(s2))) size_diff = cast(float, np.linalg.norm(np.array(s1) - np.array(s2)))
return center_dist < tolerance and size_diff < tolerance return bool(center_dist < tolerance and size_diff < tolerance)
# ** main function # ** main function
@ -168,14 +171,14 @@ def measure_length(
box = cv2.boxPoints(rbox) box = cv2.boxPoints(rbox)
box = np.array(box, dtype=np.int32) box = np.array(box, dtype=np.int32)
# order the points in the contour in top-left, top-right, bottom-right, and bottom-left # order the points in the contour in top-left, top-right, bottom-right, and bottom-left
box = perspective.order_points(box) box = cast(npt.NDArray[np.float32], perspective.order_points(box))
# unpack the bounding box # unpack the bounding box
(tl, tr, br, bl) = box (tl, tr, br, bl) = box
# compute the midpoints between the top-left and top-right as well as bottom-left and bottom-right coordinates # compute the midpoints between the top-left and top-right as well as bottom-left and bottom-right coordinates
(tltrX, tltrY) = midpoint(tl, tr) (tltrX, tltrY) = midpoint(tl, tr)
(blbrX, blbrY) = midpoint(bl, br) (blbrX, blbrY) = midpoint(bl, br)
# compute the midpoints between the top-left and top-right as well as the top-right and bottom-right coordinates # compute the midpoints between the top-left and bottom-left as well as the top-right and bottom-right coordinates
(tlblX, tlblY) = midpoint(tl, bl) (tlblX, tlblY) = midpoint(tl, bl)
(trbrX, trbrY) = midpoint(tr, br) (trbrX, trbrY) = midpoint(tr, br)