EKF-Diagnostic/sensor-anomalies
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temporally remove Engineimport

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Florian Förster 2025-10-09 07:56:05 +02:00
parent 4bf5bcc182
commit d5a9071f9d
1 changed files with 7 additions and 6 deletions
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src/dopt_sensor_anomalies/detection.py
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@ -10,7 +10,8 @@ import matplotlib.pyplot as plt
import numpy as np import numpy as np
import numpy.typing as npt import numpy.typing as npt
import torch import torch
from anomalib.engine import Engine
# from anomalib.engine import Engine
from anomalib.models import Patchcore from anomalib.models import Patchcore
from imutils import contours, perspective from imutils import contours, perspective
from pandas import DataFrame from pandas import DataFrame
@ -30,14 +31,14 @@ pixels_per_metric_Y: float = 0.251
# measuring # measuring
def midpoint(ptA: npt.NDArray, ptB: npt.NDArray) -> tuple[float, float]: def midpoint(pt_A: npt.NDArray, pt_B: npt.NDArray) -> tuple[float, float]:
"""to identify the midpoint of a 2D area """to identify the midpoint of a 2D area
Parameters Parameters
---------- ----------
ptA : npt.NDArray pt_A : npt.NDArray
tuple of coordinates x, y; shape (2, ) tuple of coordinates x, y; shape (2, )
ptB : npt.NDArray pt_B : npt.NDArray
tuple of coordinates x, y; shape (2, ) tuple of coordinates x, y; shape (2, )
Returns Returns
@ -45,7 +46,7 @@ def midpoint(ptA: npt.NDArray, ptB: npt.NDArray) -> tuple[float, float]:
tuple[float, float] tuple[float, float]
tuple of midpoint coordinates tuple of midpoint coordinates
""" """
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5) return ((pt_A[0] + pt_B[0]) * 0.5, (pt_A[1] + pt_B[1]) * 0.5)
def check_box_redundancy( def check_box_redundancy(
@ -377,7 +378,7 @@ def anomaly_detection(
backbone=const.BACKBONE, layers=const.LAYERS, coreset_sampling_ratio=const.RATIO backbone=const.BACKBONE, layers=const.LAYERS, coreset_sampling_ratio=const.RATIO
) )
# ?? benötigt? Wird nicht genutzt # ?? benötigt? Wird nicht genutzt
engine = Engine() # engine = Engine()
# preparation for plot # preparation for plot
_, axes = plt.subplots(1, 2, figsize=(12, 6)) _, axes = plt.subplots(1, 2, figsize=(12, 6))