EKF-Diagnostic/sensor-anomalies
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Apply changes to meet customer specification #27

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foefl merged 14 commits from main_adapted into main 2026-01-07 09:30:57 +00:00
Conversation 0 Commits 14 Files Changed 17 +43 -9
6 changed files with 43 additions and 9 deletions
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2
src/dopt_sensor_anomalies/_interface.py
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@ -30,11 +30,13 @@ def sensor_anomalies_detection(
user_img_path: str, user_img_path: str,
pixels_per_metric_X: float, pixels_per_metric_X: float,
pixels_per_metric_Y: float, pixels_per_metric_Y: float,
anomaly_threshold: float,
) -> int: ) -> int:
res = detection.pipeline( res = detection.pipeline(
user_img_path=user_img_path, user_img_path=user_img_path,
pixels_per_metric_X=pixels_per_metric_X, pixels_per_metric_X=pixels_per_metric_X,
pixels_per_metric_Y=pixels_per_metric_Y, pixels_per_metric_Y=pixels_per_metric_Y,
anomaly_threshold=anomaly_threshold,
) )
if res.status.code != 0: if res.status.code != 0:
_print_error_state(res.status, out_stream=sys.stderr) _print_error_state(res.status, out_stream=sys.stderr)

2
src/dopt_sensor_anomalies/constants.py
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@ -9,7 +9,7 @@ THRESHOLD_BW: Final[int] = 63
BACKBONE: Final[str] = "wide_resnet50_2" BACKBONE: Final[str] = "wide_resnet50_2"
LAYERS: Final[tuple[str, ...]] = ("layer1", "layer2", "layer3") LAYERS: Final[tuple[str, ...]] = ("layer1", "layer2", "layer3")
RATIO: Final[float] = 0.01 RATIO: Final[float] = 0.01
ANOMALY_THRESHOLD: Final[float] = 0.14 ANOMALY_THRESHOLD_DEFAULT: Final[float] = 0.14
NUM_VALID_ELECTRODES: Final[int] = 6 NUM_VALID_ELECTRODES: Final[int] = 6
HEATMAP_FILENAME_SUFFIX: Final[str] = "_Heatmap" HEATMAP_FILENAME_SUFFIX: Final[str] = "_Heatmap"

8
src/dopt_sensor_anomalies/detection.py
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@ -182,6 +182,7 @@ def measure_length(
def infer_image( def infer_image(
image: npt.NDArray[np.uint8], image: npt.NDArray[np.uint8],
model: Patchcore, model: Patchcore,
anomaly_threshold: float,
) -> t.InferenceResult: ) -> t.InferenceResult:
torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(torch_device) model.to(torch_device)
@ -200,7 +201,7 @@ def infer_image(
output = model(input_tensor) output = model(input_tensor)
anomaly_score = output.pred_score.item() anomaly_score = output.pred_score.item()
anomaly_label = bool(1 if anomaly_score >= const.ANOMALY_THRESHOLD else 0) anomaly_label = bool(1 if anomaly_score >= anomaly_threshold else 0)
anomaly_map = output.anomaly_map.squeeze().cpu().numpy() anomaly_map = output.anomaly_map.squeeze().cpu().numpy()
img_np = np.array(pil_image) img_np = np.array(pil_image)
@ -219,6 +220,7 @@ def anomaly_detection(
detection_models: t.DetectionModels, detection_models: t.DetectionModels,
data_csv: t.CsvData, data_csv: t.CsvData,
sensor_images: t.SensorImages, sensor_images: t.SensorImages,
anomaly_threshold: float,
) -> None: ) -> None:
file_stem = img_path.stem file_stem = img_path.stem
folder_path = img_path.parent folder_path = img_path.parent
@ -233,7 +235,7 @@ def anomaly_detection(
checkpoint = torch.load(detection_models[side]) checkpoint = torch.load(detection_models[side])
model.load_state_dict(checkpoint["model_state_dict"]) model.load_state_dict(checkpoint["model_state_dict"])
result = infer_image(image, model) result = infer_image(image, model, anomaly_threshold)
data_csv.extend([int(result.anomaly_label)]) data_csv.extend([int(result.anomaly_label)])
ax = axes[i] ax = axes[i]
@ -265,6 +267,7 @@ def pipeline(
user_img_path: str, user_img_path: str,
pixels_per_metric_X: float, pixels_per_metric_X: float,
pixels_per_metric_Y: float, pixels_per_metric_Y: float,
anomaly_threshold: float,
) -> None: ) -> None:
file_path = Path(user_img_path) file_path = Path(user_img_path)
if not file_path.exists(): if not file_path.exists():
@ -283,4 +286,5 @@ def pipeline(
detection_models=DETECTION_MODELS, detection_models=DETECTION_MODELS,
data_csv=data_csv, data_csv=data_csv,
sensor_images=sensor_images, sensor_images=sensor_images,
anomaly_threshold=anomaly_threshold,
) )

3
src/dopt_sensor_anomalies/detection.pyi
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@ -86,6 +86,7 @@ def measure_length(
def infer_image( def infer_image(
image: npt.NDArray[np.uint8], image: npt.NDArray[np.uint8],
model: Patchcore, model: Patchcore,
anomaly_threshold: float,
) -> t.InferenceResult: ) -> t.InferenceResult:
"""evaluate one image """evaluate one image
@ -113,6 +114,7 @@ def anomaly_detection(
detection_models: t.DetectionModels, detection_models: t.DetectionModels,
data_csv: t.CsvData, data_csv: t.CsvData,
sensor_images: t.SensorImages, sensor_images: t.SensorImages,
anomaly_threshold: float,
) -> None: ) -> None:
"""load the model, call function for anomaly detection and store the results """load the model, call function for anomaly detection and store the results
@ -134,6 +136,7 @@ def pipeline(
user_img_path: str, user_img_path: str,
pixels_per_metric_X: float, pixels_per_metric_X: float,
pixels_per_metric_Y: float, pixels_per_metric_Y: float,
anomaly_threshold: float,
) -> None: ) -> None:
"""full pipeline defined by the agreed requirements """full pipeline defined by the agreed requirements
wrapped as result pattern, handle errors on higher abstraction level wrapped as result pattern, handle errors on higher abstraction level

22
tests/test_detection.py
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@ -104,6 +104,7 @@ def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
detection_models=DETECTION_MODELS, detection_models=DETECTION_MODELS,
data_csv=data_csv, data_csv=data_csv,
sensor_images=sensor_images, sensor_images=sensor_images,
anomaly_threshold=constants.ANOMALY_THRESHOLD_DEFAULT,
) )
# check files for existence # check files for existence
root_img = path_img_with_failure_TrainedModel.parent root_img = path_img_with_failure_TrainedModel.parent
@ -124,7 +125,12 @@ def test_full_pipeline_wrapped_FailImagePath(setup_temp_dir):
pixels_per_metric_X: float = 0.251 pixels_per_metric_X: float = 0.251
pixels_per_metric_Y: float = 0.251 pixels_per_metric_Y: float = 0.251
ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y) ret = detect.pipeline(
img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
)
assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
assert ret.status.ExceptionType is FileNotFoundError assert ret.status.ExceptionType is FileNotFoundError
assert ret.status.message == MESSAGE assert ret.status.message == MESSAGE
@ -140,7 +146,12 @@ def test_full_pipeline_wrapped_FailElectrodeCount(path_img_with_failure_Electrod
pixels_per_metric_X: float = 0.251 pixels_per_metric_X: float = 0.251
pixels_per_metric_Y: float = 0.251 pixels_per_metric_Y: float = 0.251
ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y) ret = detect.pipeline(
img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
)
assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
assert ret.status.ExceptionType is errors.InvalidElectrodeCount assert ret.status.ExceptionType is errors.InvalidElectrodeCount
assert MESSAGE in ret.status.message assert MESSAGE in ret.status.message
@ -164,7 +175,12 @@ def test_full_pipeline_wrapped_Success(results_folder, path_img_with_failure_Tra
pixels_per_metric_X: float = 0.251 pixels_per_metric_X: float = 0.251
pixels_per_metric_Y: float = 0.251 pixels_per_metric_Y: float = 0.251
ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y) ret = detect.pipeline(
img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
)
assert ret.status == result_pattern.STATUS_HANDLER.SUCCESS assert ret.status == result_pattern.STATUS_HANDLER.SUCCESS
assert ret.status.code == 0 assert ret.status.code == 0
assert ret.status.ExceptionType is None assert ret.status.ExceptionType is None

15
tests/test_interface.py
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@ -54,7 +54,10 @@ def test_sensor_anomalies_detection_FailImagePath(setup_temp_dir):
with patch("sys.stderr", new_callable=StringIO) as mock_err: with patch("sys.stderr", new_callable=StringIO) as mock_err:
ret = _interface.sensor_anomalies_detection( ret = _interface.sensor_anomalies_detection(
img_path, pixels_per_metric_X, pixels_per_metric_Y img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
) )
captured = mock_err.getvalue() captured = mock_err.getvalue()
assert ret != 0 assert ret != 0
@ -72,7 +75,10 @@ def test_sensor_anomalies_detection_FailElectrodeCount(path_img_with_failure_Ele
with patch("sys.stderr", new_callable=StringIO) as mock_err: with patch("sys.stderr", new_callable=StringIO) as mock_err:
ret = _interface.sensor_anomalies_detection( ret = _interface.sensor_anomalies_detection(
img_path, pixels_per_metric_X, pixels_per_metric_Y img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
) )
captured = mock_err.getvalue() captured = mock_err.getvalue()
assert ret != 0 assert ret != 0
@ -99,7 +105,10 @@ def test_sensor_anomalies_detection_Success(
pixels_per_metric_Y: float = 0.251 pixels_per_metric_Y: float = 0.251
ret = _interface.sensor_anomalies_detection( ret = _interface.sensor_anomalies_detection(
img_path, pixels_per_metric_X, pixels_per_metric_Y img_path,
pixels_per_metric_X,
pixels_per_metric_Y,
constants.ANOMALY_THRESHOLD_DEFAULT,
) )
assert ret == 0 assert ret == 0