add output of anomaly score and sorted according to spec, related to #21 and #22

src/dopt_sensor_anomalies/constants.py

@@ -5,6 +5,8 @@ LIB_ROOT_PATH: Final[Path] = Path(__file__).parent
 STOP_FOLDER_NAME: Final[str] = "python"
 MODEL_FOLDER_NAME: Final[str] = "models"
 
+EXPORT_DATA_SORTING: Final[tuple[str, ...]] = ("sensor_sizes", "right", "left")
+
 THRESHOLD_BW: Final[int] = 63
 BACKBONE: Final[str] = "wide_resnet50_2"
 LAYERS: Final[tuple[str, ...]] = ("layer1", "layer2", "layer3")

src/dopt_sensor_anomalies/detection.py

@@ -11,6 +11,7 @@ import numpy.typing as npt
 import torch
 from anomalib.models import Patchcore
 from dopt_basics import result_pattern
+from dopt_basics.datastructures import flatten
 from imutils import contours, perspective
 from pandas import DataFrame
 from PIL import Image
@@ -56,8 +57,8 @@ def measure_length(
     img_path: Path,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
-) -> tuple[t.CsvData, t.SensorImages]:
+) -> tuple[t.ExportData, t.SensorImages]:
-    data_csv: list[str | int] = []
+    sensor_sizes: list[tuple[str, ...]] = []
     image = cv2.imread(str(img_path))
     if image is None:
         raise errors.ImageNotReadError(f"Image could not be read from: >{img_path}<")
@@ -142,14 +143,14 @@ def measure_length(
         _, binary_warped = cv2.threshold(gray_warped, 80, 255, cv2.THRESH_BINARY)
         pixel_count = np.sum(binary_warped == 0)
 
-        data_csv.extend(
+        sensor_sizes.append(
-            [
+            (
                 f"{dimB:.3f}".replace(".", ","),
                 f"{dimA:.3f}".replace(".", ","),
                 f"{pixel_count / pixels_per_metric_X / pixels_per_metric_Y:.1f}".replace(
                     ".", ","
                 ),
-            ]
+            )
         )
 
     if not filtered_cnts:  # pragma: no cover
@@ -176,7 +177,13 @@ def measure_length(
     cropped_sensor_left = orig[y_min:y_max, x_min:x_middle]
     cropped_sensor_right = orig[y_min:y_max, x_middle:x_max]
 
-    return data_csv, t.SensorImages(left=cropped_sensor_left, right=cropped_sensor_right)
+    sensor_sizes_sorted = cast(
+        tuple[str, ...],
+        tuple(flatten(reversed(sensor_sizes))),  # type: ignore
+    )
+    export_data: t.ExportData = t.ExportData(sensor_sizes=sensor_sizes_sorted)
+
+    return export_data, t.SensorImages(left=cropped_sensor_left, right=cropped_sensor_right)
 
 
 def infer_image(
@@ -218,7 +225,7 @@ def infer_image(
 def anomaly_detection(
     img_path: Path,
     detection_models: t.DetectionModels,
-    data_csv: t.CsvData,
+    export_data: t.ExportData,
     sensor_images: t.SensorImages,
     anomaly_threshold: float,
 ) -> None:
@@ -236,7 +243,10 @@ def anomaly_detection(
         model.load_state_dict(checkpoint["model_state_dict"])
 
         result = infer_image(image, model, anomaly_threshold)
-        data_csv.extend([int(result.anomaly_label)])
+        export_data[side] = (
+            f"{result.anomaly_score:.1f}".replace(".", ","),
+            str(int(result.anomaly_label)),
+        )
 
         ax = axes[i]
         ax.axis("off")
@@ -251,7 +261,12 @@ def anomaly_detection(
     )
     plt.close()
 
-    df = DataFrame([data_csv])
+    csv_data_sorted: tuple[tuple[str, ...]] = tuple(
+        export_data[key] for key in const.EXPORT_DATA_SORTING
+    )
+    csv_data: tuple[str, ...] = tuple(flatten(csv_data_sorted))
+
+    df = DataFrame([csv_data])
     df.to_csv(
         (folder_path / f"{file_stem}.csv"),
         mode="w",
@@ -284,7 +299,7 @@ def pipeline(
     anomaly_detection(
         img_path=file_path,
         detection_models=DETECTION_MODELS,
-        data_csv=data_csv,
+        export_data=data_csv,
         sensor_images=sensor_images,
         anomaly_threshold=anomaly_threshold,
     )

src/dopt_sensor_anomalies/detection.pyi

@@ -54,7 +54,7 @@ def measure_length(
     img_path: Path,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
-) -> tuple[t.CsvData, t.SensorImages]:
+) -> tuple[t.ExportData, t.SensorImages]:
     """detect and measure the size of the electrodes
 
     Parameters
@@ -68,8 +68,10 @@ def measure_length(
 
     Returns
     -------
-    tuple[t.CsvData, t.SensorImages]
+    tuple[t.ExportData, t.SensorImages]
-        t.CsvData: (list) data to save as CSV according to requirements, contains strings and ints
+        t.ExportData: (TypedDict) data to save as CSV according to requirements, contains
+        strings for sensor sizes and anomaly detection results for the left and right hand
+        sensor respectively
         t.SensorImages: (TypedDict) contains left and right image corresponding to each sensor
 
     Raises
@@ -112,7 +114,7 @@ def infer_image(
 def anomaly_detection(
     img_path: Path,
     detection_models: t.DetectionModels,
-    data_csv: t.CsvData,
+    export_data: t.ExportData,
     sensor_images: t.SensorImages,
     anomaly_threshold: float,
 ) -> None:
@@ -124,8 +126,9 @@ def anomaly_detection(
         path to file to analyse
     detection_models : t.DetectionModels
         collection of model paths for the left and right sensor
-    data_csv : t.CsvData
+    export_data: t.ExportData,
-        (list) data to save as CSV according to requirements, contains strings and ints
+        (TypedDict) data to save as CSV according to requirements, contains strings for sensor
+        sizes and anomaly detection results for the left and right hand sensor respectively
     sensor_images : t.SensorImages
         _description_
     """

src/dopt_sensor_anomalies/types.py

@@ -1,6 +1,6 @@
 import dataclasses as dc
 from pathlib import Path
-from typing import TypeAlias, TypedDict
+from typing import NotRequired, TypeAlias, TypedDict
 
 import numpy as np
 import numpy.typing as npt
@@ -17,6 +17,12 @@ class InferenceResult:
     anomaly_label: bool
 
 
+class ExportData(TypedDict):
+    sensor_sizes: tuple[str, ...]
+    left: NotRequired[tuple[str, str]]
+    right: NotRequired[tuple[str, str]]
+
+
 class SensorImages(TypedDict):
     left: npt.NDArray
     right: npt.NDArray

tests/test_detection.py

@@ -67,9 +67,9 @@ def test_measure_length(single_img_path):
         pixels_per_metric_X,
         pixels_per_metric_Y,
     )
-    assert len(data) == 18
+    assert len(data["sensor_sizes"]) == 18
-    assert isinstance(data[0], str)
+    assert isinstance(data["sensor_sizes"][0], str)
-    assert float(data[0].replace(",", ".")) == pytest.approx(1266.932)
+    assert float(data["sensor_sizes"][0].replace(",", ".")) == pytest.approx(1266.932)
     img_left = imgs["left"]
     assert 235 < img_left.shape[0] < 260
     assert 910 < img_left.shape[1] < 960
@@ -89,20 +89,20 @@ def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
     DETECTION_MODELS = dopt_sensor_anomalies._find_paths.get_detection_models(MODEL_FOLDER)
     assert DETECTION_MODELS["left"].exists()
     assert DETECTION_MODELS["right"].exists()
-    data_csv, sensor_images = detect.measure_length(
+    export_data, sensor_images = detect.measure_length(
         path_img_with_failure_TrainedModel,
         pixels_per_metric_X,
         pixels_per_metric_Y,
     )
 
     # measured sizes
-    assert len(data_csv) == 18
+    assert len(export_data["sensor_sizes"]) == 18
     assert sensor_images["left"] is not None
     assert sensor_images["right"] is not None
     detect.anomaly_detection(
         img_path=path_img_with_failure_TrainedModel,
         detection_models=DETECTION_MODELS,
-        data_csv=data_csv,
+        export_data=export_data,
         sensor_images=sensor_images,
         anomaly_threshold=constants.ANOMALY_THRESHOLD_DEFAULT,
     )