src/dopt_sensor_anomalies/detection.py

@@ -35,20 +35,6 @@ 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
-
-    Parameters
-    ----------
-    pt_A : npt.NDArray[np.floating]
-        tuple of coordinates x, y; shape (2, )
-    pt_B : npt.NDArray[np.floating]
-        tuple of coordinates x, y; shape (2, )
-
-    Returns
-    -------
-    tuple[float, float]
-        tuple of midpoint coordinates
-    """
     return ((pt_A[0] + pt_B[0]) * 0.5, (pt_A[1] + pt_B[1]) * 0.5)
 
 
@@ -57,22 +43,6 @@ def check_box_redundancy(
     box_2: t.Box,
     tolerance: float = 5.0,
 ) -> bool:
-    """to check if bounding box has already been identified and is just a redundant one
-
-    Parameters
-    ----------
-    box_1 : t.Box
-        tuple of box values: ((center_x, center_y), (width, height), angle)
-    box_2 : t.Box
-        tuple of box values: ((center_x, center_y), (width, height), angle)
-    tolerance : float, optional
-        distance threshold for width and height, by default 5.0
-
-    Returns
-    -------
-    bool
-        redundancy evaluation
-    """
     # unpack the boxes
     c1, s1, _ = box_1
     c2, s2, _ = box_2
@@ -93,32 +63,6 @@ def measure_length(
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
 ) -> tuple[t.CsvData, t.SensorImages]:
-    """detect and measure the size of the electrodes
-
-    Parameters
-    ----------
-    file_path : Path
-        path to file to analyse
-    pixels_per_metric_X : float
-        scaling parameter x dimension, Pixels per micrometer in image
-    pixels_per_metric_Y : float
-        scaling parameter y dimension, Pixels per micrometer in image
-
-    Returns
-    -------
-    tuple[t.CsvData, t.SensorImages]
-        t.CsvData: (list) data to save as CSV according to requirements, contains strings and ints
-        t.SensorImages: (TypedDict) contains left and right image corresponding to each sensor
-
-    Raises
-    ------
-    errors.ImageNotReadError
-        image was not read successfully
-    errors.ContourCalculationError
-        during contour detection there were several possible error causes
-    errors.InvalidElectrodeCount
-        an invalid number of electrodes were detected
-    """
     data_csv: list[str | int] = []
     image = cv2.imread(str(file_path))
     if image is None:
@@ -243,25 +187,6 @@ def infer_image(
     image: npt.NDArray[np.uint8],
     model: Patchcore,
 ) -> t.InferenceResult:
-    """evaluate one image
-
-    Parameters
-    ----------
-    image : npt.NDArray[np.uint8]
-        represents image to be checked for anomalies
-    model : Patchcore
-        (loaded PyTorch state dictionary): model for anomaly detection
-
-    Returns
-    -------
-    t.InferenceResult
-        contains:
-        img (numpy.ndarray)
-        anomaly_map_resized (numpy.ndarray): heatmap to visualize detected anomalies
-        anomaly_score (float): evaluation metric, in [0, 1] with close to 0 being no
-            anomaly detected
-        anomaly_label (bool): anomaly detected (1) or not (0)
-    """
     torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(torch_device)
 
@@ -270,7 +195,7 @@ def infer_image(
     pil_image = pil_image.convert("RGB")
     image_np = np.array(pil_image).astype(np.float32) / 255.0
     input_tensor = torch.from_numpy(image_np).permute(2, 0, 1)
-    
+
     input_tensor = input_tensor.unsqueeze(0)
     input_tensor = input_tensor.to(torch_device)
 
@@ -301,19 +226,6 @@ def anomaly_detection(
     data_csv: t.CsvData,
     sensor_images: t.SensorImages,
 ) -> None:
-    """load the model, call function for anomaly detection and store the results
-
-    Parameters
-    ----------
-    file_path : Path
-        path to file to analyse
-    detection_models : t.DetectionModels
-        collection of model paths for the left and right sensor
-    data_csv : t.CsvData
-        (list) data to save as CSV according to requirements, contains strings and ints
-    sensor_images : t.SensorImages
-        _description_
-    """
     file_stem = file_path.stem
     folder_path = file_path.parent
 

src/dopt_sensor_anomalies/detection.pyi

@@ -0,0 +1,135 @@
+from pathlib import Path
+
+import numpy as np
+import numpy.typing as npt
+from anomalib.models import Patchcore
+
+from dopt_sensor_anomalies import types as t
+
+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
+
+    Parameters
+    ----------
+    pt_A : npt.NDArray[np.floating]
+        tuple of coordinates x, y; shape (2, )
+    pt_B : npt.NDArray[np.floating]
+        tuple of coordinates x, y; shape (2, )
+
+    Returns
+    -------
+    tuple[float, float]
+        tuple of midpoint coordinates
+    """
+    ...
+
+def check_box_redundancy(
+    box_1: t.Box,
+    box_2: t.Box,
+    tolerance: float = 5.0,
+) -> bool:
+    """to check if bounding box has already been identified and is just a redundant one
+
+    Parameters
+    ----------
+    box_1 : t.Box
+        tuple of box values: ((center_x, center_y), (width, height), angle)
+    box_2 : t.Box
+        tuple of box values: ((center_x, center_y), (width, height), angle)
+    tolerance : float, optional
+        distance threshold for width and height, by default 5.0
+
+    Returns
+    -------
+    bool
+        redundancy evaluation
+    """
+    ...
+
+def measure_length(
+    file_path: Path,
+    pixels_per_metric_X: float,
+    pixels_per_metric_Y: float,
+) -> tuple[t.CsvData, t.SensorImages]:
+    """detect and measure the size of the electrodes
+
+    Parameters
+    ----------
+    file_path : Path
+        path to file to analyse
+    pixels_per_metric_X : float
+        scaling parameter x dimension, Pixels per micrometer in image
+    pixels_per_metric_Y : float
+        scaling parameter y dimension, Pixels per micrometer in image
+
+    Returns
+    -------
+    tuple[t.CsvData, t.SensorImages]
+        t.CsvData: (list) data to save as CSV according to requirements, contains strings and ints
+        t.SensorImages: (TypedDict) contains left and right image corresponding to each sensor
+
+    Raises
+    ------
+    errors.ImageNotReadError
+        image was not read successfully
+    errors.ContourCalculationError
+        during contour detection there were several possible error causes
+    errors.InvalidElectrodeCount
+        an invalid number of electrodes were detected
+    """
+    ...
+
+def infer_image(
+    image: npt.NDArray[np.uint8],
+    model: Patchcore,
+) -> t.InferenceResult:
+    """evaluate one image
+
+    Parameters
+    ----------
+    image : npt.NDArray[np.uint8]
+        represents image to be checked for anomalies
+    model : Patchcore
+        (loaded PyTorch state dictionary): model for anomaly detection
+
+    Returns
+    -------
+    t.InferenceResult
+        contains:
+        img (numpy.ndarray)
+        anomaly_map_resized (numpy.ndarray): heatmap to visualize detected anomalies
+        anomaly_score (float): evaluation metric, in [0, 1] with close to 0 being no
+            anomaly detected
+        anomaly_label (bool): anomaly detected (1) or not (0)
+    """
+    ...
+
+def anomaly_detection(
+    file_path: Path,
+    detection_models: t.DetectionModels,
+    data_csv: t.CsvData,
+    sensor_images: t.SensorImages,
+) -> None:
+    """load the model, call function for anomaly detection and store the results
+
+    Parameters
+    ----------
+    file_path : Path
+        path to file to analyse
+    detection_models : t.DetectionModels
+        collection of model paths for the left and right sensor
+    data_csv : t.CsvData
+        (list) data to save as CSV according to requirements, contains strings and ints
+    sensor_images : t.SensorImages
+        _description_
+    """
+    ...
+
+def pipeline(
+    user_file_path: str,
+    pixels_per_metric_X: float,
+    pixels_per_metric_Y: float,
+) -> None: ...