add full pipeline including tests

src/dopt_sensor_anomalies/detection.py

@@ -1,24 +1,20 @@
 import csv
 import warnings
-from os import path
 from pathlib import Path
 from typing import Any, Final, cast
 
-# Image.MAX_IMAGE_PIXELS = None
 import cv2
 import imutils
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import torch
-
-# from anomalib.engine import Engine
 from anomalib.models import Patchcore
+from dopt_basics import result_pattern
 from imutils import contours, perspective
 from pandas import DataFrame
 from PIL import Image
 from scipy.spatial import distance as dist
-from torchvision.transforms.v2.functional import to_dtype, to_image
 
 import dopt_sensor_anomalies._find_paths
 from dopt_sensor_anomalies import constants as const
@@ -67,14 +63,14 @@ def check_box_redundancy(
 
 # ** main function
 def measure_length(
-    file_path: Path,
+    img_path: Path,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
 ) -> tuple[t.CsvData, t.SensorImages]:
     data_csv: list[str | int] = []
-    image = cv2.imread(str(file_path))
+    image = cv2.imread(str(img_path))
     if image is None:
-        raise errors.ImageNotReadError(f"Image could not be read from: >{file_path}<")
+        raise errors.ImageNotReadError(f"Image could not be read from: >{img_path}<")
 
     cropped = image[500:1500, 100 : image.shape[1] - 100]
     orig = cropped.copy()
@@ -229,13 +225,13 @@ def infer_image(
 
 # ** main function
 def anomaly_detection(
-    file_path: Path,
+    img_path: Path,
     detection_models: t.DetectionModels,
     data_csv: t.CsvData,
     sensor_images: t.SensorImages,
 ) -> None:
-    file_stem = file_path.stem
-    folder_path = file_path.parent
+    file_stem = img_path.stem
+    folder_path = img_path.parent
 
     # reconstruct the model and initialize the engine
     model = Patchcore(
@@ -277,12 +273,13 @@ def anomaly_detection(
     )
 
 
+@result_pattern.wrap_result(100)
 def pipeline(
-    user_file_path: str,
+    user_img_path: str,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
 ) -> None:
-    file_path = Path(user_file_path)
+    file_path = Path(user_img_path)
     if not file_path.exists():
         raise FileNotFoundError("The provided path seems not to exist")
 
@@ -295,7 +292,7 @@ def pipeline(
         file_path, pixels_per_metric_X, pixels_per_metric_Y
     )
     anomaly_detection(
-        file_path=file_path,
+        img_path=file_path,
         detection_models=DETECTION_MODELS,
         data_csv=data_csv,
         sensor_images=sensor_images,

src/dopt_sensor_anomalies/detection.pyi

@@ -3,6 +3,7 @@ from pathlib import Path
 import numpy as np
 import numpy.typing as npt
 from anomalib.models import Patchcore
+from dopt_basics import result_pattern
 
 from dopt_sensor_anomalies import types as t
 
@@ -50,7 +51,7 @@ def check_box_redundancy(
     ...
 
 def measure_length(
-    file_path: Path,
+    img_path: Path,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
 ) -> tuple[t.CsvData, t.SensorImages]:
@@ -58,7 +59,7 @@ def measure_length(
 
     Parameters
     ----------
-    file_path : Path
+    img_path : Path
         path to file to analyse
     pixels_per_metric_X : float
         scaling parameter x dimension, Pixels per micrometer in image
@@ -108,7 +109,7 @@ def infer_image(
     ...
 
 def anomaly_detection(
-    file_path: Path,
+    img_path: Path,
     detection_models: t.DetectionModels,
     data_csv: t.CsvData,
     sensor_images: t.SensorImages,
@@ -117,7 +118,7 @@ def anomaly_detection(
 
     Parameters
     ----------
-    file_path : Path
+    img_path : Path
         path to file to analyse
     detection_models : t.DetectionModels
         collection of model paths for the left and right sensor
@@ -128,8 +129,26 @@ def anomaly_detection(
     """
     ...
 
+@result_pattern.wrap_result(100)
 def pipeline(
-    user_file_path: str,
+    user_img_path: str,
     pixels_per_metric_X: float,
     pixels_per_metric_Y: float,
-) -> None: ...
+) -> None:
+    """full pipeline defined by the agreed requirements
+    wrapped as result pattern, handle errors on higher abstraction level
+
+    Parameters
+    ----------
+    user_img_path : str
+        file path to the image which is to be analysed
+    pixels_per_metric_X : float
+        calibration value for the x axis to measure the size of the electrodes
+    pixels_per_metric_Y : float
+        calibration value for the y axis to measure the size of the electrodes
+
+    Raises
+    ------
+    FileNotFoundError
+        provided image path was not found
+    """

tests/test_detection.py

@@ -1,36 +1,36 @@
 import shutil
-from pathlib import Path
 from unittest.mock import patch
 
 import numpy as np
 import pytest
+from dopt_basics import result_pattern
 
 import dopt_sensor_anomalies._find_paths
 import dopt_sensor_anomalies.detection as detect
 import dopt_sensor_anomalies.types as t
-from dopt_sensor_anomalies import constants
+from dopt_sensor_anomalies import constants, errors
+
+# TODO remove
+# @pytest.fixture(scope="module")
+# def img_paths() -> tuple[Path, ...]:
+#     img_folder = Path(__file__).parent / "_img"
+#     if not img_folder.exists():
+#         raise FileNotFoundError("Img path not existing")
+#     img_paths = tuple(img_folder.glob("*.bmp"))
+#     if not img_paths:
+#         raise ValueError("No images found")
+#     return img_paths
 
 
-@pytest.fixture(scope="module")
-def img_paths() -> tuple[Path, ...]:
-    img_folder = Path(__file__).parent / "_img"
-    if not img_folder.exists():
-        raise FileNotFoundError("Img path not existing")
-    img_paths = tuple(img_folder.glob("*.bmp"))
-    if not img_paths:
-        raise ValueError("No images found")
-    return img_paths
-
-
-@pytest.fixture(scope="module")
-def single_img_path() -> Path:
-    img_folder = Path(__file__).parent / "_img"
-    if not img_folder.exists():
-        raise FileNotFoundError("Img path not existing")
-    img_paths = tuple(img_folder.glob("*_12.bmp"))
-    if not img_paths:
-        raise ValueError("No images found")
-    return img_paths[0]
+# @pytest.fixture(scope="module")
+# def single_img_path() -> Path:
+#     img_folder = Path(__file__).parent / "_img"
+#     if not img_folder.exists():
+#         raise FileNotFoundError("Img path not existing")
+#     img_paths = tuple(img_folder.glob("*_12.bmp"))
+#     if not img_paths:
+#         raise ValueError("No images found")
+#     return img_paths[0]
 
 
 def test_midpoint():
@@ -90,7 +90,6 @@ def test_measure_length(single_img_path):
     assert img_right.shape[2] == 3
 
 
-@pytest.mark.new
 @patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
 def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
     pixels_per_metric_X: float = 0.251
@@ -111,7 +110,7 @@ def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
     assert sensor_images["left"] is not None
     assert sensor_images["right"] is not None
     detect.anomaly_detection(
-        file_path=path_img_with_failure_TrainedModel,
+        img_path=path_img_with_failure_TrainedModel,
         detection_models=DETECTION_MODELS,
         data_csv=data_csv,
         sensor_images=sensor_images,
@@ -125,3 +124,57 @@ def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
     assert heatmap_file.exists()
     shutil.copy(csv_file, (results_folder / csv_file.name))
     shutil.copy(heatmap_file, (results_folder / heatmap_file.name))
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_full_pipeline_wrapped_FailImagePath(setup_temp_dir):
+    img_path = str(setup_temp_dir / "not-existing.bmp")
+    MESSAGE = "The provided path seems not to exist"
+
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
+    assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
+    assert ret.status.ExceptionType is FileNotFoundError
+    assert ret.status.message == MESSAGE
+    with pytest.raises(FileNotFoundError, match=MESSAGE):
+        _ = ret.unwrap()
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_full_pipeline_wrapped_FailElectrodeCount(path_img_with_failure_ElectrodeCount):
+    img_path = str(path_img_with_failure_ElectrodeCount)
+    MESSAGE = "Number of counted electrodes does not match the"
+
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
+    assert ret.status != result_pattern.STATUS_HANDLER.SUCCESS
+    assert ret.status.ExceptionType is errors.InvalidElectrodeCount
+    assert MESSAGE in ret.status.message
+    with pytest.raises(errors.InvalidElectrodeCount, match=MESSAGE):
+        _ = ret.unwrap()
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_full_pipeline_wrapped_Success(results_folder, path_img_with_failure_TrainedModel):
+    img_path = str(path_img_with_failure_TrainedModel)
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    ret = detect.pipeline(img_path, pixels_per_metric_X, pixels_per_metric_Y)
+    assert ret.status == result_pattern.STATUS_HANDLER.SUCCESS
+    assert ret.status.code == 0
+    assert ret.status.ExceptionType is None
+
+    # check files for existence
+    root_img = path_img_with_failure_TrainedModel.parent
+    file_stem = path_img_with_failure_TrainedModel.stem
+    csv_file = root_img / f"{file_stem}.csv"
+    heatmap_file = root_img / f"{file_stem}{constants.HEATMAP_FILENAME_SUFFIX}.png"
+    assert csv_file.exists()
+    assert heatmap_file.exists()
+    shutil.copy(csv_file, (results_folder / csv_file.name))
+    shutil.copy(heatmap_file, (results_folder / heatmap_file.name))