add hint for missing models

bump to v0.1.5
add dep dotenv to please anomalib, closes #18
2025-11-21 07:17:31 +01:00 · 2025-11-21 07:08:24 +01:00 · 2025-11-21 07:04:42 +01:00 · 2025-11-21 05:58:46 +00:00 · 2025-11-20 17:13:09 +01:00 · 2025-11-20 16:04:26 +00:00
23 changed files with 3291 additions and 3147 deletions
--- a/.gitattributes
+++ b/.gitattributes
@ -0,0 +1 @@
+*.pth filter=lfs diff=lfs merge=lfs -text
--- a/.gitignore
+++ b/.gitignore
@ -5,6 +5,7 @@ reports/
 *.code-workspace
 # credentials
 CREDENTIALS*
+*.pth

 # Byte-compiled / optimized / DLL files
 __pycache__/
--- a/README.md
+++ b/README.md
@ -1,8 +1,64 @@
 [![pdm-managed](https://img.shields.io/endpoint?url=https%3A%2F%2Fcdn.jsdelivr.net%2Fgh%2Fpdm-project%2F.github%2Fbadge.json)](https://pdm-project.org)
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

-# EKF Diagnostic: Sensor Anomalies
+# EKF Diagnostic: Detektion von Sensor-Anomalien

-This project aims to perform anomaly detection on sensor images with a CLI application.
+## Übersicht

-*Further information is provided at later project stages.*
+### Funktion
+
+Die im Rahmen des Projekts erstellte Anwendung erlaubt es, Bilder von Sensoren, die in der laufenden Fertigung während der Inline-Qualitätskontrolle erstellt werden, auf folgende Eigenschaften hin zu überprüfen:
+
+- Abmessungen der Elektroden eines jeden Sensors auf Basis von Kalibrierungswerten (in der Form Pixel/Maßeinheit)
+- Erkennung von Anomalien und damit möglichen Defekten eines Sensors
+
+Ein zu analysierendes Bild enthält zwei Sensoren mit ihrer jeweils definierten Anzahl an Elektroden. Ein solches Bild kann über das bereitgestellte Command-Line-Interface (CLI) analysiert werden. Ist die Analyse erfolgreich, wird eine nach dem Bild benannte CSV-Datei im selben Verzeichnis, in welchem sich das Bild befindet, abgelegt. Diese CSV-Datei enthält die ermittelten Werte für die Abmessungen der Elektroden sowie deren Flächeninhalte. Jeder Sensor besitzt drei Elektroden. Demzufolge ergeben sich 2x3x3 = 18 Kennwerte.
+
+Darüber hinaus wird ermittelt, ob die Sensoren jeweils Anomalien aufweisen. Die CSV-Datei enthält zusätzlich zu den 18 Maß-Kennwerten zwei Einträge mit jeweils ``0`` oder ``1``, ein Ergebnis der Anomaliedetektion für jeden Sensor. Bei einem Ergebnis von ``0`` wurde keine Anomalie festgestellt, bei ``1`` hingegen schon.
+
+Im Zuge der Analyse wird eine Heatmap für das Bild erzeugt, welche Rückschlüsse auf Anomaliebereiche erlaubt. Die Datei wird analog der CSV-Datei im entsprechenden Ordner der ursprünglichen Bilddatei mit einem Suffix abgelegt.
+
+### Architektur und Implementierung
+
+Die Anwendung ist vollständig in Python implementiert. Zur Nutzung wird eine vollständige Python-Standalone-Umgebung mit allen erforderlichen Abhängigkeiten bereitgestellt. Das Anwendungsverzeichnis wird als gepacktes ZIP-Archiv bereitgestellt. Die entpackte Größe liegt bei etwa 1,3 GB.
+
+Die Nutzung erfolgt über ein CLI, das über ein Python-Skript abgerufen werden kann. Dieses liegt im Wurzelverzeichnis der Python-Umgebung als ``cli.py`` ab.
+
+## Command-Line-Interface
+
+Die Funktionalität wird über ein CLI nutzbar gemacht. Hierfür liegt in der bereitgestellten Distribution im Ordner "python" ein Python-Skript ab. Dieses muss durch den ebenfalls in diesem Ordner befindlichen Interpreter "python.exe" aufgerufen werden. Erfolgt der Aufruf mit einem anderen Interpreter, werden die installierten Abhängigkeiten nicht gefunden und das Programm funktioniert nicht. Ausgehend vom Ordner, in dem das entpackte Applikationsverzeichnis abliegt, kann die Applikation folgendermaßen aufgerufen werden:
+
+```pwsh
+cd ekf-sensor-anomalies-deployment\python
+.\python.exe .\cli.py --help
+```
+
+Dieser Befehl gibt den folgenden Hilfetext aus:
+
+```
+usage: cli.py [-h] img_path calib_value_x calib_value_y
+
+simple CLI tool to analyse single sensor images for anomalies
+
+positional arguments:
+  img_path       file path to the image which is to be analysed
+  calib_value_x  calibration value in pixels per mcm for x axis, type: float
+  calib_value_y  calibration value in pixels per mcm for y axis, type: float
+
+options:
+  -h, --help     show this help message and exit
+```
+
+Das CLI besteht entsprechend der obigen Beschreibung aus einer Funktion. Diese benötigt die folgenden Parameter:
+
+- ``img_path``: den Pfad zur Bilddatei, in diesem Projekt Bitmap-Dateien (``.bmp``)
+- ``calib_value_x``: den Kalibrierwert in x-Richtung zur Ermittlung der Abmessungen, angegeben in Pixel/µm als Gleitkommazahl
+- ``calib_value_y``: den Kalibrierwert in y-Richtung zur Ermittlung der Abmessungen, angegeben in Pixel/µm als Gleitkommazahl
+
+Alle Parameter sind obligatorisch und müssen bereitgestellt werden. Die Analyse ist stets für nur ein Bild zur selben Zeit durchführbar. Eine Übergabe mehrerer Dateien ist nicht möglich. Ausgaben, die nicht auf Fehler zurückzuführen sind, werden standardmäßig über ``STDOUT`` ausgegeben.
+
+### Fehlermeldungen
+
+Die Anwendung führt zahlreiche Verarbeitungsschritte durch. Treten hierbei Fehler auf, werden diese mit ihrer Bezeichnung und einer Fehlerbeschreibung ausgegeben. Dies erlaubt Rückschlüsse, an welcher Stelle der Verarbeitungs-Pipeline Fehler aufgetreten sind und aus welchen Gründen das geschah. Die Fehlerausgabe erfolgt über ``STDERR``.
+
+Ein separates Loggen weiterer Ausgaben erfolgt nicht.
--- a/cli.py
+++ b/cli.py
@ -0,0 +1,55 @@
+import argparse
+import dataclasses as dc
+import sys
+from typing import cast
+
+from dopt_basics import cli
+
+from dopt_sensor_anomalies import _interface
+
+
+@dc.dataclass()
+class CliArgs:
+    img_path: str
+    calib_value_x: float
+    calib_value_y: float
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description=("simple CLI tool to analyse single sensor images for anomalies")
+    )
+    parser.add_argument(
+        "img_path",
+        help="file path to the image which is to be analysed",
+        type=str,
+    )
+    parser.add_argument(
+        "calib_value_x",
+        help="calibration value in pixels per mcm for x axis, type: float",
+        type=float,
+    )
+    parser.add_argument(
+        "calib_value_y",
+        help="calibration value in pixels per mcm for y axis, type: float",
+        type=float,
+    )
+    args = cast(CliArgs, parser.parse_args())
+
+    with cli.LoadingAnimation(
+        "Perform anomaly detection...",
+        "Pipeline ended successfully!",
+    ) as loader:
+        status = _interface.sensor_anomalies_detection(
+            args.img_path,
+            args.calib_value_x,
+            args.calib_value_y,
+        )
+        if status != 0:
+            loader.stop(interrupt=True)
+
+    sys.exit(status)
+
+
+if __name__ == "__main__":
+    main()
--- a/cli_mocked.py
+++ b/cli_mocked.py
@ -0,0 +1,58 @@
+import argparse
+import dataclasses as dc
+import sys
+from typing import cast
+from unittest.mock import patch
+
+from dopt_basics import cli
+
+from dopt_sensor_anomalies import _interface
+
+
+@dc.dataclass()
+class CliArgs:
+    img_path: str
+    calib_value_x: float
+    calib_value_y: float
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "src")
+@patch("dopt_sensor_anomalies._find_paths.MODEL_FOLDER_NAME", "tests/_models")
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description=("simple CLI tool to analyse single sensor images for anomalies")
+    )
+    parser.add_argument(
+        "img_path",
+        help="file path to the image which is to be analysed",
+        type=str,
+    )
+    parser.add_argument(
+        "calib_value_x",
+        help="calibration value in pixels per mcm for x axis, type: float",
+        type=float,
+    )
+    parser.add_argument(
+        "calib_value_y",
+        help="calibration value in pixels per mcm for y axis, type: float",
+        type=float,
+    )
+    args = cast(CliArgs, parser.parse_args())
+
+    with cli.LoadingAnimation(
+        "Perform anomaly detection...",
+        "Pipeline ended successfully!",
+    ) as loader:
+        status = _interface.sensor_anomalies_detection(
+            args.img_path,
+            args.calib_value_x,
+            args.calib_value_y,
+        )
+        if status != 0:
+            loader.stop(interrupt=True)
+
+    sys.exit(status)
+
+
+if __name__ == "__main__":
+    main()
--- a/docs/header.tex
+++ b/docs/header.tex
@ -0,0 +1,13 @@
+\usepackage{listings}
+\usepackage[scaled=0.9]{DejaVuSansMono}  % or another monospaced font
+\renewcommand{\ttdefault}{pcr}           % 'pcr' = Courier
+\lstset{
+  basicstyle=\ttfamily\small,
+  columns=fullflexible,
+  keepspaces=true,
+  breaklines=true,
+  breakatwhitespace=true,
+  showstringspaces=false,
+  frame=none,
+  backgroundcolor=\color{white}
+}
--- a/docs/manual.md
+++ b/docs/manual.md
@ -0,0 +1,63 @@
+# EKF Diagnostic: Detektion von Sensor-Anomalien
+
+## Übersicht
+
+### Funktion
+
+Die im Rahmen des Projekts erstellte Anwendung erlaubt es, Bilder von Sensoren, die in der laufenden Fertigung während der Inline-Qualitätskontrolle erstellt werden, auf folgende Eigenschaften hin zu überprüfen:
+
+- Abmessungen der Elektroden eines jeden Sensors auf Basis von Kalibrierungswerten (in der Form Pixel/Maßeinheit)
+- Erkennung von Anomalien und damit möglichen Defekten eines Sensors
+
+Ein zu analysierendes Bild enthält zwei Sensoren mit ihrer jeweils definierten Anzahl an Elektroden. Ein solches Bild kann über das bereitgestellte Command-Line-Interface (CLI) analysiert werden. Ist die Analyse erfolgreich, wird eine nach dem Bild benannte CSV-Datei im selben Verzeichnis, in welchem sich das Bild befindet, abgelegt. Diese CSV-Datei enthält die ermittelten Werte für die Abmessungen der Elektroden sowie deren Flächeninhalte. Jeder Sensor besitzt drei Elektroden. Demzufolge ergeben sich 2x3x3 = 18 Kennwerte.
+
+Darüber hinaus wird ermittelt, ob die Sensoren jeweils Anomalien aufweisen. Die CSV-Datei enthält zusätzlich zu den 18 Maß-Kennwerten zwei Einträge mit jeweils ``0`` oder ``1``, ein Ergebnis der Anomaliedetektion für jeden Sensor. Bei einem Ergebnis von ``0`` wurde keine Anomalie festgestellt, bei ``1`` hingegen schon.
+
+Im Zuge der Analyse wird eine Heatmap für das Bild erzeugt, welche Rückschlüsse auf Anomaliebereiche erlaubt. Die Datei wird analog der CSV-Datei im entsprechenden Ordner der ursprünglichen Bilddatei mit einem Suffix abgelegt.
+
+### Architektur und Implementierung
+
+Die Anwendung ist vollständig in Python implementiert. Zur Nutzung wird eine vollständige Python-Standalone-Umgebung mit allen erforderlichen Abhängigkeiten bereitgestellt. Das Anwendungsverzeichnis wird als gepacktes ZIP-Archiv bereitgestellt. Die entpackte Größe liegt bei etwa 1,3 GB.
+
+Die Nutzung erfolgt über ein CLI, das über ein Python-Skript abgerufen werden kann. Dieses liegt im Wurzelverzeichnis der Python-Umgebung als ``cli.py`` ab.
+
+## Command-Line-Interface
+
+Die Funktionalität wird über ein CLI nutzbar gemacht. Hierfür liegt in der bereitgestellten Distribution im Ordner "python" ein Python-Skript ab. Dieses muss durch den ebenfalls in diesem Ordner befindlichen Interpreter "python.exe" aufgerufen werden. Erfolgt der Aufruf mit einem anderen Interpreter, werden die installierten Abhängigkeiten nicht gefunden und das Programm funktioniert nicht. Ausgehend vom Ordner, in dem das entpackte Applikationsverzeichnis abliegt, kann die Applikation folgendermaßen aufgerufen werden:
+
+```pwsh
+cd ekf-sensor-anomalies-deployment\python
+.\python.exe .\cli.py --help
+```
+
+Dieser Befehl gibt den folgenden Hilfetext aus:
+
+```
+usage: cli.py [-h] img_path calib_value_x calib_value_y
+
+simple CLI tool to analyse single sensor images for anomalies
+
+positional arguments:
+  img_path       file path to the image which is to be analysed
+  calib_value_x  calibration value in pixels per mcm for x axis, type: float
+  calib_value_y  calibration value in pixels per mcm for y axis, type: float
+```
+\newpage
+```
+options:
+  -h, --help     show this help message and exit
+```
+
+Das CLI besteht entsprechend der obigen Beschreibung aus einer Funktion. Diese benötigt die folgenden Parameter:
+
+- ``img_path``: den Pfad zur Bilddatei, in diesem Projekt Bitmap-Dateien (``.bmp``)
+- ``calib_value_x``: den Kalibrierwert in x-Richtung zur Ermittlung der Abmessungen, angegeben in Pixel/µm als Gleitkommazahl
+- ``calib_value_y``: den Kalibrierwert in y-Richtung zur Ermittlung der Abmessungen, angegeben in Pixel/µm als Gleitkommazahl
+
+Alle Parameter sind obligatorisch und müssen bereitgestellt werden. Die Analyse ist stets für nur ein Bild zur selben Zeit durchführbar. Eine Übergabe mehrerer Dateien ist nicht möglich. Ausgaben, die nicht auf Fehler zurückzuführen sind, werden standardmäßig über ``STDOUT`` ausgegeben.
+
+### Fehlermeldungen
+
+Die Anwendung führt zahlreiche Verarbeitungsschritte durch. Treten hierbei Fehler auf, werden diese mit ihrer Bezeichnung und einer Fehlerbeschreibung ausgegeben. Dies erlaubt Rückschlüsse, an welcher Stelle der Verarbeitungs-Pipeline Fehler aufgetreten sind und aus welchen Gründen das geschah. Die Fehlerausgabe erfolgt über ``STDERR``.
+
+Ein separates Loggen weiterer Ausgaben erfolgt nicht.
--- a/docs/manual.pdf
+++ b/docs/manual.pdf
--- a/pdm.lock
+++ b/pdm.lock
@ -5,7 +5,7 @@
 groups = ["default", "dev", "lint", "nb", "tests"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:4b2f0f4a1813aaf44b68052d728903f36f705d12f720b52b18e21131ee7b605a"
+content_hash = "sha256:68a0d82fb8cdbb8c525191dfbd6b290c14ccb7b60e44ae1d07760f21fe43bb5e"

 [[metadata.targets]]
 requires_python = ">=3.11,<3.14"
@ -23,7 +23,7 @@ files = [

 [[package]]
 name = "aiohttp"
-version = "3.13.0"
+version = "3.13.2"
 requires_python = ">=3.9"
 summary = "Async http client/server framework (asyncio)"
 groups = ["default"]
@ -38,58 +38,58 @@ dependencies = [
    "yarl<2.0,>=1.17.0",
 ]
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 ]

 [[package]]
@ -3918,7 +3915,7 @@ files = [

 [[package]]
 name = "tifffile"
-version = "2025.10.4"
+version = "2025.10.16"
 requires_python = ">=3.11"
 summary = "Read and write TIFF files"
 groups = ["default"]
@ -3926,8 +3923,8 @@ dependencies = [
    "numpy",
 ]
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 ]

 [[package]]
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,15 +1,16 @@
 [project]
 name = "dopt-sensor-anomalies"
-version = "0.1.0"
+version = "0.1.5"
 description = "anomaly detection for sensor images for quality assurance processes"
 authors = [
-    {name = "d-opt GmbH", email = "f.foerster@d-opt.com"},
+    {name = "d-opt GmbH (resp.: Florian Foerster)", email = "f.foerster@d-opt.com"},
 ]
-dependencies = ["imutils>=0.5.4", "dopt-basics>=0.2.0", "numpy>=2.2.6", "anomalib[vlm_clip]>=2.1.0", "open-clip-torch>=3.2.0"]
+dependencies = ["imutils>=0.5.4", "dopt-basics>=0.2.4", "numpy>=2.2.6", "open-clip-torch>=3.2.0", "anomalib==2.1.0", "dotenv>=0.9.9"]
 requires-python = "<3.14,>=3.11"
 readme = "README.md"
 license = {text = "LicenseRef-Proprietary"}

+
 [build-system]
 requires = ["pdm-backend", "Cython", "setuptools"]
 build-backend = "pdm.backend"
@ -76,7 +77,7 @@ directory = "reports/coverage"


 [tool.bumpversion]
-current_version = "0.1.0"
+current_version = "0.1.5"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@ -148,6 +149,7 @@ dev = [
    "cython>=3.1.4",
    "setuptools>=80.9.0",
    "build>=1.3.0",
+    "rich>=14.2.0",
 ]
 nb = [
    "jupyterlab>=4.3.5",
--- a/scripts/cvt_manual.ps1
+++ b/scripts/cvt_manual.ps1
@ -0,0 +1,2 @@
+# covert the manual Markdown file to PDF
+pandoc .\docs\manual.md -o .\docs\manual.pdf -V geometry:"a4paper, margin=2.5cm" -V header-includes="\usepackage[none]{hyphenat}" # -V header-includes="\lstset{escapeinside={(*@}{@*)}}" --include-in-header=.\docs\header.tex # --syntax-highlighting=idiomatic
--- a/scripts/run_full_test_suite.ps1
+++ b/scripts/run_full_test_suite.ps1
@ -0,0 +1,17 @@
+Write-Output "[PWSH] Deleting compiled extension modules..."
+Remove-Item ".\src\dopt_sensor_anomalies\*.pyd" -Force
+Write-Output "[PWSH] Running tests..."
+pdm run pytest --cov --cov-report=html --no-cov-on-fail
+
+if ($? -eq $false){
+    Write-Output "[PWSH] Exiting script because there were failed tests"
+    Write-Output "[PWSH] No coverage report was generated"
+    Exit
+}
+
+Write-Output "[PWSH] Opening coverage report..."
+Invoke-Item .\reports\coverage\index.html
+Write-Output "[PWSH] Installing extension module..."
+pdm install
+Write-Output "[PWSH] Running test suite against extension module..."
+pdm run pytest .\tests\test_detection.py
--- a/src/dopt_sensor_anomalies/_interface.py
+++ b/src/dopt_sensor_anomalies/_interface.py
@ -0,0 +1,44 @@
+"""main pipeline interface for external calls"""
+
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING, TextIO
+
+from dopt_sensor_anomalies import detection
+
+if TYPE_CHECKING:
+    from dopt_basics.result_pattern import Status
+
+
+def _print_error_state(
+    state: Status,
+    out_stream: TextIO,
+) -> None:
+    if state.ExceptionType is None:
+        raise RuntimeError("Tried to treat state as error, but no exception is registered")
+
+    msg = (
+        f"During the procedure the following exception occurred:"
+        f"\nType: {state.ExceptionType.__name__}\nDescription: {state.description}\n"
+        f"Message: {state.message}"
+    )
+    print(f"\r{msg}", flush=True, file=out_stream)
+
+
+def sensor_anomalies_detection(
+    user_img_path: str,
+    pixels_per_metric_X: float,
+    pixels_per_metric_Y: float,
+) -> int:
+    res = detection.pipeline(
+        user_img_path=user_img_path,
+        pixels_per_metric_X=pixels_per_metric_X,
+        pixels_per_metric_Y=pixels_per_metric_Y,
+    )
+    if res.status.code != 0:
+        _print_error_state(res.status, out_stream=sys.stderr)
+        return 1
+
+    res.unwrap()
+    return 0
--- a/src/dopt_sensor_anomalies/constants.py
+++ b/src/dopt_sensor_anomalies/constants.py
@ -6,9 +6,10 @@ STOP_FOLDER_NAME: Final[str] = "python"
 MODEL_FOLDER_NAME: Final[str] = "models"

 THRESHOLD_BW: Final[int] = 63
-BACKBONE: Final[str] = "resnet18"
-LAYERS: Final[tuple[str, str]] = ("layer1", "layer2")
-RATIO: Final[float] = 0.05
+BACKBONE: Final[str] = "wide_resnet50_2"
+LAYERS: Final[tuple[str, ...]] = ("layer1", "layer2", "layer3")
+RATIO: Final[float] = 0.01
+ANOMALY_THRESHOLD: Final[float] = 0.14

 NUM_VALID_ELECTRODES: Final[int] = 6
 HEATMAP_FILENAME_SUFFIX: Final[str] = "_Heatmap"
--- a/src/dopt_sensor_anomalies/detection.c
+++ b/src/dopt_sensor_anomalies/detection.c
--- a/src/dopt_sensor_anomalies/detection.py
+++ b/src/dopt_sensor_anomalies/detection.py
@ -140,10 +140,10 @@ def measure_length(
            f"expected value: count = {num_contours}, expected = {const.NUM_VALID_ELECTRODES}"
        )

-    x_min = min(np.min(c[:, 0, 0]) for c in filtered_cnts) - 20
-    x_max = max(np.max(c[:, 0, 0]) for c in filtered_cnts) + 20
-    y_min = min(np.min(c[:, 0, 1]) for c in filtered_cnts) - 20
-    y_max = max(np.max(c[:, 0, 1]) for c in filtered_cnts) + 20
+    x_min = max(min(np.min(c[:, 0, 0]) for c in filtered_cnts) - 20, 0)
+    x_max = min(max(np.max(c[:, 0, 0]) for c in filtered_cnts) + 20, orig.shape[1])
+    y_min = max(min(np.min(c[:, 0, 1]) for c in filtered_cnts) - 20, 0)
+    y_max = min(max(np.max(c[:, 0, 1]) for c in filtered_cnts) + 20, orig.shape[0])

    rightmost_x_third = max(filtered_cnts[2][:, 0, 0])
    leftmost_x_fourth = min(filtered_cnts[3][:, 0, 0])
@ -176,7 +176,7 @@ def infer_image(
        output = model(input_tensor)

    anomaly_score = output.pred_score.item()
-    anomaly_label = output.pred_label.item()
+    anomaly_label = bool(1 if anomaly_score >= const.ANOMALY_THRESHOLD else 0)
    anomaly_map = output.anomaly_map.squeeze().cpu().numpy()

    img_np = np.array(pil_image)
--- a/tests/_models/README.md
+++ b/tests/_models/README.md
@ -0,0 +1,3 @@
+**PLACE MODELS IN THIS FOLDER**
+
+The model files are too large. Download the files from our internal distribution channel (primarily cloud).
--- a/tests/_models/patchcore_model_left_hand_side.pth
+++ b/tests/_models/patchcore_model_left_hand_side.pth
--- a/tests/_models/patchcore_model_right_hand_side.pth
+++ b/tests/_models/patchcore_model_right_hand_side.pth
--- a/tests/_results/csharp_interface/window_19_fail_model.csv
+++ b/tests/_results/csharp_interface/window_19_fail_model.csv
@ -0,0 +1 @@
+1177,318;804,803;947509,0;876,575;808,853;709020,9;952,191;804,781;766305,3;944,223;792,829;748607,2;838,797;804,902;675148,9;1203,187;792,829;953921,4;0;0
--- a/tests/_results/csharp_interface/window_19_fail_model_Heatmap.png
+++ b/tests/_results/csharp_interface/window_19_fail_model_Heatmap.png
--- a/tests/test_detection.py
+++ b/tests/test_detection.py
@ -94,7 +94,7 @@ def test_isolated_pipeline(results_folder, path_img_with_failure_TrainedModel):
        pixels_per_metric_X,
        pixels_per_metric_Y,
    )
-    print(">>>>>>> Data: ", data_csv)
+
    # measured sizes
    assert len(data_csv) == 18
    assert sensor_images["left"] is not None
@ -150,6 +150,16 @@ def test_full_pipeline_wrapped_FailElectrodeCount(path_img_with_failure_Electrod

@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
 def test_full_pipeline_wrapped_Success(results_folder, path_img_with_failure_TrainedModel):
+    # paths: check files for existence and delete because of other tests
+    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"
+    if csv_file.exists():
+        csv_file.unlink()
+    if heatmap_file.exists():
+        heatmap_file.unlink()
+
    img_path = str(path_img_with_failure_TrainedModel)
    pixels_per_metric_X: float = 0.251
    pixels_per_metric_Y: float = 0.251
@ -159,11 +169,6 @@ def test_full_pipeline_wrapped_Success(results_folder, path_img_with_failure_Tra
    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))
--- a/tests/test_interface.py
+++ b/tests/test_interface.py
@ -0,0 +1,111 @@
+import shutil
+from io import StringIO
+from unittest.mock import patch
+
+import pytest
+from dopt_basics.result_pattern import wrap_result
+
+from dopt_sensor_anomalies import _interface, constants
+
+
+def test_print_error_state_WrongState():
+    from dopt_basics.result_pattern import STATUS_HANDLER
+
+    MSG = "to treat state as error"
+    with pytest.raises(RuntimeError, match=MSG):
+        _interface._print_error_state(STATUS_HANDLER.SUCCESS, out_stream=StringIO())
+
+
+def test_print_error_state(tmp_path):
+    @wrap_result(100)
+    def error_func() -> None:
+        # do something
+        raise RuntimeError("Oops, error occurred")
+
+    err_state = error_func().status
+    output_file = tmp_path / "t_output.txt"
+    output_file.touch()
+
+    with open(output_file, "w") as stream:
+        _interface._print_error_state(err_state, stream)
+
+    lines: list[str]
+    with open(output_file, "r") as file:
+        lines = file.readlines()
+        try:
+            lines.remove("\n")
+        except ValueError:  # pragma: no cover
+            pass
+
+    assert "following exception" in lines[0]
+    assert "Type: RuntimeError" in lines[1]
+    assert "Description:" in lines[2]
+    assert "Message: Oops, error occurred" in lines[3]
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_sensor_anomalies_detection_FailImagePath(setup_temp_dir):
+    img_path = str(setup_temp_dir / "not-existing.bmp")
+
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    MESSAGE = "The provided path seems not to exist"
+
+    with patch("sys.stderr", new_callable=StringIO) as mock_err:
+        ret = _interface.sensor_anomalies_detection(
+            img_path, pixels_per_metric_X, pixels_per_metric_Y
+        )
+        captured = mock_err.getvalue()
+        assert ret != 0
+        assert "FileNotFoundError" in captured
+        assert MESSAGE in captured
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_sensor_anomalies_detection_FailElectrodeCount(path_img_with_failure_ElectrodeCount):
+    img_path = str(path_img_with_failure_ElectrodeCount)
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    MESSAGE = "Number of counted electrodes does not match the"
+
+    with patch("sys.stderr", new_callable=StringIO) as mock_err:
+        ret = _interface.sensor_anomalies_detection(
+            img_path, pixels_per_metric_X, pixels_per_metric_Y
+        )
+        captured = mock_err.getvalue()
+        assert ret != 0
+        assert "InvalidElectrodeCount" in captured
+        assert MESSAGE in captured
+
+
+@patch("dopt_sensor_anomalies._find_paths.STOP_FOLDER_NAME", "lib")
+def test_sensor_anomalies_detection_Success(
+    results_folder, path_img_with_failure_TrainedModel
+):
+    # paths: check files for existence and delete because of other tests
+    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"
+    if csv_file.exists():
+        csv_file.unlink()
+    if heatmap_file.exists():
+        heatmap_file.unlink()
+
+    img_path = str(path_img_with_failure_TrainedModel)
+    pixels_per_metric_X: float = 0.251
+    pixels_per_metric_Y: float = 0.251
+
+    ret = _interface.sensor_anomalies_detection(
+        img_path, pixels_per_metric_X, pixels_per_metric_Y
+    )
+
+    assert ret == 0
+    assert csv_file.exists()
+    assert heatmap_file.exists()
+    target_folder = results_folder / "csharp_interface"
+    target_folder.mkdir(exist_ok=True)
+    shutil.copy(csv_file, (target_folder / csv_file.name))
+    shutil.copy(heatmap_file, (target_folder / heatmap_file.name))
Author	SHA1	Message	Date
foefl	34e05476ba	add hint for missing models	2025-11-21 07:17:31 +01:00
foefl	9f5ea4af5f	bump to v0.1.5	2025-11-21 07:08:24 +01:00
foefl	507e94a73f	add dep `dotenv` to please `anomalib`, closes #18	2025-11-21 07:04:42 +01:00
Florian Förster	6e3192885b	adapted anomaly threshold constant based on tests (#19 ) ANOMALY_THRESHOLD changed Co-authored-by: frasu <frasu@noreply.localhost> Reviewed-on: #19	2025-11-21 05:58:46 +00:00
foefl	80ce3dd46d	bump version to v0.1.4	2025-11-20 17:13:09 +01:00
foefl	8884ef69be	fix result reproduction error on different computers (#17 ) fixes #16 Co-authored-by: frasu <frasu@noreply.localhost> Reviewed-on: #17 Co-authored-by: foefl <f.foerster@d-opt.com> Co-committed-by: foefl <f.foerster@d-opt.com>	2025-11-20 16:04:26 +00:00
foefl	24bba92caf	generated by `Cython` v3.2.1	2025-11-18 15:44:47 +01:00
foefl	22296a7bc3	docs: fix typo	2025-11-13 09:07:40 +01:00
foefl	4050de98c9	update docs	2025-11-13 09:00:48 +01:00
foefl	8ed9c07817	add manual as documentation	2025-11-11 11:39:35 +01:00
foefl	60a7c1f765	bump version to 0.1.3	2025-11-11 10:38:37 +01:00
foefl	7a6081815c	update test case for new exception output	2025-11-11 10:38:24 +01:00
foefl	84f1f6fb1b	remove unneeded print statement in test case	2025-11-11 10:21:50 +01:00
foefl	00214c3b1e	update CLI with loading animation, closes #13	2025-11-11 10:14:33 +01:00
foefl	04a75838e6	upgrade `dopt-basics` to newest release to use CLI spinning animation	2025-11-11 10:11:10 +01:00
foefl	5037e7733b	update CLI to propagate correct exit status code	2025-11-10 15:44:46 +01:00
foefl	4a94815bfe	add graceful error handling with return of status codes, closes #12	2025-11-10 15:27:39 +01:00
foefl	08f70ee672	fix type issue for anomaly label	2025-11-10 14:21:45 +01:00
foefl	9e6daa01ea	update README as manual, closes #15	2025-11-10 14:01:47 +01:00
foefl	7501b1b635	make CLI callable from cmd line with Python executable	2025-11-10 12:22:07 +01:00
foefl	158e242c83	add more specific author metadata	2025-11-10 09:22:22 +01:00
foefl	f9fab829a0	refactor to make cli standalone script, related to #11 ; add type hints to cli help messages, closes #10	2025-11-10 09:06:30 +01:00
foefl	5dff65992e	remove cli entry point, related to #11	2025-11-10 09:02:54 +01:00
foefl	cc7b9d8416	move cli out of lib, related to #11	2025-11-10 09:02:07 +01:00
frasu	346b905a86	src/dopt_sensor_anomalies/detection.py aktualisiert	2025-11-04 10:24:53 +00:00
frasu	7472a128dd	src/dopt_sensor_anomalies/detection.py aktualisiert	2025-11-03 16:34:15 +00:00
foefl	d074e1a840	rename model files	2025-10-24 13:58:18 +02:00
foefl	dd40440843	add simple CLI directly to package, related to #8	2025-10-24 13:03:27 +02:00
foefl	1920d01607	bump version to v0.1.1	2025-10-24 10:47:51 +02:00
foefl	3e41aa34aa	script to run full test suite with Cython modules, closes #6	2025-10-24 10:46:53 +02:00
foefl	712a1f534a	add C# interface module, closes #4	2025-10-24 10:46:08 +02:00
				`@ -0,0 +1 @@`
				`1177,318;804,803;947509,0;876,575;808,853;709020,9;952,191;804,781;766305,3;944,223;792,829;748607,2;838,797;804,902;675148,9;1203,187;792,829;953921,4;0;0`