ENH: Enable loading and saving of central correction routines.

src/darsia/corrections/color/colorcorrection.py

@@ -234,10 +234,23 @@ def __init__(
         """
 
         # Define config
-        assert config is not None, "provide config at least with 'roi' key"
-        self.config: dict = copy.deepcopy(config)
-        """Config dictionary for initialization of color correction."""
+        if config is not None:
+            self.config: dict = copy.deepcopy(config)
+            """Config dictionary for initialization of color correction."""
+            self._init_from_config(base)
+        else:
+            self.config = {}
+
+    def _init_from_config(
+        self, base: Optional[Union[darsia.Image, ColorChecker]]
+    ) -> None:
+        """Auxiliary function for initialization from config.
+
+        Args:
+            base (Image or ColorChecker, optional): reference defining a color checker; if
+                None provided, use CustomColorChecker.
 
+        """
         self.active: bool = self.config.get("active", True)
         """Flag controlling whether correction is active"""
 
@@ -265,6 +278,8 @@ def __init__(
         """Flag controlling whether values outside the feasible range [0., 1.] are clipped"""
 
         # Construct color checker
+        if base is None:
+            base = self.config.get("colorchecker", None)
         self._setup_colorchecker(base)
 
     def correct_array(
@@ -386,6 +401,34 @@ def write_config_to_file(self, path: Union[Path, str]) -> None:
         with open(Path(path), "w") as outfile:
             json.dump(self.config, outfile, indent=4)
 
+    def save(self, path: Path) -> None:
+        """Save the color correction to a file.
+
+        Args:
+            path (Path): path to the file
+
+        """
+        # Make sure that the path exists
+        path.parents[0].mkdir(parents=True, exist_ok=True)
+
+        # Save the color correction
+        np.savez(path, config=self.config)
+        print(f"Color correction saved to {path}.")
+
+    def load(self, path: Path) -> None:
+        """Load the color correction from a file.
+
+        Args:
+            path (Path): path to the file
+
+        """
+        # Make sure the file exists
+        assert path.exists(), f"File {path} does not exist."
+
+        # Load the color correction
+        self.config = np.load(path, allow_pickle=True)["config"].item()
+        self._init_from_config(base=None)
+
     # ! ---- Auxiliary files
 
     def _setup_colorchecker(

src/darsia/corrections/color/illuminationcorrection.py

@@ -1,6 +1,7 @@
 """Module containing illumination correction functionality."""
 
 from typing import Literal, Union
+from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -14,7 +15,7 @@
 class IlluminationCorrection(darsia.BaseCorrection):
     """Class for illumination correction."""
 
-    def __init__(
+    def setup(
         self,
         base: Union[darsia.Image, list[darsia.Image]],
         samples: list[tuple[slice, ...]],
@@ -226,3 +227,41 @@ def correct_array(self, img: np.ndarray) -> np.ndarray:
                     self.local_scaling[i if self.colorspace == "rgb" else 0].img,
                 )
         return img_wb
+
+    def save(self, path: Path) -> None:
+        """Save the illumination correction to a file.
+
+        Args:
+            path (Path): path to the file
+
+        """
+        # Make sure the parent directory exists
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Store color space and local scaling images as npz files
+        np.savez(
+            path,
+            config={
+                "colorspace": self.colorspace,
+                "local_scaling": self.local_scaling,
+            },
+        )
+        print(f"Illumination correction saved to {path}.")
+
+    def load(self, path: Path) -> None:
+        """Load the illumination correction from a file.
+
+        Args:
+            path (Path): path to the file
+
+        """
+        # Make sure the file exists
+        if not path.is_file():
+            raise FileNotFoundError(f"File {path} not found.")
+
+        # Load color space and local scaling images from npz file
+        data = np.load(path, allow_pickle=True)["config"].item()
+        if "colorspace" not in data or "local_scaling" not in data:
+            raise ValueError("Invalid file format.")
+        self.colorspace = data["colorspace"]
+        self.local_scaling = data["local_scaling"]

src/darsia/corrections/shape/curvature.py

@@ -101,11 +101,7 @@ def __init__(
             self.height = kwargs.get("height", 1.0)
 
         else:
-            if config is None:
-                raise Exception(
-                    "Please provide either an image as 'image' \
-                        or a config file as 'config'."
-                )
+            warn("No image provided. Please provide an image or a config file.")
 
         # The internally stored config file is tailored to when resize_factor is equal to 1.
         # For other values, it has to be adapted.
@@ -152,6 +148,40 @@ def read_config_from_file(self, path: Path) -> None:
         with open(str(path), "r") as openfile:
             self.config = json.load(openfile)
 
+    def save(self, path: Path) -> None:
+        """Save the curvature correction to a file.
+
+        Arguments:
+            path (Path): path to the file
+
+        """
+        # Make sure the parent directory exists
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Store color space and local scaling images as npz files
+        np.savez(
+            path,
+            config=self.config,
+        )
+        print(f"Curvature correction saved to {path}.")
+
+    def load(self, path: Path) -> None:
+        """Load the curvature correction from a file.
+
+        Arguments:
+            path (Path): path to the file
+
+        """
+        # Make sure the file exists
+        if not path.is_file():
+            raise FileNotFoundError(f"File {path} not found.")
+
+        # Load color space and local scaling images from npz file
+        data = np.load(path, allow_pickle=True)
+        if "config" not in data:
+            raise ValueError("Invalid file format.")
+        self.config = data["config"].item()
+
     def return_image(self) -> darsia.Image:
         """
         Returns the current image as a darsia image width provided width and height.

src/darsia/corrections/shape/drift.py

@@ -17,7 +17,7 @@ class DriftCorrection(darsia.BaseCorrection):
 
     def __init__(
         self,
-        base: Union[np.ndarray, darsia.Image],
+        base: Optional[Union[np.ndarray, darsia.Image]],
         config: Optional[dict] = None,
     ) -> None:
         """
@@ -37,6 +37,10 @@ def __init__(
                     applied or not, default is True.
 
         """
+        # Read baseline from config if not provided
+        if base is None:
+            base = config.get("base")
+            assert base is not None, "Baseline image not provided."
 
         # Read baseline image
         if isinstance(base, darsia.Image):
@@ -55,27 +59,42 @@ def __init__(
         # Establish config
         if config is None:
             config = {}
+        self._init_from_config(config)
+
+        self.translation_estimator = darsia.TranslationEstimator()
+        """Detection of effective translation based on feature detection."""
+
+    def _init_from_config(self, config: dict) -> None:
 
         self.active = config.get("active", True)
         """Flag controlling whether correction is active."""
 
-        relative_padding: float = config.get("padding", 0.0)
+        self.relative_padding: float = config.get("padding", 0.0)
         """Allow for extra padding around the provided roi (relative sense)."""
 
-        roi: Optional[Union[list, np.ndarray]] = config.get("roi")
+        roi: Optional[Union[list, np.ndarray, tuple[slice]]] = config.get("roi")
         self.roi: Optional[tuple[slice, ...]] = (
             None
             if roi is None
-            else darsia.bounding_box(
-                np.array(roi),
-                padding=round(relative_padding * np.min(self.base.shape[:2])),
-                max_size=self.base.shape[:2],
+            else (
+                roi
+                if isinstance(roi, tuple)
+                else darsia.bounding_box(
+                    np.array(roi),
+                    padding=round(self.relative_padding * np.min(self.base.shape[:2])),
+                    max_size=self.base.shape[:2],
+                )
             )
         )
         """ROI for feature detection."""
 
-        self.translation_estimator = darsia.TranslationEstimator()
-        """Detection of effective translation based on feature detection."""
+    def return_config(self) -> dict:
+        """Return config file for the drift correction."""
+        return {
+            "active": self.active,
+            "padding": self.relative_padding,
+            "roi": self.roi,
+        }
 
     # ! ---- Main correction routines
 
@@ -102,3 +121,14 @@ def correct_array(
             )
         else:
             return img
+
+    # ! ---- I/O ---- ! #
+    def load(self, path) -> None:
+        """Load the drift correction from a file."""
+        config = np.load(path, allow_pickle=True)["config"].item()
+        self._init_from_config(config)
+
+    def save(self, path) -> None:
+        """Save the drift correction to a file."""
+        np.savez(path, config=self.return_config())
+        print(f"Drift correction saved to {path}.")