Add image_processing/src/image_processing/core.py

image_processing/src/image_processing/core.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+import os
+from pathlib import Path
+from typing import List
+import logging
+import numpy as np
+import cv2
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+ch = logging.StreamHandler()
+formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+ch.setFormatter(formatter)
+logger.addHandler(ch)
+
+
+class ImageProcessingError(Exception):
+    """Custom exception for image processing errors."""
+    pass
+
+
+def _validate_image_paths(image_list: List[str]) -> List[Path]:
+    if not isinstance(image_list, list):
+        raise ValueError("image_list must be a list of file paths.")
+    valid_paths = []
+    for img_path in image_list:
+        p = Path(img_path)
+        if not p.exists() or not p.is_file():
+            logger.error(f"Invalid image path: {img_path}")
+            raise ImageProcessingError(f"File not found or invalid: {img_path}")
+        if p.suffix.lower() not in ['.jpg', '.jpeg', '.png']:
+            logger.error(f"Unsupported file type: {img_path}")
+            raise ImageProcessingError(f"Unsupported image format: {p.suffix}")
+        valid_paths.append(p)
+    return valid_paths
+
+
+def process_images(image_list: List[str]) -> np.ndarray:
+    """Verarbeitet eine Liste von Rohbildern und erstellt ein gestacktes, rauschreduziertes Ergebnisbild.
+
+    Args:
+        image_list (list[str]): Liste von Pfaden zu den zu verarbeitenden Bilddateien.
+
+    Returns:
+        np.ndarray: Gestacktes kombiniertes Bildarray.
+    """
+    valid_paths = _validate_image_paths(image_list)
+
+    if not valid_paths:
+        raise ImageProcessingError("No valid image paths provided.")
+
+    images = []
+    for path in valid_paths:
+        img = cv2.imread(str(path), cv2.IMREAD_COLOR)
+        if img is None:
+            logger.error(f"Unable to load image: {path}")
+            raise ImageProcessingError(f"Unable to load image: {path}")
+        images.append(img.astype(np.float32))
+
+    # Sicherstellen, dass alle Bilder die gleiche Größe haben
+    shapes = {im.shape for im in images}
+    if len(shapes) != 1:
+        logger.error("Images have differing shapes, unable to stack.")
+        raise ImageProcessingError("All images must have the same dimensions.")
+
+    # Stacken durch Mittelung
+    logger.info(f"Stacking {len(images)} images...")
+    stacked_image = np.mean(images, axis=0)
+    stacked_image = np.clip(stacked_image, 0, 255).astype(np.uint8)
+
+    logger.info("Stacking complete.")
+    assert isinstance(stacked_image, np.ndarray), "Output must be a numpy array."
+
+    return stacked_image