Initial commit: Speckle-Scanner 3D pipeline with setup README

02_Calibration/calibrationclasses/preprocessing.py

@@ -0,0 +1,82 @@
+from typing import List, Tuple
+import cv2
+import numpy as np
+
+
+class Preprocessing:
+    """Preprocessing class.
+
+    Parameters
+    ----------
+    clipLimit: float
+        default = 5.0
+    tileGridSize: Tuple[int, int]
+        default = (15, 15)
+    thresh1: int
+        default = 0
+    thresh2: int
+        default = 255
+    """
+
+    def __init__(
+        self,
+        tileGridSize: Tuple[int, int] = (15, 15),
+        clipLimit: float = 5.0,
+        thresh1: int = 0,
+        thresh2: int = 255,
+    ) -> None:
+        self.tileGridSize = tileGridSize
+        self.clipLimit = clipLimit
+        self.thresh1 = thresh1
+        self.thresh2 = thresh2
+
+
+    def gray(self, image: np.ndarray) -> np.ndarray:
+        """Convert to GRAY for a given image.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            image of chessboard
+
+        Returns
+        -------
+        np.ndarray
+            image of chessboard converted to GRAY
+        """
+        gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+        return gray
+    
+    def clahe(self, image: np.ndarray) -> np.ndarray:
+        """Apply Clahe to GRAY Shimage.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            image of chessboard
+
+        Returns
+        -------
+        np.ndarray
+            image of chessboard converted to GRAY and applied CLAHE
+        """
+        clahe = cv2.createCLAHE(clipLimit = self.clipLimit, tileGridSize = self.tileGridSize)
+        clahed = clahe.apply(self.gray(image))
+        return clahed
+    
+    def threshold(self, image: np.ndarray) -> np.ndarray:
+        """Apply Clahe to GRAY Shimage.
+
+        Parameters
+        ----------
+        image : np.ndarray
+            image of chessboard
+
+        Returns
+        -------
+        np.ndarray
+            image of chessboard converted to GRAY applied CLAHE and applied THRESHOLD
+        """
+        criteria = cv2.THRESH_BINARY + cv2.THRESH_OTSU+1
+        ret, threshold  = cv2.threshold(self.clahe(image), self.thresh1, self.thresh2, criteria)
+        return threshold