Lookup-Table für die Entfrenung der Gamma Korrektur implementiert

Zur Besseren Performance wird eine Lookup Table benötigt. Diese gleicht dynamisch alles Werte ab anstatt bei jedem einzelnen Pixelwert die Berechnung durchzuführen.

Code/Dyschromasie-Applikation.py

 import tkinter as tk
 import cv2
 import numpy as np
-from Farbaenderung import gammaCorrection, reverseGammaCorrection, Dyschromasie
+from Farbaenderung import Dyschromasie
 from Scrollbar import ScrollFrame
 
 root = tk.Tk()

Code/Farbaenderung.py

 import cv2
 
 
-def gammaCorrection(v):
+
+def createGammaLookup():
+    return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64")
+
+
+def createReverseGammaLookup():
+    return np.array([applyGammaCorrection(i/255) for i in np.arange(0.0, 256.0)]).astype("uint8")
+
+
+def removeGammaCorrection(v):
     if v <= 0.04045 * 255:
         return (v / 255) / 12.92
     elif v > 0.04045 * 255:
         return (((v / 255) + 0.055) / 1.055) ** 2.4
 
 
-def reverseGammaCorrection(v_reverse):
+def applyGammaCorrection(v_reverse):
     if abs(v_reverse) <= 0.0031308:
         return round(255 * (12.92 * abs(v_reverse)))
     elif abs(v_reverse) > 0.0031308:
         if (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))) > 255:
-            return 255 - (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))- 255)
+            return 255 - (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055)) - 255)
         else:
             return round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))
 
                            [0.02980165, -0.19318073, 1.16364789]])
 
     def Simulate(self):
+
+        removeGammaCorrectionLUT = createGammaLookup()
+
         if self.sim_kind == 'p':
             sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
                                 [0, 1, 0],
         # Gammakorrektur durchfuehren
         self.cb_image = np.copy(self.img_mat).astype('float64')
 
-        for i in range(self.rows):
-            for j in range(self.cols):
-                for x in range(3):
-                    self.cb_image[i, j, x] = gammaCorrection(self.img_mat[i, j, x])
+        self.cb_image = cv2.LUT(self.img_mat, removeGammaCorrectionLUT)
 
         rechen_Mat = np.copy(self.T_reversed.dot(sim_mat).dot(self.T))
-        
+
         # Einzelne Pixelwertberechnung
         for i in range(self.rows):
             for j in range(self.cols):
         for i in range(self.rows):
             for j in range(self.cols):
                 for x in range(3):
-                    self.sim_image[i, j, x] = reverseGammaCorrection(self.cb_image[i, j, x])
+                    self.sim_image[i, j, x] = applyGammaCorrection(self.cb_image[i, j, x])
 
         return self.sim_image
+