diff --git a/Code/Dyschromasie.py b/Code/Dyschromasie.py
index a328cb5..3725ec5 100644
--- a/Code/Dyschromasie.py
+++ b/Code/Dyschromasie.py
@@ -12,35 +12,44 @@ rows = image.shape[0]  # Auslesen der Zeilenanzahl
 cols = image.shape[1]  # Auslesen der Spaltenanzahl
 kanaele = image.shape[2]  # Auslesen der Kanaele (3 fuer RGB, 1 fuer Graubild)
 
-
 def gammaCorrection(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
+    if v <= 0.04045 * 255:
+        return float(((v / 255) / 12.92))
+    elif v > 0.04045 * 255:
+        return float((((v / 255) + 0.055) / 1.055) ** 2.4)
     else:
         print("Ungültiger Wert!!")
         return 1
 
+print(gammaCorrection(image[0,0,0]))
 
 def reverseGammaCorrection(v_reverse):
-    if (v_reverse <= 0.0031308):
-        return 255 * (12.92 * v_reverse)
-    elif (v_reverse > 0.0031308):
-        return 255 * (1.055 * v_reverse ** 0.41666 - 0.055)
+    if v_reverse <= 0.0031308:
+        return int(255 * (12.92 * v_reverse))
+    elif v_reverse > 0.0031308:
+        return int(255 * (1.055 * v_reverse ** 0.41666 - 0.055))
     else:
         print("Ungültiger Wert!!!")
         return 1
 
+
+cb_image = np.copy(image)
+
+for i in range(rows):
+    for j in range(cols):
+        for x in range(3):
+            cb_image[i,j,x] = gammaCorrection(float(image[i,j,x]))
+
+print(cb_image[0,0])
 '''
         0.31399022 0.63951294 0.04649755   Transformationsmatrix zum Konvertieren vom linearen RGB zum LMS Farbraum
 T   =   0.15537241 0.75789446 0.08670142   Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen 
         0.01775239 0.10944209 0.87256922   T*RGB_Farbverktor = LMS_Farbvektor
 '''
 
-T = np.array([[0.31399022,0.63951294,0.04649755],
-              [0.15537241,0.75789446,0.08670142],
-              [0.01775239,0.10944209,0.87256922]])
+T = np.array([[0.31399022, 0.63951294, 0.04649755],
+              [0.15537241, 0.75789446, 0.08670142],
+              [0.01775239, 0.10944209, 0.87256922]])
 
 '''
                5.47221206 −4.6419601   0.16963708    Rücktransformationsmatrix (Inverse von T)
@@ -48,15 +57,12 @@ T_reversed  = -1.1252419  2.29317094  −0.1678952     T_reversed Ü LMS_Farbvek
                0.02980165 −0.19318073  1.16364789
 '''
 
-T_reversed = np.array([[5.47221206,-4.6419601,0.16963708],
-                       [-1.1252419,2.29317094,-0.1678952],
-                       [0.02980165,-0.19318073,1.16364789]])
+T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
+                       [-1.1252419, 2.29317094, -0.1678952],
+                       [0.02980165, -0.19318073, 1.16364789]])
 
-
-# for i in range(rows):           #Durchgehen aller Pixel des Bildes
-#   for j in range(cols):
-#      k = image[i,j]
-#      #Umwandlungsalgorithmus
+# Multiplikation der einzelnen Pixelwerte
+# T.dot(image[x][y])
 
 
 cv2.namedWindow("Display")  # Displaywindow erstellen