Code/Dyschromasie.py

@@ -62,6 +62,11 @@ T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
                        [-1.1252419, 2.29317094, -0.1678952],
                        [0.02980165, -0.19318073, 1.16364789]])
 
+# Multiplikation der einzelnen Pixelwerte
+for i in range(rows):
+    for j in range(cols):
+        cb_image[i, j] = T.dot(cb_image[i, j])
+
 # Simulationsmatrizen fuer Protanop
 
 S_b = np.array([[0, 1.05118294, -0.05116099],  #Simulationsmatrix fuer Protanopie
@@ -76,22 +81,11 @@ S_t = np.array([[1, 0, 0],                     #Simulationsmatrix fuer Tritanopi
                 [0, 1, 0],
                 [-0.86744736, 1.86727089, 0]])
 
+#choice = input("Bitte geben Sie den Typ der zu simulierenden Farbblindheit an:(B,D,T)")
 
-#Multiplikation der einzelnen Pixel
-for i in range(rows):
-    for j in range(cols):
-        cb_image[i, j] = T_reversed.dot(S_b.dot(T.dot(cb_image[i, j]))) #T^-1*S*T*RBG_values
 
-sim_image = np.copy(cb_image)
-sim_image = sim_image.astype('uint8')
-
-#Rücktransformation der Gammawerte
-for i in range(rows):
-    for j in range(cols):
-        for x in range(3):
-            sim_image[i, j, x] = int(reverseGammaCorrection(cb_image[i, j, x]))
 
 
 cv2.namedWindow("Display")  # Displaywindow erstellen
-cv2.imshow("Display", sim_image)  # Bild zeigen
+cv2.imshow("Display", image)  # Bild zeigen
 cv2.waitKey(0)  # Fenster offen halten