Vereinfachung des Codes mit cv2.cvtColor

Statt das Array doppelt mit der flipud Funktion zu spiegeln, wird es direkt im Einleseprozess von BGR(OpenCV) zu RGB(Pillow und Algorithmus) konvertiert.

Code/Dyschromasie-Applikation.py

 
         # 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):
         # Einzelne Pixelwertberechnung
         for i in range(self.rows):
             for j in range(self.cols):
-                self.cb_image[i, j] = np.flipud(
-                    self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = self.T_reversed.dot(sim_mat).dot(self.T).dot(self.cb_image[i, j])
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
-
         # Rücktransformation der Gammawerte
         for i in range(self.rows):
             for j in range(self.cols):
         # Einzelne Pixelwertberechnung
         for i in range(self.rows):
             for j in range(self.cols):
-                self.cb_image[i, j] = np.flipud(
-                    self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = self.T_reversed.dot(sim_mat).dot(self.T).dot(self.cb_image[i, j])
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
         # Einzelne Pixelwertberechnung
         for i in range(self.rows):
             for j in range(self.cols):
-                self.cb_image[i, j] = np.flipud(
-                    self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = self.T_reversed.dot(sim_mat).dot(self.T).dot(self.cb_image[i, j])
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
         # Einspeichern der Path-Informationen
         global img, rows, cols, kanaele
         img = cv2.imread(path)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         rows, cols, kanaele = img.shape
 
 
     global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
     if sim_deut.get():
         d = Deuteranopie(img, rows, cols, kanaele, simGrad.get()/100)
-        display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB)
+        display_array_deut = d.Simulate()
 
         T = tk.Text(SB.frame, height=1, width=15)
         T.grid(columnspan=5)
         sim_pic_deut.grid(columnspan=5)
     elif sim_tri.get():
         t = Tritanopie(img, rows, cols, kanaele, simGrad.get()/100)
-        display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB)
+        display_array_tri = t.Simulate()
 
         T = tk.Text(SB.frame, height=1, width=15)
         T.grid(columnspan=5)
         sim_pic_tri.grid(columnspan=5)
     elif sim_pro.get():
         p = Protanopie(img, rows, cols, kanaele, simGrad.get()/100)
-        display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB)
+        display_array_pro = p.Simulate()
 
         T = tk.Text(SB.frame, height=1, width=15)
         T.grid(columnspan=5)