Code/Dyschromasie-Applikation.py

@@ -53,8 +53,7 @@ class Protanopie(Dyschromasie):
         # 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(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = np.flipud(self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
@@ -84,8 +83,7 @@ class Deuteranopie(Dyschromasie):
         # 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(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = np.flipud(self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
@@ -114,8 +112,7 @@ class Tritanopie(Dyschromasie):
         # 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(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
+                self.cb_image[i, j] = np.flipud(self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
 
         self.sim_image = np.copy(self.cb_image)
         self.sim_image = self.sim_image.astype('uint8')
@@ -127,7 +124,6 @@ class Tritanopie(Dyschromasie):
                     self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
         return self.sim_image
 
-
 root = tk.Tk()
 root.title("Projekt Dyschromasie")
 
@@ -139,10 +135,6 @@ kanaele = 0
 sim_pro     = tk.IntVar(root)
 sim_deut    = tk.IntVar(root)
 sim_tri     = tk.IntVar(root)
-simGrad = tk.IntVar(root)
-
-simulationsGradient = tk.Scale(root, from_=0, to_=100, variable=simGrad, orient='horizontal')
-simulationsGradient.grid(column= 0, row = 1, columnspan=10)
 
 def browse():
     # Auswahl des FilePaths
@@ -208,6 +200,7 @@ def simulate():
         sim_pic_pro.grid(columnspan=5)
 
 
+
 btn = tk.Button(root, text="Browse", width=25, command=browse, bg='light blue')
 btn.grid(column = 0, row = 0,columnspan=2)
 
@@ -219,8 +212,8 @@ checkButton_p = tk.Checkbutton(root, text="Protanop", variable=sim_pro, onvalue=
 checkButton_d = tk.Checkbutton(root, text="Deutanop", variable=sim_deut, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_t = tk.Checkbutton(root, text="Tritanop", variable=sim_tri, onvalue=1, offvalue=0, height=5, width=20)
 
-checkButton_p.grid(column=0, row=2)
-checkButton_d.grid(column=1, row=2)
-checkButton_t.grid(column=2, row=2)
+checkButton_p.grid(column = 0, row = 1)
+checkButton_d.grid(column = 1, row = 1)
+checkButton_t.grid(column = 2, row = 1)
 
 root.mainloop()

Code/Dyschromasie.py

@@ -78,6 +78,7 @@ S_t = np.array([[1, 0, 0],                     #Simulationsmatrix fuer Tritanopi
 for i in range(rows):
     for j in range(cols):
         cb_image[i,j] = T_reversed.dot(S_p).dot(T).dot(cb_image[i,j])
+        # Da OpenCV Pixelwerte in RGB speichert, aber BGR für den Algorithmus nötig ist, muss die Matrix mit flipud gedreht werden
 
 sim_image = np.copy(cb_image)
 sim_image = sim_image.astype('uint8')