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f1348cfef2
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f1348cfef2 | ||
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@ -53,7 +53,8 @@ class Protanopie(Dyschromasie):
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# Einzelne Pixelwertberechnung
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for i in range(self.rows):
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for j in range(self.cols):
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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])))
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -83,7 +84,8 @@ class Deuteranopie(Dyschromasie):
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# Einzelne Pixelwertberechnung
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for i in range(self.rows):
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for j in range(self.cols):
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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])))
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -112,7 +114,8 @@ class Tritanopie(Dyschromasie):
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# Einzelne Pixelwertberechnung
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for i in range(self.rows):
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for j in range(self.cols):
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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])))
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -124,6 +127,7 @@ class Tritanopie(Dyschromasie):
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self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
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return self.sim_image
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root = tk.Tk()
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root.title("Projekt Dyschromasie")
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@ -132,9 +136,13 @@ rows = 0
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cols = 0
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kanaele = 0
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sim_pro = tk.IntVar(root)
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sim_deut = tk.IntVar(root)
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sim_tri = tk.IntVar(root)
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sim_pro = tk.IntVar(root)
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sim_deut = tk.IntVar(root)
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sim_tri = tk.IntVar(root)
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simGrad = tk.IntVar(root)
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simulationsGradient = tk.Scale(root, from_=0, to_=100, variable=simGrad, orient='horizontal')
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simulationsGradient.grid(column= 0, row = 1, columnspan=10)
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def browse():
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# Auswahl des FilePaths
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@ -166,9 +174,9 @@ def simulate():
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d = Deuteranopie(img, rows, cols, kanaele)
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display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB)
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T = tk.Text(root,height=1,width=15)
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T = tk.Text(root, height=1, width=15)
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T.grid(columnspan=5)
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T.insert('current',"Deutranopie:")
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T.insert('current', "Deutranopie:")
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conv_SimulationPic_deut = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_deut))
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sim_pic_deut = tk.Label(root, image=conv_SimulationPic_deut)
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@ -200,20 +208,19 @@ def simulate():
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sim_pic_pro.grid(columnspan=5)
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btn = tk.Button(root, text="Browse", width=25, command=browse, bg='light blue')
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btn.grid(column = 0, row = 0,columnspan=2)
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btn.grid(column=0, row=0, columnspan=2)
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simulateButton = tk.Button(root, text="Simulate", width=25, command=simulate, bg='light blue')
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simulateButton.grid(column = 1, row = 0,columnspan=2)
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simulateButton.grid(column=1, row=0, columnspan=2)
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simulateButton.config(state='disabled')
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checkButton_p = tk.Checkbutton(root, text="Protanop", variable=sim_pro, onvalue=1, offvalue=0, height=5, width=20)
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checkButton_d = tk.Checkbutton(root, text="Deutanop", variable=sim_deut, onvalue=1, offvalue=0, height=5, width=20)
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checkButton_t = tk.Checkbutton(root, text="Tritanop", variable=sim_tri, onvalue=1, offvalue=0, height=5, width=20)
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checkButton_p.grid(column = 0, row = 1)
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checkButton_d.grid(column = 1, row = 1)
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checkButton_t.grid(column = 2, row = 1)
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checkButton_p.grid(column=0, row=2)
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checkButton_d.grid(column=1, row=2)
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checkButton_t.grid(column=2, row=2)
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root.mainloop()
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@ -78,7 +78,6 @@ S_t = np.array([[1, 0, 0], #Simulationsmatrix fuer Tritanopi
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for i in range(rows):
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for j in range(cols):
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cb_image[i,j] = T_reversed.dot(S_p).dot(T).dot(cb_image[i,j])
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# Da OpenCV Pixelwerte in RGB speichert, aber BGR für den Algorithmus nötig ist, muss die Matrix mit flipud gedreht werden
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sim_image = np.copy(cb_image)
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sim_image = sim_image.astype('uint8')
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