Applikation zur Simulation der verschiedenen Fehlsichtigkeiten

Nachdem nun die Theorie hinter der Simulation geklärt ist, wenden wir uns jetzt dem Applikationbau zu.

Code/Dyschromasie-Applikation.py

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+from PIL import Image, ImageTk
+import PIL
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import cv2
+import numpy as np
+
+
+class Dyschromasie:
+    cb_image = np.array([]).astype('float64')
+    sim_image = np.array([]).astype('uint8')
+
+    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0):
+        self.rows = rows
+        self.cols = cols
+        self.kanaele = kanaele
+        self.img_mat = img_mat
+
+    T = np.array([[0.31399022, 0.63951294, 0.04649755],
+                  [0.15537241, 0.75789446, 0.08670142],
+                  [0.01775239, 0.10944209, 0.87256922]])
+
+    T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
+                           [-1.1252419, 2.29317094, -0.1678952],
+                           [0.02980165, -0.19318073, 1.16364789]])
+
+    def gammaCorrection(self, v):
+        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)
+
+    def reverseGammaCorrection(self, v_reverse):
+        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))
+
+
+class Protanopie(Dyschromasie):
+    sim_mat = np.array([[0, 1.05118294, -0.05116099],
+                        [0, 1, 0],
+                        [0, 0, 1]])
+
+    def Simulate(self):
+        # 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):
+                    self.cb_image[i, j, x] = self.gammaCorrection(self.img_mat[i, j, x])
+
+        # 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.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):
+                for x in range(3):
+                    self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
+
+        return self.sim_image
+
+
+class Deuteranopie(Dyschromasie):
+    sim_mat = np.array([[1, 0, 0],
+                        [0.9513092, 0, 0.04866992],
+                        [0, 0, 1]])
+
+    def Simulate(self):
+        # 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):
+                    self.cb_image[i, j, x] = self.gammaCorrection(self.img_mat[i, j, x])
+
+        # 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.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):
+                for x in range(3):
+                    self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
+        return self.sim_image
+
+
+class Tritanopie(Dyschromasie):
+    sim_mat = np.array([[1, 0, 0],
+                        [0, 1, 0],
+                        [-0.86744736, 1.86727089, 0]])
+
+    def Simulate(self):
+        # 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):
+                    self.cb_image[i, j, x] = self.gammaCorrection(self.img_mat[i, j, x])
+
+        # 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.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):
+                for x in range(3):
+                    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")
+
+img = np.array([])
+rows = 0
+cols = 0
+kanaele = 0
+
+sim_pro     = tk.IntVar(root)
+sim_deut    = tk.IntVar(root)
+sim_tri     = tk.IntVar(root)
+
+def browse():
+    # Auswahl des FilePaths
+    try:
+        path = tk.filedialog.askopenfilename(filetypes=[("Image File", '.jpg')])
+        im = Image.open(path)
+    except:
+        tk.messagebox.showerror(title='Datenfehler', message='Kein Bild gefunden/ausgewählt')
+    global simulateButton
+    if len(path) > 0:
+        simulateButton.config(state='active')
+
+        # Anzeigen des Bildes
+        tkimage = ImageTk.PhotoImage(im)
+        myvar = tk.Label(root, image=tkimage)
+        myvar.image = tkimage
+        myvar.grid(columnspan=5)
+
+        # Einspeichern der Path-Informationen
+        global img, rows, cols, kanaele
+        img = cv2.imread(path)
+        rows, cols, kanaele = img.shape
+
+
+def simulate():
+    global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
+
+    if sim_deut.get():
+        d = Deuteranopie(img, rows, cols, kanaele)
+        display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB)
+
+        T = tk.Text(root,height=1,width=15)
+        T.grid(columnspan=5)
+        T.insert('current',"Deutranopie:")
+
+        conv_SimulationPic_deut = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_deut))
+        sim_pic_deut = tk.Label(root, image=conv_SimulationPic_deut)
+        sim_pic_deut.Image = conv_SimulationPic_deut
+        sim_pic_deut.grid(columnspan=5)
+    elif sim_tri.get():
+        t = Tritanopie(img, rows, cols, kanaele)
+        display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB)
+
+        T = tk.Text(root, height=1, width=15)
+        T.grid(columnspan=5)
+        T.insert('current', "Tritanopie:")
+
+        conv_SimulationPic_tri = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_tri))
+        sim_pic_tri = tk.Label(root, image=conv_SimulationPic_tri)
+        sim_pic_tri.Image = conv_SimulationPic_tri
+        sim_pic_tri.grid(columnspan=5)
+    elif sim_pro.get():
+        p = Protanopie(img, rows, cols, kanaele)
+        display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB)
+
+        T = tk.Text(root, height=1, width=15)
+        T.grid(columnspan=5)
+        T.insert('current', "Protanopie:")
+
+        conv_SimulationPic_pro = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_pro))
+        sim_pic_pro = tk.Label(root, image=conv_SimulationPic_pro)
+        sim_pic_pro.Image = conv_SimulationPic_pro
+        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)
+
+simulateButton = tk.Button(root, text="Simulate", width=25, command=simulate, bg='light blue')
+simulateButton.grid(column = 1, row = 0,columnspan=2)
+simulateButton.config(state='disabled')
+
+checkButton_p = tk.Checkbutton(root, text="Protanop", variable=sim_pro, onvalue=1, offvalue=0, height=5, width=20)
+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 = 1)
+checkButton_d.grid(column = 1, row = 1)
+checkButton_t.grid(column = 2, row = 1)
+
+root.mainloop()