Kleine Anpassungen

Der Dalt Algorithmus wird nun von dem Dalt-Button ausgeführt. Gleichzeitig wurde die Funktion showImage geschrieben, um Code zu verschlanken und lediglich die Switchfunktionen simulate und daltonize für die Auswahl der Simulationsart zu nutzen.
PS: Mehrere Bilder zum Testen eingefügt

Code/Dyschromasie-Applikation.py

@@ -1,10 +1,9 @@
 from PIL import Image, ImageTk
 import PIL
 import tkinter as tk
-from tkinter import filedialog, messagebox
 import cv2
 import numpy as np
-from Farbaenderung import gammaCorrection, reverseGammaCorrection, Dyschromasie
+from Farbaenderung import Dyschromasie
 from Scrollbar import ScrollFrame
 
 root = tk.Tk()
@@ -26,6 +25,7 @@ sim_tri = tk.IntVar(root)
 simulationsGradient = tk.Scale(SB.frame, from_=0, to_=100, variable=simGrad, orient='horizontal')
 simulationsGradient.grid(column=0, row=1, columnspan=10)
 
+
 def browse():
     # Auswahl des FilePaths
     try:
@@ -36,6 +36,7 @@ def browse():
     global simulateButton
     if len(path) > 0:
         simulateButton.config(state='active')
+        daltonButton.config(state='active')
 
         # Anzeigen des Bildes
         tkimage = ImageTk.PhotoImage(im)
@@ -47,60 +48,62 @@ def browse():
         global img, rows, cols, kanaele
         img = cv2.imread(path)
         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        #img = cv2.GaussianBlur(img, (3, 3), 0)
         rows, cols, kanaele = img.shape
 
 
+def showImage(img, rows, cols, kanaele, sim_faktor, sim_kind, sim_art):
+    color_blindness = Dyschromasie(img, rows, cols, kanaele, sim_faktor, sim_kind)
+    if sim_art == 'Sim':
+        color_blindness_mat = np.copy(color_blindness.Simulate()).astype('uint8')
+    else:
+        color_blindness_mat = np.copy(color_blindness.daltonize()).astype('uint8')
+
+    T = tk.Text(SB.frame, height=1, width=15)
+    T.grid(columnspan=5)
+    print_string = sim_kind + ' ' + str(round(color_blindness.sim_faktor * 100)) + "%"
+    T.insert('current', print_string)
+
+    conv_SimulationPic = ImageTk.PhotoImage(image=PIL.Image.fromarray(color_blindness_mat))
+    sim_pic = tk.Label(SB.frame, image=conv_SimulationPic)
+    sim_pic.Image = conv_SimulationPic
+    sim_pic.grid(columnspan=5)
+
+
 def simulate():
     global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
     if sim_deut.get():
-        d = Dyschromasie(img, rows, cols, kanaele, simGrad.get()/100, 'd')
-        display_array_deut = np.copy(d.Simulate()).astype('uint8')
+        showImage(img, rows, cols, kanaele, simGrad.get()/100, 'Deuteranop', 'Sim')
 
-        T = tk.Text(SB.frame, height=1, width=15)
-        T.grid(columnspan=5)
-        print_string = "Deutranop(" + str(round(d.sim_faktor*100)) + "%)"
-        T.insert('current', print_string)
-
-        conv_SimulationPic_deut = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_deut))
-        sim_pic_deut = tk.Label(SB.frame, image=conv_SimulationPic_deut)
-        sim_pic_deut.Image = conv_SimulationPic_deut
-        sim_pic_deut.grid(columnspan=5)
     elif sim_tri.get():
-        t = Dyschromasie(img, rows, cols, kanaele, simGrad.get()/100, 't')
-        display_array_tri = np.copy(t.Simulate()).astype('uint8')
+        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Tritanop', 'Sim')
 
-        T = tk.Text(SB.frame, height=1, width=15)
-        T.grid(columnspan=5)
-        print_string = "Tritanop(" + str(round(t.sim_faktor * 100)) + "%)"
-        T.insert('current', print_string)
-
-        conv_SimulationPic_tri = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_tri))
-        sim_pic_tri = tk.Label(SB.frame, image=conv_SimulationPic_tri)
-        sim_pic_tri.Image = conv_SimulationPic_tri
-        sim_pic_tri.grid(columnspan=5)
     elif sim_pro.get():
-        p = Dyschromasie(img, rows, cols, kanaele, simGrad.get()/100, 'p')
-        display_array_pro = np.copy(p.Simulate()).astype('uint8')
-
-        T = tk.Text(SB.frame, height=1, width=15)
-        T.grid(columnspan=5)
-        print_string = "Protanop(" + str(round(p.sim_faktor * 100)) + "%)"
-        T.insert('current', print_string)
-
-        conv_SimulationPic_pro = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_pro))
-        sim_pic_pro = tk.Label(SB.frame, image=conv_SimulationPic_pro)
-        sim_pic_pro.Image = conv_SimulationPic_pro
-        sim_pic_pro.grid(columnspan=5)
+        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Protanop', 'Sim')
 
 
-btn = tk.Button(SB.frame, text="Browse", width=25, command=browse, bg='light blue')
-btn.grid(column=0, row=0, columnspan=2)
+def daltonize():
+    global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
+    if sim_deut.get():
+        showImage(img, rows, cols, kanaele, simGrad.get()/100, 'Deuteranop', 'Dalt')
+
+    elif sim_tri.get():
+        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Tritanop', 'Dalt')
+
+    elif sim_pro.get():
+        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Protanop', 'Dalt')
+
+
+browseButton = tk.Button(SB.frame, text="Browse", width=25, command=browse, bg='light blue')
+browseButton.grid(column=0, row=0, columnspan=1)
 
 simulateButton = tk.Button(SB.frame, text="Simulate", width=25, command=simulate, bg='light blue')
-simulateButton.grid(column=1, row=0, columnspan=2)
+simulateButton.grid(column=1, row=0, columnspan=1)
 simulateButton.config(state='disabled')
 
+daltonButton = tk.Button(SB.frame, text="Daltonize", width=25, command=daltonize, bg='light blue')
+daltonButton.grid(column=2, row=0, columnspan=1)
+daltonButton.config(state='disabled')
+
 checkButton_p = tk.Checkbutton(SB.frame, text="Protanop", variable=sim_pro, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_d = tk.Checkbutton(SB.frame, text="Deutanop", variable=sim_deut, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_t = tk.Checkbutton(SB.frame, text="Tritanop", variable=sim_tri, onvalue=1, offvalue=0, height=5, width=20)

Code/Farbaenderung.py

@@ -2,28 +2,36 @@ import numpy as np
 import cv2
 
 
-def gammaCorrection(v):
+def createGammaLookup():
+    return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64")
+
+
+def createReverseGammaLookup():
+    return np.array([applyGammaCorrection(i / 255) for i in np.arange(0.0, 256.0)]).astype("uint8")
+
+
+def removeGammaCorrection(v):
     if v <= 0.04045 * 255:
         return (v / 255) / 12.92
     elif v > 0.04045 * 255:
         return (((v / 255) + 0.055) / 1.055) ** 2.4
 
 
-def reverseGammaCorrection(v_reverse):
+def applyGammaCorrection(v_reverse):
     if abs(v_reverse) <= 0.0031308:
         return round(255 * (12.92 * abs(v_reverse)))
-    elif abs(v_reverse) > 0.0031308:
+    else:
         if (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))) > 255:
-            return 255
+            return 255 - (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055)) - 255)
         else:
             return round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))
 
 
 class Dyschromasie:
     cb_image = np.array([]).astype('float64')
-    sim_image = np.array([]).astype('uint8')
+    sim_image = np.array([]).astype('int64')
 
-    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0, sim_faktor=0, sim_kind='d'):
+    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0, sim_faktor=0, sim_kind='Deuteranop'):
         self.rows = rows
         self.cols = cols
         self.kanaele = kanaele
@@ -40,15 +48,17 @@ class Dyschromasie:
                            [0.02980165, -0.19318073, 1.16364789]])
 
     def Simulate(self):
-        if self.sim_kind == 'p':
+        removeGammaCorrectionLUT = createGammaLookup()
+
+        if self.sim_kind == 'Protanop':
             sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
                                 [0, 1, 0],
                                 [0, 0, 1]])
-        elif self.sim_kind == 'd':
+        elif self.sim_kind == 'Deuteranop':
             sim_mat = np.array([[1, 0, 0],
                                 [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
                                 [0, 0, 1]])
-        else:
+        elif self.sim_kind == 'Tritanop':
             sim_mat = np.array([[1, 0, 0],
                                 [0, 1, 0],
                                 [-0.86744736 * self.sim_faktor, 1.86727089 * self.sim_faktor, (1 - self.sim_faktor)]])
@@ -56,26 +66,61 @@ class Dyschromasie:
         # 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] = gammaCorrection(self.img_mat[i, j, x])
+        self.cb_image = cv2.LUT(self.img_mat, removeGammaCorrectionLUT)
 
         rechen_Mat = np.copy(self.T_reversed.dot(sim_mat).dot(self.T))
-        print(rechen_Mat)
+
         # Einzelne Pixelwertberechnung
         for i in range(self.rows):
             for j in range(self.cols):
                 self.cb_image[i, j] = rechen_Mat.dot(self.cb_image[i, j])
 
         self.sim_image = np.copy(self.cb_image)
-        self.sim_image = self.sim_image.astype('uint8')
+        self.sim_image = self.sim_image.astype('int64')
         # 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] = reverseGammaCorrection(self.cb_image[i, j, x])
+                    self.sim_image[i, j, x] = applyGammaCorrection(self.cb_image[i, j, x])
+
+        return self.sim_image.astype('uint8')
+
+    def daltonize(self):
+
+        err2mod = np.array([[0, 0, 0], [0.7, 1, 0], [0.7, 0, 1]])
+        simulated_image = self.Simulate()
+
+        E = np.copy(simulated_image).astype('int32')
+        for i in range(self.rows):
+            for j in range(self.cols):
+                for x in range(3):
+                    E[i, j, x] = abs(int(self.img_mat[i, j, x]) - int(simulated_image[i, j, x]))
+
+        ERR = np.zeros_like(self.img_mat).astype('int32')
+
+        for i in range(self.rows):
+            for j in range(self.cols):
+                err = E[i, j, :3]
+                ERR[i, j, :3] = np.dot(err2mod, err)
+
+        dtpn = np.copy(self.img_mat).astype('int32')
+
+        for i in range(self.rows):
+            for j in range(self.cols):
+                for x in range(3):
+                    dtpn[i, j, x] = abs(int(ERR[i, j, x]) + int(self.img_mat[i, j, x]))
+
+        for i in range(self.rows):
+            for j in range(self.cols):
+                dtpn[i, j, 0] = max(0, dtpn[i, j, 0])
+                dtpn[i, j, 0] = min(255, dtpn[i, j, 0])
+                dtpn[i, j, 1] = max(0, dtpn[i, j, 1])
+                dtpn[i, j, 1] = min(255, dtpn[i, j, 1])
+                dtpn[i, j, 2] = max(0, dtpn[i, j, 2])
+                dtpn[i, j, 2] = min(255, dtpn[i, j, 2])
+
+        result = dtpn.astype('uint8')
+
+        dalt = Dyschromasie(result, self.rows, self.cols, self.kanaele, self.sim_faktor, self.sim_kind)
+        return dalt.Simulate()
 
-        print(self.img_mat[78, 86])
-        print(self.sim_image[78, 86])
-        return self.sim_image