92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
import cv2 # OpenCV fuer Bildbearbeitung
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import tkinter # Zum Erstellen von GUIs
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import numpy as np # Numpy Import
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import sys
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# Einlesen des Bildes
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script_dir = sys.path[0]
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path = script_dir[:-4] + "Beispielbilder\lena.jpg"
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image = cv2.imread(path) # Einlesen des Bildes (noch hardcodiert, sollte dann in GUI gehen)
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rows = image.shape[0] # Auslesen der Zeilenanzahl
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cols = image.shape[1] # Auslesen der Spaltenanzahl
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kanaele = image.shape[2] # Auslesen der Kanaele (3 fuer RGB, 1 fuer Graubild)
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def gammaCorrection(v):
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if v <= 0.04045 * 255:
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return float(((v / 255) / 12.92))
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elif v > 0.04045 * 255:
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return float((((v / 255) + 0.055) / 1.055) ** 2.4)
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else:
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print("Ungültiger Wert!!")
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return 1
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def reverseGammaCorrection(v_reverse):
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if v_reverse <= 0.0031308:
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return int(255 * (12.92 * v_reverse))
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elif v_reverse > 0.0031308:
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return int(255 * (1.055 * v_reverse ** 0.41666 - 0.055))
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else:
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print("Ungültiger Wert!!!")
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return 1
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cb_image = np.copy(image) # Kopie des Bildarrays
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cb_image = cb_image.astype('float64') # Casting des Arrays auf Float
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# Korrektur des Gamma Faktors für alle Bildelemente
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for i in range(rows):
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for j in range(cols):
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for x in range(3):
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cb_image[i, j, x] = gammaCorrection(float(image[i, j, x]))
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'''
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0.31399022 0.63951294 0.04649755 Transformationsmatrix zum Konvertieren vom linearen RGB zum LMS Farbraum
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T = 0.15537241 0.75789446 0.08670142 Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen
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0.01775239 0.10944209 0.87256922 T*RGB_Farbverktor = LMS_Farbvektor
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'''
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T = np.array([[0.31399022, 0.63951294, 0.04649755],
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[0.15537241, 0.75789446, 0.08670142],
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[0.01775239, 0.10944209, 0.87256922]])
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'''
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5.47221206 −4.6419601 0.16963708 Rücktransformationsmatrix (Inverse von T)
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T_reversed = -1.1252419 2.29317094 −0.1678952 T_reversed Ü LMS_Farbvektor = RBG_Farbvektor
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0.02980165 −0.19318073 1.16364789
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'''
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T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
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[-1.1252419, 2.29317094, -0.1678952],
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[0.02980165, -0.19318073, 1.16364789]])
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# Multiplikation der einzelnen Pixelwerte
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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.dot(cb_image[i, j])
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# Simulationsmatrizen fuer Protanop
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S_b = np.array([[0, 1.05118294, -0.05116099], #Simulationsmatrix fuer Protanopie
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[0, 1, 0],
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[0, 0, 1]])
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S_d = np.array([[1, 0, 0], #Simulationsmatrix fuer Deuteranopie
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[0.9513092, 0, 0.04866992],
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[0, 0, 1]])
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S_t = np.array([[1, 0, 0], #Simulationsmatrix fuer Tritanopie
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[0, 1, 0],
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[-0.86744736, 1.86727089, 0]])
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#choice = input("Bitte geben Sie den Typ der zu simulierenden Farbblindheit an:(B,D,T)")
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cv2.namedWindow("Display") # Displaywindow erstellen
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cv2.imshow("Display", image) # Bild zeigen
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cv2.waitKey(0) # Fenster offen halten
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