Simulationsmatrizen der einzelnen Fehlsichtigkeiten implementiert
Simulationsmatrizen fuer Protanopie, Deuteranopie und Tritanopie hinzugefügt.
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@ -22,6 +22,7 @@ def gammaCorrection(v):
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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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@ -32,10 +33,10 @@ def reverseGammaCorrection(v_reverse):
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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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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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# 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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@ -61,12 +62,28 @@ 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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# 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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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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#Simulationsmatrizen
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cv2.namedWindow("Display") # Displaywindow erstellen
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