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bb625895a0
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24ab0a1f85
@ -14,33 +14,24 @@ 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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if (v <= 0.04045 * 255):
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return ((v / 255) / 12.92)
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elif (v > 0.04045 * 255):
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return (((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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if (v_reverse <= 0.0031308):
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return 255 * (12.92 * v_reverse)
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elif (v_reverse > 0.0031308):
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return 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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@ -61,8 +52,11 @@ 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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# T.dot(image[x][y])
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# for i in range(rows): #Durchgehen aller Pixel des Bildes
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# for j in range(cols):
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# k = image[i,j]
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# #Umwandlungsalgorithmus
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cv2.namedWindow("Display") # Displaywindow erstellen
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