SponselMa71420/Projekt_Dyschromasie
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compare: SponselMa71420:24ab0a1f8545b884531a68aa05658b51f251236a
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1 changed files with 20 additions and 26 deletions
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Code/Dyschromasie.py
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@ -14,33 +14,24 @@ kanaele = image.shape[2] # Auslesen der Kanaele (3 fuer RGB, 1 fuer Graubild)
def gammaCorrection(v): def gammaCorrection(v):
if v <= 0.04045 * 255: if (v <= 0.04045 * 255):
return float(((v / 255) / 12.92)) return ((v / 255) / 12.92)
elif v > 0.04045 * 255: elif (v > 0.04045 * 255):
return float((((v / 255) + 0.055) / 1.055) ** 2.4) return (((v / 255) + 0.055) / 1.055) ** 2.4
else: else:
print("Ungültiger Wert!!") print("Ungültiger Wert!!")
return 1 return 1
def reverseGammaCorrection(v_reverse): def reverseGammaCorrection(v_reverse):
if v_reverse <= 0.0031308: if (v_reverse <= 0.0031308):
return int(255 * (12.92 * v_reverse)) return 255 * (12.92 * v_reverse)
elif v_reverse > 0.0031308: elif (v_reverse > 0.0031308):
return int(255 * (1.055 * v_reverse ** 0.41666 - 0.055)) return 255 * (1.055 * v_reverse ** 0.41666 - 0.055)
else: else:
print("Ungültiger Wert!!!") print("Ungültiger Wert!!!")
return 1 return 1
cb_image = np.copy(image) #Kopie des Bildarrays
cb_image = cb_image.astype('float64') #Casting des Arrays auf Float
#Korrektur des Gamma Faktors für alle Bildelemente
for i in range(rows):
for j in range(cols):
for x in range(3):
cb_image[i, j, x] = gammaCorrection(float(image[i, j, x]))
''' '''
0.31399022 0.63951294 0.04649755 Transformationsmatrix zum Konvertieren vom linearen RGB zum LMS Farbraum 0.31399022 0.63951294 0.04649755 Transformationsmatrix zum Konvertieren vom linearen RGB zum LMS Farbraum
T = 0.15537241 0.75789446 0.08670142 Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen T = 0.15537241 0.75789446 0.08670142 Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen
@ -61,8 +52,11 @@ T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
[-1.1252419,2.29317094,-0.1678952], [-1.1252419,2.29317094,-0.1678952],
[0.02980165,-0.19318073,1.16364789]]) [0.02980165,-0.19318073,1.16364789]])
# Multiplikation der einzelnen Pixelwerte
# T.dot(image[x][y]) # for i in range(rows): #Durchgehen aller Pixel des Bildes
# for j in range(cols):
# k = image[i,j]
# #Umwandlungsalgorithmus
cv2.namedWindow("Display") # Displaywindow erstellen cv2.namedWindow("Display") # Displaywindow erstellen