- import cv2 # OpenCV fuer Bildbearbeitung
- import tkinter # Zum Erstellen von GUIs
- import numpy as np # Numpy Import
- import sys
-
- # Einlesen des Bildes
- script_dir = sys.path[0]
- path = script_dir[:-4] + "Beispielbilder\grocery_store.jpg"
- image = cv2.imread(path) # Einlesen des Bildes (noch hardcodiert, sollte dann in GUI gehen)
-
- rows = image.shape[0] # Auslesen der Zeilenanzahl
- cols = image.shape[1] # Auslesen der Spaltenanzahl
- kanaele = image.shape[2] # Auslesen der Kanaele (3 fuer RGB, 1 fuer Graubild)
-
-
- def gammaCorrection(v):
- if v <= 0.04045 * 255:
- return float(((v / 255) / 12.92))
- elif v > 0.04045 * 255:
- return float((((v / 255) + 0.055) / 1.055) ** 2.4)
- else:
- print("Ungültiger Wert!!")
- return 1
-
-
- def reverseGammaCorrection(v_reverse):
- if v_reverse <= 0.0031308:
- return int(255 * (12.92 * v_reverse))
- elif v_reverse > 0.0031308:
- return int(255 * (1.055 * v_reverse ** 0.41666 - 0.055))
- else:
- print("Ungültiger Wert!!!")
- 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
- T = 0.15537241 0.75789446 0.08670142 Multiplikation aus Brucelindbloom und Hunt-Pointer-Estevez Matrixen
- 0.01775239 0.10944209 0.87256922 T*RGB_Farbverktor = LMS_Farbvektor
- '''
-
- T = np.array([[0.31399022, 0.63951294, 0.04649755],
- [0.15537241, 0.75789446, 0.08670142],
- [0.01775239, 0.10944209, 0.87256922]])
-
- '''
- 5.47221206 −4.6419601 0.16963708 Rücktransformationsmatrix (Inverse von T)
- T_reversed = -1.1252419 2.29317094 −0.1678952 T_reversed Ü LMS_Farbvektor = RBG_Farbvektor
- 0.02980165 −0.19318073 1.16364789
- '''
-
- T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
- [-1.1252419, 2.29317094, -0.1678952],
- [0.02980165, -0.19318073, 1.16364789]])
-
- # Simulationsmatrizen fuer Protanop
-
- S_p = np.array([[0, 1.05118294, -0.05116099], #Simulationsmatrix fuer Protanopie
- [0, 1, 0],
- [0, 0, 1]])
-
- S_d = np.array([[1, 0, 0], #Simulationsmatrix fuer Deuteranopie
- [0.9513092, 0, 0.04866992],
- [0, 0, 1]])
-
- S_t = np.array([[1, 0, 0], #Simulationsmatrix fuer Tritanopie
- [0, 1, 0],
- [-0.86744736, 1.86727089, 0]])
-
-
- #Multiplikation der einzelnen Pixel
- for i in range(rows):
- for j in range(cols):
- cb_image[i,j] = T_reversed.dot(S_p).dot(T).dot(cb_image[i,j])
-
- sim_image = np.copy(cb_image)
- sim_image = sim_image.astype('uint8')
-
- #Rücktransformation der Gammawerte
- for i in range(rows):
- for j in range(cols):
- for x in range(3):
- sim_image[i, j, x] = reverseGammaCorrection(cb_image[i, j, x])
-
-
- cv2.namedWindow("Display") # Displaywindow erstellen
- cv2.imshow("Display", sim_image) # Bild zeigen
- cv2.waitKey(0) # Fenster offen halten
|
