- import numpy as np
- import cv2
-
-
- def gammaCorrection(v):
- if v <= 0.04045 * 255:
- return (v / 255) / 12.92
- elif v > 0.04045 * 255:
- return (((v / 255) + 0.055) / 1.055) ** 2.4
-
-
- def reverseGammaCorrection(v_reverse):
- if abs(v_reverse) <= 0.0031308:
- return round(255 * (12.92 * abs(v_reverse)))
- elif abs(v_reverse) > 0.0031308:
- if (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))) > 255:
- return 255
- else:
- return round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))
-
-
- class Dyschromasie:
- cb_image = np.array([]).astype('float64')
- sim_image = np.array([]).astype('uint8')
-
- def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0, sim_faktor=0, sim_kind='d'):
- self.rows = rows
- self.cols = cols
- self.kanaele = kanaele
- self.img_mat = img_mat
- self.sim_faktor = sim_faktor
- self.sim_kind = sim_kind
-
- T = np.array([[0.31399022, 0.63951294, 0.04649755],
- [0.15537241, 0.75789446, 0.08670142],
- [0.01775239, 0.10944209, 0.87256922]])
-
- T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
- [-1.1252419, 2.29317094, -0.1678952],
- [0.02980165, -0.19318073, 1.16364789]])
-
- def Simulate(self):
- if self.sim_kind == 'p':
- sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
- [0, 1, 0],
- [0, 0, 1]])
- elif self.sim_kind == 'd':
- sim_mat = np.array([[1, 0, 0],
- [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
- [0, 0, 1]])
- else:
- sim_mat = np.array([[1, 0, 0],
- [0, 1, 0],
- [-0.86744736 * self.sim_faktor, 1.86727089 * self.sim_faktor, (1 - self.sim_faktor)]])
-
- # Gammakorrektur durchfuehren
- self.cb_image = np.copy(self.img_mat).astype('float64')
-
- for i in range(self.rows):
- for j in range(self.cols):
- for x in range(3):
- self.cb_image[i, j, x] = gammaCorrection(self.img_mat[i, j, x])
-
- rechen_Mat = np.copy(self.T_reversed.dot(sim_mat).dot(self.T))
- print(rechen_Mat)
- # Einzelne Pixelwertberechnung
- for i in range(self.rows):
- for j in range(self.cols):
- self.cb_image[i, j] = rechen_Mat.dot(self.cb_image[i, j])
-
- self.sim_image = np.copy(self.cb_image)
- self.sim_image = self.sim_image.astype('uint8')
- # Rücktransformation der Gammawerte
- for i in range(self.rows):
- for j in range(self.cols):
- for x in range(3):
- self.sim_image[i, j, x] = reverseGammaCorrection(self.cb_image[i, j, x])
-
- print(self.img_mat[78, 86])
- print(self.sim_image[78, 86])
- return self.sim_image
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