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