Abschwächung der Simulationsstärke
Durch Anpassungen in den Simulationsmatrizen ergeben sich Abschwächungsmuster. Somit können für verschiedene Probanden unterschiedliche Stärken der Sehschwäche simuliert werden.
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Max Sponsel
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@ -5,17 +5,19 @@ from tkinter import filedialog, messagebox
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import cv2
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import numpy as np
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root = tk.Tk()
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simGrad = tk.IntVar(root)
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class Dyschromasie:
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global simGrad
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cb_image = np.array([]).astype('float64')
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sim_image = np.array([]).astype('uint8')
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def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0):
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def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0,sim_faktor=0):
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self.rows = rows
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self.cols = cols
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self.kanaele = kanaele
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self.img_mat = img_mat
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self.sim_faktor = sim_faktor
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T = np.array([[0.31399022, 0.63951294, 0.04649755],
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[0.15537241, 0.75789446, 0.08670142],
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@ -39,11 +41,12 @@ class Dyschromasie:
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class Protanopie(Dyschromasie):
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sim_mat = np.array([[0, 1.05118294, -0.05116099],
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[0, 1, 0],
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[0, 0, 1]])
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def Simulate(self):
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sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor],
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[0, 1, 0],
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[0, 0, 1]])
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# Gammakorrektur durchfuehren
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self.cb_image = np.copy(self.img_mat).astype('float64')
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for i in range(self.rows):
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@ -55,7 +58,7 @@ class Protanopie(Dyschromasie):
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for i in range(self.rows):
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for j in range(self.cols):
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -66,25 +69,16 @@ class Protanopie(Dyschromasie):
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for x in range(3):
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self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
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# Anpassung fuer den Slider
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if(simGrad.get() != 0):
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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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if self.sim_image[i, j, x] > img[i, j, x]:
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self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x])* (simGrad.get()/100)
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elif self.sim_image[i, j, x] < img[i, j, x]:
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self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x])* (simGrad.get()/100)
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return self.sim_image
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class Deuteranopie(Dyschromasie):
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sim_mat = np.array([[1, 0, 0],
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[0.9513092, 0, 0.04866992],
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[0, 0, 1]])
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def Simulate(self):
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sim_mat = np.array([[1, 0, 0],
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[0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
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[0, 0, 1]])
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# Gammakorrektur durchfuehren
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self.cb_image = np.copy(self.img_mat).astype('float64')
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for i in range(self.rows):
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@ -96,7 +90,7 @@ class Deuteranopie(Dyschromasie):
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for i in range(self.rows):
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for j in range(self.cols):
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -107,26 +101,16 @@ class Deuteranopie(Dyschromasie):
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for x in range(3):
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self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
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# Anpassung fuer den Slider
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if (simGrad.get() != 0):
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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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if self.sim_image[i, j, x] > img[i, j, x]:
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self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100)
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elif self.sim_image[i, j, x] < img[i, j, x]:
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self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x]) * (simGrad.get() / 100)
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return self.sim_image
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class Tritanopie(Dyschromasie):
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sim_mat = np.array([[1, 0, 0],
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[0, 1, 0],
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[-0.86744736, 1.86727089, 0]])
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def Simulate(self):
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sim_mat = np.array([[1, 0, 0],
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[0, 1, 0],
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[-0.86744736 * self.sim_faktor, 1.86727089 * self.sim_faktor, (1 - self.sim_faktor)]])
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# Gammakorrektur durchfuehren
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self.cb_image = np.copy(self.img_mat).astype('float64')
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for i in range(self.rows):
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@ -138,7 +122,7 @@ class Tritanopie(Dyschromasie):
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for i in range(self.rows):
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for j in range(self.cols):
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self.cb_image[i, j] = np.flipud(
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self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j])))
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self.sim_image = np.copy(self.cb_image)
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self.sim_image = self.sim_image.astype('uint8')
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@ -149,17 +133,6 @@ class Tritanopie(Dyschromasie):
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for x in range(3):
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self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x])
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# Anpassung fuer den Slider
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if (simGrad.get() != 0):
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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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if self.sim_image[i, j, x] > img[i, j, x]:
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self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100)
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elif self.sim_image[i, j, x] < img[i, j, x]:
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self.sim_image[i, j, x] = self.sim_image[i, j, x] + abs(img[i, j, x] - self.sim_image[i, j, x]) * (simGrad.get() / 100)
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return self.sim_image
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class AutoScrollbar(tk.Scrollbar):
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@ -220,8 +193,6 @@ class ScrollFrame:
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self.canvas.config(height = self.frame.winfo_reqheight())
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root = tk.Tk()
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root.title("Projekt Dyschromasie")
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SB = ScrollFrame(root)
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@ -234,7 +205,6 @@ kanaele = 0
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sim_pro = tk.IntVar(root)
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sim_deut = tk.IntVar(root)
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sim_tri = tk.IntVar(root)
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simGrad = tk.IntVar(root)
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simulationsGradient = tk.Scale(SB.frame, from_=0, to_=100, variable=simGrad, orient='horizontal')
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simulationsGradient.grid(column= 0, row = 1, columnspan=10)
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@ -264,9 +234,8 @@ def browse():
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def simulate():
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global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
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if sim_deut.get():
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d = Deuteranopie(img, rows, cols, kanaele)
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d = Deuteranopie(img, rows, cols, kanaele, simGrad.get()/100)
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display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB)
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T = tk.Text(SB.frame, height=1, width=15)
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@ -278,7 +247,7 @@ def simulate():
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sim_pic_deut.Image = conv_SimulationPic_deut
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sim_pic_deut.grid(columnspan=5)
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elif sim_tri.get():
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t = Tritanopie(img, rows, cols, kanaele)
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t = Tritanopie(img, rows, cols, kanaele, simGrad.get()/100)
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display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB)
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T = tk.Text(SB.frame, height=1, width=15)
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@ -290,7 +259,7 @@ def simulate():
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sim_pic_tri.Image = conv_SimulationPic_tri
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sim_pic_tri.grid(columnspan=5)
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elif sim_pro.get():
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p = Protanopie(img, rows, cols, kanaele)
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p = Protanopie(img, rows, cols, kanaele, simGrad.get()/100)
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display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB)
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T = tk.Text(SB.frame, height=1, width=15)
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