From a1ebdb827c90d2b2ebf0d0313dbf762dddb15ebb Mon Sep 17 00:00:00 2001 From: Max Sponsel Date: Fri, 4 Sep 2020 11:59:25 +0200 Subject: [PATCH] =?UTF-8?q?Abschw=C3=A4chung=20der=20Simulationsst=C3=A4rk?= =?UTF-8?q?e?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Durch Anpassungen in den Simulationsmatrizen ergeben sich Abschwächungsmuster. Somit können für verschiedene Probanden unterschiedliche Stärken der Sehschwäche simuliert werden. --- Code/Dyschromasie-Applikation.py | 81 ++++++++++---------------------- 1 file changed, 25 insertions(+), 56 deletions(-) diff --git a/Code/Dyschromasie-Applikation.py b/Code/Dyschromasie-Applikation.py index 86b0463..f39d529 100644 --- a/Code/Dyschromasie-Applikation.py +++ b/Code/Dyschromasie-Applikation.py @@ -5,17 +5,19 @@ from tkinter import filedialog, messagebox import cv2 import numpy as np +root = tk.Tk() +simGrad = tk.IntVar(root) class Dyschromasie: - global simGrad 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): + def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0,sim_faktor=0): self.rows = rows self.cols = cols self.kanaele = kanaele self.img_mat = img_mat + self.sim_faktor = sim_faktor T = np.array([[0.31399022, 0.63951294, 0.04649755], [0.15537241, 0.75789446, 0.08670142], @@ -39,11 +41,12 @@ class Dyschromasie: class Protanopie(Dyschromasie): - sim_mat = np.array([[0, 1.05118294, -0.05116099], - [0, 1, 0], - [0, 0, 1]]) - def Simulate(self): + + sim_mat = np.array([[(1 - self.sim_faktor), 1.05118294 * self.sim_faktor, -0.05116099 * self.sim_faktor], + [0, 1, 0], + [0, 0, 1]]) + # Gammakorrektur durchfuehren self.cb_image = np.copy(self.img_mat).astype('float64') for i in range(self.rows): @@ -55,7 +58,7 @@ class Protanopie(Dyschromasie): for i in range(self.rows): for j in range(self.cols): self.cb_image[i, j] = np.flipud( - self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) + self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) self.sim_image = np.copy(self.cb_image) self.sim_image = self.sim_image.astype('uint8') @@ -66,25 +69,16 @@ class Protanopie(Dyschromasie): for x in range(3): self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x]) - # Anpassung fuer den Slider - if(simGrad.get() != 0): - for i in range(rows): - for j in range(cols): - for x in range(3): - if self.sim_image[i, j, x] > img[i, j, x]: - self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x])* (simGrad.get()/100) - - elif self.sim_image[i, j, x] < img[i, j, x]: - 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) return self.sim_image class Deuteranopie(Dyschromasie): - sim_mat = np.array([[1, 0, 0], - [0.9513092, 0, 0.04866992], - [0, 0, 1]]) - def Simulate(self): + + sim_mat = np.array([[1, 0, 0], + [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor], + [0, 0, 1]]) + # Gammakorrektur durchfuehren self.cb_image = np.copy(self.img_mat).astype('float64') for i in range(self.rows): @@ -96,7 +90,7 @@ class Deuteranopie(Dyschromasie): for i in range(self.rows): for j in range(self.cols): self.cb_image[i, j] = np.flipud( - self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) + self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) self.sim_image = np.copy(self.cb_image) self.sim_image = self.sim_image.astype('uint8') @@ -107,26 +101,16 @@ class Deuteranopie(Dyschromasie): for x in range(3): self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x]) - # Anpassung fuer den Slider - if (simGrad.get() != 0): - for i in range(rows): - for j in range(cols): - for x in range(3): - if self.sim_image[i, j, x] > img[i, j, x]: - self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100) - - elif self.sim_image[i, j, x] < img[i, j, x]: - 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) - return self.sim_image class Tritanopie(Dyschromasie): - sim_mat = np.array([[1, 0, 0], - [0, 1, 0], - [-0.86744736, 1.86727089, 0]]) - def Simulate(self): + + 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): @@ -138,7 +122,7 @@ class Tritanopie(Dyschromasie): for i in range(self.rows): for j in range(self.cols): self.cb_image[i, j] = np.flipud( - self.T_reversed.dot(self.sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) + self.T_reversed.dot(sim_mat).dot(self.T).dot(np.flipud(self.cb_image[i, j]))) self.sim_image = np.copy(self.cb_image) self.sim_image = self.sim_image.astype('uint8') @@ -149,17 +133,6 @@ class Tritanopie(Dyschromasie): for x in range(3): self.sim_image[i, j, x] = self.reverseGammaCorrection(self.cb_image[i, j, x]) - # Anpassung fuer den Slider - if (simGrad.get() != 0): - for i in range(rows): - for j in range(cols): - for x in range(3): - if self.sim_image[i, j, x] > img[i, j, x]: - self.sim_image[i, j, x] = img[i, j, x] + abs(self.sim_image[i, j, x] - img[i, j, x]) * (simGrad.get() / 100) - - elif self.sim_image[i, j, x] < img[i, j, x]: - 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) - return self.sim_image class AutoScrollbar(tk.Scrollbar): @@ -220,8 +193,6 @@ class ScrollFrame: self.canvas.config(height = self.frame.winfo_reqheight()) -root = tk.Tk() - root.title("Projekt Dyschromasie") SB = ScrollFrame(root) @@ -234,7 +205,6 @@ kanaele = 0 sim_pro = tk.IntVar(root) sim_deut = tk.IntVar(root) sim_tri = tk.IntVar(root) -simGrad = tk.IntVar(root) simulationsGradient = tk.Scale(SB.frame, from_=0, to_=100, variable=simGrad, orient='horizontal') simulationsGradient.grid(column= 0, row = 1, columnspan=10) @@ -264,9 +234,8 @@ def browse(): def simulate(): global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri - if sim_deut.get(): - d = Deuteranopie(img, rows, cols, kanaele) + d = Deuteranopie(img, rows, cols, kanaele, simGrad.get()/100) display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB) T = tk.Text(SB.frame, height=1, width=15) @@ -278,7 +247,7 @@ def simulate(): sim_pic_deut.Image = conv_SimulationPic_deut sim_pic_deut.grid(columnspan=5) elif sim_tri.get(): - t = Tritanopie(img, rows, cols, kanaele) + t = Tritanopie(img, rows, cols, kanaele, simGrad.get()/100) display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB) T = tk.Text(SB.frame, height=1, width=15) @@ -290,7 +259,7 @@ def simulate(): sim_pic_tri.Image = conv_SimulationPic_tri sim_pic_tri.grid(columnspan=5) elif sim_pro.get(): - p = Protanopie(img, rows, cols, kanaele) + p = Protanopie(img, rows, cols, kanaele, simGrad.get()/100) display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB) T = tk.Text(SB.frame, height=1, width=15)