Max Sponsel
4 years ago
1 changed files with 219 additions and 0 deletions
+ 219
- 0
Code/Dyschromasie-Applikation.py
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| from PIL import Image, ImageTk | |||
| import PIL | |||
| import tkinter as tk | |||
| from tkinter import filedialog, messagebox | |||
| import cv2 | |||
| import numpy as np | |||
| 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): | |||
| self.rows = rows | |||
| self.cols = cols | |||
| self.kanaele = kanaele | |||
| self.img_mat = img_mat | |||
| 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 gammaCorrection(self, 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) | |||
| def reverseGammaCorrection(self, 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)) | |||
| class Protanopie(Dyschromasie): | |||
| sim_mat = np.array([[0, 1.05118294, -0.05116099], | |||
| [0, 1, 0], | |||
| [0, 0, 1]]) | |||
| def Simulate(self): | |||
| # 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] = self.gammaCorrection(self.img_mat[i, j, x]) | |||
| # Einzelne Pixelwertberechnung | |||
| 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.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] = self.reverseGammaCorrection(self.cb_image[i, j, x]) | |||
| 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): | |||
| # 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] = self.gammaCorrection(self.img_mat[i, j, x]) | |||
| # Einzelne Pixelwertberechnung | |||
| 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.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] = self.reverseGammaCorrection(self.cb_image[i, j, x]) | |||
| 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): | |||
| # 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] = self.gammaCorrection(self.img_mat[i, j, x]) | |||
| # Einzelne Pixelwertberechnung | |||
| 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.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] = self.reverseGammaCorrection(self.cb_image[i, j, x]) | |||
| return self.sim_image | |||
| root = tk.Tk() | |||
| root.title("Projekt Dyschromasie") | |||
| img = np.array([]) | |||
| rows = 0 | |||
| cols = 0 | |||
| kanaele = 0 | |||
| sim_pro = tk.IntVar(root) | |||
| sim_deut = tk.IntVar(root) | |||
| sim_tri = tk.IntVar(root) | |||
| def browse(): | |||
| # Auswahl des FilePaths | |||
| try: | |||
| path = tk.filedialog.askopenfilename(filetypes=[("Image File", '.jpg')]) | |||
| im = Image.open(path) | |||
| except: | |||
| tk.messagebox.showerror(title='Datenfehler', message='Kein Bild gefunden/ausgewählt') | |||
| global simulateButton | |||
| if len(path) > 0: | |||
| simulateButton.config(state='active') | |||
| # Anzeigen des Bildes | |||
| tkimage = ImageTk.PhotoImage(im) | |||
| myvar = tk.Label(root, image=tkimage) | |||
| myvar.image = tkimage | |||
| myvar.grid(columnspan=5) | |||
| # Einspeichern der Path-Informationen | |||
| global img, rows, cols, kanaele | |||
| img = cv2.imread(path) | |||
| rows, cols, kanaele = img.shape | |||
| def simulate(): | |||
| global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri | |||
| if sim_deut.get(): | |||
| d = Deuteranopie(img, rows, cols, kanaele) | |||
| display_array_deut = cv2.cvtColor(np.copy(d.Simulate()), cv2.COLOR_BGR2RGB) | |||
| T = tk.Text(root,height=1,width=15) | |||
| T.grid(columnspan=5) | |||
| T.insert('current',"Deutranopie:") | |||
| conv_SimulationPic_deut = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_deut)) | |||
| sim_pic_deut = tk.Label(root, image=conv_SimulationPic_deut) | |||
| sim_pic_deut.Image = conv_SimulationPic_deut | |||
| sim_pic_deut.grid(columnspan=5) | |||
| elif sim_tri.get(): | |||
| t = Tritanopie(img, rows, cols, kanaele) | |||
| display_array_tri = cv2.cvtColor(np.copy(t.Simulate()), cv2.COLOR_BGR2RGB) | |||
| T = tk.Text(root, height=1, width=15) | |||
| T.grid(columnspan=5) | |||
| T.insert('current', "Tritanopie:") | |||
| conv_SimulationPic_tri = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_tri)) | |||
| sim_pic_tri = tk.Label(root, image=conv_SimulationPic_tri) | |||
| sim_pic_tri.Image = conv_SimulationPic_tri | |||
| sim_pic_tri.grid(columnspan=5) | |||
| elif sim_pro.get(): | |||
| p = Protanopie(img, rows, cols, kanaele) | |||
| display_array_pro = cv2.cvtColor(np.copy(p.Simulate()), cv2.COLOR_BGR2RGB) | |||
| T = tk.Text(root, height=1, width=15) | |||
| T.grid(columnspan=5) | |||
| T.insert('current', "Protanopie:") | |||
| conv_SimulationPic_pro = ImageTk.PhotoImage(image=PIL.Image.fromarray(display_array_pro)) | |||
| sim_pic_pro = tk.Label(root, image=conv_SimulationPic_pro) | |||
| sim_pic_pro.Image = conv_SimulationPic_pro | |||
| sim_pic_pro.grid(columnspan=5) | |||
| btn = tk.Button(root, text="Browse", width=25, command=browse, bg='light blue') | |||
| btn.grid(column = 0, row = 0,columnspan=2) | |||
| simulateButton = tk.Button(root, text="Simulate", width=25, command=simulate, bg='light blue') | |||
| simulateButton.grid(column = 1, row = 0,columnspan=2) | |||
| simulateButton.config(state='disabled') | |||
| checkButton_p = tk.Checkbutton(root, text="Protanop", variable=sim_pro, onvalue=1, offvalue=0, height=5, width=20) | |||
| checkButton_d = tk.Checkbutton(root, text="Deutanop", variable=sim_deut, onvalue=1, offvalue=0, height=5, width=20) | |||
| checkButton_t = tk.Checkbutton(root, text="Tritanop", variable=sim_tri, onvalue=1, offvalue=0, height=5, width=20) | |||
| checkButton_p.grid(column = 0, row = 1) | |||
| checkButton_d.grid(column = 1, row = 1) | |||
| checkButton_t.grid(column = 2, row = 1) | |||
| root.mainloop() | |||
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