Bild, welches im technical Paper verwendet wurde

Zum Ausführlichen Testen der Algorithmen.
2020-06-27 08:53:23 +00:00 · 2020-06-27 08:53:22 +00:00
24 changed files with 14 additions and 457 deletions
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@@ -1,116 +0,0 @@
 from PIL import Image, ImageTk
 import PIL
 import tkinter as tk
 import cv2
 import numpy as np
 from Farbaenderung import Dyschromasie
 from Scrollbar import ScrollFrame
 root = tk.Tk()
 simGrad = tk.IntVar(root)
 root.title("Projekt Dyschromasie")
 SB = ScrollFrame(root)
 img = np.array([])
 rows = 0
 cols = 0
 kanaele = 0
 sim_pro = tk.IntVar(root)
 sim_deut = tk.IntVar(root)
 sim_tri = tk.IntVar(root)
 simulationsGradient = tk.Scale(SB.frame, from_=0, to_=100, variable=simGrad, orient='horizontal')
 simulationsGradient.grid(column=0, row=1, columnspan=10)
 def browse():
    # Auswahl des FilePaths
    try:
        path = tk.filedialog.askopenfilename(filetypes=[("Image File", '.jpg'), ("Image File", '.png')])
        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')
        daltonButton.config(state='active')
        # Anzeigen des Bildes
        tkimage = ImageTk.PhotoImage(im)
        myvar = tk.Label(SB.frame, image=tkimage)
        myvar.image = tkimage
        myvar.grid(columnspan=5)
        # Einspeichern der Path-Informationen
        global img, rows, cols, kanaele
        img = cv2.imread(path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        rows, cols, kanaele = img.shape
 def showImage(img, rows, cols, kanaele, sim_faktor, sim_kind, sim_art):
    color_blindness = Dyschromasie(img, rows, cols, kanaele, sim_faktor, sim_kind)
    if sim_art == 'Sim':
        color_blindness_mat = np.copy(color_blindness.Simulate()).astype('uint8')
    else:
        color_blindness_mat = np.copy(color_blindness.daltonize()).astype('uint8')
    T = tk.Text(SB.frame, height=1, width=15)
    T.grid(columnspan=5)
    print_string = sim_kind + ' ' + str(round(color_blindness.sim_faktor * 100)) + "%"
    T.insert('current', print_string)
    conv_SimulationPic = ImageTk.PhotoImage(image=PIL.Image.fromarray(color_blindness_mat))
    sim_pic = tk.Label(SB.frame, image=conv_SimulationPic)
    sim_pic.Image = conv_SimulationPic
    sim_pic.grid(columnspan=5)
 def simulate():
    global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
    if sim_deut.get():
        showImage(img, rows, cols, kanaele, simGrad.get()/100, 'Deuteranop', 'Sim')
    elif sim_tri.get():
        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Tritanop', 'Sim')
    elif sim_pro.get():
        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Protanop', 'Sim')
 def daltonize():
    global img, rows, cols, kanaele, sim_pro, sim_deut, sim_tri
    if sim_deut.get():
        showImage(img, rows, cols, kanaele, simGrad.get()/100, 'Deuteranop', 'Dalt')
    elif sim_tri.get():
        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Tritanop', 'Dalt')
    elif sim_pro.get():
        showImage(img, rows, cols, kanaele, simGrad.get() / 100, 'Protanop', 'Dalt')
 browseButton = tk.Button(SB.frame, text="Browse", width=25, command=browse, bg='light blue')
 browseButton.grid(column=0, row=0, columnspan=1)
 simulateButton = tk.Button(SB.frame, text="Simulate", width=25, command=simulate, bg='light blue')
 simulateButton.grid(column=1, row=0, columnspan=1)
 simulateButton.config(state='disabled')
 daltonButton = tk.Button(SB.frame, text="Daltonize", width=25, command=daltonize, bg='light blue')
 daltonButton.grid(column=2, row=0, columnspan=1)
 daltonButton.config(state='disabled')
 checkButton_p = tk.Checkbutton(SB.frame, text="Protanop", variable=sim_pro, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_d = tk.Checkbutton(SB.frame, text="Deutanop", variable=sim_deut, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_t = tk.Checkbutton(SB.frame, text="Tritanop", variable=sim_tri, onvalue=1, offvalue=0, height=5, width=20)
 checkButton_p.grid(column=0, row=2)
 checkButton_d.grid(column=1, row=2)
 checkButton_t.grid(column=2, row=2)
 SB.update()
 root.mainloop()
@@ -2,17 +2,17 @@ import cv2  # OpenCV fuer Bildbearbeitung
 import tkinter  # Zum Erstellen von GUIs
 import numpy as np  # Numpy Import
 import sys
 from PIL import Image, ImageTk    #Wichtig zum Anzeigen der Bilder im GUI
 # Einlesen des Bildes
 script_dir = sys.path[0]
-path = script_dir[:-4] + "Beispielbilder\grocery_store.jpg"
+path = script_dir[:-4] + "Beispielbilder\lena.jpg"
-image = cv2.cvtColor(cv2.imread(path),cv2.COLOR_BGR2RGB)  # Einlesen des Bildes (noch hardcodiert, sollte dann in GUI gehen)
+image = cv2.imread(path)  # Einlesen des Bildes (noch hardcodiert, sollte dann in GUI gehen)
 rows = image.shape[0]  # Auslesen der Zeilenanzahl
 cols = image.shape[1]  # Auslesen der Spaltenanzahl
 kanaele = image.shape[2]  # Auslesen der Kanaele (3 fuer RGB, 1 fuer Graubild)
 def gammaCorrection(v):
    if v <= 0.04045 * 255:
        return float(((v / 255) / 12.92))
@@ -32,6 +32,7 @@ def reverseGammaCorrection(v_reverse):
        print("Ungültiger Wert!!!")
        return 1
 cb_image = np.copy(image)  # Kopie des Bildarrays
 cb_image = cb_image.astype('float64')  # Casting des Arrays auf Float
@@ -61,7 +62,14 @@ T_reversed = np.array([[5.47221206, -4.6419601, 0.16963708],
                       [-1.1252419, 2.29317094, -0.1678952],
                       [0.02980165, -0.19318073, 1.16364789]])
-S_p = np.array([[0, 1.05118294, -0.05116099],  #Simulationsmatrix fuer Protanopie
+# Multiplikation der einzelnen Pixelwerte
 for i in range(rows):
    for j in range(cols):
        cb_image[i, j] = T.dot(cb_image[i, j])
 # Simulationsmatrizen fuer Protanop
 S_b = np.array([[0, 1.05118294, -0.05116099],  #Simulationsmatrix fuer Protanopie
                [0, 1, 0],
                [0, 0, 1]])
@@ -73,21 +81,11 @@ S_t = np.array([[1, 0, 0],                     #Simulationsmatrix fuer Tritanopi
                [0, 1, 0],
                [-0.86744736, 1.86727089, 0]])
 #choice = input("Bitte geben Sie den Typ der zu simulierenden Farbblindheit an:(B,D,T)")
 #Multiplikation der einzelnen Pixel
 for i in range(rows):
    for j in range(cols):
        cb_image[i,j] = T_reversed.dot(S_t).dot(T).dot(cb_image[i,j])
 sim_image = np.copy(cb_image)
 sim_image = sim_image.astype('uint8')
 #Rücktransformation der Gammawerte
 for i in range(rows):
    for j in range(cols):
        for x in range(3):
            sim_image[i, j, x] = reverseGammaCorrection(cb_image[i, j, x])
 cv2.namedWindow("Display")  # Displaywindow erstellen
-cv2.imshow("Display", cv2.cvtColor(sim_image,cv2.COLOR_RGB2BGR))  # Bild zeigen
+cv2.imshow("Display", image)  # Bild zeigen
 cv2.waitKey(0)  # Fenster offen halten
@@ -1,126 +0,0 @@
 import numpy as np
 import cv2
 def createGammaLookup():
    return np.array([removeGammaCorrection(i) for i in np.arange(0, 256)]).astype("float64")
 def createReverseGammaLookup():
    return np.array([applyGammaCorrection(i / 255) for i in np.arange(0.0, 256.0)]).astype("uint8")
 def removeGammaCorrection(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 applyGammaCorrection(v_reverse):
    if abs(v_reverse) <= 0.0031308:
        return round(255 * (12.92 * abs(v_reverse)))
    else:
        if (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055))) > 255:
            return 255 - (round(255 * (1.055 * abs(v_reverse) ** 0.41666 - 0.055)) - 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('int64')
    def __init__(self, img_mat=np.array([]), rows=0, cols=0, kanaele=0, sim_faktor=0, sim_kind='Deuteranop'):
        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):
        removeGammaCorrectionLUT = createGammaLookup()
        if self.sim_kind == 'Protanop':
            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 == 'Deuteranop':
            sim_mat = np.array([[1, 0, 0],
                                [0.9513092 * self.sim_faktor, (1 - self.sim_faktor), 0.04866992 * self.sim_faktor],
                                [0, 0, 1]])
        elif self.sim_kind == 'Tritanop':
            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')
        self.cb_image = cv2.LUT(self.img_mat, removeGammaCorrectionLUT)
        rechen_Mat = np.copy(self.T_reversed.dot(sim_mat).dot(self.T))
        # 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('int64')
        # 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] = applyGammaCorrection(self.cb_image[i, j, x])
        return self.sim_image.astype('uint8')
    def daltonize(self):
        err2mod = np.array([[0, 0, 0], [0.7, 1, 0], [0.7, 0, 1]])
        simulated_image = self.Simulate()
        E = np.copy(simulated_image).astype('int32')
        for i in range(self.rows):
            for j in range(self.cols):
                for x in range(3):
                    E[i, j, x] = abs(int(self.img_mat[i, j, x]) - int(simulated_image[i, j, x]))
        ERR = np.zeros_like(self.img_mat).astype('int32')
        for i in range(self.rows):
            for j in range(self.cols):
                err = E[i, j, :3]
                ERR[i, j, :3] = np.dot(err2mod, err)
        dtpn = np.copy(self.img_mat).astype('int32')
        for i in range(self.rows):
            for j in range(self.cols):
                for x in range(3):
                    dtpn[i, j, x] = abs(int(ERR[i, j, x]) + int(self.img_mat[i, j, x]))
        for i in range(self.rows):
            for j in range(self.cols):
                dtpn[i, j, 0] = max(0, dtpn[i, j, 0])
                dtpn[i, j, 0] = min(255, dtpn[i, j, 0])
                dtpn[i, j, 1] = max(0, dtpn[i, j, 1])
                dtpn[i, j, 1] = min(255, dtpn[i, j, 1])
                dtpn[i, j, 2] = max(0, dtpn[i, j, 2])
                dtpn[i, j, 2] = min(255, dtpn[i, j, 2])
        result = dtpn.astype('uint8')
        dalt = Dyschromasie(result, self.rows, self.cols, self.kanaele, self.sim_faktor, self.sim_kind)
        return dalt.Simulate()
@@ -1,59 +0,0 @@
 import tkinter as tk
 from tkinter import filedialog, messagebox
 class AutoScrollbar(tk.Scrollbar):
   # A scrollbar that hides itself if it's not needed.
   # Only works if you use the grid geometry manager!
    def set(self, lo, hi):
        if float(lo) <= 0.0 and float(hi) >= 1.0:
            # grid_remove is currently missing from Tkinter!
            self.tk.call("grid", "remove", self)
        else:
            self.grid()
        tk.Scrollbar.set(self, lo, hi)
    def pack(self, **kw):
        raise TclError("cannot use pack with this widget")
    def place(self, **kw):
        raise TclError("cannot use place with this widget")
 class ScrollFrame:
    def __init__(self, master):
        self.vscrollbar = AutoScrollbar(master)
        self.vscrollbar.grid(row=0, column=1, sticky='ns')
        self.hscrollbar = AutoScrollbar(master, orient='horizontal')
        self.hscrollbar.grid(row=1, column=0, sticky='ew')
        self.canvas = tk.Canvas(master, yscrollcommand=self.vscrollbar.set,
                        xscrollcommand=self.hscrollbar.set)
        self.canvas.grid(row=0, column=0, sticky='nsew')
        self.vscrollbar.config(command=self.canvas.yview)
        self.hscrollbar.config(command=self.canvas.xview)
        # make the canvas expandable
        master.grid_rowconfigure(0, weight=1)
        master.grid_columnconfigure(0, weight=1)
        # create frame inside canvas
        self.frame = tk.Frame(self.canvas)
        self.frame.rowconfigure(1, weight=1)
        self.frame.columnconfigure(1, weight=1)
        # update the frame
        self.frame.bind("<Configure>", self.reset_scrollregion)
    def reset_scrollregion(self, event):
        self.canvas.configure(scrollregion=self.canvas.bbox('all'))
    def update(self):
        self.canvas.create_window(0, 0, anchor='nw', window=self.frame)
        self.frame.update_idletasks()
        self.canvas.config(scrollregion=self.canvas.bbox("all"))
        if self.frame.winfo_reqwidth() != self.canvas.winfo_width():
            # update the canvas's width to fit the inner frame
            self.canvas.config(width = self.frame.winfo_reqwidth())
        if self.frame.winfo_reqheight() != self.canvas.winfo_height():
            # update the canvas's width to fit the inner frame
            self.canvas.config(height = self.frame.winfo_reqheight())
Author	SHA1	Message	Date
SponselMa71420	a7eab231ef	Bild, welches im technical Paper verwendet wurde Zum Ausführlichen Testen der Algorithmen.	2020-06-27 08:53:23 +00:00
SponselMa71420	3738084980	Bild, welches im technical Paper verwendet wurde Zum Ausführlichen Testen der Algorithmen.	2020-06-27 08:53:22 +00:00