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gui.py
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gui.py
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import os
import cv2
import tensorflow as tf
from efimg import Exposures
from tkinter import *
from tkinter import messagebox
from tkinter import ttk
from tkinter import filedialog
from PIL import Image, ImageTk
tf.compat.v1.enable_eager_execution()
BASE_PATH = os.path.abspath(os.path.dirname(__file__))
MODEL_TO_USE = os.path.join(BASE_PATH, 'model-256-04-0.93-0.00342.hdf5')
class Root(Tk):
def open_file(self, t):
try:
filename = filedialog.askopenfilename(title = "Select A File")
if not filename:
return
self.exposures[t] = cv2.imread(filename)
self.exposures_resized[t] = cv2.resize( self.exposures[t], (self.CANVAS_SIZE_X, self.CANVAS_SIZE_Y) )
self.to_display[t] = ImageTk.PhotoImage(Image.fromarray(cv2.cvtColor(self.exposures_resized[t], cv2.COLOR_BGR2RGB)))
self.canvas[t].create_image(0, 0, anchor = NW, image = self.to_display[t])
except Exception as e:
messagebox.showerror("Error", str(e))
def fusion_popup(self):
try:
res_window = Toplevel()
self.canvas['mertens'] = Canvas(res_window, width = self.BIG_CANVAS_SIZE_X, height = self.BIG_CANVAS_SIZE_Y)
self.canvas['mertens'].grid(row = 0, column = 0, padx = 5, pady = 20)
self.canvas['mertens'].config(background="#fff")
self.canvas['mertens'].create_text(self.BIG_CANVAS_SIZE_X / 2, self.BIG_CANVAS_SIZE_Y / 2, fill="#000", font="Times 20 bold", text="COULD NOT CALCULATE METENS")
self.canvas['efnn'] = Canvas(res_window, width = self.BIG_CANVAS_SIZE_X, height = self.BIG_CANVAS_SIZE_Y)
self.canvas['efnn'].grid(row = 0, column = 1, padx = (5,10,), pady = 20)
self.canvas['efnn'].config(background="#fff")
self.canvas['efnn'].create_text(self.BIG_CANVAS_SIZE_X / 2, self.BIG_CANVAS_SIZE_Y / 2, fill="#000", font="Times 20 bold", text="COULD NOT CALCULATE EFNN")
self.mertens_time = Label(res_window, text="MERTENS : ? seconds")
self.mertens_time.grid(row=1, column=0, padx = 5, pady = 10)
self.efnn_time = Label(res_window, text="EFNN : ? seconds")
self.efnn_time.grid(row=1, column=1, padx = 5, pady = 10)
self.run_fusion()
except Exception as e:
messagebox.showerror("Error", str(e))
def run_fusion(self):
try:
if any([self.exposures[key] is None for key in self.exposures.keys()]):
messagebox.showerror("Error", "You must load all three exposures")
return
if not ( self.exposures['under'].shape == self.exposures['normal'].shape and self.exposures['over'].shape == self.exposures['normal'].shape ):
messagebox.showerror("Error", "Exposures must have the same resolutions/shape")
return
x = Exposures(BASE_PATH, exposures_files = self.exposures, model = MODEL_TO_USE)
try:
ef_t = x.create_ef()
self.ef_resized = cv2.resize(x.ef, (self.BIG_CANVAS_SIZE_X, self.BIG_CANVAS_SIZE_Y) )
self.ef_to_display = ImageTk.PhotoImage( Image.fromarray(cv2.cvtColor(self.ef_resized, cv2.COLOR_BGR2RGB)) )
self.canvas['mertens'].create_image(0, 0, anchor = NW, image = self.ef_to_display)
self.mertens_time['text'] = f"MERTENS : {ef_t:.2f} seconds"
except Exception as e:
messagebox.showerror("Error", str(e))
try:
efnn_t = x.predict_ef()
self.efnn_resized = cv2.resize(x.ef_prediction, (self.BIG_CANVAS_SIZE_X, self.BIG_CANVAS_SIZE_Y) )
self.efnn_to_display = ImageTk.PhotoImage( Image.fromarray(cv2.cvtColor(self.efnn_resized, cv2.COLOR_BGR2RGB)) )
self.canvas['efnn'].create_image(0, 0, anchor = NW, image = self.efnn_to_display)
self.efnn_time['text'] = f"EFNN : {efnn_t:.2f} seconds"
except Exception as e:
messagebox.showerror("Error", str(e))
except Exception as e:
messagebox.showerror("Error", str(e))
def __init__(self):
super(Root, self).__init__()
self.exposures = {
'under': None,
'normal': None,
'over': None,
}
self.exposures_resized = {
'under': None,
'normal': None,
'over': None,
}
self.to_display = {
'under': None,
'normal': None,
'over': None,
}
self.canvas = {}
self.title("EFNN vs Mertens")
self.CANVAS_SIZE_X = 300
self.CANVAS_SIZE_Y = 200
self.BIG_CANVAS_SIZE_X = 600
self.BIG_CANVAS_SIZE_Y = 400
self.w = Label(self, text="Choose 3 different exposures. The images must be jpg or png and of the same resolution.\nBecause Metens is quite slow, it is better to use images under 1000x1000")
self.w.grid(row=0, column=0, columnspan=3, padx = 50, pady = 10)
self.btn1 = Button(self, text ='Select Under exposed', command = lambda : self.open_file('under'))
self.btn1.grid(row=1, column=0, padx=0, pady= (10,10,))
self.btn2 = Button(self, text ='Select Normal exposed', command = lambda : self.open_file('normal'))
self.btn2.grid(row=1, column=1, padx=0, pady= (0,10,))
self.btn3 = Button(self, text ='Select Over exposed', command = lambda : self.open_file('over'))
self.btn3.grid(row=1, column=2, padx=0, pady= (0,10,))
self.canvas['under'] = Canvas(self, width = self.CANVAS_SIZE_X, height = self.CANVAS_SIZE_Y)
self.canvas['under'].grid(row = 2, column = 0, padx = (10,5,), pady = 20)
self.canvas['under'].config(background="#fff")
self.canvas['under'].create_text(self.CANVAS_SIZE_X / 2, self.CANVAS_SIZE_Y / 2, fill="#000", font="Times 20 bold", text="UNDER")
self.canvas['normal'] = Canvas(self, width = self.CANVAS_SIZE_X, height = self.CANVAS_SIZE_Y)
self.canvas['normal'].grid(row = 2, column = 1, padx = 5, pady = 20)
self.canvas['normal'].config(background="#fff")
self.canvas['normal'].create_text(self.CANVAS_SIZE_X / 2, self.CANVAS_SIZE_Y / 2, fill="#000", font="Times 20 bold", text="NORMAL")
self.canvas['over'] = Canvas(self, width = self.CANVAS_SIZE_X, height = self.CANVAS_SIZE_Y)
self.canvas['over'].grid(row = 2, column = 2, padx = (5,10,), pady = 20)
self.canvas['over'].config(background="#fff")
self.canvas['over'].create_text(self.CANVAS_SIZE_X / 2, self.CANVAS_SIZE_Y / 2, fill="#000", font="Times 20 bold", text="OVER")
self.runBtn = Button(self, text ='Run', command = self.fusion_popup)
self.runBtn.grid(row = 3, column = 0, columnspan = 3, padx = 0, pady = 10)
app = Root()
app.mainloop()
# # tf.compat.v1.enable_eager_execution()
# MODEL_TO_USE = './model-256-04-0.93-0.00342.hdf5'
# SAMPLES_DIR = './'
# SAMPLE = 'sample'
# # SAMPLES_DIR = "./dataset/jpg"
# # SAMPLE = 'OtterPoint'
# # EVS = ['-6', '-4', '-2']
# # SAMPLE = 'Zentrum'
# # EVS = ['-3', '+0', '+3']
# # SAMPLE = 'BandonSunset(2)'
# # EVS = ['-6', '-2', '+1']
# # SAMPLE = 'HDRMark'
# # EVS = ['-8', '-4', '+1']
# # SAMPLE = 'WallDrug'
# # EVS = ['-8', '-3', '+0']
# x = Exposures(
# os.path.join(SAMPLES_DIR, SAMPLE),
# # evs = [f'{SAMPLE}-EV{ev}' for ev in EVS],
# model = MODEL_TO_USE
# )
# x.create_ef()
# x.predict_ef()