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gui.py
214 lines (160 loc) · 8.63 KB
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gui.py
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import tkFileDialog
import threading
import winsound, sys
import spectogram
from Tkinter import *
from recorder import Recorder
from plot import Plot
from neural_network import NeuralNetwork
from image_transform import ImageTransform
from display_output import Display
class Gui:
timer = None
def __init__(self, root):
self.l_selected_file_name_var = StringVar() # variable for label dynamic text
self.l_timer_var = StringVar()
self.t_result_str_var = StringVar()
self.selected_file_name = "" # variable for storaging global selected file name
self.counter = 0 # clock counter
self.b_waveform = [] # declaring plot buttons
self.b_fft = []
self.l_status = []
self.full_file_path = []
self.radioIntVar = [] # 2D or more dimensions plot
self.menu_bar = Menu(root)
self.file_menu = Menu(self.menu_bar, tearoff=0)
self.ds_menu = Menu(self.menu_bar, tearoff=0)
self.nn_menu = Menu(self.menu_bar, tearoff=0)
self.frame_record = ""
self.frame_record1 = ""
self.frame_record2 = ""
self.frame_record3 = ""
self.root = root
self.create_window(root)
self.create_record(root)
self.create_result(root)
self.create_menu_bar(root)
self.create_presentation(root) #izbacen preview iz gui-a
self.disp = ""
self.root.config(menu=self.menu_bar)
self.root.mainloop()
def handleRadioSel(self):
if(self.radioIntVar.get() == 1):
self.b_spectrogram['state'] = 'active'
elif(self.radioIntVar.get() == 2):
self.b_spectrogram['state'] = 'disabled'
def open_audio_file(self):
sys.stdout.write("Searching for file...")
options = {}
options['filetypes'] = [('WAV audio files', '.wav')]
self.full_file_path = tkFileDialog.askopenfilename(**options)
splitted_path = self.full_file_path.split('/')
file_name = splitted_path[len(splitted_path)-1]
self.selected_file_name = file_name #global var, selected file_name
self.l_selected_file_name_var.set("[Selected file name]: " + self.selected_file_name)
self.b_fft['state'] = 'active' # enable plot buttons
self.b_waveform['state'] = 'active'
self.b_spectrogram['state'] = 'active'
print "\n[Selected file name:] " + file_name
def create_menu_bar(self, root):
self.file_menu.add_command(label = "Open audio file", command = self.open_audio_file)
self.menu_bar.add_cascade(label="File", menu=self.file_menu)
self.ds_menu.add_command(label = "Generate graphics", command = lambda: spectogram.create_data_set_graphs())
self.ds_menu.add_command(label = "Graphics augmentation", command = lambda: ImageTransform.gen_dataset_augmens())
self.menu_bar.add_cascade(label = "Data-Set", menu=self.ds_menu)
self.nn_menu.add_command(label = "Train", command = lambda: NeuralNetwork.create_and_train_nn())
self.nn_menu.add_command(label = "Load last model weights", command=lambda : NeuralNetwork.load_model_weights())
self.menu_bar.add_cascade(label = "Neural Network", menu=self.nn_menu)
def create_window(self, root):
root.title("Sound Recognition - Soft Computing")
root.geometry("550x580")
root.resizable(height=FALSE, width=FALSE)
def create_record(self, root):
self.frame_record = Frame(root)
self.frame_record.pack(side=TOP, fill=BOTH, pady=(0,5))
self.frame_record1 = Frame(self.frame_record)
self.frame_record1.pack(side=TOP, fill=BOTH, pady=(0,10))
self.frame_record2 = Frame(self.frame_record) #12 between 1 and 2
self.frame_record2.pack(side=TOP, fill=BOTH, pady=(0,10))
self.frame_record3 = Frame(self.frame_record)
self.frame_record3.pack(side=BOTTOM, fill=NONE)
l_caption = Label(self.frame_record1, text="Record sound:")
l_caption.pack(side=LEFT)
b_help = Button(self.frame_record1, text="info", width=3, height=1)
b_help.pack(side=RIGHT)
self.l_selected_file_name_var = StringVar()
self.l_selected_file_name_var.set("[Selected file name:] none")
l_selected_file_name = Label(self.frame_record2, textvariable = self.l_selected_file_name_var, width = 30, height = 1, anchor = 'w')
l_selected_file_name.pack(side = LEFT)
self.b_waveform = Button(self.frame_record2, text = "WaveForm", width = 8, height = 1, command = lambda : Plot.plot_audio(self.full_file_path, "raw", self.radioIntVar))
self.b_waveform.pack(side = RIGHT)
self.b_waveform['state'] = 'disabled'
self.b_fft = Button(self.frame_record2, text = "FFT", width = 8, height = 1, command = lambda : Plot.plot_audio(self.full_file_path, "fft", self.radioIntVar))
self.b_fft.pack(side = RIGHT, padx = 3)
self.b_fft['state'] = 'disabled'
self.b_spectrogram = Button(self.frame_record2, text = "Spectrogram", width = 10, height = 1, command = lambda : Plot.plot_audio(self.full_file_path, "spectrogram", self.radioIntVar))
self.b_spectrogram.pack(side = RIGHT, padx = 3)
self.b_spectrogram['state'] = 'disabled'
self.radioIntVar = IntVar()
R1 = Radiobutton(self.frame_record2, text="2D", variable=self.radioIntVar, value=1, command= lambda: self.handleRadioSel())
R1.pack( side = RIGHT)
self.radioIntVar.set(1) # init 2D as default
R2 = Radiobutton(self.frame_record2, text="3D", variable=self.radioIntVar, value=2, command= lambda: self.handleRadioSel())
R2.pack( side = RIGHT)
global b_start
global l_time
b_start = Button(self.frame_record3, text='Record', width=12, height=2, command=lambda: self.main_button_click())
b_start.pack(pady=10, padx=15, side=LEFT)
self.l_timer_var.set('00:00')
l_time = Label(self.frame_record3, height=1, width=5, state='disabled', bg='white', textvariable=self.l_timer_var, foreground='black')
l_time.pack(pady=10, padx=(10,0), side=LEFT)
l_status = Label(self.frame_record3, text="...recording", foreground='red')
l_status.pack(pady=10, padx=(5,10), side=LEFT)
b_reset = Button(self.frame_record3, text='Reset', padx=2, command=self.reset_button_click())
b_reset.pack(pady=10, padx=20, side=LEFT)
def create_presentation(self, root):
self.disp = Display(self.frame_record)
self.disp.pack()
print "==================INSTRUCTIONS==================="
print "1. Load Convolution2D Neural Network weights model..."
print "2. Hit *Record* button and wait 1 sec after Beep signal, then start whistling..."
print "3. Hit *Recognize* button and check the results..."
print "================================================\n"
def create_result(self, root):
frame_result = Frame(root)
frame_result.pack(fill=BOTH)
l_result = Label(frame_result, text="Recognized sound:")
l_result.pack(pady=10, padx=5, side=LEFT)
self.t_result_str_var.set("Output is in console..")
t_result = Label(frame_result, height=1, width=20, textvariable=self.t_result_str_var, bg="white")
t_result.pack(pady=10, padx=5, side=LEFT)
b_predict = Button(frame_result, text='Recognize', command= lambda: NeuralNetwork.predict_results())
b_predict.pack(pady=10, padx=5, side=LEFT)
b_details = Button(frame_result, text='Details')
b_details.pack(pady=10, padx=5, side=RIGHT)
def tick_timer(self):
timer = threading.Timer(1, self.tick_timer)
timer.start()
self.counter += 1
if self.counter > 9:
self.l_timer_var.set('00:' + str(self.counter))
else:
self.l_timer_var.set('00:0' + str(self.counter))
if self.counter > 3: # 3 secs for duration of recording
timer.cancel()
self.play_beep()
self.counter = 0
self.l_timer_var.set('00:00')
return
print "tick..." + str(self.counter)
def main_button_click(self):
self.play_beep()
self.tick_timer()
Recorder.start_recording()
self.full_file_path = "test.wav"
self.l_selected_file_name_var.set("[Selected file name]: " + "test.wav")
self.b_spectrogram['state'] = 'active'
def reset_button_click(self):
b_start["text"] = "Record"
def play_beep(self):
winsound.PlaySound("beep.wav", winsound.SND_ALIAS)