CharRecognitionGUI.py

#! /usr/bin/env python
#
# GUI module generated by PAGE version 4.5
# In conjunction with Tcl version 8.6
#    Nov 21, 2015 03:29:17 PM
import sys
import os
import numpy
from tkFileDialog import askopenfilename
import matplotlib.pyplot as plt
import pickle

try:
    from Tkinter import *
except ImportError:
    from tkinter import *

try:
    import ttk
    py3 = 0
except ImportError:
    import tkinter.ttk as ttk
    py3 = 1

import CharRecognitionGUI_support
from FreemanEncoder import FreemanEncoder
from PIL import Image, ImageDraw, ImageWin, ImageTk
import KNN
import HMM
import NaiveBayes
import RandomForest
import svm
import LogisticReg
import adaboost
import gbrt

def vp_start_gui():
    '''Starting point when module is the main routine.'''
    global val, w, root
    root = Tk()
    root.title("Digit Recognizer")
    geom = "848x493+374+90"
    root.geometry(geom)
    CharRecognitionGUI_support.set_Tk_var()
    w = New_Toplevel_1 (root)
    CharRecognitionGUI_support.init(root, w)
    root.mainloop()

w = None
def create_New_Toplevel_1(root, param=None):
    '''Starting point when module is imported by another program.'''
    global w, w_win, rt
    rt = root
    w = Toplevel (root)
    w.title('New_Toplevel_1')
    geom = "848x493+374+90"
    w.geometry(geom)
    CharRecognitionGUI_support.set_Tk_var()
    w_win = New_Toplevel_1 (w)
    CharRecognitionGUI_support.init(w, w_win, param)
    return w_win

def destroy_New_Toplevel_1():
    global w
    w.destroy()
    w = None


class New_Toplevel_1:
    def __init__(self, master=None):
        _bgcolor = '#d9d9d9'  # X11 color: 'gray85'
        _fgcolor = '#000000'  # X11 color: 'black'
        _compcolor = '#d9d9d9' # X11 color: 'gray85'
        _ana1color = '#d9d9d9' # X11 color: 'gray85' 
        _ana2color = '#d9d9d9' # X11 color: 'gray85' 
        self.style = ttk.Style()
        if sys.platform == "win32":
            self.style.theme_use('winnative')
        self.style.configure('.',background=_bgcolor)
        self.style.configure('.',foreground=_fgcolor)
        self.style.configure('.',font="TkDefaultFont")
        self.style.map('.',background=
            [('selected', _compcolor), ('active',_ana2color)])
        master.configure(background="#d9d9d9")


        self.style.configure('TNotebook.Tab', background=_bgcolor)
        self.style.configure('TNotebook.Tab', foreground=_fgcolor)
        self.style.map('TNotebook.Tab', background=
            [('selected', _compcolor), ('active',_ana2color)])
        self.TNotebook1 = ttk.Notebook(master)
        self.TNotebook1.place(relx=0.02, rely=0.02, relheight=0.95
                , relwidth=0.96)
        self.TNotebook1.configure(width=574)
        self.TNotebook1.configure(takefocus="")
        self.TNotebook1_predict = ttk.Frame(self.TNotebook1)
        self.TNotebook1.add(self.TNotebook1_predict, padding=3)
        self.TNotebook1.tab(0, text="Recognize",underline="-1",)
        self.TNotebook1_train = ttk.Frame(self.TNotebook1)
        self.TNotebook1.add(self.TNotebook1_train, padding=3)
        self.TNotebook1.tab(1, text="Train",underline="-1",)
        
        #user defined variables
        self.thumbnails = self.load_thumbnails(CharRecognitionGUI_support.thumbnails_path)
        self.PIL_image = Image.new("1", (300, 300), "white")
        self.hidden_canvas = ImageDraw.Draw(self.PIL_image)
        self.x = None
        self.y = None
        
        # Initialize and train all classifiers
        self._init_classifiers()

        self.Clear = ttk.Button(self.TNotebook1_predict)
        self.Clear.place(relx=0.44, rely=0.14, height=24, width=78)
        self.Clear.configure(text='''Clear Canvas''')
        self.Clear.bind("<Button-1>",self.clear)

        self.Canvas1 = Canvas(self.TNotebook1_predict)
        self.Canvas1.place(relx=0.01, rely=0.02, relheight=0.61, relwidth=0.35)
        self.Canvas1.configure(background="white")
        self.Canvas1.configure(borderwidth="2")
        self.Canvas1.configure(highlightbackground="#d9d9d9")
        self.Canvas1.configure(highlightcolor="black")
        self.Canvas1.configure(insertbackground="black")
        self.Canvas1.configure(relief=RIDGE)
        self.Canvas1.configure(selectbackground="#c4c4c4")
        self.Canvas1.configure(selectforeground="black")
        self.Canvas1.configure(width=378)
        self.Canvas1.bind("<B1-Motion>",self.drag)
        self.Canvas1.bind("<ButtonRelease-1>",self.drag_end)

        self.Save = ttk.Button(self.TNotebook1_predict)
        self.Save.place(relx=0.5, rely=0.04, height=24, width=77)
        self.Save.configure(text='''Save''')
        self.Save.bind("<Button-1>",self.save)

        self.Select = ttk.Button(self.TNotebook1_predict)
        self.Select.place(relx=0.44, rely=0.24, height=24, width=77)
        self.Select.configure(text='''Select Image''')
        self.Select.bind("<Button-1>",self.select)

#         self.Frame1 = Frame(self.TNotebook1_predict)
#         self.Frame1.place(relx=0.61, rely=0.02, relheight=0.6, relwidth=0.36)
#         self.Frame1.configure(relief=GROOVE)
#         self.Frame1.configure(borderwidth="2")
#         self.Frame1.configure(relief=GROOVE)
#         self.Frame1.configure(background=_bgcolor)
#         self.Frame1.configure(highlightbackground="#d9d9d9")
#         self.Frame1.configure(highlightcolor="black")
#         self.Frame1.configure(width=305)
        
        self.CanvasHist = Canvas(self.TNotebook1_predict)
        self.CanvasHist.place(relx=0.61, rely=0.02, relheight=0.6, relwidth=0.36)
        self.CanvasHist.configure(background="white")
        self.CanvasHist.configure(borderwidth="2")
        self.CanvasHist.configure(highlightbackground="#d9d9d9")
        self.CanvasHist.configure(highlightcolor="black")
        self.CanvasHist.configure(insertbackground="black")
        self.CanvasHist.configure(relief=RIDGE)
        self.CanvasHist.configure(selectbackground="#c4c4c4")
        self.CanvasHist.configure(selectforeground="black")
        self.CanvasHist.configure(width=378)
        
        self.freeman_textbox = Text(self.TNotebook1_predict)
        self.freeman_textbox.place(relx=0.01, rely=0.69, height=131, width=514)
        self.freeman_textbox.configure(background=_bgcolor)
        self.freeman_textbox.configure(foreground="#000000")
        self.freeman_textbox.configure(highlightbackground="#d9d9d9")
        self.freeman_textbox.configure(highlightcolor="black")
        
        self.scrollbar = Scrollbar(self.freeman_textbox)
        self.scrollbar.pack(side=RIGHT, fill=Y)
        self.scrollbar.config(command=self.freeman_textbox.yview)
        self.freeman_textbox.configure(yscrollcommand=self.scrollbar.set)

        self.TCombobox1 = ttk.Combobox(self.TNotebook1_predict)
        self.TCombobox1.place(relx=0.41, rely=0.35, relheight=0.06
                , relwidth=0.16)
        self.value_list = ['kNN (Freeman Code)','HMM (Freeman Code)','RandomForest (EFD)',
                           'NaiveBayes (RAW)', 'SVM (EFD)', 'LogisticReg (EFD)',
                           'AdaBoost (EFD)', 'GradientBoosting (EFD)']
        self.TCombobox1.configure(values=self.value_list)
        self.TCombobox1.configure(textvariable=CharRecognitionGUI_support.combobox)
        self.TCombobox1.configure(takefocus="")
        
        self.TCombobox2 = ttk.Combobox(self.TNotebook1_predict)
        self.TCombobox2.place(relx=0.4, rely=0.04, height=24, width=77)
        self.value_list = ['0','1','2','3','4','5','6','7','8','9',]
        self.TCombobox2.configure(values=self.value_list)
        self.TCombobox2.configure(textvariable=CharRecognitionGUI_support.combobox2)
        self.TCombobox2.configure(takefocus="")
        self.TCombobox2.current(0)
        
        self.Frame2 = Frame(self.TNotebook1_predict)
        self.Frame2.place(relx=0.737, rely=0.715, relheight=0.26, relwidth=0.15)
        self.Frame2.configure(relief=GROOVE)
        self.Frame2.configure(borderwidth="2")
        self.Frame2.configure(relief=GROOVE)
        self.Frame2.configure(background=_bgcolor)
        self.Frame2.configure(highlightbackground="#d9d9d9")
        self.Frame2.configure(highlightcolor="black")
        self.Frame2.configure(width=150, height=150)

        self.Thumbnail = Label(self.TNotebook1_predict)
        self.Thumbnail.place(relx=0.75, rely=0.73, relheight=0.225, relwidth=0.125)
        self.Thumbnail.configure(background=_bgcolor)
        self.Thumbnail.configure(disabledforeground="#a3a3a3")
        self.Thumbnail.configure(foreground="#000000")
        self._img1 = PhotoImage(file='./thumbnails/blank.gif')
        self.Thumbnail.configure(image=self._img1)
        self.Thumbnail.configure(text='''Label''')
        self.Thumbnail.configure(width=94)

        self.Recognize = ttk.Button(self.TNotebook1_predict)
        self.Recognize.place(relx=0.44, rely=0.45, height=24, width=87)
        self.Recognize.configure(text='''Recognize''')
        self.Recognize.bind("<Button-1>",self.recognize)
        
        self.Clear_Results = ttk.Button(self.TNotebook1_predict)
        self.Clear_Results.place(relx=0.44, rely=0.55, height=24, width=87)
        self.Clear_Results.configure(text='''Clear Results''')
        self.Clear_Results.bind("<Button-1>",self.clear_results)

    def _init_classifiers(self):
        # Initialize classifier objects
        self.fenc = FreemanEncoder()
        self.knn = KNN.KNN()
        self.HMM = HMM.HMM()
        self.NaiveBayes = NaiveBayes.NaiveBayes()
        self.RandomForest = RandomForest.RandomForests()
        self.SVM = svm.SVM_SVC()
        self.LogisticReg = LogisticReg.LogisticReg()
        self.AdaBoost = adaboost.AdaBoost()
        self.GBRT = gbrt.GBRT()
        
        #Train initially on the default data set, if no model saved already
        
        # Initialize KNN, no saved model for KNN
        self.knn.knn_train(CharRecognitionGUI_support.training_dataset, 1.0)
        
        # Initialize HMM
        self.HMM.training(CharRecognitionGUI_support.training_dataset)
        
        # Initialize Naive Bayes
        try:
            pickle.load( open( "./Models/naivebayes_model.p", "rb" ) )
        except IOError:
            self.NaiveBayes.training(CharRecognitionGUI_support.training_dataset)
        
        # Initialize Random Forest
        try:
            pickle.load( open( "./Models/random_forest.p", "rb" ) )
        except IOError:
            self.RandomForest.training(CharRecognitionGUI_support.training_dataset)
        
        # Initialize SVM
        try:
            pickle.load( open( "./Models/svm.p", "rb" ) )
        except IOError:
            self.SVM.training(CharRecognitionGUI_support.training_dataset)
        
        # Initialize Logistic Regression
        try:
            pickle.load( open( "./Models/logistic_model.p", "rb" ) )
        except IOError:
            self.LogisticReg.training(CharRecognitionGUI_support.training_dataset)
            
        # Initialize AdaBoost
        try:
            pickle.load( open( "./Models/AdaBoostClassifier.p", "rb" ) )
        except IOError:
            self.AdaBoost.training(CharRecognitionGUI_support.training_dataset)
            
        # Initialize GBRT
        try:
            pickle.load( open( "./Models/GradientBoostingClassifier.p", "rb" ) )
        except IOError:
            self.GBRT.training(CharRecognitionGUI_support.training_dataset)
        
    
    def load_thumbnails(self, thumbnails_path):
        images = {}
        for thumb in os.listdir(thumbnails_path):
            thumb_name = os.path.splitext(thumb)[0]
            images[thumb_name] = thumbnails_path + '/' + thumb
        
        return images
    
    def quit(self, event):
        '''
        Event function to quit the drawer window
        '''
        sys.exit()
        
    def select(self, event):
        Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
        self.clear("<Button-1>")
        image_path = askopenfilename()
        self.PIL_image = Image.open(image_path)
        self.selected_image = ImageTk.PhotoImage(self.PIL_image)
        self.Canvas1.create_image(150, 150, image=self.selected_image)
       
    def clear(self, event):
        '''
        Event function to clear the drawing canvas (draw white fill)
        '''
        self.Canvas1.delete("all")
        self.PIL_image = Image.new("1", (300, 300), "white")
        self.hidden_canvas = ImageDraw.Draw(self.PIL_image)
    
    def drag(self, event):
        '''
        Event function to start drawing on canvas when left mouse drag happens
        '''
        newx,newy=event.x,event.y
        if self.x is None:
            self.x,self.y=newx,newy
            return
        self.Canvas1.create_line((self.x,self.y,newx,newy), width=5, smooth=True)
        self.hidden_canvas.line((self.x,self.y,newx,newy), width=12)
        self.x,self.y=newx,newy
    
    def drag_end(self, event):
        '''
        Event function to stop drawing on canvas when mouse drag stops
        '''
        self.x,self.y=None,None
       
    def save(self, event):
        '''
        Event function to save the current canvas image in JPG format
        '''
        image_cnt = 1
        if not os.path.exists(CharRecognitionGUI_support.save_dir):
            os.makedirs(CharRecognitionGUI_support.save_dir)
        file_name = CharRecognitionGUI_support.save_dir + self.TCombobox2.get() + '_' + str(image_cnt) + ".jpg"
        
        while os.path.isfile(file_name):
            image_cnt += 1
            file_name = CharRecognitionGUI_support.save_dir + self.TCombobox2.get() + '_' + str(image_cnt) + ".jpg"
        
        self.PIL_image.save(file_name)
        self.freeman_textbox.delete("1.0", END)
        self.freeman_textbox.insert(END, 'SAVED!')
        self.freeman_textbox.see(END)

    def clear_results(self, event):
        self.freeman_textbox.delete("1.0", END)
        self._img1 = PhotoImage(file='./thumbnails/blank.gif')
        self.Thumbnail.configure(image=self._img1)
        self.CanvasHist.delete("all")
    
    def recognize(self, event):
        image = ~numpy.array(self.PIL_image.convert('L'))
        try:
            code = self.fenc.encode_freeman(image)
            
            # Plotting the histogram
            figure = plt.figure()
            hist = list(map(int, list(code)))
            plt_hist = plt.hist(hist)
            plt.savefig('hist.png')
            self.hist_im = ImageTk.PhotoImage(Image.open('hist.png').resize((280,280), Image.LANCZOS))
            os.remove('hist.png')
            self.CanvasHist.create_image(290, 265, image=self.hist_im, anchor=SE)
            
        except ValueError:
            self.freeman_textbox.delete("1.0", END)
            self.freeman_textbox.insert(END, 'Please redraw the image')
            self.freeman_textbox.see(END)
        
        self.freeman_textbox.delete("1.0", END)
        self.freeman_textbox.insert(END, str(code))
        self.freeman_textbox.see(END)
        
        if self.TCombobox1.get() == '':
            pass
        
        elif self.TCombobox1.get().split(" ")[0] == 'kNN':
            
            pred = self.knn.knn_predict_one(code, 1)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
            
        elif self.TCombobox1.get().split(" ")[0] == 'HMM':
            
            pred = self.HMM.predict(code)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
                    
        elif self.TCombobox1.get().split(" ")[0] == 'NaiveBayes':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.NaiveBayes.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
        
        elif self.TCombobox1.get().split(" ")[0] == 'RandomForest':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.RandomForest.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
        
        elif self.TCombobox1.get().split(" ")[0] == 'SVM':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.SVM.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
        
        elif self.TCombobox1.get().split(" ")[0] == 'LogisticReg':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.LogisticReg.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
            
        elif self.TCombobox1.get().split(" ")[0] == 'AdaBoost':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.AdaBoost.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
            
        elif self.TCombobox1.get().split(" ")[0] == 'GradientBoosting':
            image = numpy.array(self.PIL_image.convert('L').resize((100,100), Image.LANCZOS))
            image[image < 128] = 1
            image[image >= 128] = 0
            image[image == 1] = 255
            
            pred = self.GBRT.predict(image)
            pred_thumb = self.thumbnails[pred[0]]
            self._image = PhotoImage(file=pred_thumb)
            self.Thumbnail.configure(image=self._image)
        
        else:
            self.freeman_textbox.delete("1.0", END)
            self.freeman_textbox.insert(END, 'Not Implemented yet')
            self.freeman_textbox.see(END)


if __name__ == '__main__':
    vp_start_gui()