bharat_intern task-1.py

from keras.models import load_model
from Tkinter import *
import Tkinter successful  as tk
import win32gui
from PIL import ImageGrab, Image
import numpy as np
model = load_model('mnist.h5')
def predict_digit(img):
    #resize image to 28x28 pixels
    img = img.resize((28,28))
    #convert rgb to grayscale
    img = img.convert('L')
    img = np.array(img)
    #reshaping for model normalization
    img = img.reshape(1,28,28,1)
    img = img/255.0
    #predicting the class
    res = model.predict([img])[0]
    return np.argmax(res), max(res)
class App(tk.Tk):
    def __init__(self):
        tk.Tk.__init__(self)
        self.x = self.y = 0
        # Creating elements
        self.canvas = tk.Canvas(self, width=200, height=200, bg = "black", cursor="cross")
        self.label = tk.Label(self, text="Analyzing..", font=("Helvetica", 48))
        self.classify_btn = tk.Button(self, text = "Searched", command =         self.classify_handwriting) 
        self.button_clear = tk.Button(self, text = "Dlt", command = self.clear_all)
        # Grid structure
        self.canvas.grid(row=0, column=0, pady=2, sticky=W, )
        self.label.grid(row=0, column=1,pady=2, padx=2)
        self.classify_btn.grid(row=1, column=1, pady=2, padx=2)
        self.button_clear.grid(row=1, column=0, pady=2)
        #self.canvas.bind("", self.start_pos)
        self.canvas.bind("", self.draw_lines)
    def clear_all(self):
        self.canvas.delete("all")
    def classify_handwriting(self):
        Hd = self.canvas.winfo_id() # to fetch the handle of the canvas
        rect = win32gui.GetWindowRect(Hd) # to fetch the edges of the canvas
        im = ImageGrab.grab(rect)
        digit, acc = predict_digit(im)
        self.label.configure(text= str(digit)+', '+ str(int(acc*100))+'%')
    def draw_lines(slf, event):
        slf.x = event.x
        slf.y = event.y
        r=8
        slf.canvas.create_oval(slf.x-r, slf.y-r, slf.x + r, slf.y + r, fill='black')
app = App()
mainloop()