interactive.py

import sys
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tkinter import *
import pyscreenshot as ImageGrab

def main():
     class_names = check_args()
     print(f"--Load Model {sys.argv[2]}--")
     #Load the model that should be in sys.argv[2]
     model = tf.keras.models.load_model(sys.argv[2])
     draw(model, class_names)

def predict(model, class_names, img, true_label):
    img = np.array([img])
    #Replace these two lines with code to make a prediction
    prediction = model.predict(img)[0]
    print("Prediction: ", prediction)
    #Determine what the predicted label is
    predicted_label = prediction.argmax(axis=-1)
    print("Prediction label: ", predicted_label)
    plot(class_names, prediction, true_label, predicted_label, img[0])
    plt.show()

def check_args():
     if(len(sys.argv) == 1):
        print("No arguments so using defaults")
        if input("Y for MNIST, otherwise notMNIST:") == "Y":
             sys.argv = ["interactive.py", "MNIST", "MNIST.h5"]
        else:
             sys.argv = ["interactive.py", "notMNIST", "notMNIST.h5"]
     if(len(sys.argv) != 3):
          print("Usage python interactive.py <MNIST,notMNIST> <model.h5>")
          sys.exit(1)
     if sys.argv[1] == "MNIST":
          print("--Dataset MNIST--")
          class_names = list(range(10))
     elif sys.argv[1] == "notMNIST":
          print("--Dataset notMNIST--")
          class_names = ["A","B","C","D","E","F","G","H","I","J"]
     else:
          print(f"Choose MNIST or notMNIST, not {sys.argv[1]}")
          sys.exit(2)
     if sys.argv[2][-3:] != ".h5":
          print(f"{sys.argv[2]} is not a h5 extension")
          sys.exit(3)
     return class_names

def plot(class_names, prediction, true_label, predicted_label, img):
    plt.figure(figsize=(6,3))
    plt.subplot(1,2,1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(img, cmap=plt.cm.binary)
    predicted_label = np.argmax(prediction)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'
    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],100*np.max(prediction),class_names[true_label]),color=color)
    plt.subplot(1,2,2)
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(class_names, prediction, color="#777777")
    plt.ylim([0, 1])
    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')

#State of mouse
b1 = "up"
def b1down(event):
    global b1
    b1 = "down"
def b1up(event):
    global b1
    b1 = "up"
def motion(event):
    if b1 == "down":
        event.widget.create_oval(event.x,event.y,event.x,event.y, width=16)

def draw(model, class_names):
    root = Tk()
    root.title("Draw")
    drawing_area = Canvas(root,bg="white",width=28*8,height=28*8)
    drawing_area.pack()
    drawing_area.bind("<Motion>", motion)
    drawing_area.bind("<ButtonPress-1>", b1down)
    drawing_area.bind("<ButtonRelease-1>", b1up)
    button=Button(root,fg="green",text=class_names[0],command=lambda:getter(drawing_area, 0))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[1],command=lambda:getter(drawing_area, 1))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[2],command=lambda:getter(drawing_area, 2))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[3],command=lambda:getter(drawing_area, 3))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[4],command=lambda:getter(drawing_area, 4))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[5],command=lambda:getter(drawing_area, 5))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[6],command=lambda:getter(drawing_area, 6))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[7],command=lambda:getter(drawing_area, 7))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[8],command=lambda:getter(drawing_area, 8))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text=class_names[9],command=lambda:getter(drawing_area, 9))
    button.pack(side=LEFT)
    button=Button(root,fg="green",text="Clear",command=lambda:delete(drawing_area))
    button.pack(side=RIGHT)
    def delete(widget):
        widget.delete("all")
    def getter(widget, value):
        x=root.winfo_rootx()+widget.winfo_x()
        y=root.winfo_rooty()+widget.winfo_y()
        x1=x+widget.winfo_width()
        y1=y+widget.winfo_height()
        grabbed = ImageGrab.grab().crop((x,y,x1,y1)).resize((28,28)).convert(mode="L")
        array = np.array(grabbed)
        if(np.amax(array.flatten()) > 1):
            array = array / 255
        array = 1 - array
        predict(model, class_names, array, value)
    root.mainloop()

if __name__ == "__main__":
    main()