{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Machine Learning\n",
    "\n",
    "This notebook is an example of a CNN for recognizing handwritten characters.\n",
    "\n",
    "Most of this code is from https://keras.io/examples/vision/mnist_convnet/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# Model / data parameters\n",
    "num_classes = 10\n",
    "input_shape = (28, 28, 1)\n",
    "\n",
    "# Load the data and split it between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "\n",
    "# Scale images to the [0, 1] range\n",
    "x_train = x_train.astype(\"float32\") / 255\n",
    "x_test = x_test.astype(\"float32\") / 255\n",
    "\n",
    "# Make sure images have shape (28, 28, 1)\n",
    "x_train = np.expand_dims(x_train, -1)\n",
    "x_test = np.expand_dims(x_test, -1)\n",
    "print(\"x_train shape:\", x_train.shape)\n",
    "print(x_train.shape[0], \"train samples\")\n",
    "print(x_test.shape[0], \"test samples\")\n",
    "\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                16010     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=input_shape),\n",
    "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Flatten(),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(num_classes, activation=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "422/422 [==============================] - 6s 3ms/step - loss: 0.3744 - accuracy: 0.8868 - val_loss: 0.0892 - val_accuracy: 0.9763\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.1177 - accuracy: 0.9634 - val_loss: 0.0660 - val_accuracy: 0.9817\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0876 - accuracy: 0.9732 - val_loss: 0.0480 - val_accuracy: 0.9865\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0738 - accuracy: 0.9774 - val_loss: 0.0462 - val_accuracy: 0.9872\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0642 - accuracy: 0.9805 - val_loss: 0.0440 - val_accuracy: 0.9872\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0585 - accuracy: 0.9818 - val_loss: 0.0373 - val_accuracy: 0.9898\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0544 - accuracy: 0.9832 - val_loss: 0.0348 - val_accuracy: 0.9908\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9845 - val_loss: 0.0342 - val_accuracy: 0.9907\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0462 - accuracy: 0.9853 - val_loss: 0.0313 - val_accuracy: 0.9910\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0444 - accuracy: 0.9858 - val_loss: 0.0320 - val_accuracy: 0.9907\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0418 - accuracy: 0.9872 - val_loss: 0.0303 - val_accuracy: 0.9913\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9874 - val_loss: 0.0276 - val_accuracy: 0.9922\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 1s 3ms/step - loss: 0.0381 - accuracy: 0.9875 - val_loss: 0.0292 - val_accuracy: 0.9912\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0368 - accuracy: 0.9879 - val_loss: 0.0291 - val_accuracy: 0.9920\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 1s 2ms/step - loss: 0.0356 - accuracy: 0.9888 - val_loss: 0.0257 - val_accuracy: 0.9925\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c9d4871f40>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "epochs = 15\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.026043808087706566\n",
      "Test accuracy: 0.9907000064849854\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"mnist.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "from tkinter import *\n",
    "from PIL import ImageGrab\n",
    "import imageio\n",
    "import tkinter.font as font\n",
    "\n",
    "class Paint(object):\n",
    "    def __init__(self):\n",
    "        self.root=Tk()\n",
    "        self.root.title('Playing with numbers')\n",
    "        # self.root.wm_iconbitmap('44143.ico')\n",
    "        self.root.configure(background='light salmon')\n",
    "        self.c = Canvas(self.root,bg='light cyan', height=330, width=400)\n",
    "        self.label = Label(self.root, text='Draw any numer', font=20, bg='light salmon')\n",
    "        self.label.grid(row=0, column=3)\n",
    "        self.c.grid(row=1, columnspan=9)\n",
    "        self.c.create_line(0,0,400,0,width=20,fill='midnight blue')\n",
    "        self.c.create_line(0,0,0,330,width=20,fill='midnight blue')\n",
    "        self.c.create_line(400,0,400,330,width=20,fill='midnight blue')\n",
    "        self.c.create_line(0,330,400,330,width=20,fill='midnight blue')\n",
    "        self.myfont = font.Font(size=20,weight='bold')\n",
    "        self.predicting_button=Button(self.root,text='Predict', fg='white', bg='blue', height=2, width=6, font=self.myfont, command=lambda:self.classify(self.c))\n",
    "        self.predicting_button.grid(row=2,column=1)\n",
    "        self.clear=Button(self.root,text='Clear', fg='white', bg='orange', height=2, width=6, font=self.myfont, command=self.clear)\n",
    "        self.clear.grid(row=2,column=5)\n",
    "        self.prediction_text = Text(self.root, height=5, width=5)\n",
    "        self.prediction_text.grid(row=4, column=3)\n",
    "        self.label=Label(self.root, text=\"Predicted Number is\", fg=\"black\", font=30, bg='light salmon')\n",
    "\n",
    "        self.label.grid(row=3,column=3)\n",
    "        self.model=model\n",
    "        self.setup()\n",
    "        self.root.mainloop()\n",
    "\n",
    "\n",
    "    def setup(self):\n",
    "        self.old_x=None\n",
    "        self.old_y=None\n",
    "        self.color='black'\n",
    "        self.linewidth=15\n",
    "        self.c.bind('<B1-Motion>', self.paint)\n",
    "        self.c.bind('<ButtonRelease-1>', self.reset)\n",
    "\n",
    "\n",
    "    def paint(self,event):\n",
    "        paint_color=self.color\n",
    "        if self.old_x and self.old_y:\n",
    "            self.c.create_line(self.old_x,self.old_y,event.x,event.y,fill=paint_color,width=self.linewidth,capstyle=ROUND,\n",
    "                              smooth=TRUE,splinesteps=48)\n",
    "        self.old_x=event.x\n",
    "        self.old_y=event.y\n",
    "\n",
    "\n",
    "    def clear(self):\n",
    "        \"\"\"Clear drawing area\"\"\"\n",
    "        self.c.delete(\"all\")\n",
    "\n",
    "    def reset(self, event):\n",
    "        \"\"\"reset old_x and old_y if the left mouse button is released\"\"\"\n",
    "        self.old_x, self.old_y = None, None\n",
    "\n",
    "\n",
    "    def classify(self,widget):\n",
    "        x=self.root.winfo_rootx()+widget.winfo_x()\n",
    "        y=self.root.winfo_rooty()+widget.winfo_y()\n",
    "        x1=widget.winfo_width()\n",
    "        y1=widget.winfo_height()\n",
    "        ImageGrab.grab().crop((x,y,x1,y1)).resize((28,28)).save('classify.png')\n",
    "        img=imageio.imread('classify.png', as_gray=True, pilmode='P')\n",
    "        img=np.array(img)\n",
    "        img=np.reshape(img,(1,28,28,1))\n",
    "        img[img==0] = 255\n",
    "        img[img==225] = 0\n",
    "        # Predict digit\n",
    "        pred = self.model.predict([img])\n",
    "        # Get index with highest probability\n",
    "        pred = np.argmax(pred)\n",
    "        print(pred)\n",
    "        self.prediction_text.delete(\"1.0\", END)\n",
    "        self.prediction_text.insert(END, pred)\n",
    "        labelfont = ('times', 30, 'bold')\n",
    "        self.prediction_text.config(font=labelfont)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    Paint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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