VFR_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbcf899e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL\n",
    "import cv2\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "120e1a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pil_image(img_path):\n",
    "    pil_img = PIL.Image.open(img_path).convert('L') # transform to grayscale\n",
    "    pil_img = pil_img.resize((105, 105)) # resize image to 1058105\n",
    "    return pil_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f619306",
   "metadata": {},
   "outputs": [],
   "source": [
    "def noise_image(img):\n",
    "    img_array = np.asarray(img)\n",
    "    mean = 0.0\n",
    "    std = 3\n",
    "    noisy_img = img_array + np.random.normal(mean, std, img_array.shape)\n",
    "    noisy_img_clipped = np.clip(noisy_img, 0, 255)\n",
    "    noise_img = PIL.Image.fromarray(np.uint8(noisy_img_clipped))\n",
    "    noise_img = noise_img.resize((105, 105))\n",
    "    return noise_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37860ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def blur_image(img):\n",
    "    blur_img = img.filter(PIL.ImageFilter.GaussianBlur(radius = 3))\n",
    "    blur_img = blur_img.resize((105, 105))\n",
    "    return blur_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eae1e91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def affine_rotation(img):\n",
    "    rows, columns = img.shape\n",
    "\n",
    "    point1 = np.float32([[10, 10], [30, 10], [10, 30]])\n",
    "    point2 = np.float32([[20, 15], [40, 10], [20, 40]])\n",
    "\n",
    "    anchor = cv2.getAffineTransform(point1, point2)\n",
    "\n",
    "    output = cv2.warpAffine(img, anchor, (columns, rows))\n",
    "    affine_img = PIL.Image.fromarray(np.uint8(output))\n",
    "    affine_img = affine_img.resize((105, 105))\n",
    "    return affine_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3592ad7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_fill(img):\n",
    "    output = cv2.Laplacian(img, cv2.CV_64F)\n",
    "    laplacian_img = PIL.Image.fromarray(np.uint8(output))\n",
    "    laplacian_img = laplacian_img.resize((105, 105))\n",
    "    return laplacian_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d537f11b",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 3] The system cannot find the path specified: 'C/Lucas/XJTLU/Y3/1st_term/DTS201TC/founts'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16964\\2889172367.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdata_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'/dataset'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mttf_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.ttf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mttf_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'C/Lucas/XJTLU/Y3/1st_term/DTS201TC/founts'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "ttf_path = 'C/Lucas/XJTLU/Y3/1st_term/DTS201TC/founts'\n",
    "data_path = '/dataset'\n",
    "\n",
    "for file in os.listdir(ttf_path):\n",
    "    if file.endswith('.ttf'):\n",
    "        path = os.path.join(ttf_path, file)\n",
    "        name, ext = os.path.splitext(os.path.basename(path))\n",
    "        out_path = data_path + '/' + name\n",
    "        command = 'trdg -l en -c 10 -rs -let -num -r --length 1 -b 1 -e .png -fi -f 105 -ft ' + path + ' --output_dir ' + out_path\n",
    "        os.system(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17ec491d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Font Names ->  ['Aller_Bd', 'BEBAS___', 'Capture_it', 'Lato-ThinItalic', 'Walkway_Black']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from imutils import paths\n",
    "#from random import shuffle\n",
    "import random\n",
    "data_path = './dataset'\n",
    "image_paths = sorted(list(paths.list_images(data_path)))\n",
    "#random.seed(10)\n",
    "random.shuffle(image_paths)\n",
    "\n",
    "font_names = []\n",
    "\n",
    "for f in os.listdir(data_path):\n",
    "    if not f.startswith('.'):\n",
    "        font_names.append(f)\n",
    "\n",
    "font_names.sort()\n",
    "\n",
    "print('Font Names -> ', font_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc3ea796",
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_label(label):\n",
    "    return font_names.index(label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b1075e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools\n",
    "import numpy as np\n",
    "from keras.utils import image_utils\n",
    "from PIL import ImageFilter\n",
    "import PIL\n",
    "data = []\n",
    "labels = []\n",
    "auguments = [\"blur\", \"noise\", \"affine\", \"gradient\"]\n",
    "\n",
    "for path in image_paths:\n",
    "    label = path.split(os.path.sep)[-2]\n",
    "\n",
    "    if not label.startswith('.'):\n",
    "        label = conv_label(label)\n",
    "    else:\n",
    "        continue\n",
    "\n",
    "    pil_img = pil_image(path)\n",
    "    org_img = image_utils.img_to_array(pil_img)\n",
    "\n",
    "    data.append(org_img)\n",
    "    labels.append(label)\n",
    "\n",
    "    for i in range(0, len(auguments)):\n",
    "        for augument in list(itertools.combinations(auguments, i + 1)):\n",
    "\n",
    "            temp_img = pil_img\n",
    "            combinations = list(augument)\n",
    "\n",
    "            for method in combinations:\n",
    "                if method == 'noise':\n",
    "                    temp_img = noise_image(temp_img)\n",
    "\n",
    "                elif method == 'blur':\n",
    "                    temp_img = blur_image(temp_img)\n",
    "\n",
    "                elif method == 'affine':\n",
    "                    open_cv_affine = np.array(pil_img)\n",
    "                    temp_img = affine_rotation(open_cv_affine)\n",
    "\n",
    "                elif method == 'gradient':\n",
    "                    open_cv_gradient = np.array(pil_img)\n",
    "                    temp_img = gradient_fill(open_cv_gradient)\n",
    "\n",
    "            temp_img = image_utils.img_to_array(temp_img)\n",
    "\n",
    "            data.append(temp_img)\n",
    "            labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2b41f59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0132eb55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = np.asarray(data, dtype = \"float\") / 255.0\n",
    "labels = np.array(labels)\n",
    "\n",
    "(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size = 0.25, random_state = 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b0891dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "trainY = to_categorical(trainY, num_classes = len(font_names))\n",
    "testY = to_categorical(testY, num_classes = len(font_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dcbd9055",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "augmented_images = ImageDataGenerator(\n",
    "    rotation_range = 30,\n",
    "    width_shift_range = 0.1,\n",
    "    height_shift_range = 0.1,\n",
    "    shear_range = 0.2,\n",
    "    zoom_range = 0.2,\n",
    "    horizontal_flip = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7033ed98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "K.set_image_data_format('channels_last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc31f549",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.normalization.batch_normalization_v1 import BatchNormalization\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D , UpSampling2D ,Conv2DTranspose\n",
    "\n",
    "def create_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    #Cu Layers\n",
    "    model.add(Conv2D(64, kernel_size = (48, 48), activation = 'relu', input_shape = (105, 105, 1)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(MaxPooling2D(pool_size = (2, 2)))\n",
    "\n",
    "    model.add(Conv2D(128, kernel_size = (24, 24), activation = 'relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(MaxPooling2D(pool_size = (2, 2)))\n",
    "\n",
    "    model.add(Conv2DTranspose(128, (24, 24), strides = (2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'uniform'))\n",
    "    model.add(UpSampling2D(size = (2, 2)))\n",
    "\n",
    "    model.add(Conv2DTranspose(64, (12, 12), strides = (2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'uniform'))\n",
    "    model.add(UpSampling2D(size = (2, 2)))\n",
    "\n",
    "    #Cs Layers\n",
    "    model.add(Conv2D(256, kernel_size=(12, 12), activation = 'relu'))\n",
    "    model.add(Conv2D(256, kernel_size=(12, 12), activation = 'relu'))\n",
    "    model.add(Conv2D(256, kernel_size=(12, 12), activation = 'relu'))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(4096, activation = 'relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(4096, activation = 'relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(2383, activation = 'relu'))\n",
    "    model.add(Dense(len(font_names), activation = 'softmax'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa1c7e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8e39b7e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Lucas\\Anaconda\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from keras import optimizers\n",
    "\n",
    "batch_size = 128\n",
    "epochs = 10\n",
    "model= create_model()\n",
    "opt = optimizers.SGD(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)\n",
    "model.compile(loss = 'mean_squared_error', optimizer = opt, metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8457609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 58, 58, 64)        147520    \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 58, 58, 64)       256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 29, 29, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 6, 6, 128)         4718720   \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 6, 6, 128)        512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 3, 3, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 6, 6, 128)        9437312   \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2D  (None, 12, 12, 128)      0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 24, 24, 64)       1179712   \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " up_sampling2d_1 (UpSampling  (None, 48, 48, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 37, 37, 256)       2359552   \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 26, 26, 256)       9437440   \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 15, 15, 256)       9437440   \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 57600)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4096)              235933696 \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2383)              9763151   \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 5)                 11920     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 299,208,543\n",
      "Trainable params: 299,208,159\n",
      "Non-trainable params: 384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ee4a72",
   "metadata": {},
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c088ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.1293 - accuracy: 0.4280 \n",
      "Epoch 1: val_loss improved from inf to 0.17203, saving model to model_store_path\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 7). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B1F892B80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B324C4E80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47/47 [==============================] - 2826s 60s/step - loss: 0.1293 - accuracy: 0.4280 - val_loss: 0.1720 - val_accuracy: 0.2005\n",
      "Epoch 2/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0627 - accuracy: 0.7645 \n",
      "Epoch 2: val_loss improved from 0.17203 to 0.13554, saving model to model_store_path\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 7). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B1F892B80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B324C4E80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47/47 [==============================] - 3027s 65s/step - loss: 0.0627 - accuracy: 0.7645 - val_loss: 0.1355 - val_accuracy: 0.3605\n",
      "Epoch 3/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0131 - accuracy: 0.9625 \n",
      "Epoch 3: val_loss improved from 0.13554 to 0.08467, saving model to model_store_path\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 7). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model_store_path\\assets\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B1F892B80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.normalization.batch_normalization_v1.BatchNormalization object at 0x0000023B324C4E80> has the same name 'BatchNormalization' as a built-in Keras object. Consider renaming <class 'keras.layers.normalization.batch_normalization_v1.BatchNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47/47 [==============================] - 2804s 60s/step - loss: 0.0131 - accuracy: 0.9625 - val_loss: 0.0847 - val_accuracy: 0.7210\n",
      "Epoch 4/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0069 - accuracy: 0.9795 \n",
      "Epoch 4: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2710s 58s/step - loss: 0.0069 - accuracy: 0.9795 - val_loss: 0.0975 - val_accuracy: 0.6450\n",
      "Epoch 5/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0043 - accuracy: 0.9883 \n",
      "Epoch 5: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2739s 58s/step - loss: 0.0043 - accuracy: 0.9883 - val_loss: 0.1166 - val_accuracy: 0.6165\n",
      "Epoch 6/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9833 \n",
      "Epoch 6: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2741s 58s/step - loss: 0.0056 - accuracy: 0.9833 - val_loss: 0.1576 - val_accuracy: 0.4555\n",
      "Epoch 7/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9847 \n",
      "Epoch 7: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2785s 59s/step - loss: 0.0052 - accuracy: 0.9847 - val_loss: 0.1582 - val_accuracy: 0.5315\n",
      "Epoch 8/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0045 - accuracy: 0.9860 \n",
      "Epoch 8: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2761s 59s/step - loss: 0.0045 - accuracy: 0.9860 - val_loss: 0.2024 - val_accuracy: 0.4295\n",
      "Epoch 9/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9905 \n",
      "Epoch 9: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2783s 59s/step - loss: 0.0033 - accuracy: 0.9905 - val_loss: 0.0995 - val_accuracy: 0.6880\n",
      "Epoch 10/10\n",
      "47/47 [==============================] - ETA: 0s - loss: 0.0023 - accuracy: 0.9938 \n",
      "Epoch 10: val_loss did not improve from 0.08467\n",
      "47/47 [==============================] - 2786s 59s/step - loss: 0.0023 - accuracy: 0.9938 - val_loss: 0.1414 - val_accuracy: 0.5930\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x23b86891130>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras import callbacks\n",
    "#from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "model_path = \"model_store_path\"\n",
    "my_callbacks = [\n",
    "    callbacks.EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 10, verbose = 0, mode = 'min'),\n",
    "    callbacks.ModelCheckpoint(model_path, monitor = 'val_loss', verbose = 1, save_best_only = True, mode = 'min')\n",
    "]\n",
    "\n",
    "model.fit(\n",
    "    trainX,\n",
    "    trainY,\n",
    "    shuffle = True,\n",
    "    batch_size = batch_size,\n",
    "    epochs = epochs,\n",
    "    verbose = 1,\n",
    "    validation_data = (testX, testY),\n",
    "    callbacks = my_callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "45af350a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss -> 0.08466523140668869\n",
      "Test accuracy -> 0.7210000157356262\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model_path = \"model_store_path\"\n",
    "model = load_model(model_path)\n",
    "score = model.evaluate(testX, testY, verbose = 0)\n",
    "\n",
    "print('Test loss ->', score[0])\n",
    "print('Test accuracy ->', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e815e087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL\n",
    "from keras.utils import image_utils\n",
    "\n",
    "img_path = \"./imgs/after_69.jpg\"\n",
    "\n",
    "org_img = PIL.Image.open(img_path).convert('L')\n",
    "pil_img = blur_image(org_img)\n",
    "pil_img = image_utils.img_to_array(pil_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "271914e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rev_conv_label(label):\n",
    "    return font_names[label]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ab5515ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 412ms/step\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = []\n",
    "data.append(pil_img)\n",
    "data = np.asarray(data, dtype = \"float\") / 255.0\n",
    "y = np.argmax(model.predict(data), axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f2d08303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "label = rev_conv_label(int(y[0]))\n",
    "fig, ax = plt.subplots(1)\n",
    "ax.imshow(org_img, interpolation = 'nearest', cmap = cm.gray)\n",
    "ax.text(5, 5, label, bbox = { 'facecolor': 'white', 'pad': 8 })\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2f30975",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}