Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from PIL import Image,ImageOps\n",
    "import CharacterSegmentation as cs\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_IMAGE = './input/input_1.jpg'\n",
    "SEGMENTED_OUTPUT_DIR = './segmented/'\n",
    "EMNIST_PATH = './data/emnist/'\n",
    "MODEL_PATH = './model/alphanum_model_binary_ex_88.h5'\n",
    "mapping_processed = EMNIST_PATH + 'processed-mapping.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x253e96758d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image.open(INPUT_IMAGE)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "........Program Initiated.......\n",
      "\n",
      "\n",
      " Resizing Image........\n",
      "#---------Image Info:--------#\n",
      "\tHeight = 342 \n",
      "\tWidth = 1320\n",
      "#----------------------------#\n",
      "Applying Adaptive Threshold with kernel :- 21 X 21\n",
      "Noise Removal From Image.........\n",
      "Beginning Character Semenation..............\n",
      "\n",
      "Given Text has   #  1  #   no. of lines\n",
      "\n",
      "Average Width of Each Letter:-  95.4\n"
     ]
    }
   ],
   "source": [
    "cs.image_segmentation(INPUT_IMAGE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "segmented_images = []\n",
    "files = [f for r, d, f in os.walk(SEGMENTED_OUTPUT_DIR)][0]\n",
    "for f in files:\n",
    "    segmented_images.append(Image.open(SEGMENTED_OUTPUT_DIR + f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(18,18))\n",
    "\n",
    "size = len(segmented_images)\n",
    "for i in range(size):\n",
    "    img = segmented_images[i]\n",
    "    plt.subplot(2, size, i + 1)\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making images bolder to avoid artifacts on resizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "files = [f for r, d, f in os.walk(SEGMENTED_OUTPUT_DIR)][0]\n",
    "for f in files:\n",
    "    filename = SEGMENTED_OUTPUT_DIR + f\n",
    "    img = cv2.imread(filename, 0)\n",
    "\n",
    "    kernel = np.ones((2,2), np.uint8)\n",
    "    dilation = cv2.erode(img, kernel, iterations = 1)\n",
    "    cv2.imwrite(filename, dilation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Segmented Characters to EMNIST format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img2emnist(filepath, char_code):\n",
    "    img = Image.open(filepath).resize((28, 28))\n",
    "    inv_img = ImageOps.invert(img)\n",
    "    \n",
    "    flatten = np.array(inv_img).flatten()\n",
    "    flatten = flatten / 255\n",
    "    flatten = np.where(flatten > 0.5, 1, 0)\n",
    "    \n",
    "    csv_img = ','.join([str(num) for num in flatten])\n",
    "    csv_str = '{},{}'.format(char_code, csv_img)\n",
    "    return csv_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_filename = 'test.csv'\n",
    "if os.path.exists(temp_filename):\n",
    "    os.remove(temp_filename)\n",
    "\n",
    "f_test = open(temp_filename, 'a+')\n",
    "column_names = ','.join([\"label\"] + [\"pixel\" + str(i) for i in range(784)])\n",
    "print(column_names, file=f_test)\n",
    "\n",
    "files = [f for r, d, f in os.walk(SEGMENTED_OUTPUT_DIR)][0]\n",
    "for f in files:\n",
    "    csv = img2emnist(SEGMENTED_OUTPUT_DIR + f, -1)\n",
    "    print(csv, file=f_test)\n",
    "    \n",
    "f_test.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0     -1       0       0       0       0       0       0       0       0   \n",
       "1     -1       0       0       0       0       0       0       0       0   \n",
       "2     -1       0       0       0       0       0       0       0       0   \n",
       "3     -1       0       0       0       0       0       0       0       0   \n",
       "4     -1       0       0       0       0       0       0       0       0   \n",
       "5     -1       0       0       0       0       0       0       0       0   \n",
       "6     -1       0       0       0       0       0       0       0       0   \n",
       "7     -1       0       0       0       0       0       0       0       0   \n",
       "8     -1       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0       0    ...            0         0         0         0         0   \n",
       "1       0    ...            0         0         0         0         0   \n",
       "2       0    ...            0         0         0         0         0   \n",
       "3       0    ...            0         0         0         0         0   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "5       0    ...            0         0         0         0         0   \n",
       "6       0    ...            0         0         0         0         0   \n",
       "7       0    ...            0         0         0         0         0   \n",
       "8       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         0  \n",
       "3         0         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "5         0         0         0         0         0  \n",
       "6         0         0         0         0         0  \n",
       "7         0         0         0         0         0  \n",
       "8         0         0         0         0         0  \n",
       "\n",
       "[9 rows x 785 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv(temp_filename)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(temp_filename)\n",
    "X_data = data.drop(labels = [\"label\"], axis = 1)\n",
    "X_data = X_data.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(14,14))\n",
    "\n",
    "size = X_data.shape[0]\n",
    "for i in range(size):\n",
    "    v = X_data[i][:,:,0].astype('uint8')\n",
    "    img = Image.fromarray(255* v)\n",
    "    \n",
    "    plt.subplot(2, size, i + 1)\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(mapping_processed)\n",
    "code2char = {}\n",
    "for index, row in df.iterrows():\n",
    "    code2char[row['id']] = row['char']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = load_model(MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X2+3X+5=0'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict results\n",
    "results = model.predict(X_data)\n",
    "\n",
    "# select the index with the maximum probability\n",
    "results = np.argmax(results, axis = 1)\n",
    "parsed_str = \"\"\n",
    "for r in results:\n",
    "    parsed_str += code2char[r]\n",
    "\n",
    "parsed_str"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}