captcha_solver2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version:  2.7.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "print(\"Tensorflow version: \", tf.__version__)\n",
    "\n",
    "seed = 1243\n",
    "np.random.seed(seed)\n",
    "tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of train images found:  10240\n",
      "Number of test images found:  1104\n",
      "Number of test images found:  1104\n",
      "Shape of image:  (45, 126, 3)\n",
      "Shape of image:  (45, 126, 3)\n",
      "Shape of image:  (45, 126, 3)\n",
      "Shape of image:  (45, 126, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Path to the data directory\n",
    "train_dir = Path(\"C:\\\\Users\\\\soham\\\\OneDrive\\\\Desktop\\\\Dataset\")\n",
    "test_dir = Path(\"C:\\\\Users\\\\soham\\\\OneDrive\\\\Desktop\\\\Test\")\n",
    "chal_dir = Path(\"C:\\\\Users\\\\soham\\\\OneDrive\\\\Desktop\\\\wrongs\")\n",
    "\n",
    "\n",
    "# Get list of all the images\n",
    "train_images = list(train_dir.glob(\"*.png\"))\n",
    "print(\"Number of train images found: \", len(train_images))\n",
    "test_images = list(test_dir.glob(\"*.png\"))\n",
    "print(\"Number of test images found: \", len(test_images))\n",
    "chal_images = list(chal_dir.glob(\"*.png\"))\n",
    "print(\"Number of test images found: \", len(test_images))\n",
    "\n",
    "\n",
    "# Let's take a look at some samples first. \n",
    "# Always look at your data!\n",
    "sample_images = train_images[:4]\n",
    "\n",
    "_,ax = plt.subplots(2,2, figsize=(5,3))\n",
    "for i in range(4):\n",
    "    img = cv2.imread(str(sample_images[i]))\n",
    "    print(\"Shape of image: \", img.shape)\n",
    "    ax[i//2, i%2].imshow(img)\n",
    "    ax[i//2, i%2].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unqiue charcaters in the whole dataset:  23\n",
      "Maximum length of any captcha:  5\n",
      "Characters present:  ['2', '3', '4', '5', '6', '7', '8', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'k', 'm', 'n', 'p', 'r', 'w', 'x', 'y']\n",
      "Total number of samples in the train dataset:  10240\n",
      "Total number of samples in the test dataset:  1104\n",
      "Total number of samples in the test dataset:  14\n"
     ]
    }
   ],
   "source": [
    "# Store all the characters in a set\n",
    "characters = set()\n",
    "\n",
    "# A list to store the length of each captcha\n",
    "captcha_length = []\n",
    "\n",
    "# Store image-label info\n",
    "train_dataset = []\n",
    "test_dataset = []\n",
    "chal_dataset = []\n",
    "\n",
    "# Iterate over the dataset and store the\n",
    "# information needed\n",
    "for img_path in train_images:\n",
    "    # 1. Get the label associated with each image\n",
    "    train_label = img_path.name.split(\".png\")[0]\n",
    "    # 2. Store the length of this cpatcha\n",
    "    captcha_length.append(len(train_label))\n",
    "    # 3. Store the image-label pair info\n",
    "    train_dataset.append((str(img_path), train_label))\n",
    "    # 4. Store the characters present\n",
    "    for ch in train_label:\n",
    "        characters.add(ch)\n",
    "\n",
    "for img_path in test_images:\n",
    "    # 1. Get the label associated with each image\n",
    "    test_label = img_path.name.split(\".png\")[0]\n",
    "    # 2. Store the length of this cpatcha\n",
    "    captcha_length.append(len(test_label))\n",
    "    # 3. Store the image-label pair info\n",
    "    test_dataset.append((str(img_path), test_label))\n",
    "    # 4. Store the characters present\n",
    "    for ch in test_label:\n",
    "        characters.add(ch)\n",
    "\n",
    "for img_path in chal_images:\n",
    "    # 1. Get the label associated with each image\n",
    "    chal_label = img_path.name.split(\".png\")[0]\n",
    "    # 2. Store the length of this cpatcha\n",
    "    captcha_length.append(len(chal_label))\n",
    "    # 3. Store the image-label pair info\n",
    "    chal_dataset.append((str(img_path), chal_label))\n",
    "    # 4. Store the characters present\n",
    "    for ch in chal_label:\n",
    "        characters.add(ch)\n",
    "\n",
    "# Sort the characters        \n",
    "characters = sorted(characters)\n",
    "\n",
    "def CountFrequency(my_list):\n",
    "\n",
    "\t# Creating an empty dictionary\n",
    "\tfreq = {}\n",
    "\tfor item in my_list:\n",
    "\t\tif (item in freq):\n",
    "\t\t\tfreq[item] += 1\n",
    "\t\telse:\n",
    "\t\t\tfreq[item] = 1\n",
    "\n",
    "\tfor key, value in freq.items():\n",
    "\t\tprint (\"% d : % d\"%(key, value))\n",
    "        \n",
    "# my_list =[i for g,i in train_dataset if len(i)!=5]\n",
    "# Convert the dataset info into a dataframe\n",
    "train_dataset = pd.DataFrame(train_dataset, columns=[\"img_path\", \"label\"], index=None)\n",
    "train_dataset = train_dataset.sample(frac=1.).reset_index(drop=True)\n",
    "\n",
    "# Shuffle the dataset\n",
    "test_dataset = pd.DataFrame(test_dataset, columns=[\"img_path\", \"label\"], index=None)\n",
    "test_dataset= test_dataset.sample(frac=1.).reset_index(drop=True)\n",
    "\n",
    "chal_dataset = pd.DataFrame(chal_dataset, columns=[\"img_path\", \"label\"], index=None)\n",
    "chal_dataset= chal_dataset.sample(frac=1.).reset_index(drop=True)\n",
    "\n",
    "print(\"Number of unqiue charcaters in the whole dataset: \", len(characters))\n",
    "print(\"Maximum length of any captcha: \", max(Counter(captcha_length).keys()))\n",
    "print(\"Characters present: \", characters)\n",
    "print(\"Total number of samples in the train dataset: \", len(train_dataset))\n",
    "print(\"Total number of samples in the test dataset: \", len(test_dataset))\n",
    "print(\"Total number of samples in the test dataset: \", len(chal_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>img_path</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\8xky4.png</td>\n",
       "      <td>8xky4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\26r4n.png</td>\n",
       "      <td>26r4n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\3fdem.png</td>\n",
       "      <td>3fdem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\fxh2k.png</td>\n",
       "      <td>fxh2k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\by3fe.png</td>\n",
       "      <td>by3fe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            img_path  label\n",
       "0  C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\8xky4.png  8xky4\n",
       "1  C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\26r4n.png  26r4n\n",
       "2  C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\3fdem.png  3fdem\n",
       "3  C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\fxh2k.png  fxh2k\n",
       "4  C:\\Users\\soham\\OneDrive\\Desktop\\Dataset\\by3fe.png  by3fe"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>img_path</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Test\\k3f7h.png</td>\n",
       "      <td>k3f7h</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Test\\4kphe.png</td>\n",
       "      <td>4kphe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C:\\Users\\soham\\OneDrive\\Desktop\\Test\\m3ewk.png</td>\n",
       "      <td>m3ewk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         img_path  label\n",
       "0  C:\\Users\\soham\\OneDrive\\Desktop\\Test\\k3f7h.png  k3f7h\n",
       "1  C:\\Users\\soham\\OneDrive\\Desktop\\Test\\yrgad.png  yrgad\n",
       "2  C:\\Users\\soham\\OneDrive\\Desktop\\Test\\2xgdy.png  2xgdy\n",
       "3  C:\\Users\\soham\\OneDrive\\Desktop\\Test\\4kphe.png  4kphe\n",
       "4  C:\\Users\\soham\\OneDrive\\Desktop\\Test\\m3ewk.png  m3ewk"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples:  10240\n",
      "Number of validation samples:  1104\n",
      "Number of training images:  (10240, 45, 126)\n",
      "Number of training labels:  (10240,)\n",
      "Number of validation images:  (1104, 45, 126)\n",
      "Number of validation labels:  (1104,)\n"
     ]
    }
   ],
   "source": [
    "# Split the dataset into training and validation sets\n",
    "training_data, validation_data, chal_data = train_dataset, test_dataset, chal_dataset\n",
    "\n",
    "training_data = training_data.reset_index(drop=True)\n",
    "validation_data = validation_data.reset_index(drop=True)\n",
    "chal_data = chal_data.reset_index(drop=True)\n",
    "\n",
    "print(\"Number of training samples: \", len(training_data))\n",
    "print(\"Number of validation samples: \", len(validation_data))\n",
    "\n",
    "\n",
    "\n",
    "# Map text to numeric labels \n",
    "char_to_labels = {char:idx for idx, char in enumerate(characters)}\n",
    "\n",
    "# Map numeric labels to text\n",
    "labels_to_char = {val:key for key, val in char_to_labels.items()}\n",
    "\n",
    "\n",
    "\n",
    "# Sanity check for corrupted images\n",
    "def is_valid_captcha(captcha):\n",
    "    for ch in captcha:\n",
    "        if not ch in characters:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "\n",
    "\n",
    "# Store arrays in memory as it's not a muvh big dataset\n",
    "def generate_arrays(df, resize=True, img_height=45, img_width=126):\n",
    "    \n",
    "    num_items = len(df)\n",
    "    images = np.zeros((num_items, img_height, img_width), dtype=np.float32)\n",
    "    labels = [0]*num_items\n",
    "    \n",
    "    for i in range(num_items):\n",
    "        img = cv2.imread(df[\"img_path\"][i])\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        \n",
    "        if resize: \n",
    "            img = cv2.resize(img, (img_width, img_height))\n",
    "        \n",
    "        img = (img/255.).astype(np.float32)\n",
    "        label = df[\"label\"][i]\n",
    "        \n",
    "        # Add only if it is a valid captcha\n",
    "        if is_valid_captcha(label):\n",
    "            images[i, :, :] = img\n",
    "            labels[i] = label\n",
    "    \n",
    "    return images, np.array(labels)\n",
    "\n",
    "\n",
    "\n",
    "# Build training data\n",
    "training_data, training_labels = generate_arrays(df=training_data)\n",
    "print(\"Number of training images: \", training_data.shape)\n",
    "print(\"Number of training labels: \", training_labels.shape)\n",
    "\n",
    "\n",
    "# Build validation data\n",
    "validation_data, validation_labels = generate_arrays(df=validation_data)\n",
    "print(\"Number of validation images: \", validation_data.shape)\n",
    "print(\"Number of validation labels: \", validation_labels.shape)\n",
    "\n",
    "# Build challenge data\n",
    "chal_data, chal_labels = generate_arrays(df=chal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(keras.utils.Sequence):\n",
    "    \"\"\"Generates batches from a given dataset.\n",
    "    \n",
    "    Args:\n",
    "        data: training or validation data\n",
    "        labels: corresponding labels\n",
    "        char_map: dictionary mapping char to labels\n",
    "        batch_size: size of a single batch\n",
    "        img_width: width of the resized\n",
    "        img_height: height of the resized\n",
    "        downsample_factor: by what factor did the CNN downsample the images\n",
    "        max_length: maximum length of any captcha\n",
    "        shuffle: whether to shuffle data or not after each epoch\n",
    "    Returns:\n",
    "        batch_inputs: a dictionary containing batch inputs \n",
    "        batch_labels: a batch of corresponding labels \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self,\n",
    "                 data,\n",
    "                 labels,\n",
    "                 char_map,\n",
    "                 batch_size=16,\n",
    "                 img_width=126,\n",
    "                 img_height=45,\n",
    "                 downsample_factor=4,\n",
    "                 max_length=5,\n",
    "                 shuffle=True\n",
    "                ):\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "        self.char_map = char_map\n",
    "        self.batch_size = batch_size\n",
    "        self.img_width = img_width\n",
    "        self.img_height = img_height\n",
    "        self.downsample_factor = downsample_factor\n",
    "        self.max_length = max_length\n",
    "        self.shuffle = shuffle\n",
    "        self.indices = np.arange(len(data))    \n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.data) / self.batch_size))\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # 1. Get the next batch indices\n",
    "        curr_batch_idx = self.indices[idx*self.batch_size:(idx+1)*self.batch_size]\n",
    "        \n",
    "        # 2. This isn't necessary but it can help us save some memory\n",
    "        # as not all batches the last batch may not have elements\n",
    "        # equal to the batch_size \n",
    "        batch_len = len(curr_batch_idx)\n",
    "        \n",
    "        # 3. Instantiate batch arrays\n",
    "        batch_images = np.ones((batch_len, self.img_width, self.img_height, 1),\n",
    "                               dtype=np.float32)\n",
    "        batch_labels = np.ones((batch_len, self.max_length), dtype=np.float32)\n",
    "        input_length = np.ones((batch_len, 1), dtype=np.int64) * \\\n",
    "                                (self.img_width // self.downsample_factor - 2)\n",
    "        label_length = np.zeros((batch_len, 1), dtype=np.int64)\n",
    "        \n",
    "        \n",
    "        for j, idx in enumerate(curr_batch_idx):\n",
    "            # 1. Get the image and transpose it\n",
    "            img = self.data[idx].T\n",
    "            # 2. Add extra dimenison\n",
    "            img = np.expand_dims(img, axis=-1)\n",
    "            # 3. Get the correpsonding label\n",
    "            text = self.labels[idx]\n",
    "            # 4. Include the pair only if the captcha is valid\n",
    "            if is_valid_captcha(text):\n",
    "                label = [self.char_map[ch] for ch in text]\n",
    "                batch_images[j] = img\n",
    "                batch_labels[j] = label\n",
    "                label_length[j] = len(text)\n",
    "        \n",
    "        batch_inputs = {\n",
    "                'input_data': batch_images,\n",
    "                'input_label': batch_labels,\n",
    "                'input_length': input_length,\n",
    "                'label_length': label_length,\n",
    "                }\n",
    "        return batch_inputs, np.zeros(batch_len).astype(np.float32)\n",
    "        \n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            np.random.shuffle(self.indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batch size for training and validation\n",
    "batch_size = 16\n",
    "\n",
    "# Desired image dimensions\n",
    "img_width=126\n",
    "img_height=45 \n",
    "\n",
    "# Factor  by which the image is going to be downsampled\n",
    "# by the convolutional blocks\n",
    "downsample_factor=4\n",
    "\n",
    "# Maximum length of any captcha in the data\n",
    "max_length=5\n",
    "\n",
    "# Get a generator object for the training data\n",
    "train_data_generator = DataGenerator(data=training_data,\n",
    "                                     labels=training_labels,\n",
    "                                     char_map=char_to_labels,\n",
    "                                     batch_size=batch_size,\n",
    "                                     img_width=img_width,\n",
    "                                     img_height=img_height,\n",
    "                                     downsample_factor=downsample_factor,\n",
    "                                     max_length=max_length,\n",
    "                                     shuffle=True\n",
    "                                    )\n",
    "\n",
    "# Get a generator object for the validation data \n",
    "valid_data_generator = DataGenerator(data=validation_data,\n",
    "                                     labels=validation_labels,\n",
    "                                     char_map=char_to_labels,\n",
    "                                     batch_size=batch_size,\n",
    "                                     img_width=img_width,\n",
    "                                     img_height=img_height,\n",
    "                                     downsample_factor=downsample_factor,\n",
    "                                     max_length=max_length,\n",
    "                                     shuffle=False\n",
    "                                    )\n",
    "\n",
    "challenge_data_gen = DataGenerator(data=chal_data,\n",
    "                                     labels=chal_labels,\n",
    "                                     char_map=char_to_labels,\n",
    "                                     batch_size=14,\n",
    "                                     img_width=img_width,\n",
    "                                     img_height=img_height,\n",
    "                                     downsample_factor=downsample_factor,\n",
    "                                     max_length=max_length,\n",
    "                                     shuffle=False\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A utility to decode the output of the network\n",
    "def decode_batch_predictions(pred):\n",
    "    pred = pred[:, :-2]\n",
    "    input_len = np.ones(pred.shape[0])*pred.shape[1]\n",
    "    \n",
    "    # Use greedy search. For complex tasks, you can use beam search\n",
    "    results = keras.backend.ctc_decode(pred, \n",
    "                                        input_length=input_len,\n",
    "                                        greedy=True)[0][0]\n",
    "    \n",
    "    # Iterate over the results and get back the text\n",
    "    output_text = []\n",
    "    for res in results.numpy():\n",
    "        outstr = ''\n",
    "        for c in res:\n",
    "            if c < len(characters) and c >=0:\n",
    "                outstr += labels_to_char[c]\n",
    "        output_text.append(outstr)\n",
    "    \n",
    "    # return final text results\n",
    "    return output_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AccuracyCallback(keras.callbacks.Callback):\n",
    "    def __init__(self):\n",
    "        self.precision = np.array([])\n",
    "        self.accuracy = np.array([])\n",
    "        self.val_precision = np.array([])\n",
    "        self.val_accuracy = np.array([])\n",
    "        a1 = np.array([])\n",
    "        a2 = np.array([])\n",
    "        a3 = np.array([])\n",
    "        a4 = np.array([])\n",
    "        a5 = np.array([])\n",
    "\n",
    "        v1 = np.array([])\n",
    "        v2 = np.array([])\n",
    "        v3 = np.array([])\n",
    "        v4 = np.array([])\n",
    "        v5 = np.array([])\n",
    "\n",
    "        self.an = [a1,a2,a3,a4,a5]\n",
    "        self.val_an = [v1,v2,v3,v4,v5]\n",
    "\n",
    "    def calculator(self, name, dataset_generator):\n",
    "        prec, acc = 0,0\n",
    "        char_acc = [0,0,0,0,0]\n",
    "        for p, (inp_value, _) in enumerate(dataset_generator):\n",
    "            bs = inp_value['input_data'].shape[0]\n",
    "            X_data = inp_value['input_data']\n",
    "            labels = inp_value['input_label']\n",
    "            preds = self.prediction_model.predict(X_data, batch_size=32)\n",
    "            pred_texts = decode_batch_predictions(preds)\n",
    "            \n",
    "            \n",
    "            orig_texts = []\n",
    "            for label in labels:\n",
    "                text = ''.join([labels_to_char[int(x)] for x in label])\n",
    "                orig_texts.append(text)\n",
    "            \n",
    "            for i in range(bs):\n",
    "                j = 0\n",
    "                sum =0\n",
    "                while j<len(pred_texts[i]) and j<len(orig_texts[i]):\n",
    "                    if orig_texts[i][j] == pred_texts[i][j]:\n",
    "                        sum += 1\n",
    "                        char_acc[j] +=1\n",
    "                    j += 1\n",
    "                sum /= 5\n",
    "                prec += sum\n",
    "                if pred_texts[i] == orig_texts[i]:\n",
    "                    acc += 1\n",
    "                else:\n",
    "                    if name != 'val_':\n",
    "                        self.val_wrong.append((orig_texts[i],pred_texts[i]))\n",
    "                    else:\n",
    "                        self.wrong.append((orig_texts[i],pred_texts[i]))\n",
    "                    \n",
    "        prec /= bs*len(dataset_generator)\n",
    "        acc /= bs*len(dataset_generator)\n",
    "        for zz in range(5):\n",
    "            char_acc[zz] /= bs*len(dataset_generator)\n",
    "        if name != 'val_':\n",
    "            self.precision = np.append(self.precision, prec)\n",
    "            self.accuracy =  np.append(self.accuracy, acc)\n",
    "            for j in range(5):\n",
    "                self.an[j] = np.append(self.an[j], char_acc[j])\n",
    "        else:\n",
    "            self.val_precision = np.append(self.val_precision, prec)\n",
    "            self.val_accuracy =  np.append(self.val_accuracy, acc)\n",
    "            for j in range(5):\n",
    "                self.val_an[j] = np.append(self.val_an[j], char_acc[j])\n",
    "        print(f\" {name}precision: {prec} - {name}accuracy: {acc} - {name}cg: {char_acc[0]} \")\n",
    "    \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        self.prediction_model = keras.models.Model(model.get_layer(name='input_data').input,\n",
    "                                        model.get_layer(name='dense2').output)\n",
    "                                        \n",
    "        self.wrong, self.val_wrong = [], []\n",
    "        self.calculator('', train_data_generator)\n",
    "        self.calculator('val_', valid_data_generator)\n",
    "        if(logs.get('val_loss') <= 0.1):\n",
    "            print(\"\\n\\n\\nReached 0.09 loss value so cancelling training!\\n\\n\\n\")\n",
    "            self.model.stop_training = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CTCLayer(layers.Layer):\n",
    "    def __init__(self, name=None):\n",
    "        super().__init__(name=name)\n",
    "        self.loss_fn = keras.backend.ctc_batch_cost\n",
    "\n",
    "    def call(self, y_true, y_pred, input_length, label_length):\n",
    "        loss = self.loss_fn(y_true, y_pred, input_length, label_length)\n",
    "        self.add_loss(loss)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    # Inputs to the model\n",
    "    input_img = layers.Input(shape=(img_width, img_height, 1),\n",
    "                            name='input_data',\n",
    "                            dtype='float32')\n",
    "    labels = layers.Input(name='input_label', shape=[max_length], dtype='float32')\n",
    "    input_length = layers.Input(name='input_length', shape=[1], dtype='int64')\n",
    "    label_length = layers.Input(name='label_length', shape=[1], dtype='int64')\n",
    "    \n",
    "    # First conv block\n",
    "    x = layers.Conv2D(32,\n",
    "               (3,3),\n",
    "               activation='relu',\n",
    "               kernel_initializer='he_normal',\n",
    "               padding='same',\n",
    "               name='Conv1')(input_img)\n",
    "    x = layers.MaxPooling2D((2,2), name='pool1')(x)\n",
    "    \n",
    "    # Second conv block\n",
    "    x = layers.Conv2D(64,\n",
    "               (3,3),\n",
    "               activation='relu',\n",
    "               kernel_initializer='he_normal',\n",
    "               padding='same',\n",
    "               name='Conv2')(x)\n",
    "    x = layers.MaxPooling2D((2,2), name='pool2')(x)\n",
    "    \n",
    "    # We have used two max pool with pool size and strides of 2.\n",
    "    # Hence, downsampled feature maps are 4x smaller. The number of\n",
    "    # filters in the last layer is 64. Reshape accordingly before\n",
    "    # passing it to RNNs\n",
    "    new_shape = ((img_width // 4), (img_height // 4)*64)\n",
    "    x = layers.Reshape(target_shape=new_shape, name='reshape')(x)\n",
    "    x = layers.Dense(64, activation='relu', name='dense1')(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "    \n",
    "    # RNNs\n",
    "    x = layers.Bidirectional(layers.LSTM(128,\n",
    "                                         return_sequences=True,\n",
    "                                         dropout=0.2))(x)\n",
    "    x = layers.Bidirectional(layers.LSTM(64,\n",
    "                                         return_sequences=True,\n",
    "                                         dropout=0.25))(x)\n",
    "    \n",
    "    # Predictions\n",
    "    x = layers.Dense(len(characters)+1,\n",
    "              activation='softmax', \n",
    "              name='dense2',\n",
    "              kernel_initializer='he_normal')(x)\n",
    "    \n",
    "    # Calculate CTC\n",
    "    output = CTCLayer(name='ctc_loss')(labels, x, input_length, label_length)\n",
    "    \n",
    "    # Define the model\n",
    "    model = keras.models.Model(inputs=[input_img,\n",
    "                                       labels,\n",
    "                                       input_length,\n",
    "                                       label_length],\n",
    "                                outputs=output,\n",
    "                                name='ocr_model_v1')\n",
    "    \n",
    "    # Optimizer\n",
    "    sgd = keras.optimizers.SGD(learning_rate=0.001,\n",
    "                               decay=1e-6,\n",
    "                               momentum=0.9,\n",
    "                               nesterov=True,\n",
    "                               clipnorm=5)\n",
    "    \n",
    "    # Compile the model and return \n",
    "    model.compile(optimizer=sgd)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ocr_model_v1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_data (InputLayer)        [(None, 126, 45, 1)  0           []                               \n",
      "                                ]                                                                 \n",
      "                                                                                                  \n",
      " Conv1 (Conv2D)                 (None, 126, 45, 32)  320         ['input_data[0][0]']             \n",
      "                                                                                                  \n",
      " pool1 (MaxPooling2D)           (None, 63, 22, 32)   0           ['Conv1[0][0]']                  \n",
      "                                                                                                  \n",
      " Conv2 (Conv2D)                 (None, 63, 22, 64)   18496       ['pool1[0][0]']                  \n",
      "                                                                                                  \n",
      " pool2 (MaxPooling2D)           (None, 31, 11, 64)   0           ['Conv2[0][0]']                  \n",
      "                                                                                                  \n",
      " reshape (Reshape)              (None, 31, 704)      0           ['pool2[0][0]']                  \n",
      "                                                                                                  \n",
      " dense1 (Dense)                 (None, 31, 64)       45120       ['reshape[0][0]']                \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 31, 64)       0           ['dense1[0][0]']                 \n",
      "                                                                                                  \n",
      " bidirectional (Bidirectional)  (None, 31, 256)      197632      ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " bidirectional_1 (Bidirectional  (None, 31, 128)     164352      ['bidirectional[0][0]']          \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " input_label (InputLayer)       [(None, 5)]          0           []                               \n",
      "                                                                                                  \n",
      " dense2 (Dense)                 (None, 31, 24)       3096        ['bidirectional_1[0][0]']        \n",
      "                                                                                                  \n",
      " input_length (InputLayer)      [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " label_length (InputLayer)      [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " ctc_loss (CTCLayer)            (None, 1)            0           ['input_label[0][0]',            \n",
      "                                                                  'dense2[0][0]',                 \n",
      "                                                                  'input_length[0][0]',           \n",
      "                                                                  'label_length[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 429,016\n",
      "Trainable params: 429,016\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "640/640 [==============================] - ETA: 0s - loss: 12.7784 precision: 0.6824804687499952 - accuracy: 0.47919921875 - cg: 0.89208984375 \n",
      " val_precision: 0.6909420289855069 - val_accuracy: 0.483695652173913 - val_cg: 0.9130434782608695 \n",
      "640/640 [==============================] - 106s 158ms/step - loss: 12.7784 - val_loss: 2.3375\n",
      "Epoch 2/10\n",
      "640/640 [==============================] - ETA: 0s - loss: 1.0770 precision: 0.9966015624999998 - accuracy: 0.9890625 - cg: 0.9998046875 \n",
      " val_precision: 0.9932971014492753 - val_accuracy: 0.9846014492753623 - val_cg: 0.9981884057971014 \n",
      "\n",
      "\n",
      "\n",
      "Reached 0.09 loss value so cancelling training!\n",
      "\n",
      "\n",
      "\n",
      "640/640 [==============================] - 103s 161ms/step - loss: 1.0770 - val_loss: 0.0875\n"
     ]
    }
   ],
   "source": [
    "# Add early stopping\n",
    "es = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                   patience=5,\n",
    "                                   restore_best_weights=True)\n",
    "\n",
    "accCallback = AccuracyCallback()\n",
    "# Train the model\n",
    "history = model.fit(train_data_generator,\n",
    "                    validation_data=valid_data_generator,\n",
    "                    epochs=10,\n",
    "                    callbacks=[accCallback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\soham\\Downloads\\stc graphs\\final\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\soham\\Downloads\\stc graphs\\final\\assets\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x0000018E3A6F72B0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> 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.recurrent.LSTMCell object at 0x0000018E3A6AD730> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> 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.recurrent.LSTMCell object at 0x0000018E3A81CF70> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> 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.recurrent.LSTMCell object at 0x0000018E3A888A60> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> 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"
     ]
    }
   ],
   "source": [
    "model.save('C:\\\\Users\\\\soham\\\\Downloads\\\\stc graphs\\\\final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_data (InputLayer)     [(None, 126, 45, 1)]      0         \n",
      "                                                                 \n",
      " Conv1 (Conv2D)              (None, 126, 45, 32)       320       \n",
      "                                                                 \n",
      " pool1 (MaxPooling2D)        (None, 63, 22, 32)        0         \n",
      "                                                                 \n",
      " Conv2 (Conv2D)              (None, 63, 22, 64)        18496     \n",
      "                                                                 \n",
      " pool2 (MaxPooling2D)        (None, 31, 11, 64)        0         \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 31, 704)           0         \n",
      "                                                                 \n",
      " dense1 (Dense)              (None, 31, 64)            45120     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 31, 64)            0         \n",
      "                                                                 \n",
      " bidirectional (Bidirectiona  (None, 31, 256)          197632    \n",
      " l)                                                              \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirectio  (None, 31, 128)          164352    \n",
      " nal)                                                            \n",
      "                                                                 \n",
      " dense2 (Dense)              (None, 31, 24)            3096      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 429,016\n",
      "Trainable params: 429,016\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "prediction_model = keras.models.Model(model.get_layer(name='input_data').input,\n",
    "                                        model.get_layer(name='dense2').output, )\n",
    "prediction_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x2700 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Let's check results on some validation samples\n",
    "\n",
    "\n",
    "for p, (inp_value, _) in enumerate(valid_data_generator):\n",
    "    bs = inp_value['input_data'].shape[0]\n",
    "    X_data = inp_value['input_data']\n",
    "    labels = inp_value['input_label']\n",
    "    preds = prediction_model.predict(X_data, batch_size=16)\n",
    "    pred_texts = decode_batch_predictions(preds)\n",
    "    \n",
    "    \n",
    "    orig_texts = []\n",
    "    for label in labels:\n",
    "        text = ''.join([labels_to_char[int(x)] for x in label])\n",
    "        orig_texts.append(text)\n",
    "\n",
    "    _, ax = plt.subplots(4, 4, figsize=(15, 9))\n",
    "    for i in range(len(pred_texts)):\n",
    "        img = (X_data[i, :, :, 0] * 255).astype(np.uint8)\n",
    "        img = img.T\n",
    "        title = f\"Prediction: {pred_texts[i]}\"\n",
    "        ax[i // 4, i % 4].imshow(img)\n",
    "        ax[i // 4, i % 4].set_title(title)\n",
    "        ax[i // 4, i % 4].axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "    for i in range(bs):\n",
    "        j = 0\n",
    "        sum =0\n",
    "        while j<len(pred_texts[i]) and j<len(orig_texts[i]):\n",
    "            if orig_texts[i][j] != pred_texts[i][j]:\n",
    "                sum += 1\n",
    "            j += 1\n",
    "        sum /= 5\n",
    "        acc = 1 if pred_texts[i] == orig_texts[i] else 0\n",
    "        # print(f'Ground truth: {orig_texts[i]} \\t Predicted: {pred_texts[i]}')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6eiiqkUkWRcQtUc009aSIeG5ETLjpZ6wTyrlRjQhed1lEnBHVNNl3j8UeF9UX5ZdFxHrj1l0vIr6VUto953xxh7l/LSL2LsTviGoUgV9ExK1RfTHeLqovG48fW+fpEfHlDtvpq5TSP0XEexue/k1UP5L/Kqr3MEVVjDw1Ip4TEX8Qk59jr4jHXustYuVCZtnY81O5r4N1uvWFiHhzIf5gVF8E/zcifhfVl/iFUX3R26G27voRcUpKaa+c8896zOMPI+Iz8Vgxfk9Ur/fZUR0rq4y1//Kojvnx1omI41NKu4yNbDlT7ZlSem5Uf9/jo/p83B8Rd0Y1a8J5UY0AcmrO+d4BtP/GlNJTo/phYdOo3td7o/rcLo6In0V13jgj5/xoPxtO1cwQbxkX+m1UP9BcFtXfPz+q1+XAmPjjxSdTSj+KiBujOvfsPO65n0d1HF8XEUsiYrOoisRXxsodC9eKiGNTSjtPcQx9JqrRZFZYLaofkf5pyj9yoj+M6pge75iccy6sCwDMQmpGNWONmnFyc6VmPDUi9hz3eNeU0jo556VTbDfZhbt9I+K0yTZOKT05IrYq5NKNX0X12b0yqmNiSUQ8HNU5Youoas0XR3Ws/F/TEfFPKaVLc84nT7H/cyJiaax83twnevvbVtg1pbRuznmqz0799b0u53ztFNvUHR0Rh4x7vDgiTo6IS6OanWPdqM5vBxTy3TYiPhwRR3XZJgAww6kb1Y016sbJzZW6MXLO/zN2rL1vXHjTiPhaSulFk13HG/tcfzUmDkTyxpzz9Q3t/Tal9JuI2HpceJ+I+I/J8hy7IXv3hqcXpJS2zTn/arJ9xMR67IGorlnWzfSasSMppXUi4j9j4uwol0fE/wyiTQCgXdSJ6sQadeLk5kqdOKOuL6aU/iSqQTLrE0M8PNbmeVFdR7snqvPQwqg++8+rrb9rVLPVvahh8JqpvCrGddyL6h7dH0TERVFdu1svIp4VEQdFdd/peO9JKX0n53x+VB0G/3Dcc1eN7efqqD6fG43l/upYuWPhKhHxuZTSopxzaebWTnw8qln7Vrg9Ir4dEb8c+/9Noprt9I+j+t1gvB0i4n9SSrv1+PoBg5JztlgskyxRfTnItWVRD/v5y8J+HoqIdQrr7llY95Fx///NiFgwSVtr1h6vEtVICfV9/iwinjtF3vOj+hKwvLbttRGxXgd/9xsL7S6PiH+JiLUatpkX1QjnD4/b5v5e3oNC2x/ocLtXF7bNEXFxRLy4g+03jmr69F9ExAlTrHtCrY3rp3nMLurxtTq44W/+TkQ8YZLtXh5V56r6dtdFxAY9HiMPjDtWPhYR8yfZ/qBx649fXtPFa1bKoevP+TTes4UNr30nyz0R8dGI2Hwa7Zf+/k6Xa6O6yXDNHtv+QGGfD479d2lEHBkRqzZsu1lU09DXtz8mIv5q3ONrImLvSXJ4TlQFWn0/r+0g/18WjvtVengdvl94DTYe1jFosVgsFoul96Xhu9yiHvajZlQzNq17Qq2N66d5zC7q8bVSMw65ZozqYmK93Zd1sN1Ntc/T+O0v6GD7txTabXyPx7Y5PqoLom+JiCd1+PetG9UsEg/V2ro1Gj7/te1PqW13fgfbHFHbpv76/FGXr2+OiC9MsX79M/zQuHbvHXvNmure1aPq3Fc65z9x0MegxWKxWCyW/iyhbpwf6sbxy8Whbqxvp26c5hJVh73TCzn88yTbNH2uP9FBe5+rbXNLRKQptnlx4XM8/vHbO2j3Z7VtfjzJujOiZuzhvV4lqoFg/rrQVo7qGuMuwzr2LBaLxWKx9LaEOnF+qBPHLxeHOrG+nTqx9/dqJl1f3C6qe0THb7MsqvthN51i2x0j4sJCm//S43uz4p7VR6I6VxTvh42q8+m3Ctv/T1Qd6VY8vjUiDpwkh62j6txb3897O3yfTyjkv+J9ezSq64tNf8OaEfHPY+vV2//7QR+jFoulu6XeGxoYgJTSJlH94Fp3Vp561IQVVoyq9+mc86tzzoubVsw5P1gLvTMmTnX82Yh4Xs75vMkazTnfnXP+f1HNWjXeVjHFlMMppQ0i4hOFp/485/yunPMDDW0+knP+x4j4k6i+UESsPOL7QKWUNo3yCIQnRsSuOecfT7WPnPMdOedjc87PiurmtVZLKa0b1YWZui9GxP4555ubts05/yCqUSFuqj21MCL+sceU1ozqy+OhOed35pzvnqT9E2Pi8RlRFbdzwfoR8a6IuDyl9PIRtL9VRPxbRJyfUtq2T/tcI6ofVPbJOX8+N4w8mnP+fVTF/a21p14XER8c+//LI2K3nHPj9PA55wujmjWh7k0d5PrZ2uOFsfIILVMaG23rZbXwiTnnO7rZDwAwc6kZJ1AztoyacWQujMdGYl1hstE1I6X0jIh4wrjQBVGNernCs1NKG03Rbr2NKyd7j8e8I+e8e875iznnG6dYNyIics735Zz/ISL+KKqLZytsGhGv72AX9VFBd04pbVhc8zH1v+1bUzy/kpTS02Pl17eUx1RWj2r02Dsj4vljr1lT3ftwzvk9UX3Wxls1OqtZAYBZQt04gbqxZdSNozVWU7w2qgEux3t3SqnputXfx8TP9XkR8e4OmqzXQZtFNUvAZKZbj60X1Ywmk+Ux2XMzpWaMlNL8lNIlteWXKaVro5px5aqI+EihrSUR8aqc8wXdtgkAzDzqxAnUiS2jThyZGXF9MaW0SlQzda49Lrw0qs6vf51znnSm0pzzJVHNIv+T2lN/nlJ60hS5lqwRVYe5A3PO/1g4561o996IeE1Us5iOt29UE1VEVDNW7pFz/kZTYznn30R1z+hDtafe2H3qEVHlv2L2yrfnnN8zyd/wYM75vRHx9sLT70spPaXHHIAB0OEPBmxs2vLvRcTmhafrN+tM5fyoZq3qpv21o+oMNN7JOeejcs650/3knI+PiGNr4b9MKa0+yWZviJWnBY+I+ErO+TMdtvnNqEZqGLa/jJWnjI+opg1/bc65/uVqSjnn3/Ylq8F6Y0x8ry6IiCM7OU5yztdFxIFRFUTjvamDCydN/jXn/JVOVsw5fzWqz8d4L0wprdlj223xcFRF6xVRzVJ39yTrbhQR30spvbOP7d8XEb+NqtPcDVF1wmvyzIi4MKX0wj61/Y6c8zlTrTRWeH+sFl47ItaJatSSg3LO9Q6Bpf2cFRE/qoVflFJao7T+OP8Z1SyL4036w1PBETHxO1npxw4AYBZSM6oZ1YxqxiZjN2wuqoX3nWKz+sW0n8TKNxeuEhMvwP+flFIqPD/lzYk553pd1LGc86kR8clauJMLpvW8uv3bHo6Iv6ut1u3rmyPitCm2afKmnPMvOlz3PVHVuOO9pMd2AYAZRt2oblQ3qhs7kXO+JSIOieoGxRVSRHy5frNjSmnvqGYsGO+uiDg457ysg+ZOq7UT0X099Q9RzZixwotSSqtGsz2jmpVlvG46/M2kmnFeRDyrtjwzqhvgSzel54j4dkQ8K+dcv94JAMxC6kR1ojpRndhkBl1fPCAitq/F3pRzPn2K7f5PzvnhqI6R28eFV48uz2njfDjnfFIH7S6LxyakWCFFxCZj///6nPPVHeznmog4vhZ+6jQ73J2Qc/58JyuOrXdCLbxadH//KzBAOvzBgKSUnpxSek9E/DIidiusckFEfL3L3b4rN4z4PYk3x2NfIiKqH97/rMt9rPDBWPnL8+ZR/ttWOLL2+MGYWOhN5R8j4pYut+nZ2Kgi9VELHo7qC1j9osVsclQh9vZu/uaxzllfqoXXid5GnL83qos83fjP2uN5EbFDJxvmnE/IOafasmeX7ffDfRHxzag+t8+IiHVyzk/KOW+Xc94657xhRGwREYdGNcJmXYqIj6aUXtNj+zdHxBciYv+I2DLnvF7OeWHOefuc85YRsW5Ur+lfR9UBsG7diDgppfS0Httf4dcx8cecydRH11zhKznnK7rYzzdrj6c8hnI1ItYJtfAfjs3aN6WU0moxcTSgX3bS2REAmNnUjBGhZpxJ1Iyjqxnrs5VvP3Yhv0n9gtypMfGC2mSjeO4YK58TSjkMwpdrj3cau2FgMr+MiPronpP9bTtFxMbjHp+Tc74yIsZfcNsupVS6QaJp/7/IOd9eXHNyZ+Scv9fpyjnnOyPih7XwjmOjnwIAs5S6MSLUjTOJurEF1xpzzj+NiTcdbhIRX0spzYuISCk9PiK+GivfL7RilouObhrOOd8VERfXwo31WEpp46jqzRV+m3O+LCLOGhfbICJ2maTZ+v7viIhLJll/JteM3Tgzqo5+B4zdEA0AzGLqxIhQJ84k6kTXFydTn11+UZ5kRrwmY4OSfqoW3r/b/UQ1Gcc/dbH+D2Li7HwRET8Zq807Vb9nNSLi2V1sP94DMfF1ncq7Y+XBeCKqTrVTTZQBDIkbAqA3z0kpXdKw/CqldEdUs3H9c0Q8rrD9TVFN+9vxaCYRcXXO+cwecn117fFpvf7Qm6sp2y+thfcsrZtS2iKqDkvjfS/n/Psu27w/Jn5pHqQXRsT6tdjXZ8iIKD1JKS2IiG1r4Qtzzhf0sLt/L8SmGh2k5MRcTX3djfpoKhET/662ui+qUTGekHM+MOd8fM75ypzzI/UVc86Lc85fzjnvGhEHx8TZ5VJE/EeX05JfGRH7RcQWOec/zTl/J+c8oUNfrlyac/6XiHhKRBwdE0fv3DAivj7Nmw6P73K0p2tj4usQEXFcl+3WL0xGdHYMfTZW/uFplahm7evE/hFRL6bN7gcAM5+a8TF7ltZVM84casaRK41+WbygNnbz5vhZ1++PiLPH9jH+fDLZBbn6c49GRMcjaU5DfZTLeRHxnMk2GDtH1mdK6OZvW/Ha/qSTfYzNNLFnwz661e0IyxETj+F1I+KJPbYPAIyeuvExe5bWVTfOHOrG1vlQTKxV9oiIfxqra74aE88rn8g5f7/LduptPH+SmVj2juoaZn3bjuqxhudOn+xG4RleM3bjBRFxfkrp2JTSk4fQHgAwOOrEx+xZWledOHOoE0eu1dcXU0oLI2LnWribCSLqTq49XtjphA3jfH1s8oeO5JwfiIhfFZ4a1j2rJd/NOd/azQZj63+nFt4oIv6gxxyAPtPhD3qzTkQ8q2F5alT/2DW5JCL27uHL+qJukxzrYf/cWvhn3e6npl6Y7dSw3q6F2Ld7bLPX7XqxZyHW0RTeM9gehVhp1IgpjRVk9WNkt7Epu7txRg/NX1OIbdDDfoYu53x7zvnzOeclXW53YkQ8P6rRZ8ZbKyI+0MV+zss5f6+b0Zpyzo/knP82qhGb6naIiD/pdF8FvfyQVO+geH9E/LzLfVxfiM2faqNcTb/+41r4sLHZ+6ZSn/58SQz3ByUAYDDUjI9RM858asYRyjlfFRE31sJNF9T+IFa+YHxmzvnhsQs14y+WP2XsQlrJ3rXHF/Rw8TNSSqumlJ6XUjoqpXRMSul7KaXTU0oXlm5WiIhzC7vZooOm6hcstx674F9Sf91W3LRZ30fTReJdYuIF+V5v3pwzxzAA0Ejd+Bh148ynbmyRsU5wh0TE72pPvTMivhcTj9FzI+K9PTRVr4fWieaZWKZVj43NSli/sbuTemxG1oxj145XmgkkIlaPahDRP4hq9pxzxm2yZkQcFhG/TCmVrt0CADODOvEx6sSZT504QjPg+uILC7HpnEdKHY6bziNNerlntXTO/d9udpBzvjOqe0bHm99DLhETO+51qnQ+LJ1vgRGYN+oEYA65IapZoP4157ysh+0v6mGbnaP6cXe8N6eUXtXDvlao/wBen4Z5fNt13Xa+WeGSqEZ8WLXH7btRvwixPMo3nc0mpemfL5zG/i6MiK3GPd4gqtngftPFPuoj+3eiNMNb64ur6co5X5pSemNM/NL9xpTSu3POdwy4/S+llJ4dEX9ee+qd0XvHtW6OlRXqRc9vSzMkdrmPiM6Poc9ExEvGPd4sIv44Ir7etEFK6ekx8Qed/8w539dhmwDA7KJmVDO2lZpx9H4aEYeOe1y/aLZC02wEK/5/h3GP943aLHNjMzE8f5J9TCmltHlEvCciXhMTZzPv1vwO1inlt2/URs8cuwHheeNCd8djx/HpsfI5pNPX96Ho8qLdmAdzzvWLrJ2YyccwANAf6kZ1Y1upG1sm53xrSum1UdWTK477FBEvq616Z0Qc3OM55ayIeDBWPkfsE+WbaMfXU3ksr4jqs3l7PHYe2DWltE5hVoVSndZLh7+I9teMRWPv0a1jywUR8ZmU0l5R/S0Lx1ZbJyKOSymtm3P+dL/aBgBaTZ2oTmwrdeLotfn6YqlD6He678M5qabzSJN+3LP6QM755h73s964x70eY72eD0v/FpTOt8AI6PAH/fdQVLNt3R0Rv47qH9AzI+L0sdH0etXVNLtjnlSILRhb+mXjhnh92vhHojzaxZRyzg+klG6Ilb+wD0r9ZrTru511bQYqfbG9chr7u6KhjW6+EN/ZbaM552WFL/ydzLA24+WcT0op/SxWLoRWjYgXR8R/DyGFf4iIN8XKRcezUkqPzznXRw/txF09bFP/0arrfUzzGPphVCPFjD9PvTUm6fAXEUcWYp/rsD0AYOZSM1bUjDOHmnH0To2VL8gtSCltm3P+VW29qS7I/dW4xxMuyEXE7hGx9iT7mFRK6YiI+JeYOKNBr6a8mJVzvj6ldE1UF3VX2CdqN29GVS+vNe7xohWz3Oec704p/TyqEUwjIp6YUnpGzrl+rNZf33NyzvdPlWNB18fvmNLNGjPlGAYAuqNurKgbZw51YwvlnM9IKf1dRBzdtEpEHJpzvqHH/T84dn1y/E2j+0TE+8evl1J6cqz82ftFzvn2sX3klNJpEXHQ2HOrRzXTww9rzdXrsetzzlOeD2ZozdixnPNpKaXdo+pYOP5v/ERK6YKc8zkNmwIAM486saJOnDnUiaPX5uuLpfPIs6bYpltN55EmI7lntWE/vRxj0zkfXptSejiqmnyF+vkWGJFVRp0AzFBn5JxTw7JmzvlxOeen5pxfnnP++5zzT6dZWEVUBVu3uv3C0ou1GuLza4+X5JzzNNopjZQxCBvVHvf6BWwm2bAQu3sa+yu9ZvXXdSq9jDg01321EGsalaWvxqYV/1G/2u9xxKm6oR5DY+f4eme9F47N4jdBSmntiHhDLfyznPOlg8gPABg6NeNj1Iwzn5px9JpmJPg/KaV1ImLXcaFbI+KX4x6fGREPj3u8V5p4hbJ+QW9pRHR0c2BK6a8j4pjoX2e/iM4vZtVfn707+Nt+MsU+Vlp/rIarj8Db1eyH48y14xcAKFM3PkbdOPOpG9vrn2Ni/bPCx3POP5jm/ut10S4ppfrgLdOqx8bUrzl2U4/NtJqxK2ODrx4SVQfOFVaNakAeAGBmUSc+Rp0486kTR6/N1xdHeR5pMuPuWa2Z7vmw/u/B/GnsC+gjHf5g5nikh21KX5qHZb3a46XT3N90t+9U/ca0+4bU7ijV36s8zdEGS+9VvQ36b1EhtsUcar8NjouIB2qx0ix+ERGviYlFkdn9AIDpUDMOh5pRzTh0OedbIuLyWrh+8eyFsXIHuZ+Ov6iTc14aEeeOe37jiNhpin3+b8754ZhCSul5EfGRwlNLoxqc5m0RsWdEbB3VeWftiFilfsPCVO1Mon7BctOYOAroZKOTlh7X139BrDyqZWkbAIC2UzcOh7pR3dgm60bzjCU/78P+63XRqhHxolpsWvVYSulpMXH2h+l0+Jt1NWPO+byY2Elxj5TSdsPKAQCYsdSJw6FOVCcOXcuvL47yPDJb9ft86PMFLaHDH8xu9Y4nERFvnWQkmF6WhQ1t16ccX2eaf8t0t+9UfZSCdYfU7ijV36s0Ngphr0rv1Wyfgr4NfleIbTqH2h+5sZkO/7sWfkPD5+mttce3R8Q3B5IYAEAzNWP31IxqxlH5ae3xnimlVcc9nurmxIiJN//93yieYzMwPGeKNpv8ayF2QkQ8Ked8SM75cznnM3LO1+Sc7845P1AfYTKl1O0om+OdHhH1UYz/7/VIKc2PiJ3HPbc45/zr2vpnR8T4C817ppTmjXtcn03inoi4sKdsAQBmFnVj99SN6sY2OTaqwVdKPp9SevI0939RTJxpY3w9liJir3HPPRQR/zt+5ZzzdRFxzbjQM1NKm417XK/HckSc1kWOc6VmPKUQ23PIOQAAc4M6sXvqRHXiqLT1+mLpPLJWn88jH+ggj9mk3+dDny9oCR3+YHa7vRDrdhrrXt1de7xeYSrnbmwwjW27cUft8VwYSaI03fn8aeyvtO2d09gfnSmN0DGdGyZnWvtt8Zna4/kR8drxgZTSc2JioXt8zvmhAeYFAFCiZuyemrEyfxr7K22rZpxa/QLbBhGxy7jH9Qty9YtvpX2M32bPqGZimGz9CVJKW8fE+ub7Oec35Zzvnmr7cXo+9+Sc74iIS2rh8X/bXrHy7+ATXpuxemz8TafrRcRzG/YXEbEo5/xo18kCAMw86sbuqRsr86exv9K26sYupZTeFhEHTbLKBhFxYkppjV7byDkvj6pD3Xjj66edopoBYoWf5ZxLN3ZOVq/W67Ff5pxv6yLHuVIzXl+ITbdDJwBAiTqxe+rEyvxp7K+0rTpxaq28vhijPY/MVtM9H9ZnIr17GvsC+kiHP5jdfl+IbTmktm+tPZ4XEU/pZUdjo7xvMe2MOnNL7fHClNJsn5q4dEHk6dPY3zMKsdIXdPqrNJveMF/3UbffCjnni6Ma5XO8I6d4nCPimIElBQDQTM3YPTVjRc04fIsi4pFabJ+IiJTS4yJi+3HxX+ecFxf2cUFUswys8LyU0prj9zXObRHxiw7y2rcQ+2AH29VN9ybE+sXD56eUVh/7/05GJy3FV7y+m0TEszrcBwDAbKNu7J66saJuHKGU0k4R8YlaeGlEnFWL7RwRH59mc/X6aNuU0pPG/n+69diqMXGWul7qsblQMz5YiM32cw8AMBrqxO6pEyvqxOFbFO28vjjK88hsNS96vN6aUtoqIlavhevnW2BEdPiD2e3CiFhei71gSG3/vBDbucd97RgTR4EYlHNqj1eJiN2G1PaoXFSI1Ufn78Yutcd3R8Q109gfnSkVxB2PbjkL2m+Tz9YeP2dsVr8V09i/tvb8j3POPiMAwCioGbunZqyoGYcs57wkIs6vhVd0ttsnIsaP2Fi8sXBsdoFF40JrRsTzxu1jvNNyzrmD1BbUHj+Yc76wg+3qpvs5qv/Na0fEHmP/P/5vyxHx0w73seL13TtWfn1L6wIAzFbqxu6pGyvqxhFJKa0fEd+IiPrMfW+LiP0j4sZa/O0ppVdPo8lSfTS+Xp1q3YiI02Llc82K7Z4TE2fy6EeHv9lYMz6uEHMDNAAwCOrE7qkTK+rEIWvx9cXzCrFhnUdms17Ph6XtSudbYAR0+INZLOd8Z0z8R/dpKaXSaBf9dm4htn+P+/rj6STSpUWF2OsH3GZ9BI1hFZIr1Gcji4jo6aJOSmnniNiqFj63wy/xTM/LCrFORkuZLe23yTdj4ig0bx3776FRXUQc73MDzwgAoEDN2JNFhZiasUNqxmmrX2jbNaW0Tky8mPaTLvaxT0rpiRHxtCnWa7JJ7fGdHW5Xd1CP261wVkQ8VIvtk1LaIiK2GRf7Zc65aUTKX8TKA9c8N6W0bkx8fW/MOV81rWwBAGYIdWNPFhVi6sYOqRv74tiYOMvJ8TnnL+ecb49qYMr6MXNsSqmnmQByzldHxA218D4ppTXisZtAI6p6sXjD4Ni55uJxoSellJ4WE+uxhyPizB7SnAs1466FWH0mGQCAaVMn9mRRIaZO7JA6cdraeH2x1NYwP5Oz1at63K702pfOt8AI6PAHs993C7H3DLrRnPMNEXFFLbzf2DTQHRubOv11fUtsamdExF212EEppUFOF72k9njdAbY1wdg03FfWws8ZK5S69bZC7Mc97IcupJQ2iYjDCk+dMqT2XxwRO9XCSyLiZ8Nov21yzg9HxBdq4deklOZHxJ/W4jdGxA+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      "text/plain": [
       "<Figure size 4500x2100 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p, (inp_value, _) in enumerate(challenge_data_gen):\n",
    "    bs = inp_value['input_data'].shape[0]\n",
    "    X_data = inp_value['input_data']\n",
    "    labels = inp_value['input_label']\n",
    "    preds = prediction_model.predict(X_data, batch_size=16)\n",
    "    pred_texts = decode_batch_predictions(preds)\n",
    "    \n",
    "    \n",
    "    orig_texts = []\n",
    "    for label in labels:\n",
    "        text = ''.join([labels_to_char[int(x)] for x in label])\n",
    "        orig_texts.append(text)\n",
    "\n",
    "    _, ax = plt.subplots(4, 4, figsize=(15, 7))\n",
    "    for i in range(len(pred_texts)):\n",
    "        img = (X_data[i, :, :, 0] * 255).astype(np.uint8)\n",
    "        img = img.T\n",
    "        # img = plt.imread(f\"C:\\\\Users\\\\soham\\\\OneDrive\\\\Desktop\\\\show\\\\{pred_texts[i]}.png\")\n",
    "        title = f\"Prediction: {pred_texts[i]}\"\n",
    "        ax[i // 4, i % 4].imshow(img, cmap='gray')\n",
    "        ax[i // 4, i % 4].set_title(title)\n",
    "        ax[i // 4, i % 4].axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "    for i in range(bs):\n",
    "        j = 0\n",
    "        sum =0\n",
    "        while j<len(pred_texts[i]) and j<len(orig_texts[i]):\n",
    "            if orig_texts[i][j] != pred_texts[i][j]:\n",
    "                sum += 1\n",
    "            j += 1\n",
    "        sum /= 5\n",
    "        acc = 1 if pred_texts[i] == orig_texts[i] else 0\n",
    "        # print(f'Ground truth: {orig_texts[i]} \\t Predicted: {pred_texts[i]}')\n",
    "    break"
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}