bill-extractor-v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pyplot\n",
    "import imutils\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_iou(y_true, y_pred):\n",
    "    prec = []\n",
    "    for t in np.arange(0.5, 1.0, 0.05):\n",
    "        y_pred_ = tf.to_int32(y_pred > t)\n",
    "        score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)\n",
    "        K.get_session().run(tf.local_variables_initializer())\n",
    "        with tf.control_dependencies([up_opt]):\n",
    "            score = tf.identity(score)\n",
    "        prec.append(score)\n",
    "    return K.mean(K.stack(prec), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "InternalError",
     "evalue": "failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 8335327232",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-6a4442a6b47b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bill-segmentation-model-0.9797.h5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'mean_iou'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmean_iou\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deserialize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    420\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mopened_new_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36m_deserialize_model\u001b[0;34m(f, custom_objects, compile)\u001b[0m\n\u001b[1;32m    285\u001b[0m                              ' elements.')\n\u001b[1;32m    286\u001b[0m         \u001b[0mweight_value_tuples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m     \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m   2468\u001b[0m             \u001b[0massign_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2469\u001b[0m             \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0massign_placeholder\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2470\u001b[0;31m         \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_ops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2471\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mget_session\u001b[0;34m()\u001b[0m\n\u001b[1;32m    184\u001b[0m                 config = tf.ConfigProto(intra_op_parallelism_threads=num_thread,\n\u001b[1;32m    185\u001b[0m                                         allow_soft_placement=True)\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[0m_SESSION\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_SESSION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_MANUAL_VAR_INIT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m   1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m     \"\"\"\n\u001b[0;32m-> 1551\u001b[0;31m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1552\u001b[0m     \u001b[0;31m# NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1553\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_graph_context_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m    674\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m       \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_NewSessionRef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m       \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 8335327232"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model('bill-segmentation-model-0.9797.h5', custom_objects={'mean_iou': mean_iou})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.resize(cv2.cvtColor(cv2.imread('images/2.jpg'), cv2.COLOR_BGR2RGB), (1280, 960))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-c9855f8ab13f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "mask = model.predict(np.array([img / 255.0]))[0]\n",
    "mask = mask > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def warp_document(mask, img):\n",
    "    mask[0:3, :] = 0\n",
    "    mask[:, 0:3] = 0\n",
    "    mask[mask.shape[0]-3:mask.shape[0], :] = 0\n",
    "    mask[:, mask.shape[1]-3:mask.shape[1]] = 0\n",
    "    mask = np.uint8(mask * 255)\n",
    "\n",
    "    ###\n",
    "\n",
    "    kernel = np.ones((3,3),np.uint8)\n",
    "    opening = cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernel, iterations = 2)\n",
    "    # sure background area\n",
    "    sure_bg = cv2.dilate(opening,kernel,iterations=3)\n",
    "    # Finding sure foreground area\n",
    "    dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)\n",
    "    ret, sure_fg = cv2.threshold(dist_transform,0.01*dist_transform.max(),255,0)\n",
    "    # Finding unknown region\n",
    "    sure_fg = np.uint8(sure_fg)\n",
    "    unknown = cv2.subtract(sure_bg,sure_fg)\n",
    "\n",
    "    ###\n",
    "\n",
    "    ret, markers = cv2.connectedComponents(sure_fg)\n",
    "    markers = markers+1\n",
    "    markers[unknown==255] = 0\n",
    "\n",
    "    ###\n",
    "\n",
    "    markers = cv2.watershed(cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB),markers)\n",
    "    markers = np.where(markers!=1, 1, 0)\n",
    "    ret, m2 = cv2.threshold(markers.astype(np.uint8), 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
    "    m2[0:3, :] = 0\n",
    "    m2[:, 0:3] = 0\n",
    "    m2[m2.shape[0]-3:mask.shape[0], :] = 0\n",
    "    m2[:, m2.shape[1]-3:mask.shape[1]] = 0\n",
    "\n",
    "    ###\n",
    "\n",
    "    contours = cv2.findContours(m2, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) \n",
    "    cnts = imutils.grab_contours(contours)\n",
    "    cnt = sorted(cnts, key = cv2.contourArea, reverse = True)[0]\n",
    "    rect = cv2.minAreaRect(cnt)\n",
    "\n",
    "    box = cv2.boxPoints(rect)\n",
    "\n",
    "    w, h = 300, 800\n",
    "    if rect[2] >= -45:\n",
    "        dst_pts = np.array([[0, h],   [0, 0], [w, 0], [w, h]], dtype=np.float32)\n",
    "    else:\n",
    "        dst_pts = np.array([[w, h], [0, h], [0, 0], [w, 0],], dtype=np.float32)\n",
    "    M = cv2.getPerspectiveTransform(box, dst_pts)\n",
    "    return cv2.warpPerspective(img, M, (w, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "warped_bill = warp_document(mask, img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_smoothening(img):\n",
    "    ret1, th1 = cv2.threshold(img, cv2.THRESH_BINARY, 255, cv2.THRESH_BINARY)\n",
    "    ret2, th2 = cv2.threshold(th1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
    "    blur = cv2.GaussianBlur(th2, (5, 5), 0)\n",
    "    ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
    "    return th3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_noise_and_smooth(img):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    filtered = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 21, 41)\n",
    "    kernel = np.ones((1, 1), np.uint8)\n",
    "    opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)\n",
    "    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)\n",
    "    #img = image_smoothening(img)\n",
    "    or_image = cv2.bitwise_or(img, closing)\n",
    "    return or_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f57474f8780>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(20, 20))\n",
    "pyplot.imshow(remove_noise_and_smooth(warped_bill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'remove_noise_and_smooth' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-cca5870c82dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdenoised\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mremove_noise_and_smooth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwarped_bill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1_warped.jpg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwarped_bill\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2RGB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'remove_noise_and_smooth' is not defined"
     ]
    }
   ],
   "source": [
    "denoised = remove_noise_and_smooth(warped_bill)\n",
    "cv2.imwrite('1_warped.jpg', cv2.cvtColor(warped_bill, cv2.COLOR_BGR2RGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytesseract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.00\n"
     ]
    }
   ],
   "source": [
    "print(pytesseract.image_to_string('text2.png', lang='eng', config='--psm 13 --oem 3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ЧІКЕН МИКНаГЕТС 9 ШТ\n"
     ]
    }
   ],
   "source": [
    "print(pytesseract.image_to_string('text3.png', lang='ukr', config='--psm 13 --oem 3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ЧІКЕН НИКНАГЕТС 9 ШТ\n",
      "СОУСОСИРНИЙ 2. Х 8.00 з\n",
      "Макчикен МЕНЮ\n",
      "\" 1 КАРТОПЛЯНІ ДІПИ МЕНЯ\n",
      "« | ЦЯй 3 БЕРГАМОТОМ МЕН\n"
     ]
    }
   ],
   "source": [
    "print(pytesseract.image_to_string('text4.png', lang='ukr', config='--psm 6 --oem 3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(pytesseract.image_to_string('text4.png', lang='ukr', config='--psm 7 --oem 3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ЧІКЕН МИКНАГЕТС 9 о\n",
      "СОУСОСИРНИЙ 2. Х 8 х\n",
      "Макчикен МЕНЮ\n",
      "« 1 КАРТОПЛЯНІ ДІПИ МЕНЯ\n",
      "« | ЦЯй 3 БЕРГАМОТОМ МЕН\n"
     ]
    }
   ],
   "source": [
    "print(pytesseract.image_to_string('text4.png', lang='ukr', config='--psm 4 --oem 3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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