{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "from imutils.perspective import four_point_transform\n",
    "import pytesseract\n",
    "import argparse\n",
    "import imutils\n",
    "import cv2\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] -i INPUT [-d DEBUG]\n",
      "ipykernel_launcher.py: error: the following arguments are required: -i/--input\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": [
    "input= \"sample_711.jpg\"\n",
    "\n",
    "# Construct the argument parser\n",
    "ap = argparse.ArgumentParser()\n",
    "ap.add_argument(\"-i\", \"--input\", required=True,\n",
    "\thelp=\"path to input receipt image\")\n",
    "ap.add_argument(\"-d\", \"--debug\", type=int, default=-1,\n",
    "\thelp=\"whether or not we are visualizing each step of the pipeline\")\n",
    "\n",
    "# Parse the arguments\n",
    "args = vars(ap.parse_args())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'args' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# load the input image from disk, resize it, and compute the ratio\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# of the *new* width to the *old* width\u001b[39;00m\n\u001b[0;32m      3\u001b[0m image\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_711.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 4\u001b[0m orig \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[43margs\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m      5\u001b[0m image \u001b[38;5;241m=\u001b[39m orig\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m      6\u001b[0m image \u001b[38;5;241m=\u001b[39m imutils\u001b[38;5;241m.\u001b[39mresize(image, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'args' is not defined"
     ]
    }
   ],
   "source": [
    "# load the input image from disk, resize it, and compute the ratio\n",
    "# of the *new* width to the *old* width\n",
    "image= \"sample_711.jpg\"\n",
    "orig = cv2.imread(args[\"image\"])\n",
    "image = orig.copy()\n",
    "image = imutils.resize(image, width=500)\n",
    "ratio = orig.shape[1] / float(image.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the image to grayscale, blur it slightly, and then apply\n",
    "# edge detection\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "blurred = cv2.GaussianBlur(gray, (5, 5,), 0)\n",
    "edged = cv2.Canny(blurred, 75, 200)\n",
    "# check to see if we should show the output of our edge detection\n",
    "# procedure\n",
    "if args[\"debug\"] > 0:\n",
    "\tcv2.imshow(\"Input\", image)\n",
    "\tcv2.imshow(\"Edged\", edged)\n",
    "\tcv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find contours in the edge map and sort them by size in descending\n",
    "# order\n",
    "cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\n",
    "\tcv2.CHAIN_APPROX_SIMPLE)\n",
    "cnts = imutils.grab_contours(cnts)\n",
    "cnts = sorted(cnts, key=cv2.contourArea, reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize a contour that corresponds to the receipt outline\n",
    "receiptCnt = None\n",
    "# loop over the contours\n",
    "for c in cnts:\n",
    "\t# approximate the contour\n",
    "\tperi = cv2.arcLength(c, True)\n",
    "\tapprox = cv2.approxPolyDP(c, 0.02 * peri, True)\n",
    "\t# if our approximated contour has four points, then we can\n",
    "\t# assume we have found the outline of the receipt\n",
    "\tif len(approx) == 4:\n",
    "\t\treceiptCnt = approx\n",
    "\t\tbreak\n",
    "# if the receipt contour is empty then our script could not find the\n",
    "# outline and we should be notified\n",
    "if receiptCnt is None:\n",
    "\traise Exception((\"Could not find receipt outline. \"\n",
    "\t\t\"Try debugging your edge detection and contour steps.\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check to see if we should draw the contour of the receipt on the\n",
    "# image and then display it to our screen\n",
    "if args[\"debug\"] > 0:\n",
    "\toutput = image.copy()\n",
    "\tcv2.drawContours(output, [receiptCnt], -1, (0, 255, 0), 2)\n",
    "\tcv2.imshow(\"Receipt Outline\", output)\n",
    "\tcv2.waitKey(0)\n",
    "# apply a four-point perspective transform to the *original* image to\n",
    "# obtain a top-down bird's-eye view of the receipt\n",
    "receipt = four_point_transform(orig, receiptCnt.reshape(4, 2) * ratio)\n",
    "# show transformed image\n",
    "cv2.imshow(\"Receipt Transform\", imutils.resize(receipt, width=500))\n",
    "cv2.waitKey(0)"
   ]
  }
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