Seperate_All_Captchas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path\n",
    "import cv2\n",
    "import glob\n",
    "import imutils\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CAPTCHA_IMAGE_FOLDER = 'electoral-tagged'\n",
    "OUTPUT_FOLDER = 'seperate'\n",
    "\n",
    "captcha_image_files = glob.glob(os.path.join(CAPTCHA_IMAGE_FOLDER, \"*\"))\n",
    "counts = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[INFO] processing image 932/940\n",
      "72844.png\n",
      "72844\n",
      "[INFO] processing image 933/940\n",
      "73237.png\n",
      "73237\n",
      "[INFO] processing image 934/940\n",
      "81619.png\n",
      "81619\n",
      "[INFO] processing image 935/940\n",
      "34227.png\n",
      "34227\n",
      "[INFO] processing image 936/940\n",
      "72624.png\n",
      "72624\n",
      "[INFO] processing image 937/940\n",
      "24757.png\n",
      "24757\n",
      "[INFO] processing image 938/940\n",
      "61275.png\n",
      "61275\n",
      "[INFO] processing image 939/940\n",
      "41379.png\n",
      "41379\n",
      "[INFO] processing image 940/940\n",
      "15984.png\n",
      "15984\n"
     ]
    }
   ],
   "source": [
    "for (i, captcha_image_file) in enumerate(captcha_image_files):\n",
    "    print(\"[INFO] processing image {}/{}\".format(i + 1, len(captcha_image_files)))\n",
    "\n",
    "    filename = os.path.basename(captcha_image_file)\n",
    "    print(filename)\n",
    "\n",
    "    captcha_correct_text = os.path.splitext(filename)[0]\n",
    "    print(captcha_correct_text)\n",
    "\n",
    "    image = cv2.imread(captcha_image_file)\n",
    "    image =cv2.resize(image, (700,250))\n",
    "    image = cv2.GaussianBlur(image,(1,9),0)\n",
    "    image = cv2.pyrMeanShiftFiltering(image, sp=8, sr=16, maxLevel=1, termcrit=(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 3, 1))\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#     plt.imshow(gray, cmap='gray')\n",
    "#     plt.show()\n",
    "\n",
    "    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n",
    "    thresh = cv2.erode(thresh, None, iterations = 3)\n",
    "\n",
    "#     plt.imshow(thresh, cmap='gray')\n",
    "#     plt.show()\n",
    "\n",
    "    contours = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    contours = contours[0] if imutils.is_cv2() else contours[1]\n",
    "\n",
    "    cv2.drawContours(image, contours, -1, (255,120,0), thickness = 2)\n",
    "\n",
    "#     plt.imshow(image, cmap='gray')\n",
    "#     plt.show()\n",
    "\n",
    "    letter_image_regions = []\n",
    "\n",
    "#    print(len(contours))\n",
    "\n",
    "    for contour in contours:\n",
    "        # Get the rectangle that contains the contour\n",
    "        (x, y, w, h) = cv2.boundingRect(contour)\n",
    "\n",
    "        letter_image_regions.append((x, y, w, h))\n",
    "\n",
    "    len(letter_image_regions)\n",
    "\n",
    "    letter_image_regions = sorted(letter_image_regions, key = lambda x: x[0])\n",
    "\n",
    "    letter_image_regions\n",
    "\n",
    "\n",
    "    res = [i for i in letter_image_regions if min(i[1],i[2],i[0],i[3]) > 20]\n",
    "\n",
    "    letter_image_regions = res\n",
    "\n",
    "#     print(letter_image_regions)\n",
    "\n",
    "\n",
    "#     len(letter_image_regions)\n",
    "\n",
    "\n",
    "#     col = 20\n",
    "#     for i in letter_image_regions :\n",
    "#         e = i[0]\n",
    "#         f = i[1]\n",
    "#         g = i[2]\n",
    "#         h = i[3]\n",
    "#         img = cv2.rectangle(image,(e,f),(e+g,f+h),(255,col,0),3)\n",
    "#         col = col + 50\n",
    "#         print((e,f),(e+g,f+h))\n",
    "\n",
    "\n",
    "#     plt.imshow(img, cmap='gray')\n",
    "#     plt.show()\n",
    "\n",
    "    for letter_bounding_box, letter_text in zip(letter_image_regions, captcha_correct_text):\n",
    "        # Grab the coordinates of the letter in the image\n",
    "        x, y, w, h = letter_bounding_box\n",
    "\n",
    "        # Extract the letter from the original image with a 2-pixel margin around the edge\n",
    "        letter_image = gray[y - 2:y + h + 2, x - 2:x + w + 2]\n",
    "\n",
    "#         plt.imshow(letter_image, cmap='gray')\n",
    "#         plt.show()\n",
    "\n",
    "\n",
    "        save_path = os.path.join(OUTPUT_FOLDER, letter_text)\n",
    "\n",
    "        if not os.path.exists(save_path):\n",
    "            os.makedirs(save_path)\n",
    "\n",
    "        count = counts.get(letter_text, 1)\n",
    "        p = os.path.join(save_path, \"{}.png\".format(str(count).zfill(6)))\n",
    "        cv2.imwrite(p, letter_image)\n",
    "\n",
    "        counts[letter_text] = count + 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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