2529 lines
1.1 MiB
2529 lines
1.1 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "49811aa9-7ffb-4d09-98f6-2839b37f4e3a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"3\n",
|
||
"548\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
" \n",
|
||
"image = cv2.imread('img/1.jpg')\n",
|
||
"\n",
|
||
"img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
"ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
"contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"for i in range(len(contours)):\n",
|
||
" x ,y, w, h = cv2.boundingRect(contours[i])\n",
|
||
" a=w*h \n",
|
||
" aspectRatio = float(w)/h\n",
|
||
" if aspectRatio >= 3 and a>600:\n",
|
||
" approx = cv2.approxPolyDP(contours[i], 0.05* cv2.arcLength(contours[i], True), True)\n",
|
||
" print(len(approx))\n",
|
||
" print(x)\n",
|
||
" if len(approx) <= 4 and x>15 :\n",
|
||
" width=w\n",
|
||
" height=h \n",
|
||
" start_x=x\n",
|
||
" start_y=y\n",
|
||
" end_x=start_x+width\n",
|
||
" end_y=start_y+height \n",
|
||
"\n",
|
||
"plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"cv2.imshow('nomer', image)\n",
|
||
"cv2.imshow('nomer2', plate)\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "137b37e4-d210-4fcb-a39f-19a68ef903bd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
"_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)\n",
|
||
"\n",
|
||
"contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
"largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
"\n",
|
||
"\n",
|
||
"rect = cv2.minAreaRect(largest_contour)\n",
|
||
"box = cv2.boxPoints(rect)\n",
|
||
"box = np.intp(box)\n",
|
||
"\n",
|
||
"\n",
|
||
"angle = rect[-1]\n",
|
||
"if angle < -45:\n",
|
||
" angle += 90\n",
|
||
"if (angle < 90):\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated = cv2.warpAffine(plate, M, (w, h))\n",
|
||
"else:\n",
|
||
" angle = 0.5;\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated = cv2.warpAffine(plate, M, (w, h))\n",
|
||
"\n",
|
||
"cv2.imshow('nomer2', rotated)\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "0ced0394",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"images = ['img/1.jpg', 'img/2.jpg', 'img/3.jpg']\n",
|
||
"\n",
|
||
"processed_plates = []\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
"\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" for i in range(len(contours)):\n",
|
||
" x, y, w, h = cv2.boundingRect(contours[i])\n",
|
||
" a = w * h\n",
|
||
" aspectRatio = float(w) / h\n",
|
||
" if aspectRatio >= 3 and a > 600:\n",
|
||
" approx = cv2.approxPolyDP(contours[i], 0.05 * cv2.arcLength(contours[i], True), True)\n",
|
||
" if len(approx) <= 4 and x > 15:\n",
|
||
" width = w\n",
|
||
" height = h\n",
|
||
" start_x = x\n",
|
||
" start_y = y\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
" rect = cv2.minAreaRect(largest_contour)\n",
|
||
" box = cv2.boxPoints(rect)\n",
|
||
" box = np.intp(box)\n",
|
||
" angle = rect[-1]\n",
|
||
"\n",
|
||
" if angle < -45:\n",
|
||
" angle += 90\n",
|
||
" if angle < 90:\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated = cv2.warpAffine(plate, M, (w, h))\n",
|
||
" else:\n",
|
||
" angle = 0.5\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated = cv2.warpAffine(plate, M, (w, h))\n",
|
||
"\n",
|
||
" processed_plates.append(rotated)\n",
|
||
"\n",
|
||
"for i, rotated_plate in enumerate(processed_plates):\n",
|
||
" cv2.imshow(f'Номер {i+1}', rotated_plate)\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "4cf0eacd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"images = ['img/1.jpg']\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" # Загрузка изображения\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" \n",
|
||
" # Пороговое преобразование для выделения контуров\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Поиск контура номерного знака\n",
|
||
" plate_contour = None\n",
|
||
" for i in range(len(contours)):\n",
|
||
" x, y, w, h = cv2.boundingRect(contours[i])\n",
|
||
" area = w * h\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if aspect_ratio >= 3 and area > 600: # Условие для фильтрации по форме и размеру\n",
|
||
" approx = cv2.approxPolyDP(contours[i], 0.05 * cv2.arcLength(contours[i], True), True)\n",
|
||
" if len(approx) <= 4 and x > 15:\n",
|
||
" plate_contour = contours[i]\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Обрезка области номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" # Коррекция наклона и поворот номерного знака\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, plate_thresh = cv2.threshold(gray_plate, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" contours, _ = cv2.findContours(plate_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Корректируем наклон, если требуется\n",
|
||
" largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
" rect = cv2.minAreaRect(largest_contour)\n",
|
||
" box = cv2.boxPoints(rect)\n",
|
||
" box = np.intp(box)\n",
|
||
" angle = rect[-1]\n",
|
||
"\n",
|
||
" if angle < -45:\n",
|
||
" angle += 90\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated_plate = cv2.warpAffine(plate, M, (w, h))\n",
|
||
"\n",
|
||
" # Обводим номерной знак на исходном изображении\n",
|
||
" cv2.drawContours(image, [plate_contour], -1, (0, 255, 0), 2)\n",
|
||
"\n",
|
||
" # Отображение и сохранение результатов\n",
|
||
" cv2.imshow('Исходное изображение с обведённым номером', image)\n",
|
||
" cv2.imshow('Изображение номерного знака', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_plate_{img_path.split(\"/\")[-1]}', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_image_{img_path.split(\"/\")[-1]}', image)\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "7a7f83e8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"ename": "TesseractNotFoundError",
|
||
"evalue": "tesseract is not installed or it's not in your PATH. See README file for more information.",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
||
"File \u001b[1;32mc:\\Users\\leonk\\Documents\\code\\study\\.venv\\lib\\site-packages\\pytesseract\\pytesseract.py:275\u001b[0m, in \u001b[0;36mrun_tesseract\u001b[1;34m(input_filename, output_filename_base, extension, lang, config, nice, timeout)\u001b[0m\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 275\u001b[0m proc \u001b[38;5;241m=\u001b[39m subprocess\u001b[38;5;241m.\u001b[39mPopen(cmd_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msubprocess_args())\n\u001b[0;32m 276\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
|
||
"File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py:951\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[0;32m 948\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mTextIOWrapper(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstderr,\n\u001b[0;32m 949\u001b[0m encoding\u001b[38;5;241m=\u001b[39mencoding, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m--> 951\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreexec_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 952\u001b[0m \u001b[43m \u001b[49m\u001b[43mpass_fds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 953\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 954\u001b[0m \u001b[43m \u001b[49m\u001b[43mp2cread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp2cwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 955\u001b[0m \u001b[43m \u001b[49m\u001b[43mc2pread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc2pwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 956\u001b[0m \u001b[43m \u001b[49m\u001b[43merrread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwrite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 957\u001b[0m \u001b[43m \u001b[49m\u001b[43mrestore_signals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 958\u001b[0m \u001b[43m \u001b[49m\u001b[43mgid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mumask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 959\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_new_session\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 960\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m 961\u001b[0m \u001b[38;5;66;03m# Cleanup if the child failed starting.\u001b[39;00m\n",
|
||
"File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py:1420\u001b[0m, in \u001b[0;36mPopen._execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session)\u001b[0m\n\u001b[0;32m 1419\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1420\u001b[0m hp, ht, pid, tid \u001b[38;5;241m=\u001b[39m \u001b[43m_winapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCreateProcess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1421\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# no special security\u001b[39;49;00m\n\u001b[0;32m 1422\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 1423\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mclose_fds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1424\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreationflags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1425\u001b[0m \u001b[43m \u001b[49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1426\u001b[0m \u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1427\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartupinfo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1428\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 1429\u001b[0m \u001b[38;5;66;03m# Child is launched. Close the parent's copy of those pipe\u001b[39;00m\n\u001b[0;32m 1430\u001b[0m \u001b[38;5;66;03m# handles that only the child should have open. You need\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1433\u001b[0m \u001b[38;5;66;03m# pipe will not close when the child process exits and the\u001b[39;00m\n\u001b[0;32m 1434\u001b[0m \u001b[38;5;66;03m# ReadFile will hang.\u001b[39;00m\n",
|
||
"\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] Не удается найти указанный файл",
|
||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||
"\u001b[1;31mTesseractNotFoundError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[1;32mIn[4], line 54\u001b[0m\n\u001b[0;32m 51\u001b[0m rotated_plate \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mwarpAffine(plate, M, (w, h))\n\u001b[0;32m 53\u001b[0m \u001b[38;5;66;03m# Распознавание текста с номерного знака\u001b[39;00m\n\u001b[1;32m---> 54\u001b[0m plate_text \u001b[38;5;241m=\u001b[39m \u001b[43mpytesseract\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage_to_string\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrotated_plate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m--psm 8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mРаспознанный номер:\u001b[39m\u001b[38;5;124m\"\u001b[39m, plate_text)\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# Обводим номер на исходном изображении\u001b[39;00m\n",
|
||
"File \u001b[1;32mc:\\Users\\leonk\\Documents\\code\\study\\.venv\\lib\\site-packages\\pytesseract\\pytesseract.py:486\u001b[0m, in \u001b[0;36mimage_to_string\u001b[1;34m(image, lang, config, nice, output_type, timeout)\u001b[0m\n\u001b[0;32m 481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 482\u001b[0m \u001b[38;5;124;03mReturns the result of a Tesseract OCR run on the provided image to string\u001b[39;00m\n\u001b[0;32m 483\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 484\u001b[0m args \u001b[38;5;241m=\u001b[39m [image, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtxt\u001b[39m\u001b[38;5;124m'\u001b[39m, lang, config, nice, timeout]\n\u001b[1;32m--> 486\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m{\u001b[49m\n\u001b[0;32m 487\u001b[0m \u001b[43m \u001b[49m\u001b[43mOutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBYTES\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_and_get_output\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 488\u001b[0m \u001b[43m \u001b[49m\u001b[43mOutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDICT\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_and_get_output\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[43mOutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSTRING\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_and_get_output\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 490\u001b[0m \u001b[43m\u001b[49m\u001b[43m}\u001b[49m\u001b[43m[\u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"File \u001b[1;32mc:\\Users\\leonk\\Documents\\code\\study\\.venv\\lib\\site-packages\\pytesseract\\pytesseract.py:489\u001b[0m, in \u001b[0;36mimage_to_string.<locals>.<lambda>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 481\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 482\u001b[0m \u001b[38;5;124;03mReturns the result of a Tesseract OCR run on the provided image to string\u001b[39;00m\n\u001b[0;32m 483\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 484\u001b[0m args \u001b[38;5;241m=\u001b[39m [image, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtxt\u001b[39m\u001b[38;5;124m'\u001b[39m, lang, config, nice, timeout]\n\u001b[0;32m 486\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m 487\u001b[0m Output\u001b[38;5;241m.\u001b[39mBYTES: \u001b[38;5;28;01mlambda\u001b[39;00m: run_and_get_output(\u001b[38;5;241m*\u001b[39m(args \u001b[38;5;241m+\u001b[39m [\u001b[38;5;28;01mTrue\u001b[39;00m])),\n\u001b[0;32m 488\u001b[0m Output\u001b[38;5;241m.\u001b[39mDICT: \u001b[38;5;28;01mlambda\u001b[39;00m: {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m: run_and_get_output(\u001b[38;5;241m*\u001b[39margs)},\n\u001b[1;32m--> 489\u001b[0m Output\u001b[38;5;241m.\u001b[39mSTRING: \u001b[38;5;28;01mlambda\u001b[39;00m: \u001b[43mrun_and_get_output\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m 490\u001b[0m }[output_type]()\n",
|
||
"File \u001b[1;32mc:\\Users\\leonk\\Documents\\code\\study\\.venv\\lib\\site-packages\\pytesseract\\pytesseract.py:352\u001b[0m, in \u001b[0;36mrun_and_get_output\u001b[1;34m(image, extension, lang, config, nice, timeout, return_bytes)\u001b[0m\n\u001b[0;32m 341\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m save(image) \u001b[38;5;28;01mas\u001b[39;00m (temp_name, input_filename):\n\u001b[0;32m 342\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 343\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_filename\u001b[39m\u001b[38;5;124m'\u001b[39m: input_filename,\n\u001b[0;32m 344\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput_filename_base\u001b[39m\u001b[38;5;124m'\u001b[39m: temp_name,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 349\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m: timeout,\n\u001b[0;32m 350\u001b[0m }\n\u001b[1;32m--> 352\u001b[0m run_tesseract(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 353\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read_output(\n\u001b[0;32m 354\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput_filename_base\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mextsep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mextension\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 355\u001b[0m return_bytes,\n\u001b[0;32m 356\u001b[0m )\n",
|
||
"File \u001b[1;32mc:\\Users\\leonk\\Documents\\code\\study\\.venv\\lib\\site-packages\\pytesseract\\pytesseract.py:280\u001b[0m, in \u001b[0;36mrun_tesseract\u001b[1;34m(input_filename, output_filename_base, extension, lang, config, nice, timeout)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m 279\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 280\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TesseractNotFoundError()\n\u001b[0;32m 282\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m timeout_manager(proc, timeout) \u001b[38;5;28;01mas\u001b[39;00m error_string:\n\u001b[0;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m proc\u001b[38;5;241m.\u001b[39mreturncode:\n",
|
||
"\u001b[1;31mTesseractNotFoundError\u001b[0m: tesseract is not installed or it's not in your PATH. See README file for more information."
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import pytesseract\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Путь к Tesseract OCR, если требуется\n",
|
||
"# pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n",
|
||
"\n",
|
||
"images = ['img/1.jpg', 'img/2.jpg', 'img/3.jpg']\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Поиск контура номерного знака\n",
|
||
" plate_contour = None\n",
|
||
" for i in range(len(contours)):\n",
|
||
" x, y, w, h = cv2.boundingRect(contours[i])\n",
|
||
" a = w * h\n",
|
||
" aspectRatio = float(w) / h\n",
|
||
" if aspectRatio >= 3 and a > 600:\n",
|
||
" approx = cv2.approxPolyDP(contours[i], 0.05 * cv2.arcLength(contours[i], True), True)\n",
|
||
" if len(approx) <= 4 and x > 15:\n",
|
||
" plate_contour = contours[i]\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Извлечение области номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" # Коррекция наклона и поворот номерного знака\n",
|
||
" gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
" rect = cv2.minAreaRect(largest_contour)\n",
|
||
" box = cv2.boxPoints(rect)\n",
|
||
" box = np.intp(box)\n",
|
||
" angle = rect[-1]\n",
|
||
"\n",
|
||
" if angle < -45:\n",
|
||
" angle += 90\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated_plate = cv2.warpAffine(plate, M, (w, h))\n",
|
||
"\n",
|
||
" # Распознавание текста с номерного знака\n",
|
||
" plate_text = pytesseract.image_to_string(rotated_plate, config='--psm 8')\n",
|
||
" print(\"Распознанный номер:\", plate_text)\n",
|
||
"\n",
|
||
" # Обводим номер на исходном изображении\n",
|
||
" cv2.drawContours(image, [plate_contour], -1, (0, 255, 0), 2)\n",
|
||
" cv2.putText(image, plate_text.strip(), (start_x, start_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)\n",
|
||
"\n",
|
||
" # Показ результатов\n",
|
||
" cv2.imshow('Исходное изображение с обведённым номером', image)\n",
|
||
" cv2.imshow('Изображение номерного знака', rotated_plate)\n",
|
||
"\n",
|
||
" # Сохранение результатов\n",
|
||
" cv2.imwrite(f'processed_plate_{img_path.split(\"/\")[-1]}', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_image_{img_path.split(\"/\")[-1]}', image)\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "9e3f2a58",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Номерной знак успешно извлечён и сохранён как 'extracted_plate.jpg'\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"\n",
|
||
"# Загрузка изображения\n",
|
||
"image = cv2.imread('img/1.jpg')\n",
|
||
"img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
"# Улучшение контраста с использованием порогового преобразования\n",
|
||
"_, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
"# Применение морфологических операций (закрытие) для объединения разрывов\n",
|
||
"kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
|
||
"closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
|
||
"\n",
|
||
"# Поиск контуров после морфологической обработки\n",
|
||
"contours, hierarchy = cv2.findContours(closed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
"# Поиск контура номерного знака\n",
|
||
"plate_contour = None\n",
|
||
"for contour in contours:\n",
|
||
" # Прямоугольник вокруг контура\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" \n",
|
||
" # Фильтры по соотношению сторон и площади\n",
|
||
" area = w * h\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if 2.5 < aspect_ratio < 5.5 and 1500 < area < 15000: # Уточнённые условия\n",
|
||
" approx = cv2.approxPolyDP(contour, 0.02 * cv2.arcLength(contour, True), True)\n",
|
||
" if len(approx) == 4: # Проверяем, что контур прямоугольный\n",
|
||
" plate_contour = approx\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
"# Проверяем, нашли ли контур номерного знака\n",
|
||
"if plate_contour is not None:\n",
|
||
" # Обрезаем и сохраняем область номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
" cv2.imwrite('extracted_plate.jpg', plate)\n",
|
||
" print(\"Номерной знак успешно извлечён и сохранён как 'extracted_plate.jpg'\")\n",
|
||
"else:\n",
|
||
" print(\"Не удалось найти номерной знак на изображении.\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "a9d5e8d5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"\n",
|
||
"# Список изображений для обработки\n",
|
||
"images = ['img/1.jpg']\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" # Загрузка изображения\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" \n",
|
||
" # Улучшение контраста с использованием порогового преобразования\n",
|
||
" _, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
" # Применение морфологических операций (закрытие) для объединения разрывов\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
|
||
" closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
|
||
"\n",
|
||
" # Поиск контуров после морфологической обработки\n",
|
||
" contours, hierarchy = cv2.findContours(closed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Поиск контура номерного знака\n",
|
||
" plate_contour = None\n",
|
||
" for contour in contours:\n",
|
||
" # Прямоугольник вокруг контура\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" \n",
|
||
" # Фильтры по соотношению сторон и площади\n",
|
||
" area = w * h\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if 2.5 < aspect_ratio < 5.5 and 1500 < area < 15000: # Уточнённые условия\n",
|
||
" approx = cv2.approxPolyDP(contour, 0.02 * cv2.arcLength(contour, True), True)\n",
|
||
" if len(approx) == 4: # Проверяем, что контур прямоугольный\n",
|
||
" plate_contour = approx\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Проверяем, нашли ли контур номерного знака\n",
|
||
" if plate_contour is not None:\n",
|
||
" # Обрезаем область номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" # Коррекция наклона номерного знака\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, plate_thresh = cv2.threshold(gray_plate, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" contours, _ = cv2.findContours(plate_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" if contours:\n",
|
||
" largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
" rect = cv2.minAreaRect(largest_contour)\n",
|
||
" box = cv2.boxPoints(rect)\n",
|
||
" box = np.intp(box)\n",
|
||
" angle = rect[-1]\n",
|
||
"\n",
|
||
" if angle < -45:\n",
|
||
" angle += 90\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated_plate = cv2.warpAffine(plate, M, (w, h))\n",
|
||
" else:\n",
|
||
" rotated_plate = plate\n",
|
||
"\n",
|
||
" # Обводим контур номерного знака на исходном изображении\n",
|
||
" cv2.drawContours(image, [plate_contour], -1, (0, 255, 0), 2)\n",
|
||
"\n",
|
||
" # Отображение и сохранение результатов\n",
|
||
" cv2.imshow('Исходное изображение с обведённым номером', image)\n",
|
||
" cv2.imshow('Изображение номерного знака', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_plate_{img_path.split(\"/\")[-1]}', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_image_{img_path.split(\"/\")[-1]}', image)\n",
|
||
" else:\n",
|
||
" print(f\"Не удалось найти номерной знак на изображении {img_path}\")\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "b36cc4fc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер на изображении img/1.jpg: “TBDOMK OL\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import pytesseract\n",
|
||
"\n",
|
||
"# Путь к Tesseract OCR, если требуется\n",
|
||
"pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n",
|
||
"\n",
|
||
"# Список изображений для обработки\n",
|
||
"images = ['img/1.jpg']\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" # Загрузка изображения\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" \n",
|
||
" # Улучшение контраста с использованием порогового преобразования\n",
|
||
" _, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
" # Применение морфологических операций (закрытие) для объединения разрывов\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
|
||
" closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
|
||
"\n",
|
||
" # Поиск контуров после морфологической обработки\n",
|
||
" contours, hierarchy = cv2.findContours(closed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Поиск контура номерного знака\n",
|
||
" plate_contour = None\n",
|
||
" for contour in contours:\n",
|
||
" # Прямоугольник вокруг контура\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" \n",
|
||
" # Фильтры по соотношению сторон и площади\n",
|
||
" area = w * h\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if 2.5 < aspect_ratio < 5.5 and 1500 < area < 15000: # Уточнённые условия\n",
|
||
" approx = cv2.approxPolyDP(contour, 0.02 * cv2.arcLength(contour, True), True)\n",
|
||
" if len(approx) == 4: # Проверяем, что контур прямоугольный\n",
|
||
" plate_contour = approx\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Проверяем, нашли ли контур номерного знака\n",
|
||
" if plate_contour is not None:\n",
|
||
" # Обрезаем область номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" # Коррекция наклона номерного знака\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, plate_thresh = cv2.threshold(gray_plate, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" contours, _ = cv2.findContours(plate_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" if contours:\n",
|
||
" largest_contour = max(contours, key=cv2.contourArea)\n",
|
||
" rect = cv2.minAreaRect(largest_contour)\n",
|
||
" box = cv2.boxPoints(rect)\n",
|
||
" box = np.intp(box)\n",
|
||
" angle = rect[-1]\n",
|
||
"\n",
|
||
" if angle < -45:\n",
|
||
" angle += 90\n",
|
||
" (h, w) = plate.shape[:2]\n",
|
||
" center = (w // 2, h // 2)\n",
|
||
" M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
|
||
" rotated_plate = cv2.warpAffine(plate, M, (w, h))\n",
|
||
" else:\n",
|
||
" rotated_plate = plate\n",
|
||
"\n",
|
||
" # Распознавание текста с помощью Tesseract\n",
|
||
" plate_text = pytesseract.image_to_string(rotated_plate, config='--psm 8')\n",
|
||
" print(f\"Распознанный номер на изображении {img_path}: {plate_text.strip()}\")\n",
|
||
"\n",
|
||
" # Обводим контур номерного знака на исходном изображении\n",
|
||
" cv2.drawContours(image, [plate_contour], -1, (0, 255, 0), 2)\n",
|
||
" cv2.putText(image, plate_text.strip(), (start_x, start_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)\n",
|
||
"\n",
|
||
" # Отображение и сохранение результатов\n",
|
||
" cv2.imshow('Исходное изображение с обведённым номером', image)\n",
|
||
" cv2.imshow('Изображение номерного знака', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_plate_{img_path.split(\"/\")[-1]}', rotated_plate)\n",
|
||
" cv2.imwrite(f'processed_image_{img_path.split(\"/\")[-1]}', image)\n",
|
||
" else:\n",
|
||
" print(f\"Не удалось найти номерной знак на изображении {img_path}\")\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "37f43981",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер на изображении img/1.jpg: RE\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import pytesseract\n",
|
||
"\n",
|
||
"# Путь к Tesseract OCR, если требуется\n",
|
||
"# pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n",
|
||
"\n",
|
||
"# Список изображений для обработки\n",
|
||
"images = ['img/1.jpg']\n",
|
||
"\n",
|
||
"for img_path in images:\n",
|
||
" # Загрузка изображения\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" \n",
|
||
" # Улучшение контраста с использованием порогового преобразования\n",
|
||
" _, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
" # Применение морфологических операций (закрытие) для объединения разрывов\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
|
||
" closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
|
||
"\n",
|
||
" # Поиск контуров после морфологической обработки\n",
|
||
" contours, hierarchy = cv2.findContours(closed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Поиск контура номерного знака\n",
|
||
" plate_contour = None\n",
|
||
" for contour in contours:\n",
|
||
" # Прямоугольник вокруг контура\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" \n",
|
||
" # Фильтры по соотношению сторон и площади\n",
|
||
" area = w * h\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if 2.5 < aspect_ratio < 5.5 and 1500 < area < 15000: # Уточнённые условия\n",
|
||
" approx = cv2.approxPolyDP(contour, 0.02 * cv2.arcLength(contour, True), True)\n",
|
||
" if len(approx) == 4: # Проверяем, что контур прямоугольный\n",
|
||
" plate_contour = approx\n",
|
||
" start_x, start_y, width, height = x, y, w, h\n",
|
||
" end_x = start_x + width\n",
|
||
" end_y = start_y + height\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Проверяем, нашли ли контур номерного знака\n",
|
||
" if plate_contour is not None:\n",
|
||
" # Обрезаем область номерного знака\n",
|
||
" plate = image[start_y:end_y, start_x:end_x]\n",
|
||
"\n",
|
||
" # Дополнительное улучшение для OCR\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" # Увеличение контрастности\n",
|
||
" plate_enhanced = cv2.convertScaleAbs(gray_plate, alpha=1.5, beta=20)\n",
|
||
" \n",
|
||
" # Бинаризация и морфология для улучшения качества текста\n",
|
||
" _, plate_thresh = cv2.threshold(plate_enhanced, 150, 255, cv2.THRESH_BINARY)\n",
|
||
" plate_cleaned = cv2.morphologyEx(plate_thresh, cv2.MORPH_OPEN, kernel)\n",
|
||
"\n",
|
||
" # Отправка улучшенного изображения в Tesseract\n",
|
||
" custom_config = r'--oem 3 --psm 8 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'\n",
|
||
" plate_text = pytesseract.image_to_string(plate_cleaned, config=custom_config)\n",
|
||
" \n",
|
||
" # Вывод распознанного текста в консоль\n",
|
||
" print(f\"Распознанный номер на изображении {img_path}: {plate_text.strip()}\")\n",
|
||
"\n",
|
||
" # Обводим контур номерного знака на исходном изображении (без текста)\n",
|
||
" cv2.drawContours(image, [plate_contour], -1, (0, 255, 0), 2)\n",
|
||
"\n",
|
||
" # Отображение и сохранение результатов\n",
|
||
" cv2.imshow('Исходное изображение с обведённым номером', image)\n",
|
||
" cv2.imshow('Изображение номерного знака', plate_cleaned)\n",
|
||
" cv2.imwrite(f'processed_plate_{img_path.split(\"/\")[-1]}', plate_cleaned)\n",
|
||
" cv2.imwrite(f'processed_image_{img_path.split(\"/\")[-1]}', image)\n",
|
||
" else:\n",
|
||
" print(f\"Не удалось найти номерной знак на изображении {img_path}\")\n",
|
||
"\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "61c9766c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Результат распознавания на улучшенном изображении:\n",
|
||
"Распознанный номер: TB29MKOP\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"import pytesseract\n",
|
||
"\n",
|
||
"# Путь к Tesseract OCR, если требуется\n",
|
||
"# pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n",
|
||
"\n",
|
||
"# Загрузка изображения номерного знака\n",
|
||
"plate = cv2.imread('extracted_plate.jpg')\n",
|
||
"\n",
|
||
"# Увеличение изображения для улучшения детализации\n",
|
||
"scale_percent = 300 # Увеличение на 300%\n",
|
||
"width = int(plate.shape[1] * scale_percent / 100)\n",
|
||
"height = int(plate.shape[0] * scale_percent / 100)\n",
|
||
"dim = (width, height)\n",
|
||
"resized_plate = cv2.resize(plate, dim, interpolation=cv2.INTER_CUBIC)\n",
|
||
"cv2.imshow(\"Enhanced Plate\", resized_plate)\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n",
|
||
"# Преобразование в оттенки серого\n",
|
||
"gray_plate = cv2.cvtColor(resized_plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"cv2.imshow(\"Enhanced Plate\", gray_plate)\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n",
|
||
"# Применение адаптивного порогового преобразования\n",
|
||
"binary_plate = cv2.adaptiveThreshold(gray_plate, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \n",
|
||
" cv2.THRESH_BINARY, 11, 2)\n",
|
||
"\n",
|
||
"# Сохранение и отображение для визуальной проверки\n",
|
||
"cv2.imshow(\"Enhanced Plate\", binary_plate)\n",
|
||
"cv2.waitKey(0)\n",
|
||
"cv2.destroyAllWindows()\n",
|
||
"\n",
|
||
"# Обновлённый вайтлист для распознавания\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 3 --psm 8 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Распознавание текста на улучшенном изображении\n",
|
||
"print(\"Результат распознавания на улучшенном изображении:\")\n",
|
||
"text = pytesseract.image_to_string(gray_plate, config=custom_config)\n",
|
||
"print(f\"Распознанный номер: {text.strip()}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"id": "e0df04cf",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер: \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"import pytesseract\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Загрузка изображения номерного знака\n",
|
||
"plate = cv2.imread('extracted_plate.jpg')\n",
|
||
"\n",
|
||
"# 1. Изменение размера изображения\n",
|
||
"resized_plate = cv2.resize(\n",
|
||
" plate, None, fx=2, fy=2,\n",
|
||
" interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
"# 2. Конвертация в градации серого\n",
|
||
"gray = cv2.cvtColor(resized_plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
"# 3. Применение \"чёрной шляпы\" для выделения тёмных символов на светлом фоне\n",
|
||
"rectKern = cv2.getStructuringElement(cv2.MORPH_RECT, (13, 5))\n",
|
||
"blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, rectKern)\n",
|
||
"\n",
|
||
"# 4. Выделение светлых областей с использованием операции закрытия и бинаризации\n",
|
||
"squareKern = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n",
|
||
"light = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, squareKern)\n",
|
||
"_, light = cv2.threshold(light, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
"# 5. Вычисление градиента Шарра для выделения границ символов\n",
|
||
"gradX = cv2.Sobel(blackhat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\n",
|
||
"gradX = np.absolute(gradX)\n",
|
||
"(minVal, maxVal) = (np.min(gradX), np.max(gradX))\n",
|
||
"gradX = 255 * ((gradX - minVal) / (maxVal - minVal))\n",
|
||
"gradX = gradX.astype(\"uint8\")\n",
|
||
"\n",
|
||
"# 6. Сглаживание, закрытие и пороговая обработка\n",
|
||
"gradX = cv2.GaussianBlur(gradX, (5, 5), 0)\n",
|
||
"gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKern)\n",
|
||
"_, thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
|
||
"\n",
|
||
"# 7. Очистка с помощью эрозии и дилатации\n",
|
||
"thresh = cv2.erode(thresh, None, iterations=2)\n",
|
||
"thresh = cv2.dilate(thresh, None, iterations=2)\n",
|
||
"\n",
|
||
"# 8. Маскирование светлых областей и финальная обработка\n",
|
||
"thresh = cv2.bitwise_and(thresh, thresh, mask=light)\n",
|
||
"thresh = cv2.dilate(thresh, None, iterations=2)\n",
|
||
"thresh = cv2.erode(thresh, None, iterations=1)\n",
|
||
"\n",
|
||
"# Обновлённый вайтлист для Tesseract\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 3 --psm 8 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Распознавание текста на финальном изображении\n",
|
||
"text = pytesseract.image_to_string(thresh, config=custom_config)\n",
|
||
"print(f\"Распознанный номер: {text.strip()}\")\n",
|
||
"\n",
|
||
"# Отображение всех этапов обработки в Matplotlib\n",
|
||
"fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
|
||
"fig.suptitle('Этапы обработки изображения номерного знака')\n",
|
||
"\n",
|
||
"# Отображение оригинального изображения в градациях серого\n",
|
||
"axs[0, 0].imshow(gray, cmap='gray')\n",
|
||
"axs[0, 0].set_title(\"Оригинальное изображение (серое)\")\n",
|
||
"axs[0, 0].axis('off')\n",
|
||
"\n",
|
||
"# Отображение изображения после \"чёрной шляпы\"\n",
|
||
"axs[0, 1].imshow(blackhat, cmap='gray')\n",
|
||
"axs[0, 1].set_title(\"Blackhat\")\n",
|
||
"axs[0, 1].axis('off')\n",
|
||
"\n",
|
||
"# Отображение выделения светлых областей\n",
|
||
"axs[0, 2].imshow(light, cmap='gray')\n",
|
||
"axs[0, 2].set_title(\"Светлые области\")\n",
|
||
"axs[0, 2].axis('off')\n",
|
||
"\n",
|
||
"# Отображение градиента Шарра\n",
|
||
"axs[1, 0].imshow(gradX, cmap='gray')\n",
|
||
"axs[1, 0].set_title(\"Градиент Шарра\")\n",
|
||
"axs[1, 0].axis('off')\n",
|
||
"\n",
|
||
"# Отображение изображения после пороговой обработки\n",
|
||
"axs[1, 1].imshow(thresh, cmap='gray')\n",
|
||
"axs[1, 1].set_title(\"Пороговое изображение\")\n",
|
||
"axs[1, 1].axis('off')\n",
|
||
"\n",
|
||
"# Отображение финального результата\n",
|
||
"axs[1, 2].imshow(thresh, cmap='gray')\n",
|
||
"axs[1, 2].set_title(\"Финальное обработанное изображение\")\n",
|
||
"axs[1, 2].axis('off')\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"id": "8ef0267e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер: TB29MKOP\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import re\n",
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"import pytesseract\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Загрузка изображения номерного знака\n",
|
||
"plate = cv2.imread('extracted_plate.jpg')\n",
|
||
"\n",
|
||
"# Изменение размера для улучшения качества\n",
|
||
"resized_plate = cv2.resize(plate, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
"# Преобразование в градации серого\n",
|
||
"gray = cv2.cvtColor(resized_plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
"# Применение адаптивного порогового преобразования для бинаризации изображения\n",
|
||
"thresh = cv2.adaptiveThreshold(gray, 255, \n",
|
||
" cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \n",
|
||
" cv2.THRESH_BINARY_INV, 41, 10)\n",
|
||
"\n",
|
||
"# Удаление мелких шумов с помощью морфологического открытия\n",
|
||
"kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n",
|
||
"thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)\n",
|
||
"\n",
|
||
"# Удаление рамок номерного знака с помощью удаления крупных контуров по периметру\n",
|
||
"contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
"\n",
|
||
"# Дополнительная очистка изображения с помощью морфологических операций\n",
|
||
"# Заполнение возможных разрывов в символах\n",
|
||
"thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)\n",
|
||
"\n",
|
||
"# Отображение финального результата\n",
|
||
"plt.imshow(thresh, cmap='gray')\n",
|
||
"plt.title('Обработанное изображение с чёрными буквами на белом фоне')\n",
|
||
"plt.axis('off')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Настройки Tesseract для русского и английского языков (если необходимо)\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 1 --psm 7 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Распознавание текста на финальном изображении\n",
|
||
"text = pytesseract.image_to_string(thresh, config=custom_config)\n",
|
||
"print(f\"Распознанный номер: {text.strip()}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 91,
|
||
"id": "d1a98d92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер: T829MK97\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import re\n",
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"import pytesseract\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Загрузка изображения номерного знака\n",
|
||
"plate = cv2.imread('extracted_plate.jpg')\n",
|
||
"\n",
|
||
"# Изменение размера для улучшения качества\n",
|
||
"resized_plate = cv2.resize(plate, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
"# Преобразование в градации серого\n",
|
||
"gray = cv2.cvtColor(resized_plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
"# Применение фильтра размытия для уменьшения шумов\n",
|
||
"gray = cv2.GaussianBlur(gray, (3, 3), 0)\n",
|
||
"\n",
|
||
"# Применение адаптивного порогового преобразования для бинаризации изображения\n",
|
||
"thresh = cv2.adaptiveThreshold(gray, 255, \n",
|
||
" cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \n",
|
||
" cv2.THRESH_BINARY_INV, 41, 10)\n",
|
||
"\n",
|
||
"# Удаление мелких шумов с помощью морфологического открытия\n",
|
||
"kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n",
|
||
"thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)\n",
|
||
"\n",
|
||
"# Дополнительная очистка изображения с помощью морфологических операций\n",
|
||
"thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)\n",
|
||
"\n",
|
||
"# Отображение финального результата\n",
|
||
"plt.imshow(thresh, cmap='gray')\n",
|
||
"plt.title('Обработанное и выровненное изображение')\n",
|
||
"plt.axis('off')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Настройки Tesseract для русского и английского языков (если необходимо)\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 1 --psm 10 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Распознавание текста на финальном изображении\n",
|
||
"text = pytesseract.image_to_string(thresh, config=custom_config)\n",
|
||
"print(f\"Распознанный номер: {text.strip()}\")\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"id": "f2b19b5d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер для img/1.jpg: CT829MK97\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер для img/2.jpg: AO2397\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер для img/3.jpg: K263C097\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import pytesseract\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Список входных изображений\n",
|
||
"images = ['img/1.jpg', 'img/2.jpg', 'img/3.jpg']\n",
|
||
"\n",
|
||
"# Настройки для Tesseract\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 1 --psm 10 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Обработка изображений\n",
|
||
"for img_path in images:\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" if image is None:\n",
|
||
" print(f\"Не удалось загрузить изображение: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Преобразование в градации серого и бинаризация\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
"\n",
|
||
" # Поиск контуров\n",
|
||
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" plate = None\n",
|
||
"\n",
|
||
" for i in range(len(contours)):\n",
|
||
" x, y, w, h = cv2.boundingRect(contours[i])\n",
|
||
" a = w * h\n",
|
||
" aspectRatio = float(w) / h\n",
|
||
" if aspectRatio >= 3 and a > 600:\n",
|
||
" approx = cv2.approxPolyDP(contours[i], 0.05 * cv2.arcLength(contours[i], True), True)\n",
|
||
" if len(approx) <= 4 and x > 15:\n",
|
||
" plate = image[y:y + h, x:x + w]\n",
|
||
" break\n",
|
||
"\n",
|
||
" if plate is None:\n",
|
||
" print(f\"Номерной знак не найден на изображении: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Преобразование в градации серого для номерного знака\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
" # Увеличение размера для улучшения качества\n",
|
||
" resized_plate = cv2.resize(gray_plate, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
" # Фильтрация и бинаризация\n",
|
||
" gray = cv2.GaussianBlur(resized_plate, (3, 3), 0)\n",
|
||
" thresh = cv2.adaptiveThreshold(gray, 255,\n",
|
||
" cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n",
|
||
" cv2.THRESH_BINARY_INV, 41, 10)\n",
|
||
"\n",
|
||
" # Морфологические операции для очистки изображения\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n",
|
||
" thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)\n",
|
||
" thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)\n",
|
||
"\n",
|
||
" # Распознавание текста\n",
|
||
" text = pytesseract.image_to_string(thresh, config=custom_config)\n",
|
||
" print(f\"Распознанный номер для {img_path}: {text.strip()}\")\n",
|
||
"\n",
|
||
" # Отображение исходного номерного знака и обработанной версии\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||
" axes[0].imshow(cv2.cvtColor(plate, cv2.COLOR_BGR2RGB))\n",
|
||
" axes[0].set_title('Вырезанный номерной знак')\n",
|
||
" axes[0].axis('off')\n",
|
||
"\n",
|
||
" axes[1].imshow(thresh, cmap='gray')\n",
|
||
" axes[1].set_title('Обработанный номерной знак')\n",
|
||
" axes[1].axis('off')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 173,
|
||
"id": "3ce0582c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер для img/3.jpg: K2O3COT\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import scipy.fftpack\n",
|
||
"import pytesseract\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Настройки для Tesseract\n",
|
||
"whitelist = 'ABCEHMOPTXYK0123456789'\n",
|
||
"custom_config = f'--oem 1 --psm 10 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"# Определение функции imclearborder\n",
|
||
"def imclearborder(imgBW, radius):\n",
|
||
" imgBWcopy = imgBW.copy()\n",
|
||
" contours, _ = cv2.findContours(imgBWcopy.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" imgRows, imgCols = imgBW.shape\n",
|
||
"\n",
|
||
" contourList = []\n",
|
||
" for idx in range(len(contours)):\n",
|
||
" cnt = contours[idx]\n",
|
||
" for pt in cnt:\n",
|
||
" rowCnt = pt[0][1]\n",
|
||
" colCnt = pt[0][0]\n",
|
||
" check1 = (rowCnt >= 0 and rowCnt < radius) or (rowCnt >= imgRows - radius and rowCnt < imgRows)\n",
|
||
" check2 = (colCnt >= 0 and colCnt < radius) or (colCnt >= imgCols - radius and colCnt < imgCols)\n",
|
||
" if check1 or check2:\n",
|
||
" contourList.append(idx)\n",
|
||
" break\n",
|
||
"\n",
|
||
" for idx in contourList:\n",
|
||
" cv2.drawContours(imgBWcopy, contours, idx, (0, 0, 0), -1)\n",
|
||
"\n",
|
||
" return imgBWcopy\n",
|
||
"\n",
|
||
"# Определение функции bwareaopen\n",
|
||
"def bwareaopen(imgBW, areaPixels):\n",
|
||
" imgBWcopy = imgBW.copy()\n",
|
||
" contours, _ = cv2.findContours(imgBWcopy.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" for idx in range(len(contours)):\n",
|
||
" area = cv2.contourArea(contours[idx])\n",
|
||
" if 0 < area <= areaPixels:\n",
|
||
" cv2.drawContours(imgBWcopy, contours, idx, (0, 0, 0), -1)\n",
|
||
" return imgBWcopy\n",
|
||
"\n",
|
||
"# Гомоморфная фильтрация\n",
|
||
"def homomorphic_filter(image):\n",
|
||
" rows, cols = image.shape\n",
|
||
" \n",
|
||
" imgLog = np.log1p(np.array(image, dtype=\"float\") / 255)\n",
|
||
" \n",
|
||
" M, N = rows + 1, cols + 1\n",
|
||
" sigma = 10\n",
|
||
" X, Y = np.meshgrid(np.linspace(0, N - 1, N), np.linspace(0, M - 1, M))\n",
|
||
" centerX, centerY = np.ceil(N / 2), np.ceil(M / 2)\n",
|
||
" gaussianNumerator = (X - centerX) ** 2 + (Y - centerY) ** 2\n",
|
||
"\n",
|
||
" Hlow = np.exp(-gaussianNumerator / (2 * sigma ** 2))\n",
|
||
" Hhigh = 1 - Hlow\n",
|
||
" HlowShift = scipy.fftpack.ifftshift(Hlow)\n",
|
||
" HhighShift = scipy.fftpack.ifftshift(Hhigh)\n",
|
||
"\n",
|
||
" If = scipy.fftpack.fft2(imgLog, (M, N))\n",
|
||
" Ioutlow = np.real(scipy.fftpack.ifft2(If * HlowShift, (M, N)))\n",
|
||
" Iouthigh = np.real(scipy.fftpack.ifft2(If * HhighShift, (M, N)))\n",
|
||
" \n",
|
||
" gamma1, gamma2 = 0.3, 1.5\n",
|
||
" Iout = gamma1 * Ioutlow[:rows, :cols] + gamma2 * Iouthigh[:rows, :cols]\n",
|
||
" \n",
|
||
" Ihmf = np.expm1(Iout)\n",
|
||
" Ihmf = (Ihmf - np.min(Ihmf)) / (np.max(Ihmf) - np.min(Ihmf))\n",
|
||
" Ihmf2 = np.array(255 * Ihmf, dtype=\"uint8\")\n",
|
||
" cv2.imshow(\"\", Ihmf2)\n",
|
||
" cv2.waitKey(0)\n",
|
||
" cv2.destroyAllWindows()\n",
|
||
" Ithresh = Ihmf2 < 110\n",
|
||
" Ithresh = 255 * Ithresh.astype(\"uint8\")\n",
|
||
" \n",
|
||
"\n",
|
||
" Iopen = bwareaopen(Ihmf2, 11)\n",
|
||
" return Iopen\n",
|
||
"\n",
|
||
"# Список входных изображений\n",
|
||
"images = ['img/3.jpg']#, 'img/2.jpg', 'img/3.jpg']\n",
|
||
"\n",
|
||
"# Основной обработчик\n",
|
||
"for img_path in images:\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" if image is None:\n",
|
||
" print(f\"Не удалось загрузить изображение: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
" contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" plate = None\n",
|
||
" for contour in contours:\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" aspectRatio = float(w) / h\n",
|
||
" if aspectRatio >= 3 and w * h > 600:\n",
|
||
" plate = image[y:y + h, x:x + w]\n",
|
||
" break\n",
|
||
"\n",
|
||
" if plate is None:\n",
|
||
" print(f\"Номерной знак не найден на изображении: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
" processed_plate = homomorphic_filter(gray_plate)\n",
|
||
"\n",
|
||
" text = pytesseract.image_to_string(processed_plate, config=custom_config)\n",
|
||
" print(f\"Распознанный номер для {img_path}: {text.strip()}\")\n",
|
||
"\n",
|
||
" # Отображение результатов\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||
" axes[0].imshow(cv2.cvtColor(plate, cv2.COLOR_BGR2RGB))\n",
|
||
" axes[0].set_title('Вырезанный номерной знак')\n",
|
||
" axes[0].axis('off')\n",
|
||
"\n",
|
||
" axes[1].imshow(processed_plate, cmap='gray')\n",
|
||
" axes[1].set_title('Обработанный номерной знак')\n",
|
||
" axes[1].axis('off')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 380,
|
||
"id": "e0e546e4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Найдено контуров: 15\n",
|
||
"Распознанный номер для img/1.jpg: \n",
|
||
"Распознанный номер для img/1.jpg: 1829MK97\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Найдено контуров: 32\n",
|
||
"Распознанный номер для img/2.jpg: \n",
|
||
"Распознанный номер для img/2.jpg: A023y92\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Найдено контуров: 24\n",
|
||
"Распознанный номер для img/3.jpg: \n",
|
||
"Распознанный номер для img/3.jpg: K263097\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Список входных изображений\n",
|
||
"images = ['img/1.jpg', 'img/2.jpg', 'img/3.jpg']\n",
|
||
"\n",
|
||
"# Белый список символов\n",
|
||
"whitelist = 'ABCEHMOPTXyK0123456789'\n",
|
||
"custom_config = f'--oem 1 --psm 7 -c tessedit_char_whitelist={whitelist}'\n",
|
||
"\n",
|
||
"def expand_characters(image, kernel_size=(3, 3), iterations=1):\n",
|
||
" \"\"\"\n",
|
||
" Функция для наращивания (расширения) символов на изображении.\n",
|
||
" \n",
|
||
" :param image: Входное бинарное изображение (чёрно-белое).\n",
|
||
" :param kernel_size: Размер ядра для морфологической операции (ширина, высота).\n",
|
||
" :param iterations: Количество итераций расширения.\n",
|
||
" :return: Изображение с расширенными символами.\n",
|
||
" \"\"\"\n",
|
||
" # Создаём ядро\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)\n",
|
||
" \n",
|
||
" # Применяем морфологическое расширение\n",
|
||
" dilated_image = cv2.dilate(image, kernel, iterations=iterations)\n",
|
||
" \n",
|
||
" return dilated_image\n",
|
||
"\n",
|
||
"\n",
|
||
"def imclearborder1(imgBW, radius=1):\n",
|
||
" \"\"\"\n",
|
||
" Удаляет только те объекты, которые непосредственно касаются границ изображения.\n",
|
||
" \n",
|
||
" :param imgBW: Бинарное изображение (чёрно-белое, 0 и 255).\n",
|
||
" :param radius: Расстояние от границы, в пределах которого объект считается касающимся.\n",
|
||
" :return: Изображение с удалёнными объектами, касающимися границ.\n",
|
||
" \"\"\"\n",
|
||
" imgBWcopy = imgBW.copy()\n",
|
||
"\n",
|
||
" # Находим контуры\n",
|
||
" contours, _ = cv2.findContours(imgBWcopy.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" imgRows, imgCols = imgBW.shape\n",
|
||
"\n",
|
||
" # Список контуров, которые касаются границы\n",
|
||
" contourList = []\n",
|
||
"\n",
|
||
" for idx, cnt in enumerate(contours):\n",
|
||
" for pt in cnt:\n",
|
||
" rowCnt = pt[0][1] # y-координата\n",
|
||
" colCnt = pt[0][0] # x-координата\n",
|
||
"\n",
|
||
" # Проверка на касание границы\n",
|
||
" check1 = (rowCnt >= 0 and rowCnt < radius) or (rowCnt >= imgRows - radius and rowCnt < imgRows)\n",
|
||
" check2 = (colCnt >= 0 and colCnt < radius) or (colCnt >= imgCols - radius and colCnt < imgCols)\n",
|
||
" if check1 or check2:\n",
|
||
" contourList.append(idx)\n",
|
||
" break\n",
|
||
"\n",
|
||
" # Удаляем найденные контуры\n",
|
||
" for idx in contourList:\n",
|
||
" cv2.drawContours(imgBWcopy, contours, idx, (0, 0, 0), -1)\n",
|
||
"\n",
|
||
" return imgBWcopy\n",
|
||
"\n",
|
||
"\n",
|
||
"# Функция для создания эталонных шаблонов символов\n",
|
||
"def generate_templates(whitelist):\n",
|
||
" templates = {}\n",
|
||
" for char in whitelist:\n",
|
||
" img = np.zeros((50, 30), dtype=np.uint8) # Пустое изображение для символа\n",
|
||
" font = cv2.FONT_HERSHEY_SIMPLEX\n",
|
||
" cv2.putText(img, char, (5, 40), font, 1.2, 255, 2, cv2.LINE_AA)\n",
|
||
" templates[char] = img\n",
|
||
" return templates\n",
|
||
"\n",
|
||
"# Функция для фильтрации маленьких областей (bwareaopen)\n",
|
||
"def bwareaopen(imgBW, areaPixels):\n",
|
||
" imgBWcopy = imgBW.copy()\n",
|
||
" contours, _ = cv2.findContours(imgBWcopy, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" for contour in contours:\n",
|
||
" if cv2.contourArea(contour) < areaPixels:\n",
|
||
" cv2.drawContours(imgBWcopy, [contour], -1, 0, -1)\n",
|
||
" return imgBWcopy\n",
|
||
"\n",
|
||
"# Распознавание символов\n",
|
||
"def recognize_characters(image, templates):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" recognized_text = \"\"\n",
|
||
"\n",
|
||
" for contour in sorted(contours, key=lambda x: cv2.boundingRect(x)[0]): # Сортировка слева направо\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" if w > 10 and h > 20: # Фильтр по размеру символов\n",
|
||
" roi = image[y:y+h, x:x+w]\n",
|
||
" roi_resized = cv2.resize(roi, (30, 50), interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
" # Сравнение с шаблонами\n",
|
||
" best_match = None\n",
|
||
" max_corr = -1\n",
|
||
" for char, template in templates.items():\n",
|
||
" res = cv2.matchTemplate(roi_resized, template, cv2.TM_CCOEFF_NORMED)\n",
|
||
" _, corr, _, _ = cv2.minMaxLoc(res)\n",
|
||
" if corr > max_corr:\n",
|
||
" max_corr = corr\n",
|
||
" best_match = char\n",
|
||
"\n",
|
||
" if best_match is not None and max_corr > 0.5: # Порог корреляции\n",
|
||
" recognized_text += best_match\n",
|
||
"\n",
|
||
" return recognized_text\n",
|
||
"def imclearborder(imgBW, radius=1, area_threshold=50, aspect_ratio_threshold=0.1):\n",
|
||
" \"\"\"\n",
|
||
" Удаляет объекты, которые выходят за границу изображения,\n",
|
||
" сохраняя важные объекты, даже если они касаются границ.\n",
|
||
"\n",
|
||
" :param imgBW: Бинарное изображение (чёрно-белое, 0 и 255).\n",
|
||
" :param radius: Радиус касания границы.\n",
|
||
" :param area_threshold: Минимальная площадь объекта для его сохранения.\n",
|
||
" :param aspect_ratio_threshold: Минимальное отношение ширины к высоте.\n",
|
||
" :return: Изображение с удалёнными объектами.\n",
|
||
" \"\"\"\n",
|
||
" imgBWcopy = imgBW.copy()\n",
|
||
"\n",
|
||
" # ШАГ 1: Морфологическая эрозия для разъединения объектов\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))\n",
|
||
" eroded = cv2.erode(imgBWcopy, kernel, iterations=1)\n",
|
||
"\n",
|
||
" # ШАГ 2: Находим контуры\n",
|
||
" contours, _ = cv2.findContours(eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" imgRows, imgCols = imgBW.shape\n",
|
||
"\n",
|
||
" print(\"Найдено контуров:\", len(contours))\n",
|
||
" for cnt_idx, cnt in enumerate(contours):\n",
|
||
" # Вычисляем ограничивающий прямоугольник\n",
|
||
" x, y, w, h = cv2.boundingRect(cnt)\n",
|
||
" area = cv2.contourArea(cnt)\n",
|
||
"\n",
|
||
" # Проверяем вытянутость объекта\n",
|
||
" aspect_ratio = w / h if h != 0 else 0\n",
|
||
"\n",
|
||
" # Определяем точки, касающиеся границы\n",
|
||
" touches_border = (\n",
|
||
" x <= radius or y <= radius or \n",
|
||
" x + w >= imgCols - radius or \n",
|
||
" y + h >= imgRows - radius\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Условие для удаления: объект выходит за границу, мал по площади и не является вытянутым\n",
|
||
" if touches_border :\n",
|
||
" cv2.drawContours(imgBWcopy, [cnt], -1, (0, 0, 0), -1)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
" return imgBWcopy\n",
|
||
"# Генерация шаблонов символов\n",
|
||
"templates = generate_templates(whitelist)\n",
|
||
"\n",
|
||
"# Обработка изображений\n",
|
||
"for img_path in images:\n",
|
||
" image = cv2.imread(img_path)\n",
|
||
" if image is None:\n",
|
||
" print(f\"Не удалось загрузить изображение: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Преобразование в градации серого и бинаризация\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" ret, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
"\n",
|
||
" # Поиск контуров\n",
|
||
" contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" plate = None\n",
|
||
"\n",
|
||
" for contour in contours:\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" a = w * h\n",
|
||
" aspectRatio = float(w) / h\n",
|
||
" if aspectRatio >= 3 and a > 600:\n",
|
||
" plate = image[y:y + h, x:x + w]\n",
|
||
" break\n",
|
||
"\n",
|
||
" if plate is None:\n",
|
||
" print(f\"Номерной знак не найден на изображении: {img_path}\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Преобразование в градации серого для номерного знака\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
" # Увеличение размера для улучшения качества\n",
|
||
" resized_plate = cv2.resize(gray_plate, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
" # Фильтрация и бинаризация\n",
|
||
" gray = cv2.GaussianBlur(resized_plate, (3, 3), 0)\n",
|
||
" thresh = cv2.adaptiveThreshold(gray, 255,\n",
|
||
" cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n",
|
||
" cv2.THRESH_BINARY_INV, 41, 10)\n",
|
||
"\n",
|
||
" # Удаление мелких областей\n",
|
||
" expanded_image = expand_characters(imclearborder1(imclearborder(thresh, 2), 5), kernel_size=(2, 2), iterations=1)\n",
|
||
" processed_plate = bwareaopen(expanded_image, 30)\n",
|
||
"\n",
|
||
" # Распознавание текста с использованием шаблонов\n",
|
||
" recognized_text = recognize_characters(processed_plate, templates)\n",
|
||
"\n",
|
||
" print(f\"Распознанный номер для {img_path}: {recognized_text}\")\n",
|
||
" text = pytesseract.image_to_string(processed_plate, config=custom_config)\n",
|
||
" print(f\"Распознанный номер для {img_path}: {text.strip()}\")\n",
|
||
" # Отображение исходного номерного знака и обработанной версии\n",
|
||
" fig, axes = plt.subplots(1, 3, figsize=(10, 5))\n",
|
||
" axes[0].imshow(cv2.cvtColor(plate, cv2.COLOR_BGR2RGB))\n",
|
||
" axes[0].set_title('Вырезанный номерной знак')\n",
|
||
" axes[0].axis('off')\n",
|
||
"\n",
|
||
" axes[1].imshow(thresh, cmap='gray')\n",
|
||
" axes[1].set_title('Обработанный1 номерной знак')\n",
|
||
" axes[1].axis('off')\n",
|
||
"\n",
|
||
" axes[2].imshow(processed_plate, cmap='gray')\n",
|
||
" axes[2].set_title('Обработанный2 номерной знак')\n",
|
||
" axes[2].axis('off')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 310,
|
||
"id": "7bf47d00",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Найдено контуров для распознавания: 9\n",
|
||
"Не удалось распознать символ: x=17, y=14, w=22, h=31, корреляция: 0.27\n",
|
||
"Не удалось распознать символ: x=47, y=9, w=23, h=36, корреляция: 0.15\n",
|
||
"Не удалось распознать символ: x=72, y=7, w=25, h=38, корреляция: 0.24\n",
|
||
"Не удалось распознать символ: x=98, y=8, w=24, h=40, корреляция: 0.30\n",
|
||
"Не удалось распознать символ: x=127, y=15, w=24, h=33, корреляция: 0.14\n",
|
||
"Не удалось распознать символ: x=151, y=17, w=27, h=30, корреляция: 0.20\n",
|
||
"Не удалось распознать символ: x=190, y=11, w=18, h=24, корреляция: 0.30\n",
|
||
"Не удалось распознать символ: x=213, y=9, w=14, h=27, корреляция: 0.30\n",
|
||
"Распознанный номер: \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Белый список символов (набор символов, которые могут быть в номере)\n",
|
||
"whitelist = 'ABCEHKMOPTXy0123456789'\n",
|
||
"\n",
|
||
"# Генерация шаблонов для каждого символа\n",
|
||
"def generate_templates(whitelist):\n",
|
||
" templates = {}\n",
|
||
" for char in whitelist:\n",
|
||
" img = np.zeros((50, 30), dtype=np.uint8) # Пустое изображение\n",
|
||
" font = cv2.FONT_HERSHEY_SIMPLEX\n",
|
||
" cv2.putText(img, char, (5, 40), font, 1.2, 255, 2, cv2.LINE_AA) # Рисуем символ\n",
|
||
" templates[char] = img\n",
|
||
" return templates\n",
|
||
"\n",
|
||
"def merge_close_contours(image, distance_threshold=10):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" merged_contours = []\n",
|
||
"\n",
|
||
" used = set() # Множество индексов использованных контуров\n",
|
||
"\n",
|
||
" for i, cnt1 in enumerate(contours):\n",
|
||
" if i in used:\n",
|
||
" continue\n",
|
||
" new_contour = cnt1.copy()\n",
|
||
" for j, cnt2 in enumerate(contours):\n",
|
||
" if i != j and j not in used:\n",
|
||
" # Вычисляем минимальное расстояние от cnt1 к cnt2\n",
|
||
" min_distance = float(\"inf\")\n",
|
||
" for pt1 in cnt1:\n",
|
||
" for pt2 in cnt2:\n",
|
||
" dist = np.linalg.norm(np.array(pt1[0]) - np.array(pt2[0]))\n",
|
||
" if dist < min_distance:\n",
|
||
" min_distance = dist\n",
|
||
" if min_distance < distance_threshold: # Если расстояние меньше порога\n",
|
||
" new_contour = np.vstack((new_contour, cnt2)) # Объединяем контуры\n",
|
||
" used.add(j)\n",
|
||
" used.add(i)\n",
|
||
" merged_contours.append(new_contour)\n",
|
||
"\n",
|
||
" # Рисуем новые контуры на чистом изображении\n",
|
||
" merged_image = np.zeros_like(image)\n",
|
||
" cv2.drawContours(merged_image, merged_contours, -1, 255, -1)\n",
|
||
" return merged_image\n",
|
||
"\n",
|
||
"\n",
|
||
"# Распознавание символов через сопоставление шаблонов\n",
|
||
"def recognize_characters(image, templates):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" recognized_text = \"\"\n",
|
||
"\n",
|
||
" # Сортируем контуры слева направо\n",
|
||
" sorted_contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[0])\n",
|
||
"\n",
|
||
" print(f\"Найдено контуров для распознавания: {len(sorted_contours)}\")\n",
|
||
" for contour in sorted_contours:\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" if w > 10 and h > 20: # Фильтруем слишком маленькие объекты\n",
|
||
" roi = image[y:y+h, x:x+w]\n",
|
||
" roi_resized = cv2.resize(roi, (30, 50), interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
" # Сравниваем ROI с каждым шаблоном\n",
|
||
" best_match = None\n",
|
||
" max_corr = -1\n",
|
||
" for char, template in templates.items():\n",
|
||
" res = cv2.matchTemplate(roi_resized, template, cv2.TM_CCOEFF_NORMED)\n",
|
||
" _, corr, _, _ = cv2.minMaxLoc(res)\n",
|
||
" if corr > max_corr:\n",
|
||
" max_corr = corr\n",
|
||
" best_match = char\n",
|
||
"\n",
|
||
" if best_match is not None and max_corr > 0.5: # Порог корреляции\n",
|
||
" print(f\"Распознан символ: {best_match}, корреляция: {max_corr:.2f}\")\n",
|
||
" recognized_text += best_match\n",
|
||
" else:\n",
|
||
" print(f\"Не удалось распознать символ: x={x}, y={y}, w={w}, h={h}, корреляция: {max_corr:.2f}\")\n",
|
||
"\n",
|
||
" return recognized_text\n",
|
||
"\n",
|
||
"def selective_dilation(image, distance_threshold=10, iterations=1):\n",
|
||
" \"\"\"\n",
|
||
" Выполняет \"наращивание\" (дилатацию) символов, чтобы соединить близкие контуры.\n",
|
||
" \n",
|
||
" :param image: Бинарное изображение.\n",
|
||
" :param distance_threshold: Максимальное расстояние между контурами для их соединения.\n",
|
||
" :param iterations: Количество итераций для расширения.\n",
|
||
" :return: Обработанное изображение.\n",
|
||
" \"\"\"\n",
|
||
" # Копируем изображение\n",
|
||
" image_copy = image.copy()\n",
|
||
"\n",
|
||
" # Находим контуры\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Создаём маску для временного соединения контуров\n",
|
||
" temp_mask = np.zeros_like(image)\n",
|
||
"\n",
|
||
" for i, cnt1 in enumerate(contours):\n",
|
||
" for j, cnt2 in enumerate(contours):\n",
|
||
" if i != j: # Сравниваем контуры только с другими\n",
|
||
" # Находим минимальное расстояние между точками двух контуров\n",
|
||
" min_distance = float(\"inf\")\n",
|
||
" for pt1 in cnt1:\n",
|
||
" for pt2 in cnt2:\n",
|
||
" dist = np.linalg.norm(np.array(pt1[0]) - np.array(pt2[0]))\n",
|
||
" if dist < min_distance:\n",
|
||
" min_distance = dist\n",
|
||
"\n",
|
||
" # Если расстояние меньше порога, соединяем контуры\n",
|
||
" if min_distance < distance_threshold:\n",
|
||
" # Рисуем оба контура на временной маске\n",
|
||
" cv2.drawContours(temp_mask, [cnt1], -1, 255, thickness=-1)\n",
|
||
" cv2.drawContours(temp_mask, [cnt2], -1, 255, thickness=-1)\n",
|
||
"\n",
|
||
" # Выполняем дилатацию только на временной маске\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n",
|
||
" dilated_mask = cv2.dilate(temp_mask, kernel, iterations=iterations)\n",
|
||
"\n",
|
||
" # Объединяем исходное изображение с обработанной маской\n",
|
||
" result = cv2.bitwise_or(image_copy, dilated_mask)\n",
|
||
"\n",
|
||
" return result\n",
|
||
"\n",
|
||
"templates = generate_templates(whitelist)\n",
|
||
"processed_image1 = selective_dilation(processed_plate, distance_threshold=7, iterations=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"# Распознавание текста\n",
|
||
"recognized_text = recognize_characters(processed_image1, templates)\n",
|
||
"\n",
|
||
"print(f\"Распознанный номер: {recognized_text}\")\n",
|
||
"\n",
|
||
"# Визуализация результата\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.imshow(processed_image1, cmap='gray')\n",
|
||
"plt.title('После объединения контуров')\n",
|
||
"plt.axis('off')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 346,
|
||
"id": "bd042f82",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Найдено контуров для распознавания: 9\n",
|
||
"\n",
|
||
"Символ 1 (x=17, y=14, w=22, h=31):\n",
|
||
"\n",
|
||
"Распознанный номер: None\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Белый список символов (набор символов, которые могут быть в номере)\n",
|
||
"whitelist = 'ABCEHKMOPTXy0123456789'\n",
|
||
"\n",
|
||
"# Генерация шаблонов для каждого символа\n",
|
||
"def generate_templates(whitelist):\n",
|
||
" templates = {}\n",
|
||
" for char in whitelist:\n",
|
||
" img = np.zeros((50, 30), dtype=np.uint8) # Пустое изображение для шаблона\n",
|
||
" font = cv2.FONT_HERSHEY_SIMPLEX\n",
|
||
" cv2.putText(img, char, (5, 40), font, 1.2, 255, 2, cv2.LINE_AA) # Рисуем символ\n",
|
||
" templates[char] = img\n",
|
||
" return templates\n",
|
||
"\n",
|
||
"# Нормализация ROI (приведение к фиксированным размерам)\n",
|
||
"def normalize_roi(roi):\n",
|
||
" # Приведение ROI к фиксированным размерам 50x30\n",
|
||
" resized = cv2.resize(roi, (30, 50), interpolation=cv2.INTER_CUBIC)\n",
|
||
"\n",
|
||
" # Утоньшение символов\n",
|
||
" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n",
|
||
" eroded = cv2.erode(resized, kernel, iterations=1)\n",
|
||
"\n",
|
||
" return eroded\n",
|
||
"\n",
|
||
"# Сравнение символов через XOR\n",
|
||
"def compare_symbols(roi, template):\n",
|
||
" # Используем XOR для подсчёта несовпадающих пикселей\n",
|
||
" diff = cv2.bitwise_xor(roi, template)\n",
|
||
" total_pixels = roi.size\n",
|
||
" mismatched_pixels = cv2.countNonZero(diff)\n",
|
||
" similarity = 1 - (mismatched_pixels / total_pixels) # Схожесть в диапазоне [0, 1]\n",
|
||
" return similarity * 100 # Переводим в проценты\n",
|
||
"\n",
|
||
"# Распознавание символов с гибким сравнением\n",
|
||
"def debug_character_recognition(image, templates):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" recognized_text = \"\"\n",
|
||
"\n",
|
||
" # Сортируем контуры слева направо\n",
|
||
" sorted_contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[0])\n",
|
||
"\n",
|
||
" print(f\"Найдено контуров для распознавания: {len(sorted_contours)}\")\n",
|
||
" for idx, contour in enumerate(sorted_contours):\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" if w > 10 and h > 20: # Фильтруем слишком маленькие объекты\n",
|
||
" roi = image[y:y+h, x:x+w]\n",
|
||
" normalized_roi = normalize_roi(roi)\n",
|
||
"\n",
|
||
" print(f\"\\nСимвол {idx + 1} (x={x}, y={y}, w={w}, h={h}):\")\n",
|
||
" best_match = None\n",
|
||
" max_similarity = -1\n",
|
||
" debug_info = []\n",
|
||
" return\n",
|
||
" # Сравниваем ROI с каждым шаблоном\n",
|
||
" for char, template in templates.items():\n",
|
||
" similarity = compare_symbols(normalized_roi, template)\n",
|
||
" debug_info.append((char, similarity))\n",
|
||
" if similarity > max_similarity:\n",
|
||
" max_similarity = similarity\n",
|
||
" best_match = char\n",
|
||
"\n",
|
||
" # Визуализация текущего ROI и шаблона\n",
|
||
" plt.figure(figsize=(8, 4))\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.imshow(normalized_roi, cmap='gray')\n",
|
||
" plt.title(f'Символ {idx + 1} (ROI, утоньшено)')\n",
|
||
" plt.axis('off')\n",
|
||
"\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.imshow(template, cmap='gray')\n",
|
||
" plt.title(f'Шаблон: {char}, Схожесть: {similarity:.2f}%')\n",
|
||
" plt.axis('off')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Выводим проценты схожести с каждым символом из шаблона\n",
|
||
" for char, similarity in debug_info:\n",
|
||
" #print(f\" Шаблон: {char}, Схожесть: {similarity:.2f}%\")\n",
|
||
" pass\n",
|
||
" if best_match is not None and max_similarity > 50: # Порог схожести\n",
|
||
" #print(f\" -> Распознан символ: {best_match}, Максимальная схожесть: {max_similarity:.2f}%\")\n",
|
||
" recognized_text += best_match\n",
|
||
" else:\n",
|
||
" print(f\" -> Не удалось распознать символ.\")\n",
|
||
"\n",
|
||
" return recognized_text\n",
|
||
"\n",
|
||
"\n",
|
||
"# Генерация шаблонов\n",
|
||
"templates = generate_templates(whitelist)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"# Распознавание текста с улучшенным дебагом\n",
|
||
"recognized_text = debug_character_recognition(processed_image1, templates)\n",
|
||
"\n",
|
||
"print(f\"\\nРаспознанный номер: {recognized_text}\")\n",
|
||
"\n",
|
||
"# Визуализация результата\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.imshow(processed_image1, cmap='gray')\n",
|
||
"plt.title('Обработанное изображение')\n",
|
||
"plt.axis('off')\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 383,
|
||
"id": "986222d3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import easyocr\n",
|
||
"import cv2\n",
|
||
"\n",
|
||
"def ocr_image_from_variable(img):\n",
|
||
" \"\"\"\n",
|
||
" Выполняет OCR для изображения из переменной.\n",
|
||
" \n",
|
||
" :param img: Изображение в формате numpy (например, после загрузки с помощью OpenCV).\n",
|
||
" :return: Распознанный текст.\n",
|
||
" \"\"\"\n",
|
||
" reader = easyocr.Reader(['en'], gpu=True) # Инициализация easyOCR\n",
|
||
" gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Конвертация в оттенки серого\n",
|
||
" \n",
|
||
" # Запуск OCR\n",
|
||
" results = reader.readtext(gray)\n",
|
||
" \n",
|
||
" text = \"\"\n",
|
||
" for res in results:\n",
|
||
" if len(results) == 1:\n",
|
||
" text = res[1]\n",
|
||
" if len(results) > 1 and len(res[1]) > 6 and res[2] > 0.2: # Фильтрация текста\n",
|
||
" text = res[1]\n",
|
||
" \n",
|
||
" return text"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 384,
|
||
"id": "c568f0d0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "error",
|
||
"evalue": "OpenCV(4.10.0) d:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function '__cdecl cv::impl::`anonymous-namespace'::CvtHelper<struct cv::impl::`anonymous namespace'::Set<3,4,-1>,struct cv::impl::A0x46dff480::Set<1,-1,-1>,struct cv::impl::A0x46dff480::Set<0,2,5>,4>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)'\n> Invalid number of channels in input image:\n> 'VScn::contains(scn)'\n> where\n> 'scn' is 1\n",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[1;32mIn[384], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m recognized_text \u001b[38;5;241m=\u001b[39m \u001b[43mocr_image_from_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresized_plate\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"Cell \u001b[1;32mIn[383], line 12\u001b[0m, in \u001b[0;36mocr_image_from_variable\u001b[1;34m(img)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;124;03mВыполняет OCR для изображения из переменной.\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;124;03m\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;124;03m:param img: Изображение в формате numpy (например, после загрузки с помощью OpenCV).\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;124;03m:return: Распознанный текст.\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 11\u001b[0m reader \u001b[38;5;241m=\u001b[39m easyocr\u001b[38;5;241m.\u001b[39mReader([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124men\u001b[39m\u001b[38;5;124m'\u001b[39m], gpu\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# Инициализация easyOCR\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m gray \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcvtColor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOLOR_BGR2GRAY\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Конвертация в оттенки серого\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# Запуск OCR\u001b[39;00m\n\u001b[0;32m 15\u001b[0m results \u001b[38;5;241m=\u001b[39m reader\u001b[38;5;241m.\u001b[39mreadtext(gray)\n",
|
||
"\u001b[1;31merror\u001b[0m: OpenCV(4.10.0) d:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function '__cdecl cv::impl::`anonymous-namespace'::CvtHelper<struct cv::impl::`anonymous namespace'::Set<3,4,-1>,struct cv::impl::A0x46dff480::Set<1,-1,-1>,struct cv::impl::A0x46dff480::Set<0,2,5>,4>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)'\n> Invalid number of channels in input image:\n> 'VScn::contains(scn)'\n> where\n> 'scn' is 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"recognized_text = ocr_image_from_variable(resized_plate)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 350,
|
||
"id": "0225f69f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.nn as nn\n",
|
||
"import torch.nn.functional as F\n",
|
||
"\n",
|
||
"# Белый список символов (набор символов, которые могут быть в номере)\n",
|
||
"whitelist = 'ABCEHKMOPTXy0123456789'\n",
|
||
"\n",
|
||
"# Архитектура модели CNN\n",
|
||
"def compute_output_size(input_size, kernel_size, stride, padding):\n",
|
||
" return (input_size - kernel_size + 2 * padding) // stride + 1\n",
|
||
"\n",
|
||
"class CNN(nn.Module):\n",
|
||
" def __init__(self, num_classes):\n",
|
||
" super(CNN, self).__init__()\n",
|
||
" # Сверточные слои\n",
|
||
" self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n",
|
||
" self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n",
|
||
" self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
|
||
" \n",
|
||
" # Вычисляем размеры после сверточных слоев\n",
|
||
" height = compute_output_size(28, kernel_size=3, stride=1, padding=1) # conv1\n",
|
||
" height = compute_output_size(height, kernel_size=3, stride=1, padding=1) # conv2\n",
|
||
" height = compute_output_size(height, kernel_size=2, stride=2, padding=0) # pool\n",
|
||
" width = height # Для квадратного входа\n",
|
||
"\n",
|
||
" # Полносвязные слои\n",
|
||
" self.fc1 = nn.Linear(64 * height * width, 128)\n",
|
||
" self.fc2 = nn.Linear(128, num_classes)\n",
|
||
" self.dropout = nn.Dropout(0.5)\n",
|
||
"\n",
|
||
" def forward(self, x):\n",
|
||
" x = F.relu(self.conv1(x))\n",
|
||
" x = self.pool(F.relu(self.conv2(x)))\n",
|
||
" x = x.view(x.size(0), -1)\n",
|
||
" x = F.relu(self.fc1(x))\n",
|
||
" x = self.dropout(x)\n",
|
||
" x = self.fc2(x)\n",
|
||
" return x\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 366,
|
||
"id": "29fdb225",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\leonk\\AppData\\Local\\Temp\\ipykernel_22288\\1427365130.py:9: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
|
||
" model.load_state_dict(torch.load(\"letter_recognition_model1.pth\", map_location=device))\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"CNN(\n",
|
||
" (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (fc1): Linear(in_features=12544, out_features=128, bias=True)\n",
|
||
" (fc2): Linear(in_features=128, out_features=22, bias=True)\n",
|
||
" (dropout): Dropout(p=0.5, inplace=False)\n",
|
||
")"
|
||
]
|
||
},
|
||
"execution_count": 366,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Устройство (CPU или GPU)\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"\n",
|
||
"# Инициализация модели\n",
|
||
"num_classes = len(whitelist)\n",
|
||
"model = CNN(num_classes).to(device)\n",
|
||
"\n",
|
||
"# Загрузка сохранённых весов\n",
|
||
"model.load_state_dict(torch.load(\"letter_recognition_model1.pth\", map_location=device))\n",
|
||
"model.eval()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 375,
|
||
"id": "21d9c521",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"from torchvision import transforms\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Преобразование для модели\n",
|
||
"transform = transforms.Compose([\n",
|
||
" transforms.ToPILImage(),\n",
|
||
" transforms.Resize((28, 28)),\n",
|
||
" transforms.ToTensor(),\n",
|
||
" transforms.Normalize((0.5,), (0.5,)) # Приведение в диапазон [-1, 1]\n",
|
||
"])\n",
|
||
"\n",
|
||
"# Функция для распознавания символов\n",
|
||
"def recognize_characters(image, model, whitelist):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" recognized_text = \"\"\n",
|
||
"\n",
|
||
" # Сортируем контуры слева направо\n",
|
||
" sorted_contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[0])\n",
|
||
"\n",
|
||
" for idx, contour in enumerate(sorted_contours):\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" if w > 10 and h > 20: # Фильтруем слишком маленькие объекты\n",
|
||
" roi = image[y:y+h, x:x+w]\n",
|
||
"\n",
|
||
" # Приведение ROI к формату модели (28x28)\n",
|
||
" roi_resized = transform(roi).unsqueeze(0).to(device) # Добавляем размер батча\n",
|
||
"\n",
|
||
" # Предсказание\n",
|
||
" with torch.no_grad():\n",
|
||
" output = model(roi_resized)\n",
|
||
" probabilities = torch.softmax(output, dim=1).cpu().numpy().flatten()\n",
|
||
"\n",
|
||
" # Топ-3 предсказания\n",
|
||
" top3_indices = probabilities.argsort()[-3:][::-1]\n",
|
||
" top3_characters = [whitelist[idx] for idx in top3_indices]\n",
|
||
" \n",
|
||
" top3_probs = [probabilities[idx] * 100 for idx in top3_indices]\n",
|
||
"\n",
|
||
" # Лучшее предсказание\n",
|
||
" best_character = top3_characters[0]\n",
|
||
" #recognized_text += best_character\n",
|
||
"\n",
|
||
" # Визуализация\n",
|
||
" fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||
" ax[0].imshow(roi, cmap='gray')\n",
|
||
" ax[0].set_title(f\"Contour {idx + 1}: Best Match '{best_character}'\")\n",
|
||
" ax[0].axis('off')\n",
|
||
"\n",
|
||
" bar_positions = range(len(top3_characters))\n",
|
||
" ax[1].barh(bar_positions, top3_probs, align='center')\n",
|
||
" ax[1].set_yticks(bar_positions)\n",
|
||
" ax[1].set_yticklabels(top3_characters)\n",
|
||
" ax[1].invert_yaxis() # Ставим сверху лучший результат\n",
|
||
" ax[1].set_xlabel('Probability (%)')\n",
|
||
" ax[1].set_title(\"Top-3 Predictions\")\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" return recognized_text\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 376,
|
||
"id": "230c39ed",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA6cAAAHqCAYAAAD4RTjrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA8QElEQVR4nO3dd5hU5fk/4GeWZXcRWEBpolRRQVE0YEEUFIgNsQVNiAVUxIK9xRYFNYom0cSG0SiaKFHR2EiMImLBii2xIJavIEEQC01EUPb8/vDHxHEpCwKvwH1f11ww57znnGfend0zn3lPyWVZlgUAAAAkVJS6AAAAABBOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE06Bxbr11lsjl8vFSy+9lLoUAFhpJk6cGLlcLm699db8tEGDBkUul1tp23jiiScil8vFE088sdLWCesC4XQ1ef/99+OYY46JVq1aRVlZWZSXl0fnzp3jj3/8Y8ybN2+Vbfett96KQYMGxcSJE1fZNn6oOXPmxFlnnRUtW7aM0tLS2GijjaJ3797x5ZdfrtD6Fu1gFj2Kiopiww03jH322Seef/75lVz9/3z00UcxaNCgeO2116rUflH4y+VyMXbs2ErzsyyLpk2bRi6Xi3322WeFarr00kvj/vvvX6Flf6hBgwZFixYtljh/5syZ0bBhw8jlcnHPPfcUzFvUNwB867v7taU9VkcYeuqpp2LfffeNpk2bRllZWTRu3Dj23HPPeOaZZ6q0fL9+/QpqLi8vj/bt28fvf//7mD9//iqufuW6/vrrC0Iu8MMUpy5gXfCPf/wjDjrooCgtLY3DDz882rVrFwsWLIixY8fGmWeeGW+++WbceOONq2Tbb731VgwePDh23XXXpQaFVGbNmhVdu3aN//73vzFgwIBo3bp1fPLJJ/H000/H/PnzY7311lvhdQ8dOjRq1aoVFRUVMXny5LjpppuiS5cu8eKLL8Y222yz8l7E//fRRx/F4MGDo0WLFsu1/rKyshg+fHjsvPPOBdOffPLJ+O9//xulpaUrXNOll14avXv3jv3333+F17GqXHDBBSv8BQTAuuavf/1rwfO//OUvMWrUqErT27Ztu8preeedd6KoqCiOPfbYaNy4ccyYMSNuv/326NKlS/zjH/+IPffcc5nrKC0tjT//+c8R8e2Xlffee2+cccYZMW7cuLjzzjtX9Uuo5Pzzz4+zzz57uZe7/vrro379+tGvX7+C6V26dIl58+ZFSUnJSqoQ1g3C6Sr2wQcfxC9+8Yto3rx5PP7447Hhhhvm5w0cODDee++9+Mc//pGwwlXrq6++ipKSkigqWvwg/TnnnBOTJk2KV155JVq2bJmf/qtf/eoHb7t3795Rv379/PP9998/2rVrFyNGjFgl4XRF7b333jFixIi4+uqro7j4f7+Sw4cPjw4dOsSnn36asLpV44033oihQ4fGBRdcEBdccEHqcgB+9A499NCC588//3yMGjWq0vTVoX///tG/f/+Caccff3y0atUq/vCHP1QpnBYXFxfUfvzxx8cOO+wQd911V1x55ZXRpEmTSstkWRZfffVV1KhR44e/iMXU89198A9VVFQUZWVlK219sK5wWO8qdsUVV8QXX3wRN998c0EwXaR169Zx8skn559/8803cfHFF8cmm2wSpaWl0aJFizj33HMrHebSokWL2GeffWLs2LGx/fbbR1lZWbRq1Sr+8pe/5NvceuutcdBBB0VExG677bbYQ36uv/762HLLLaO0tDSaNGkSAwcOjJkzZ1ba1ve/EYyI2HXXXWPXXXfNP190fsWdd94Z559/fmy00Uax3nrrxezZsxfbNzNnzoxhw4bFgAEDomXLlrFgwYIlHs7z9ddfx9tvvx1Tp05d7PyqaNy4cUREpZ3P/Pnz48ILL4zWrVtHaWlpNG3aNM4666xKtYwaNSp23nnnqFu3btSqVSs233zzOPfcc/OvfbvttouIiCOOOCLf11U51KdPnz7x2WefxahRo/LTFixYEPfcc0/88pe/XOwyv/vd72KnnXaKDTbYIGrUqBEdOnSodGhsLpeLuXPnxm233Zav57s/xylTpsRRRx0VTZo0idLS0mjZsmUcd9xxsWDBgkr9c9ppp0WDBg2iZs2accABB8Qnn3yyzNe1NCeffHIccMABscsuu/yg9QDwP3Pnzo3TTz89mjZtGqWlpbH55pvH7373u8iyrKBdLpeLE044Ie64447YfPPNo6ysLDp06BBPPfXUCm97vfXWiwYNGlT6DFFVRUVF+c8Ui05FWvRZ55FHHomOHTtGjRo14k9/+lNEfPsZ4pRTTsm/1tatW8fll18eFRUVBeudOXNm9OvXL+rUqRN169aNvn37LrbGJZ1zevvtt8f2228f6623XtSrVy+6dOkSjz76aL6+N998M5588sn8fnbRa1jSOacjRoyIDh06RI0aNaJ+/fpx6KGHxpQpUwra9OvXL2rVqhVTpkyJ/fffP2rVqhUNGjSIM844IxYuXFjQ9s4774wOHTpE7dq1o7y8PLbaaqv44x//WJUuhx8lI6er2EMPPRStWrWKnXbaqUrt+/fvH7fddlv07t07Tj/99HjhhRfisssui/Hjx8d9991X0Pa9996L3r17x1FHHRV9+/aNW265Jfr16xcdOnSILbfcMrp06RInnXRSXH311XHuuefmD/VZ9O+gQYNi8ODB0aNHjzjuuONiwoQJMXTo0Bg3blw888wzUb169RV6zRdffHGUlJTEGWecEfPnz1/iIS1jx46Nr776Klq3bh29e/eO+++/PyoqKqJTp05x3XXXFYxuTpkyJdq2bRt9+/at8rkdn3/+eUREVFRUxJQpU+Liiy+OsrKyOPjgg/NtKioqYt99942xY8fGgAEDom3btvH666/HVVddFe+8807+fM0333wz9tlnn9h6663joosuitLS0njvvffy59e0bds2LrroorjgggtiwIAB+dBVlZ97ixYtolOnTvG3v/0t9tprr4iIePjhh2PWrFnxi1/8Iq6++upKy/zxj3+MfffdNw455JBYsGBB3HnnnXHQQQfFyJEjo2fPnhHx7SFg/fv3j+233z4GDBgQERGbbLJJRHx7CPL2228fM2fOjAEDBkSbNm1iypQpcc8998SXX35Z8DM78cQTo169enHhhRfGxIkT4w9/+EOccMIJcdddd1Xp5/B9I0aMiGeffTbGjx//oz4XGmBNkmVZ7LvvvjFmzJg46qijYptttolHHnkkzjzzzJgyZUpcddVVBe2ffPLJuOuuu+Kkk06K0tLSuP7662PPPfeMF198Mdq1a1elbc6ePTsWLFgQn376afzlL3+JN954I/+l7Yp4//33IyJigw02yE+bMGFC9OnTJ4455pg4+uijY/PNN48vv/wyunbtGlOmTIljjjkmmjVrFs8++2ycc845MXXq1PjDH/6Q75P99tsvxo4dG8cee2y0bds27rvvvujbt2+V6hk8eHAMGjQodtppp7jooouipKQkXnjhhXj88cdj9913jz/84Q9x4oknRq1ateK8886LiIhGjRotcX233nprHHHEEbHddtvFZZddFh9//HH88Y9/jGeeeSZeffXVqFu3br7twoULY4899ogddtghfve738Vjjz0Wv//972OTTTaJ4447LiK+/dK8T58+0b1797j88ssjImL8+PHxzDPPFAx8wBolY5WZNWtWFhHZfvvtV6X2r732WhYRWf/+/Qumn3HGGVlEZI8//nh+WvPmzbOIyJ566qn8tOnTp2elpaXZ6aefnp82YsSILCKyMWPGFKxz+vTpWUlJSbb77rtnCxcuzE+/9tprs4jIbrnlloJt9e3bt1K9Xbt2zbp27Zp/PmbMmCwislatWmVffvnlMl/vlVdemUVEtsEGG2Tbb799dscdd2TXX3991qhRo6xevXrZRx99lG/7wQcfZBGx2Dq+78ILL8wiotKjbt262b/+9a+Ctn/961+zoqKi7Omnny6YfsMNN2QRkT3zzDNZlmXZVVddlUVE9sknnyxxu+PGjcsiIhs2bNgya8yyLBs2bFgWEdm4ceOya6+9Nqtdu3a+3w466KBst912y7Ls2/7v2bNnwbLf798FCxZk7dq1y7p161YwvWbNmovts8MPPzwrKirKxo0bV2leRUVFQX09evTIT8uyLDv11FOzatWqZTNnzqzS6/x+3c2aNcvOOeecLMv+954ZMWLEcq8LYF02cODA7Lsf4+6///4sIrJLLrmkoF3v3r2zXC6Xvffee/lpi/aLL730Un7apEmTsrKysuyAAw6ocg177LFHfl0lJSXZMccck82bN2+Zy/Xt2zerWbNm9sknn2SffPJJ9t5772WXXnpplsvlsq233jrfbtFnne/vuy+++OKsZs2a2TvvvFMw/eyzz86qVauWffjhhwV9csUVV+TbfPPNN9kuu+xSaX+96LPDIu+++25WVFSUHXDAAQWfk7IsK9gnbrnllgWfhRZZtH9b9PlrwYIFWcOGDbN27doV9NHIkSOziMguuOCCgv6JiOyiiy4qWOe2226bdejQIf/85JNPzsrLy7Nvvvmm0vZhTeWw3lVo0eGstWvXrlL7f/7znxERcdpppxVMP/300yMiKp2busUWWxQcFtmgQYPYfPPN4//+7/+Wua3HHnssFixYEKecckrB+aBHH310lJeX/6DzYPv27Vul80G++OKLiPj28KLRo0fHL3/5yzjuuOPi/vvvjxkzZsR1112Xb9uiRYvIsmy5roh37733xqhRo+LRRx+NYcOGxWabbRY/+9nP4tlnn823GTFiRLRt2zbatGkTn376af7RrVu3iIgYM2ZMRET+28wHHnig0iFDK8PBBx8c8+bNi5EjR8acOXNi5MiRSzykNyIK+nfGjBkxa9as2GWXXeKVV15Z5rYqKiri/vvvj169ekXHjh0rzf/+YU0DBgwomLbLLrvEwoULY9KkSVV5aQWGDBkSX3/99Q/6Zh2Ayv75z39GtWrV4qSTTiqYfvrpp0eWZfHwww8XTO/UqVN06NAh/7xZs2ax3377xSOPPFLp0NElGTJkSDz66KNx8803x4477hgLFiyIb775pkrLzp07Nxo0aBANGjSI1q1bx7nnnhudOnWqdJRYy5YtY4899iiYNmLEiNhll12iXr16BfvuHj16xMKFC/OHJ//zn/+M4uLi/EhjRES1atXixBNPXGZ9i47muuCCCypdN2NFrib/0ksvxfTp0+P4448vOBe1Z8+e0aZNm8V+7jr22GMLnu+yyy4Fn/Hq1q0bc+fOLTgtCNZ0DutdhcrLyyPi21ulVMWkSZOiqKgoWrduXTC9cePGUbdu3UphoFmzZpXWUa9evZgxY0aVthURsfnmmxdMLykpiVatWq1Q8Fjkuxc2WppFAatXr15Rq1at/PQdd9wxWrZsWRAiV0SXLl0KLojUu3fv2HTTTePEE0+Ml19+OSIi3n333Rg/fnw0aNBgseuYPn16RET8/Oc/jz//+c/Rv3//OPvss6N79+5x4IEHRu/evZd4safl0aBBg+jRo0cMHz48vvzyy1i4cGH07t17ie1HjhwZl1xySbz22msF58ZWZYf5ySefxOzZs6t82Nb332f16tWLiKjS++y7Jk6cGL/97W/juuuuK/h5A/DDTZo0KZo0aVLpC/FFp/J8f7++6aabVlrHZpttFl9++WV88sknsf766+dPj1mkQYMGUa1atfzz755+c+ihh8ZPfvKT6NevX6VrICxOWVlZPPTQQxER+esebLzxxpXaLe4zxbvvvhv/+c9/lrnvnjRpUmy44YaV9jnf/+yzOO+//34UFRXFFltsscy2VbGkz10REW3atKl0S7mysrJKr+/7n/GOP/74uPvuu2OvvfaKjTbaKHbfffc4+OCDq3RBKvixEk5XofLy8mjSpEm88cYby7VcVb+R++4O4ruy71344IdaUj0LFy5cbA1VvYreoivxLe78jIYNGy53+FmWWrVqxQ477BAPPPBAzJ07N2rWrBkVFRWx1VZbxZVXXrnYZZo2bRoR376mp556KsaMGRP/+Mc/4l//+lfcdddd0a1bt3j00UeX+LNYHr/85S/j6KOPjmnTpsVee+1VcO7Jdz399NOx7777RpcuXeL666+PDTfcMKpXrx7Dhg2L4cOH/+A6vm9lvc8uuOCC2GijjWLXXXfNn2s6bdq0iPg2ME+cODGaNWu2UsI+AD/Ms88+G7vttlvBtA8++GCJt6UrKSmJfffdN4YMGRLz5s1b5meBatWqRY8ePZZZx+LWU1FRET/96U/jrLPOWuwym2222TLX+2NXlc8VDRs2jNdeey0eeeSRePjhh+Phhx+OYcOGxeGHHx633XbbaqgSVj7hdBXbZ5994sYbb4znnnsuOnXqtNS2zZs3j4qKinj33XcL7lP28ccfx8yZM6N58+bLvf0lBctF65owYUK0atUqP33BggXxwQcfFOww6tWrt9gr202aNKlg2eW16HCi71+lLuLbC/a0adNmhde9JIsON/riiy+iZs2asckmm8S///3v6N69+zK/FCgqKoru3btH9+7d48orr4xLL700zjvvvBgzZkz06NFjhQ7z+a4DDjggjjnmmHj++eeXerGhe++9N8rKyuKRRx4puAfqsGHDKrVdXE0NGjSI8vLy5f7S5If68MMP47333lvse+b444+PiG9HY5cUygFYsubNm8djjz0Wc+bMKRg9ffvtt/Pzv+vdd9+ttI533nknf9Xd0tLSSoeLLrrq/ZLMmzcvsiyLOXPmrJLbvSyyySabxBdffLHMcNu8efMYPXp0fPHFFwWjpxMmTKjSNioqKuKtt95a6u3nqrrv/+7nrkWnDn23nhX5jBfx7ZcCvXr1il69ekVFRUUcf/zx8ac//Sl+/etfVzoSD9YEhihWsbPOOitq1qwZ/fv3j48//rjS/Pfffz9/ye+99947IiJ/lblFFo3qLboK6/KoWbNmRESlcNmjR48oKSmJq6++umAE7Oabb45Zs2YVbGuTTTaJ559/vuAWIyNHjozJkycvdz3ftfnmm0f79u3jgQceKLiX56OPPhqTJ0+On/70p/lpK+NWMp9//nk8++yz0bhx42jYsGFEfHuu55QpU+Kmm26q1H7evHkxd+7c/LLft2hnteiw2iX1dVXVqlUrhg4dGoMGDYpevXotsV21atUil8sVnBM0ceLE/JWFv6tmzZqV6ikqKor9998/HnrooXjppZcqLbOyR94XueSSS+K+++4reFx88cUR8e3vyX333ZfvQwCWz9577x0LFy6Ma6+9tmD6VVddFblcLn81+EWee+65gusUTJ48OR544IHYfffdo1q1alGvXr3o0aNHwWPRuZKLDpv9rpkzZ8a9994bTZs2ze9jV5WDDz44nnvuuXjkkUcWW8eiL6L33nvv+Oabb2Lo0KH5+QsXLoxrrrlmmdvYf//9o6ioKC666KJK15r47n5ycfvZxenYsWM0bNgwbrjhhoLTcR5++OEYP378Cn3G++yzzwqeFxUVxdZbbx0RscRb88GPnZHTVWyTTTaJ4cOHx89//vNo27ZtHH744dGuXbtYsGBBPPvsszFixIj8vSfbt28fffv2jRtvvDFmzpwZXbt2jRdffDFuu+222H///SsdXlMV22yzTVSrVi0uv/zymDVrVpSWlka3bt2iYcOGcc4558TgwYNjzz33jH333TcmTJgQ119/fWy33XYFN8bu379/3HPPPbHnnnvGwQcfHO+//37cfvvt+duS/BBXXXVV/PSnP42dd945jjnmmJg1a1ZceeWVsdlmmxVcwGBFbiVzzz33RK1atSLLsvjoo4/i5ptvjhkzZsQNN9yQ/6bzsMMOi7vvvjuOPfbYGDNmTHTu3DkWLlwYb7/9dtx99935e6tddNFF8dRTT0XPnj2jefPmMX369Lj++utj4403jp133jkivv1Z161bN2644YaoXbt21KxZM3bYYYcqn4MbEVW6vH3Pnj3jyiuvjD333DN++ctfxvTp0+O6666L1q1bx3/+85+Cth06dIjHHnssf0Pzli1bxg477BCXXnppPProo9G1a9f8LXSmTp0aI0aMiLFjx66S0ctF/fRdi7az3Xbbxf7777/StwmwrujVq1fstttucd5558XEiROjffv28eijj8YDDzwQp5xySqV9drt27WKPPfYouJVMxLe3T1mWvfbaKzbeeOPYYYcdomHDhvHhhx/GsGHD4qOPPlrh24wtjzPPPDMefPDB2GefffK30Js7d268/vrrcc8998TEiROjfv360atXr+jcuXOcffbZMXHixNhiiy3i73//e8yaNWuZ22jdunWcd955cfHFF8cuu+wSBx54YJSWlsa4ceOiSZMmcdlll0XEt/vZoUOHxiWXXBKtW7eOhg0bVhoZjYioXr16XH755XHEEUdE165do0+fPvlbybRo0SJOPfXU5e6H/v37x+effx7dunWLjTfeOCZNmhTXXHNNbLPNNgVH4MEaJdl1gtcx77zzTnb00UdnLVq0yEpKSrLatWtnnTt3zq655prsq6++yrf7+uuvs8GDB2ctW7bMqlevnjVt2jQ755xzCtpk2eJvL5JllW/vkmVZdtNNN2WtWrXKqlWrVum2Mtdee23Wpk2brHr16lmjRo2y4447LpsxY0al9f7+97/PNtpoo6y0tDTr3Llz9tJLLy3xVjLLe1uQUaNGZTvuuGNWVlaWrb/++tlhhx2WTZ06taDND72VTM2aNbNOnTpld999d6X2CxYsyC6//PJsyy23zEpLS7N69eplHTp0yAYPHpzNmjUry7IsGz16dLbffvtlTZo0yUpKSrImTZpkffr0qXQZ+wceeCDbYostsuLi4mXeVua7t5JZmsX9rG+++eZs0003zUpLS7M2bdpkw4YNq3QZ/CzLsrfffjvr0qVLVqNGjUr9N2nSpOzwww/PGjRokJWWlmatWrXKBg4cmM2fP3+p9X3/8vg/hFvJAKyY799KJsuybM6cOdmpp56aNWnSJKtevXq26aabZr/97W8Lbn2SZd/eSmbgwIHZ7bffnt+XbLvttlX+u37ttddmO++8c1a/fv2suLg4a9CgQdarV6+C29stzaJbySzLkj7rZNm3r/Wcc87JWrdunZWUlGT169fPdtppp+x3v/tdtmDBgny7zz77LDvssMOy8vLyrE6dOtlhhx2Wvfrqq8u8lcwit9xyS7btttvmPx907do1GzVqVH7+tGnTsp49e2a1a9fOIiL/uWhJ+8q77rorv771118/O+SQQ7L//ve/Veqf79d4zz33ZLvvvnvWsGHDrKSkJGvWrFl2zDHHVPoMBWuSXJatomP4AAD40cnlcjFw4MBKhwADpOacUwAAAJITTgEAAEhOOAUAACA5V+sFAFiHuNwI8GNl5BQAAIDkhFMAAACSc1gvACxGRUVFfPTRR1G7du3I5XKpywGANVKWZTFnzpxo0qRJFBUtfWy0yuHUjpmVyfkurGz+Rv2P36+V46OPPoqmTZumLgMA1gqTJ0+OjTfeeKltjJwCwGLUrl07Ir7dmZaXlyeuBgDWTLNnz46mTZvm96tLI5wCwGIsGo0vLy8XTgHgB6rKUW4uiAQAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkVpy4AYGXIsmy5l8nlcqugEgAAVoSRUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSc7VeAFiKdhc+EkWl66UuAwBWuYlDeibdvpFTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA59zkF1llZlq3QcrlcbiVXAgCAkVMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkilMXsCbKsix1CQAAAGsVI6cAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJCccAoAAEBywikAAADJCacAAAAkV5y6gNSyLEtdArCGWZG/G7lcbhVUAgCw9jByCgAAQHLCKQAAAMkJpwAAACQnnAIAAJCccAoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkNxaE06zLFuhB8Dq4O9TWkOGDIlcLhennHJK6lIAgCVYa8IpACzOuHHj4k9/+lNsvfXWqUsBAJZCOAVgrfXFF1/EIYccEjfddFPUq1cvdTkAwFIIpwCstQYOHBg9e/aMHj16LLPt/PnzY/bs2QUPAGD1KU5dAACsCnfeeWe88sorMW7cuCq1v+yyy2Lw4MGruCoAYEmMnAKw1pk8eXKcfPLJcccdd0RZWVmVljnnnHNi1qxZ+cfkyZNXcZUAwHcZOQVgrfPyyy/H9OnT4yc/+Ul+2sKFC+Opp56Ka6+9NubPnx/VqlUrWKa0tDRKS0tXd6kAwP8nnAKw1unevXu8/vrrBdOOOOKIaNOmTfzqV7+qFEwBgPSEUwDWOrVr14527doVTKtZs2ZssMEGlaYDAD8OzjkFAAAgOSOnAKwTnnjiidQlAABLYeQUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAILni1AUsTpZlqUsASM7fQgBgXWLkFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSKUxcAq1oul1uh5bIsW8mVAAAAS2LkFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAILni1AXA8sjlcj/qbWVZtgoqAQCAtZ+RUwAAAJITTgEAAEjOYb0AsBRvDN4jysvLU5cBAGs9I6cAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJBc8apceZZlq3L1rMFyuVzqElaJFX1dflcAAFjXGTkFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABIrjh1Aaz5crlc6hLWeCvSh1mWrYJKAAAgDSOnAAAAJGfkFACWot2Fj0RR6XqLnTdxSM/VXA0ArL2MnAIAAJCccAoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJCccAoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACRXnLoAfjxyuVzqElgOK/rzyrJsJVcCAAA/nJFTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFYK2Sy+WW+hg0aFDqEgGAxShOXQAArExTp07N//+uu+6KCy64ICZMmJCfVqtWrRRlAQDLIJwCsFZp3Lhx/v916tSJXC5XMA0A+HFyWC8AAADJGTkFgIiYP39+zJ8/P/989uzZCasBgHWPkVMAiIjLLrss6tSpk380bdo0dUkAsE4RTgEgIs4555yYNWtW/jF58uTUJQHAOsVhvQAQEaWlpVFaWpq6DABYZxk5BQAAIDnhFAAAgOSEUwDWWv369YuZM2emLgMAqALhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABIrriqDbMsW5V1AAAAsA4zcgoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJBcceoC+PHIsmyFlsvlciu5EgAAYF1j5BQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkilMXAAA/Zm8M3iPKy8tTlwEAaz3hlB8sy7LlXiaXy62CSgAAgDWVw3oBAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABIrriqDXO53HKvPMuy5V6GdcOKvjdW5H24tvL7tfZb0fe79wYAsCYycgoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJBcceoCYHlkWbbcy+RyuVVQycq1Iq8LAADWJkZOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASK54Va48l8ut0HJZlq3kSmD18N4FAIAVY+QUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgueLUBcCPVZZlqUtgLZLL5VKXAADwo2bkFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSKUxcAsKbJ5XKpSwAAWOsYOQUAACA54RQAAIDkhFMAAACSE04BAABITjgFYK3TokWLyOVylR4DBw5MXRoAsASu1gvAWmfcuHGxcOHC/PM33ngjfvrTn8ZBBx2UsCoAYGmEUwDWOg0aNCh4PmTIkNhkk02ia9euiSoCAJbFYb0ArNUWLFgQt99+exx55JHuUQsAP2JGTgFYq91///0xc+bM6Nev31LbzZ8/P+bPn59/Pnv27FVcGQDwXUZOAVir3XzzzbHXXntFkyZNltrusssuizp16uQfTZs2XU0VAgAREbksy7IqNVyNh0JVsSSokhV973ofsiQ/9kNDvXf/Z9KkSdGqVav4+9//Hvvtt99S2y5u5LRp06Yxa9asKC8vX9WlAsBaafbs2VGnTp0q7U8d1gvAWmvYsGHRsGHD6Nmz5zLblpaWRmlp6WqoCgBYHOGUtZ5RJJbkxz4Cyg9TUVERw4YNi759+0Zxsd0dAPzYOecUgLXSY489Fh9++GEceeSRqUsBAKrAV8kArJV23313R04AwBrEyCkAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJBcceoCFieXyy33MlmWrYJKgDXFivzdAADgx8PIKQAAAMkJpwAAACQnnAIAAJCccAoAAEBywikAAADJCacAAAAkJ5wCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLFqQtYWXK53Aotl2XZSq6EtcWKvKe8n364Ff1dBgBgzWbkFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSKUxeQWi6XW23byrJstW2LNFbn+wkAANYmRk4BAABITjgFAAAgOeEUAACA5IRTAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSK05dwLokl8st9zJZlq2CSgAAAH5cjJwCAACQnHAKAABAcsIpAAAAyQmnAAAAJCecAgAAkJxwCgAAQHLCKQAAAMkJpwAAACQnnAIAAJCccAoAAEBywikAAADJCacAAAAkV5y6AAD4McqyLCIiZs+enbgSAFhzLdqPLtqvLk2Vw2lVVgZrE+95WLd99tlnERHRtGnTxJUAwJpvzpw5UadOnaW2MXIKAIux/vrrR0TEhx9+uMydKT/M7Nmzo2nTpjF58uQoLy9PXc5aTV+vHvp59dHXq8+K9nWWZTFnzpxo0qTJMtsKpwCwGEVF316WoU6dOj7wrCbl5eX6ejXR16uHfl599PXqsyJ9XdUveV0QCQAAgOSEUwAAAJITTgFgMUpLS+PCCy+M0tLS1KWs9fT16qOvVw/9vPro69VndfR1LnNJUgAAABIzcgoAAEBywikAAADJCacAAAAkJ5wCwPdcd9110aJFiygrK4sddtghXnzxxdQlrfEuu+yy2G677aJ27drRsGHD2H///WPChAkFbb766qsYOHBgbLDBBlGrVq342c9+Fh9//HGiitcOQ4YMiVwuF6ecckp+mn5euaZMmRKHHnpobLDBBlGjRo3Yaqut4qWXXsrPz7IsLrjggthwww2jRo0a0aNHj3j33XcTVrxmWrhwYfz617+Oli1bRo0aNWKTTTaJiy++OL57+Rx9vWKeeuqp6NWrVzRp0iRyuVzcf//9BfOr0q+ff/55HHLIIVFeXh5169aNo446Kr744ovlrkU4BYDvuOuuu+K0006LCy+8MF555ZVo37597LHHHjF9+vTUpa3RnnzyyRg4cGA8//zzMWrUqPj6669j9913j7lz5+bbnHrqqfHQQw/FiBEj4sknn4yPPvooDjzwwIRVr9nGjRsXf/rTn2LrrbcumK6fV54ZM2ZE586do3r16vHwww/HW2+9Fb///e+jXr16+TZXXHFFXH311XHDDTfECy+8EDVr1ow99tgjvvrqq4SVr3kuv/zyGDp0aFx77bUxfvz4uPzyy+OKK66Ia665Jt9GX6+YuXPnRvv27eO6665b7Pyq9OshhxwSb775ZowaNSpGjhwZTz31VAwYMGD5i8kAgLztt98+GzhwYP75woULsyZNmmSXXXZZwqrWPtOnT88iInvyySezLMuymTNnZtWrV89GjBiRbzN+/PgsIrLnnnsuVZlrrDlz5mSbbrppNmrUqKxr167ZySefnGWZfl7ZfvWrX2U777zzEudXVFRkjRs3zn7729/mp82cOTMrLS3N/va3v62OEtcaPXv2zI488siCaQceeGB2yCGHZFmmr1eWiMjuu+++/POq9Otbb72VRUQ2bty4fJuHH344y+Vy2ZQpU5Zr+0ZOAeD/W7BgQbz88svRo0eP/LSioqLo0aNHPPfccwkrW/vMmjUrIiLWX3/9iIh4+eWX4+uvvy7o+zZt2kSzZs30/QoYOHBg9OzZs6A/I/Tzyvbggw9Gx44d46CDDoqGDRvGtttuGzfddFN+/gcffBDTpk0r6O86derEDjvsoL+X00477RSjR4+Od955JyIi/v3vf8fYsWNjr732igh9vapUpV+fe+65qFu3bnTs2DHfpkePHlFUVBQvvPDCcm2veOWUDQBrvk8//TQWLlwYjRo1KpjeqFGjePvttxNVtfapqKiIU045JTp37hzt2rWLiIhp06ZFSUlJ1K1bt6Bto0aNYtq0aQmqXHPdeeed8corr8S4ceMqzdPPK9f//d//xdChQ+O0006Lc889N8aNGxcnnXRSlJSURN++ffN9uri/Kfp7+Zx99tkxe/bsaNOmTVSrVi0WLlwYv/nNb+KQQw6JiNDXq0hV+nXatGnRsGHDgvnFxcWx/vrrL3ffC6cAwGo1cODAeOONN2Ls2LGpS1nrTJ48OU4++eQYNWpUlJWVpS5nrVdRUREdO3aMSy+9NCIitt1223jjjTfihhtuiL59+yaubu1y9913xx133BHDhw+PLbfcMl577bU45ZRTokmTJvp6LeKwXgD4/+rXrx/VqlWrdOXSjz/+OBo3bpyoqrXLCSecECNHjowxY8bExhtvnJ/euHHjWLBgQcycObOgvb5fPi+//HJMnz49fvKTn0RxcXEUFxfHk08+GVdffXUUFxdHo0aN9PNKtOGGG8YWW2xRMK1t27bx4YcfRkTk+9TflB/uzDPPjLPPPjt+8YtfxFZbbRWHHXZYnHrqqXHZZZdFhL5eVarSr40bN6500cBvvvkmPv/88+Xue+EUAP6/kpKS6NChQ4wePTo/raKiIkaPHh2dOnVKWNmaL8uyOOGEE+K+++6Lxx9/PFq2bFkwv0OHDlG9evWCvp8wYUJ8+OGH+n45dO/ePV5//fV47bXX8o+OHTvGIYcckv+/fl55OnfuXOmWSO+88040b948IiJatmwZjRs3Lujv2bNnxwsvvKC/l9OXX34ZRUWF0aVatWpRUVEREfp6ValKv3bq1ClmzpwZL7/8cr7N448/HhUVFbHDDjss3wZ/0OWcAGAtc+edd2alpaXZrbfemr311lvZgAEDsrp162bTpk1LXdoa7bjjjsvq1KmTPfHEE9nUqVPzjy+//DLf5thjj82aNWuWPf7449lLL72UderUKevUqVPCqtcO371ab5bp55XpxRdfzIqLi7Pf/OY32bvvvpvdcccd2XrrrZfdfvvt+TZDhgzJ6tatmz3wwAPZf/7zn2y//fbLWrZsmc2bNy9h5Wuevn37ZhtttFE2cuTI7IMPPsj+/ve/Z/Xr18/OOuusfBt9vWLmzJmTvfrqq9mrr76aRUR25ZVXZq+++mo2adKkLMuq1q977rlntu2222YvvPBCNnbs2GzTTTfN+vTps9y1CKcA8D3XXHNN1qxZs6ykpCTbfvvts+effz51SWu8iFjsY9iwYfk28+bNy44//visXr162XrrrZcdcMAB2dSpU9MVvZb4fjjVzyvXQw89lLVr1y4rLS3N2rRpk914440F8ysqKrJf//rXWaNGjbLS0tKse/fu2YQJExJVu+aaPXt2dvLJJ2fNmjXLysrKslatWmXnnXdeNn/+/Hwbfb1ixowZs9i/z3379s2yrGr9+tlnn2V9+vTJatWqlZWXl2dHHHFENmfOnOWuJZdlWbbC47wAAACwEjjnFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACSE04BAABITjgFAAAgOeEUAICVpl+/frH//vv/oHVMnDgxcrlcvPbaa0ts88QTT0Qul4uZM2dGRMStt94adevWzc8fNGhQbLPNNj+ojqXp0qVLDB8+/AetY8cdd4x77713JVUEaz7hFABgHdSvX7/I5XKRy+WipKQkWrduHRdddFF88803qUurkp122immTp0aderUWez8M844I0aPHp1/vjJC8yIPPvhgfPzxx/GLX/wiP+20006L9ddfP5o2bRp33HFHQfsRI0ZEr169Kq3n/PPPj7PPPjsqKipWSl2wphNOAQDWUXvuuWdMnTo13n333Tj99NNj0KBB8dvf/naxbRcsWLCaq1u6kpKSaNy4ceRyucXOr1WrVmywwQarZNtXX311HHHEEVFU9O1H6YceeiiGDx8ejz76aFxxxRXRv3//+PTTTyMiYtasWXHeeefFddddV2k9e+21V8yZMycefvjhVVInrGmEUwCAdVRpaWk0btw4mjdvHscdd1z06NEjHnzwwYj430jjb37zm2jSpElsvvnmERHx+uuvR7du3aJGjRqxwQYbxIABA+KLL76otO7BgwdHgwYNory8PI499tiCcPuvf/0rdt5556hbt25ssMEGsc8++8T7779faR1vv/127LTTTlFWVhbt2rWLJ598Mj/v+4f1ft93D+sdNGhQ3HbbbfHAAw/kR4ufeOKJ6NatW5xwwgkFy33yySdRUlJSMOr6/fmPP/54wUjo+PHjY9ddd42OHTtGnz59ory8PD744IOIiDjrrLPiuOOOi2bNmlVaV7Vq1WLvvfeOO++8c7HbgnWNcAoAQERE1KhRoyBEjh49OiZMmBCjRo2KkSNHxty5c2OPPfaIevXqxbhx42LEiBHx2GOPVQp4o0ePjvHjx8cTTzwRf/vb3+Lvf/97DB48OD9/7ty5cdppp8VLL70Uo0ePjqKiojjggAMqHd565plnxumnnx6vvvpqdOrUKXr16hWfffbZcr+uM844Iw4++OD8SPHUqVNjp512iv79+8fw4cNj/vz5+ba33357bLTRRtGtW7fFrmvs2LGx3nrrRdu2bfPT2rdvHy+99FLMmDEjXn755Zg3b160bt06xo4dG6+88kqcdNJJS6xt++23j6effnq5XxOsjYRTAIB1XJZl8dhjj8UjjzxSEMpq1qwZf/7zn2PLLbeMLbfcMoYPHx5fffVV/OUvf4l27dpFt27d4tprr42//vWv8fHHH+eXKykpiVtuuSW23HLL6NmzZ1x00UVx9dVX58Pnz372szjwwAOjdevWsc0228Qtt9wSr7/+erz11lsFdZ1wwgnxs5/9LNq2bRtDhw6NOnXqxM0337zcr69WrVpRo0aN/Ehx48aNo6SkJA488MCIiHjggQfybW+99db8+biLM2nSpGjUqFH+kN6IiD322CMOPfTQ2G677aJfv35x2223Rc2aNeO4446LG264IYYOHRqbb755dO7cOd58882C9TVp0iQmT57svFMI4RQAYJ01cuTIqFWrVpSVlcVee+0VP//5z2PQoEH5+VtttVWUlJTkn48fPz7at28fNWvWzE/r3LlzVFRUxIQJE/LT2rdvH+utt17+eadOneKLL76IyZMnR0TEu+++G3369IlWrVpFeXl5tGjRIiIiPvzww4L6OnXqlP9/cXFxdOzYMcaPH79SXntERFlZWRx22GFxyy23RETEK6+8Em+88Ub069dvicvMmzcvysrKKk0fNGhQvPfee/H666/HAQccEJdddln06NEjqlevHpdcckmMHTs2+vfvH4cffnjBcjVq1IiKioqC0VtYVxWnLgAAgDR22223GDp0aJSUlESTJk2iuLjwo+F3Q+jK1KtXr2jevHncdNNN0aRJk6ioqIh27doluehS//79Y5ttton//ve/MWzYsOjWrVs0b958ie3r168fM2bMWOo633777bj99tvj1VdfjVtuuSW6dOkSDRo0iIMPPjiOPPLImDNnTtSuXTsiIj7//POoWbNm1KhRY6W+LlgTGTkFAFhH1axZM1q3bh3NmjWrFEwXp23btvHvf/875s6dm5/2zDPPRFFRUf6CSRER//73v2PevHn5588//3zUqlUrmjZtGp999llMmDAhzj///OjevXu0bdt2iWHv+eefz///m2++iZdffrngXM/lUVJSEgsXLqw0fauttoqOHTvGTTfdFMOHD48jjzxyqevZdtttY9q0aUusOcuyOOaYY+LKK6+MWrVqxcKFC+Prr7+OiMj/+9063njjjdh2221X6DXB2kY4BQCgSg455JAoKyuLvn37xhtvvBFjxoyJE088MQ477LBo1KhRvt2CBQviqKOOirfeeiv++c9/xoUXXhgnnHBCFBUVRb169WKDDTaIG2+8Md577714/PHH47TTTlvs9q677rq477774u23346BAwfGjBkzlhkel6RFixbxn//8JyZMmBCffvppPihGfDt6OmTIkMiyLA444IClrmfbbbeN+vXrxzPPPLPY+X/+85+jQYMG+av5du7cOR5//PF4/vnn46qrrootttgi6tatm2//9NNPx+67775CrwnWNsIpAABVst5668UjjzwSn3/+eWy33XbRu3fv6N69e1x77bUF7bp37x6bbrppdOnSJX7+85/Hvvvumz+XtaioKO688854+eWXo127dnHqqacu8d6qQ4YMiSFDhkT79u1j7Nix8eCDD0b9+vVXqPajjz46Nt988+jYsWM0aNCgIFz26dMniouLo0+fPos9n/S7qlWrFkcccUTccccdleZ9/PHH8Zvf/Cauvvrq/LTtt98+Tj/99OjZs2fcfffdMWzYsPy8KVOmxLPPPhtHHHHECr0mWNvksizLUhcBAACpTJw4MTbZZJMYN25c/OQnP1lm+2nTpsWWW24Zr7zyylLPT12WX/3qVzFjxoy48cYbV3gdsDYxcgoAwDrp66+/jmnTpsX5558fO+64Y5WCaURE48aN4+abb650deHl1bBhw7j44ot/0DpgbWLkFACAddITTzwRu+22W2y22WZxzz33xFZbbZW6JFinCacAAAAk57BeAAAAkhNOAQAASE44BQAAIDnhFAAAgOSEUwAAAJITTgEAAEhOOAUAACA54RQAAIDkhFMAAACS+3/KNGEXt1r+LAAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Распознанный номер: \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Распознавание текста\n",
|
||
"recognized_text = recognize_characters(processed_image1, model, whitelist)\n",
|
||
"\n",
|
||
"# Вывод результата\n",
|
||
"print(f\"\\nРаспознанный номер: {recognized_text}\")\n",
|
||
"\n",
|
||
"# Визуализация результата\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.imshow(processed_image1, cmap='gray')\n",
|
||
"plt.title('Обработанное изображение')\n",
|
||
"plt.axis('off')\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 217,
|
||
"id": "bc797665",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import cv2\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"def imclearborder(imgBW, connectivity=8):\n",
|
||
" \"\"\"\n",
|
||
" Удаляет только те пиксели объектов, которые непосредственно касаются границ изображения.\n",
|
||
" \n",
|
||
" :param imgBW: Бинарное изображение (чёрно-белое, 0 и 255).\n",
|
||
" :param connectivity: Тип связности (4 или 8). 8-связность по умолчанию.\n",
|
||
" :return: Изображение с удалёнными пикселями, касающимися границ.\n",
|
||
" \"\"\"\n",
|
||
" # Находим связные компоненты\n",
|
||
" num_labels, labels = cv2.connectedComponents(imgBW, connectivity)\n",
|
||
"\n",
|
||
" # Создаем маску для граничных пикселей\n",
|
||
" border_mask = np.zeros_like(labels)\n",
|
||
"\n",
|
||
" # Помечаем пиксели на границах\n",
|
||
" border_mask[0, :] = labels[0, :] # Верхняя граница\n",
|
||
" border_mask[-1, :] = labels[-1, :] # Нижняя граница\n",
|
||
" border_mask[:, 0] = labels[:, 0] # Левая граница\n",
|
||
" border_mask[:, -1] = labels[:, -1] # Правая граница\n",
|
||
"\n",
|
||
" # Уникальные метки на границах\n",
|
||
" border_labels = np.unique(border_mask)\n",
|
||
"\n",
|
||
" # Удаляем только пиксели на границе, не трогая внутренние области\n",
|
||
" result = imgBW.copy()\n",
|
||
" for label in border_labels:\n",
|
||
" if label != 0: # Пропускаем фон\n",
|
||
" result[(labels == label) & (border_mask == label)] = 0\n",
|
||
"\n",
|
||
" return result\n",
|
||
"\n",
|
||
"# Бинарное изображение\n",
|
||
"imgBW = np.array([\n",
|
||
" [1, 1, 1, 1, 1],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 1, 1, 2],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 1, 0, 1],\n",
|
||
" [1, 0, 0, 0, 1],\n",
|
||
" [1, 1, 1, 1, 1]\n",
|
||
"], dtype=np.uint8) * 255\n",
|
||
"\n",
|
||
"# Удаление объектов, касающихся границы\n",
|
||
"result = imclearborder(imgBW)\n",
|
||
"\n",
|
||
"# Визуализация\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"plt.imshow(imgBW, cmap='gray')\n",
|
||
"plt.title('Оригинальное изображение')\n",
|
||
"plt.axis('off')\n",
|
||
"\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"plt.imshow(result, cmap='gray')\n",
|
||
"plt.title('Без объектов на границе')\n",
|
||
"plt.axis('off')\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 168,
|
||
"id": "c2854f0f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распознанный номер для img/1.jpg: \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import cv2\n",
|
||
"import pytesseract\n",
|
||
"from matplotlib import pyplot as plt\n",
|
||
"\n",
|
||
"# Настройки для распознавания\n",
|
||
"whitelist = 'ABCEHMOPTXyK0123456789'\n",
|
||
"\n",
|
||
"# Функция для создания эталонных шаблонов символов\n",
|
||
"def generate_templates(whitelist):\n",
|
||
" templates = {}\n",
|
||
" for char in whitelist:\n",
|
||
" img = np.zeros((50, 30), dtype=np.uint8) # Пустое изображение для символа\n",
|
||
" font = cv2.FONT_HERSHEY_SIMPLEX\n",
|
||
" cv2.putText(img, char, (5, 40), font, 1.2, 255, 2, cv2.LINE_AA)\n",
|
||
" templates[char] = img\n",
|
||
" return templates\n",
|
||
"\n",
|
||
"# Нахождение символов и сопоставление\n",
|
||
"def recognize_characters(image, templates):\n",
|
||
" contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
" recognized_text = \"\"\n",
|
||
"\n",
|
||
" for contour in sorted(contours, key=lambda x: cv2.boundingRect(x)[0]): # Сортировка слева направо\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" if w > 10 and h > 20: # Фильтр по размеру символов\n",
|
||
" roi = image[y:y+h, x:x+w]\n",
|
||
" roi_resized = cv2.resize(roi, (30, 50), interpolation=cv2.INTER_CUBIC) # Приведение к стандартному размеру\n",
|
||
"\n",
|
||
" # Сравнение с шаблонами\n",
|
||
" best_match = None\n",
|
||
" max_corr = -1\n",
|
||
" for char, template in templates.items():\n",
|
||
" res = cv2.matchTemplate(roi_resized, template, cv2.TM_CCOEFF_NORMED)\n",
|
||
" _, corr, _, _ = cv2.minMaxLoc(res)\n",
|
||
" if corr > max_corr:\n",
|
||
" max_corr = corr\n",
|
||
" best_match = char\n",
|
||
"\n",
|
||
" if best_match is not None and max_corr > 0.5: # Порог корреляции\n",
|
||
" recognized_text += best_match\n",
|
||
"\n",
|
||
" return recognized_text\n",
|
||
"\n",
|
||
"# Основной обработчик\n",
|
||
"def process_plate(image_path):\n",
|
||
" image = cv2.imread(image_path)\n",
|
||
" if image is None:\n",
|
||
" print(f\"Не удалось загрузить изображение: {image_path}\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # Преобразование в градации серого и пороговая обработка\n",
|
||
" img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
|
||
" _, thresh = cv2.threshold(img_gray, 100, 200, cv2.THRESH_TOZERO_INV)\n",
|
||
" contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
|
||
"\n",
|
||
" # Нахождение области номерного знака\n",
|
||
" plate = None\n",
|
||
" for contour in contours:\n",
|
||
" x, y, w, h = cv2.boundingRect(contour)\n",
|
||
" aspect_ratio = float(w) / h\n",
|
||
" if aspect_ratio >= 3 and w * h > 600:\n",
|
||
" plate = image[y:y + h, x:x + w]\n",
|
||
" break\n",
|
||
"\n",
|
||
" if plate is None:\n",
|
||
" print(f\"Номерной знак не найден на изображении: {image_path}\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # Преобразование вырезанного номера\n",
|
||
" gray_plate = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)\n",
|
||
"\n",
|
||
" # Применение гомоморфной фильтрации\n",
|
||
" processed_plate = homomorphic_filter(gray_plate)\n",
|
||
"\n",
|
||
" # Нахождение и распознавание символов\n",
|
||
" templates = generate_templates(whitelist)\n",
|
||
" recognized_text = recognize_characters(processed_plate, templates)\n",
|
||
"\n",
|
||
" print(f\"Распознанный номер для {image_path}: {recognized_text}\")\n",
|
||
"\n",
|
||
" # Визуализация\n",
|
||
" plt.figure(figsize=(10, 5))\n",
|
||
" plt.subplot(1, 2, 1)\n",
|
||
" plt.imshow(cv2.cvtColor(plate, cv2.COLOR_BGR2RGB))\n",
|
||
" plt.title(\"Вырезанный номер\")\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
" plt.subplot(1, 2, 2)\n",
|
||
" plt.imshow(processed_plate, cmap=\"gray\")\n",
|
||
" plt.title(\"Обработанный номер\")\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"# Пример обработки\n",
|
||
"images = ['img/1.jpg']\n",
|
||
"for img_path in images:\n",
|
||
" process_plate(img_path)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 160,
|
||
"id": "63cc871f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA5IAAAHvCAYAAAAmUuAkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACZtElEQVR4nOzdd1yVdf8/8Nd1Fod54DAFVFBURM2NZg6cae5t5irbw8ysLLvTsrupd2lLsxRzW2quzI1bEMWtKCgCykb2OuP6/cGP6yuCylEOh/F6Ph48imu+kfPhc72vzxJEURRBREREREREVEEySwdARERERERENQsTSSIiIiIiIjIJE0kiIiIiIiIyCRNJIiIiIiIiMgkTSSIiIiIiIjIJE0kiIiIiIiIyCRNJIiIiIiIiMgkTSSIiIiIiIjKJwtIBEBHVBQUFBTh48CBOnz6N27dvIzc3F9nZ2WjcuDG++uorS4dHREREZBJBFEXR0kEQET0uHx8f3Lx5E8uXL8eUKVPK7A8JCUHPnj0BAPf7szdlyhSsWLECkydPRnBwcKXEJYoi/ve//+G///0v7ty5U2Z/o0aNcO3aNchk7CBCRERENQefXIiIzOiVV17BzJkzIZfLsXDhQly/fh0GgwGiKEIURURHRzOJJCIiohqHXVuJiMxk3bp1WLp0KVq2bIndu3ejXr16lg6JiIiIqFLwNTgRkZl89dVXUKlU2Lx5M5NIIiIiqlWYSBIRVcCmTZvw4osvomXLlnBycoJarYavry9eeOEFREZGljn+9u3bOHv2LLp06YIzZ86gT58+0Gq1UKlU8Pb2xoQJE3DmzJkH3nPKlCkQBOG+X+WNBQWKW0J79+4NrVYLKysrNGzYEC+88AKuXr1a7vE+Pj4QBAExMTHl7g8ODi73fvfbfj9BQUEQBAEhISEVOv5u+/fvx+jRo+Ht7Q0rKyu4urqiY8eOmDNnDtLS0ioUU2FhIZo2bSr9+92rZLtMJkNUVFS5caxevVo6LigoqNS+kJCQMr8jhUIBLy8v9O7dG+vXr7/vz3flyhU8//zzaNiwIaysrKDVatG7d29s2LDhgf8uJT/v/b58fHzKPS8sLAxjxoyBp6cnVCoV3NzcMHjwYOzZs6fc48v7LNrY2KBJkyaYOnUqLl269MA4iYio9mHXViKiChgzZgysrKwQEBCAXr16Qa/X48KFC1i+fDk2bNiA3bt3o0uXLtLx8fHxAIDQ0FCEhIRALpeja9eu8PT0xPnz57F69Wps2LABy5cvx3PPPffAez/11FPw8/OTvo+KisLRo0fLHCeKIqZMmYI//vgDCoUC3bt3h5ubG06fPo3ly5dj/fr12LhxI/r3719J/ypVY9q0afjhhx8AAG3atEG3bt2QmZmJyMhIfPbZZ+jZs2eZpK483377La5du/bQ40RRxI8//ojvv/++zL6FCxc+9Hx3d3fp31in0yEqKgr79+/H/v37ceHCBcybN6/U8Tt27MCoUaNQUFCAZs2aYcSIEUhOTsbBgwexf/9+7Nq1C7///vsD79m4cWN07dpV+j4nJwcbN24s99ilS5fi1VdfhdFoRNu2bREUFISbN29i+/bt2L59O+bOnYs5c+aUe+7dn8Xs7GyEhoZi2bJlWLt2LcLCwtCyZcuH/vsQEVEtIRIR1QINGzYUAYjLly8vd/+BAwdEAOKD/uxNnjxZBCBOnjy5zL5169aJOTk5pbYZjUbxp59+EgGILVq0EI1GY7n302q14smTJ0ud++OPP4oARCsrKzEyMrLceCZMmCACEIODg0ttX758eblx/vLLLyIA0cXFRYyIiCgV55w5c0QAoqOjo5icnFzqvJJ/uxs3bpQbx/3ud7/t99OjRw8RgHjgwIEKHS+Korho0SIRgOjs7Czu37+/zP7Q0FAxNjb2oTHduHFDtLa2Fhs0aHDfz0HJ9l69eokODg5idnZ2qf3Hjh0TAYi9e/cWAYg9evQotb/kd37vdlEs/vyU/Bx3S0xMFDUajQhA/Pzzz0t9hk6ePCk6OTmJAMRff/213H+f3377TQQgTpkypczPC0Bs2LBhqe3nzp0TFQqFKAiC+Mcff5Ta988//4gqlUoEIO7evbvUvpKycW/50ul04qBBg0QA4rvvvltujEREVDuxaysRUQWMHTsWtra2pbYJgoDXX38dTz75JC5evIjLly+Xe+4nn3yCDh06lNr2xhtvoF+/figsLLxvK5dOpwMAKJXKCsU4f/586X5t2rQpFeecOXPwxBNPICMjA0uXLq3Q9SxNr9dLrXe//vqrtHzL3QIDA1G/fv2HXuvtt99Gfn4+vvvuuwodm5WVVWYJmIULF8LKygqvvPJKxX6Au/Tp0wcAYDQaS21funQpMjMz0b59e8yePbtUl9sOHTpg9uzZAIpbU8tj6mdk4cKF0Ov1GD58OCZOnFhq34ABA/Dyyy8/8H73UigUUmvwvT8bERHVbkwkiYgqKCoqCj/++COmT5+OqVOnYsqUKZgyZQqSkpIAoNyxkgDw/PPPl7v9pZdeAgAcOHCg3P35+fkAACsrq4fGFh8fj+joaADA5MmTy+wXBEGK4373q25OnTqFlJQUuLi4YPjw4Y98nR07dmDr1q14+umnMWLEiIceP2jQIDRq1Ag//vijtOZofHw8Nm7ciHHjxsHV1bXC99br9bhy5YqUoI0dO7bU/pLxouX9zgBg6tSpAIBr167h9u3bZfab8hm5+373G9dacr/Dhw/DYDA88Fo5OTnYuXMn/ve//0Eul2PUqFEVioGIiGoHjpEkInoIg8GAN998E0uWLJESi/JkZWVJ/1/SsuTs7AwHB4dyj2/cuDGA/xtPea+SSWScnJweGuOtW7cqfL+SY+/l6+v70PuUZ8WKFVixYgWA4p/b3t4e/v7+GD16NN58802o1epHuu7NmzcBAM2aNSt3cpyKKCgowLRp02BlZSWNs3wYmUyGN998EzNmzMCuXbvQv39//Pzzz9Dr9Zg2bVqp33N5Dh48WCZeKysrfPrpp1ILY4mS38X9/u0dHR2h1WqRnp6O+Ph4eHp6ltpvymekIvcr+YwUFBQgLS0Nbm5upfY///zzZV6MNGnSBPv37y81RpiIiGo/JpJERA+xcOFCLF68GB4eHvjf//6HLl26wN3dXUqQxo8fj7Vr15ZKMq2trR/7viUP/fcmD+YycuRI2NnZldl+v8l9Stw90YvBYEBcXByOHDmCsLAw/Pvvv9izZ88jJ4KP66uvvsL169fx8ccfo0mTJhU+74UXXsAnn3yCH374AUFBQfj111/x1FNPoV27dg+ddfbuyXaMRiOSkpJw6NAhzJs3D7a2tnj33Xcf50cqpao/I3dPtlNQUICrV68iIiICkydPxt9//43WrVtXSRxERGR5TCSJiB6iZAmGJUuWYMiQIWX2lzcTqLe3N4DiFqPMzExoNJoyx5R0RS059m65ubmIjY2FlZWV1Er0IF5eXtL9srKyym2VvH79eqlj7zV//vxyl4sIDg5+YCLZtWvXMuMJz5w5gy5dumDfvn04fvz4I7VWNWjQAABw9epViKJocjJ6/fp1fP311/Dx8cFHH31k0rkajQaTJ0/Gzz//jLlz5yItLQ3Tpk2r0Ln+/v5l/j1u3bqFjh074r333kP37t3RsWNHAMW/iytXrki/m3tlZmYiPT1dOvZeJctutGjRokKxeXl5ITo6GtevXy93htWSONRqNbRabZn9L774YpluscuWLcPUqVPx3HPP4cKFCxWKg4iIaj6OkSQieoiSB/mGDRuW2Xfx4sVy14P09PSUEsDly5eXe93ffvsNANCrV68y+/bu3Quj0YhOnTpVaCIVb29v6X73JjFA8ZIWJdvLm7SmsrVp00ZKVEq6qJqqQ4cOcHFxQUpKCv7++2+Tz582bRoKCgrw/fffP1IL8VtvvQUA+Prrr+Ht7V2h8ZX34+Xlha5du0IURRw6dEjaXjJRTUnX4HstW7YMQHH30XsTybS0NJw6dQrW1tZo165dheIouV95n5G779etWzcoFBV71zxu3DgAxWXh7jU9iYiodmMiSUT0EM2bNwcA/PTTT6VmpkxISMCkSZOg1+vLPW/69OkAgHnz5uHUqVOl9i1evBi7du2CWq3G22+/XWrf9evX8d577wEo7mJZUTNnzpTud/bsWWm7KIr4/PPPcebMGTg6OkqT/JjTjRs3pFlsy0vAK0KhUEhjCl9++eVSCViJkydPljvGdN++fdixYweeeeYZDB069JHu36xZM7z77rsYOHAgPvvsswonVuW5desWjhw5AgBwcXGRtr/00ktwcHDA6dOn8cUXX5TqHh0REYHPP/8cAKTPQ4mcnBxMnToVBoOh3BmF7+ftt9+GQqHA33//jVWrVpXat3v3bixZsgTA/32WKmLdunUAilsxKxoHERHVfOzaSkS1yrx587B48eIy2++eIKVz587lnlvS1fReH330Ef79918sXboUBw4cQLt27ZCVlYWDBw+iUaNGGD58ODZv3lzmvDfeeANHjhzB+vXr0alTJ3Tv3h316tXD+fPncf78eSiVSixfvrzU2L1hw4bhn3/+gU6nw5gxYzBp0qQK/+yvvPIKjh07hpUrV6JDhw7o0aMH3NzccPr0aURGRsLa2hpr1qwxadbRijh16hRmzZoFoHiMZHx8PLZv346cnBz06dMHTz755CNf++2330ZkZCQWL16MHj16oG3btmjWrBmysrKkLqEHDhwo0z04Pj4earUaixYteqyfraLLYNztypUrUvdPo9GI5ORkHDx4EAUFBQgICCg1u6m7uztWr16N0aNHY/bs2Vi5ciXatm0rnaPX6/H888+XSv4///xzLFiwABkZGQgICMA333xT4dhatWqFn376Ca+99homTpyI7777Dv7+/rh58yaOHTsGURQxd+5c9OvXr9zzf/vtN2mMaGFhISIjIxEREQEAeP/99x95YiUiIqqBLLaCJRFRJWrYsKG0oPzjft27mL0oFi/kPmTIELFevXqiWq0WmzRpIr7//vtiVlbWfRdrF0VRNBqN4h9//CH26NFDdHR0FJVKpejl5SVOmDBBPHv2bJnjGzduLPbp00dcvXp1qcXp77Z8+fL7ximKorhmzRoxKChIul/9+vXFKVOmiFeuXHngv92NGzdMul/J9ru/BEEQ7ezsxI4dO4pff/21mJ+fLx3fo0cPEYB44MCBcu/zIDt37hSHDh0quru7i0qlUnR1dRUDAwPFTz/9VExLSys3pk8++aTca5Xsr+j2ex04cEAEIPbo0aPc7fd+2djYiK1atRJnz54t3rlzp9xrXrp0SZw8ebLo7e0tKpVK0dHRUezZs6e4bt26MseOHz9e+vfNzs4u93o3btwQAYgNGzYsd/+JEyfEUaNGiR4eHqJCoRCdnZ3FgQMHirt37y73+JLP+N1fMplMdHZ2Fvv06VNunEREVLsJoviAueyJiIiIiIiI7sExkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZJJKSSSDg4MhCEK5+wIDAyEIAn755ReTzyWqqe79XAcFBUEQBOlLpVLB19cXL7/8MuLi4h54LlFtcL/P9ebNmzFgwAC4uLhApVLB09MTY8aMwf79+6VjQkJCIAgCYmJiqjBiIvN7WF1x95e/v/99zyOqDcorDy1btiz32JiYGAiCgPnz50vbWFdUPYU5L37t2jWcPHkSPj4+WL16NV577TVz3o6oWvP29saXX34JACgqKsKlS5ewePFi7Nq1C5cvX4aNjY2FIySqOqIo4oUXXkBwcDDatm2LGTNmwMPDAwkJCdi8eTN69+6No0ePokuXLpYOlahK3V1X3E2j0VggGiKi+zNrIrlq1Sq4ublhwYIFGDVqFGJiYuDj42POWxJVWxqNBhMmTCi1zdfXF2+++SaOHj2Kvn37Wigyoqq3YMECBAcHY/r06fjf//5X6i307NmzsXLlSigUZq2iiKql8uoKIqLqyKxjJNesWYNRo0Zh0KBB0Gg0WLNmjTlvR1TjeHh4AAAfmKlOyc/Px5dffgl/f3/Mnz+/3C56EydORGBgoAWiIyIiooow29NraGgooqKisHz5cqhUKowYMQKrV6/GRx99ZK5bElVrBoMBqampAACdTofLly9jzpw58PPzw1NPPWXh6IiqzpEjR5Ceno7p06dDLpdbOhyiauXuuuJu1tbWsLW1tUBERJZzv/Jw584dC0RD9xJEURTNceG33noLW7Zswc2bNyEIAvbs2YN+/fohIiICbdq0McctiaqtoKAgHDx4sMz25s2bY8eOHfD19bVAVESWsWjRIrz99tvYvHkzhg0bZulwiKqN+9UVAPDKK69g8eLFVRwRkeU8qDyU+PbbbzFz5swqiojuZZYWSb1ej/Xr12Py5MlSl6VevXrBzc0Nq1evZiJJdZKPjw+WLl0KoLiMREZG4ptvvsGAAQNw+PBhuLq6WjhCoqqRlZUFALC3t7dwJETVz911xd28vb0tEA2RZd2vPCQlJXEscTVglkRy9+7dSElJQWBgIKKioqTtPXv2xNq1a/H1119DJuMSllS32Nraok+fPtL3/fv3R9euXdGhQwd89dVXWLBggQWjI6o6Dg4OAIDs7GwLR0JU/dxbVxDVZfcrD1zio3owSyK5evVqAMCYMWPK3X/w4EH07NnTHLcmqlHat28PjUaDQ4cOWToUoipTsh7e+fPn2bWViIiohqr0RDI3NxdbtmzB2LFjMWrUqDL7p02bhtWrVzORJPr/DAYDcnJyLB0GUZXp2rUrnJycsHbtWnz00UeccIeIiKgGqvREcvPmzcjNzcUbb7yBbt26ldm/e/du/Pnnn/jpp59gZWVV2bcnqlEOHDiAnJwctG7d2tKhEFUZGxsbfPDBB5g1axY++OADfPvtt2WWAFm1ahWaNm3KJUCIiIiqqUpPJFevXg1nZ2d06dKl3P1DhgzB0qVLsWPHDowYMaKyb09UbWVmZmLVqlUA/m+ynV9++QXW1taYNWuWhaMjqlrvvfceLl68iAULFuDAgQMYNWoUPDw8kJiYiL///hthYWE4duyYpcMkqnJ31xX34uQiRFSdVGoimZycjL179+LZZ5+9b1el3r17w8bGBqtWrWIiSXVKfHw8Jk6cCAAQBAFOTk7o0aMH5syZw5mMqc6RyWT4448/MHToUPz666+YP38+srKy4Orqiu7du+Obb77Bk08+aekwiarc3XXFvZhIElF1YrZ1JImIiIiIiKh24hocREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBJFRQ8UBMGccTwUl7uk6oZlgqg0lgmi0lgmiEpjmahd2CJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmUVTFTWQyGWxsbKDX6yEIAgoKCiCKYlXcmsiiVCoV1Go15HI5BEGATqe777GCIEAmk0EQBBQVFaGwsBB6vb4KoyWyHEEQYGdnB4VCAVEUYTAYpDIBQCoTBoPBwpESVS5T6onyCIIAQRAgiiJ0Oh0KCwthNBrNFC2R+cnlcshkMilXeNRnIZlMJtUpRqOR9YcZmD2RFAQBHh4eGDx4MG7fvg2VSoU9e/YgOzubySTVara2tmjVqhV69uwJFxcXqNVqREVF3fd4tVoNe3t7KJVKXL16FcePH8eNGzdQUFDAP35Uq8nlcri7u2Ps2LHw9PSE0WhESkoKFAoFbG1tIZPJcOnSJZw4cQI3b97kCxaqNUytJ8qjVCphY2ODwsJCXL58GQcPHkRGRgafsahGUqlUcHd3h5eXFwoKCpCfn4+oqCiTn4NUKhXc3NzQsGFD6PV6JCQkID4+3kxR111mTyQdHR0xadIkfPjhh7C3t0dYWBgUCgX++ecfZGdnm/v2RBahVqvRqlUrvPDCCxg7diwcHBxMOj8vLw8xMTHYvXs31q9fjwsXLiA3N5cPBlTrqNVqNG7cGD/++COefPJJWFlZlTlGFEVkZGRgx44dWLJkCcLDw1FQUGCBaIkqz+PWE/fS6XS4cOECXn31VURGRiIzM7OSIiWqGgqFAg0aNMCgQYMwbtw4ODs74+eff8aOHTtw9erVCl9HqVSifv36GDRoEF577TVYWVnhiy++wLZt28wYfd1k9jGSOTk5eOONN+Dg4ABBENCpUyf4+vpCqVSa+9ZEFlNQUICmTZuiX79+j/RwYGNjg4CAAEyfPh1HjhzB0aNH0bFjRwiCYIZoiSzD3d0dv//+Oy5cuICgoKByk0iguGeLk5MTJkyYgD179mDDhg2oV69eFUdLVLket564l1KpRJMmTfDiiy8iLy+vEiIkqlp6vR7NmjXD6NGj0alTJzRu3BjTp0+HtbW1SdcxGAyoV68epk2bhmbNmqFhw4b4/PPPTe42Tg9n1kRSEAS4uLhAoSjd8Nm4ceP7PjAQ1RZnzpzBvn37Hvs6crkcTzzxBHbv3o333nsPcrm8EqIjsiyNRoPBgwdj/PjxJp2nVqvRo0cPfPPNN2aKjKjqVFY9ART3ZNm3bx/eeOMNPjBTjRUXF4crV64AKE4IU1NTkZiYaNI1jEYjUlNTcebMGen7GzduID8/v7LDrfPM2rVVoVCgR48e8PDwKLW9f//+WLp0KVJSUjjWheoEnU6HixcvYsqUKUhJSbnvcaIoQqvVonv37hg7diw6d+4svXSxs7PDxIkTsXDhQo6ZpBrL1tYWTz75JGbMmIGnn3661L5jx45h6tSpyMrKkrYZjUYEBgbizTffRK9evSCXy+Hg4ICxY8fiiSeewJw5c7Bt2zaWCarxKlpPlMdoNKKgoAA5OTl8rqJaw2g0PtZLkbsnneLLFfMwayKpUqkwadIk6Xuj0QiZTAZvb2907NgR0dHRSEtLM2cIRNWCKIooLCxEUlLSQ9+sJSYm4vLly9i4cSPGjRuH2bNnw83NDXK5HF5eXhg9ejRWrVpVRZETVS5vb28MHToU/fv3l7pq63Q69OvXD8ePH0dRUVGZscDbtm3Dvn37MGXKFMyaNQve3t5QKpVo3rw5Zs2ahV27dvFNM9V4ptQTRETVgdm6tsrlcvj6+qJDhw7StszMTOkBYeTIkRzjQnVORSbLKZmmOiUlBVu2bMEvv/wivUlzcHDAzJkz0bBhQ3OHSlTpVCoVAgIC0LdvXymJzM/Px7vvvoujR4+isLCw3DIiiiJyc3Oxbt06LFiwQJqoTaFQoFGjRujSpUuV/hxE5sRJ1YiopjBbImljY4Nhw4bB2dkZAKQZ90q6XLRq1QpNmjSBjY2NuUIgqtFEUURCQgJCQ0ORkZEBoHhNJGdnZ64RRjWOXC5HQEAA+vfvDz8/PwDFa0OGhoZi48aNFep2lJmZiUOHDuHPP/+ETqeDIAjQaDSYNGkS3NzczP0jEBER0V3MkkgKggBnZ2cMHTpUWkz68uXLWLlyJdLT06VxYF27dmWrJNEDFBUVIS0tDbdu3QLwf8sglCSWRDWFwWCQeqmUTBiVmZmJRYsWVbgbn16vx+XLl7F582YkJCQAKG6VDAgI4BhJIiKiKmaWRFKtVqN58+Zo0aKFtG3Tpk0IDw/HlStXoNfrIZPJ0KNHD/j5+ZWZ1ZWIigmCAGtra3h6egIobpH08PCASqWycGREplEqldBqtVIvlaKiIly7dg379u0zqYU9Pz8f4eHhWLBgASIiIhAaGop33nmHa+YRERFVMbNkcBqNBv369ZPWfUlJScHOnTul7q1t27aVJkrw9/fHyZMnkZ6ebo5QiGosuVyOJk2aYPDgwXB0dARQ3CJz4sQJrhFGNY6npycCAgKkLqjp6elYv359qRlaKyo1NRXr16/H6dOnodfrER4ezpkqiYiIqlilt0jK5XJ4eHggKCgIQHFXvJMnTyI6OhpGoxF79uxBcnIyjEYjbGxs0L59e/j4+FR2GEQ1mrW1NVq3bo2JEydi+PDhUCqVUrfW33//HQUFBZYOkajC5HI5unbtiu7du8Pa2hqiKCIzMxOhoaGPdD29Xo+kpCQcOXIEJ06cYBJJRERkAZXeImlnZ4eWLVtKkynk5uZi69at0oPv1atXcf78eXh6ekqJZPPmzXHhwgUUFRVVdjhEFiMIgjQzpUwmg62tLVq3bn3fJW/UajVcXFyg1Wrh4eGBgIAAdO7cGY0aNQJQ/PAcGxuLY8eOcVY/qlEUCgW8vLykNYWNRiNyc3O5/BPVeabUE1ZWVpDJZMjIyMDt27eRmZnJscFU65j67HQ/vr6+0Gq10vccRmcelf6v6u7ujt69e8POzg6iKCIlJQX79u2T9ufl5WHPnj3o0KEDrK2t4evri1atWuHgwYOIj4+v7HCILEIQBKjVaqjVagDFLTKenp549dVX79uaaG1tDQ8PD7i6usLJyQl2dnbSWMiSJHLLli2P1BWQyJKMRiPu3LkjfXZ1Ol2p74nqIlPrCbVaDZlMhri4OGzduhXh4eHIyspiMkm1xqM8O92PRqNB06ZNpeuqVCquFGEGlZpIqlQq+Pr6omvXrgCKZ+k7c+ZMmQTxwIEDmDBhAtzd3aUufP7+/rh16xZbWqhWEAQBDg4O0tswQRCg1WoxfPjwR7pednY29u7diz/++AOFhYWVGSqR2el0OmRmZiInJ0f6Pi0tjYkk1WmPWk9kZmaiqKgIycnJuHz5MhNJqjUq+9np3usykax8lTpGUqvVomXLltIMk3l5edi0aVOZtwgxMTG4ePGi9FDRokULtGnTRnoDQVTTGY1G6Q1YZSgqKkJSUhJEUeTLFqqRjEajNDurKIoc10h13qPWE0qlEvXq1YOtrS3LEdUqlf3sdDe1Ws2JCs2g0lokBUFAw4YN0b17d9jY2MBgMCA2NhZ79+4tc2xBQQF27dqFp556Ck5OTnB1dYW/vz88PT0RHR1dWSERWVRiYiJu3LiBnj17Aij+3MfFxT204lcoFLC1tYWDgwNsbW0hCALc3d0xffp0KJVK/Oc//+HDA9V4JWNgiOoyU+sJURQRFRWFn3/+GSdOnDBp6RyimuBRn53uZWVlBVdXV9jb28NoNCIuLo6JpBlUWiJpbW0Nf39/dOrUCUBxC8r58+dRWFgIJyenMsdfuXIFt2/fRuPGjaFWq9GqVSt07NgR169fZ4sL1To6nQ6XL1/GpEmTkJyc/MBjXVxcEBgYiIEDB6Jr165wc3ODTCaDjY0Nhg8fjm+//ZbL5VCNo1QqpbfMJZ9nuVxu4aiIqo+K1BM6nQ65ubnQ6XR8VqJaz5Rnp3s1adIEH3zwAQYPHgwA7AJuJpWWSHp6eqJt27Zwd3cHUPwLc3Z2xoIFC+57jpubm/Q2LSAgAD179sTOnTu5sDTVOqIooqioCCkpKQ/9Y5icnIxLly5hy5YtGDVqFP7zn/+gfv360tI6o0aNwm+//VZFkRM9PpVKBR8fH2nYg42NDerXrw9nZ2fk5uZaODqi6sGUeoKoLnicMqHVajmnRBWolERSLpejW7duGDZsmLTNzs4O/fr1q/A17Ozs0LZtW/To0QNbt26tjLCIarQ7d+5gx44d0Gq1+Oyzz6BSqWBvb4933nkHu3fvtnR4RBUmk8mQn59fqluRSqVCvXr1EBsba8HIiIiI6FFVymQ7jo6O8PX1ldYIe1Te3t7o3LlzZYREVCskJSXh1KlTuHPnDoD/m3mMYySpJikoKMDWrVuxfft2FBYWQiaTQaPRSGukmkoQBMjl8lLrjREREVHVqpREsnnz5njiiSdgZWX1WNdxcnJCQEAA3NzcKiMsohrPYDAgMTERp0+fBgBpwDjHSFJNExcXV2o5KA8PD0ybNg1KpdLka2k0GowZMwZXr17FqVOnYGtrW9nhEhER0UM8diKpUqnQuXNntGvXDkDx+kZr166FVquFvb39Q79atWqFFStWoKCgAGq1Gk2aNEGXLl0e+wcjqi1kMtljv6QhsrSSSUJKxqwolUr4+voiKCjIpOsIgoDWrVtjwYIF8PPzQ5s2bXD58uVyJ3UjIiIi83nsRNLT0xNNmzaFi4sLAOD27dvYtGkT7ty5g5ycnId+Xbt2DZcuXUJqaioAwMvLCyNHjuSakkQoTiJdXV3Rpk0bAMXjkZs0aQJ7e3vLBkb0CMLCwrBlyxbodDoAxZMhzJs3Dw0bNqzQ+UqlEk8++STeeOMNuLq6AihOUDdt2oTs7GyzxU1ERERlPXYi2a1bN7Rv3x7W1tYoKChAdHQ0jh07VuHzCwsLERYWhjNnzgAons2vadOmjzx2hqi2kMlkaNq0KZ555hkpcTQYDDh//jxkskrplU5UpWJjY7Fv3z6cOnUKQHFi2KJFC4wePbpC59vY2KBdu3YYMGAAFAoFRFFEcnIyFi9ezHHDREREVeyxnkbt7e0RGBgIX19fAEBqaipOnz6NxMREk65z6dIlREREICcnB0qlEg0aNMAzzzzzOKER1WgymQz+/v6YMmUKJk6cCKVSCVEUkZmZiZ9++olTw1ONZDQacevWLYSHh0tLP9na2uLDDz/Ehx9+eN8J22QyGVq0aIFPPvkEH3/8Mezs7AAUt0ZGRkaaXOcQERHR43us5T+aNWuGhg0bQq1WQ6fT4cqVKzhw4ID0gFBRaWlpuHDhAqKjo9G6dWs4Ojqib9++WLp0KdeUpFpBEARYW1ujSZMmcHZ2fuCxbm5u6Nq1K3r16oWWLVtK3caNRiNu376NkJAQLqxLNVZ8fDz+/PNPaDQaTJgwAYIgQKvVYvr06ejbty/mzZuHlJQUqR6RyWTo0aMHhgwZgvbt20Or1QIAcnJycOjQISxcuBA5OTmW/JGIKoUp9cS9DAYDsrKykJ6ezrXziKjKPFYi2bdvX7Ro0QJWVlbIzs5GVFQUzp8/b/J1DAYDLl68iKNHj6JZs2ZQqVTSpDs7d+58nBCJLObupQkUCgUaNWqEH374QRofdj9WVlbQarVwcXGRxgobDAbEx8dj2bJlyMjIMHfoRGaTm5uLU6dOIS0tDefPn8c333wDoPgFikajwcKFC1FQUCAdLwgCXFxc4ObmBhsbGwDFvV82bdqERYsWISYmht1aqcZ61HriXqIoIjc3F6dPn8bMmTPNESpRlbi7TAiCAIXi0VOVu4cBcdJC83jk346Hhwc6d+6MevXqQSaTITMzE7GxsY/8kJuQkICoqCgA/zfByOjRo5lIUo3l4+ODVq1aASj+Y2hnZydNmmMKnU6Ha9euYenSpfjrr7/40Ew1WskD75UrV6DX69GvXz/06dMHQHFFX1Jm7icvLw8RERFYt24dLl68WBUhE5lNZdUTQHGvFT8/P9y4cQM//fRTJUZJVHW0Wi28vLwAFE8wqNFoYGdnh6SkJJOuY2dnJw29k8lkcHd3f6TlpujBHjmRLCwsxLZt2+Dl5QUvLy8cPHgQhw8ffuQud9nZ2Thz5gxCQkLQqVMnXLt2Df/++y8EQYAoio8aJpHFREdH48SJE2jatCkcHBxMOjczMxM3btzAhQsXcOnSJVy7dg1HjhzhWDCqNQwGA+Li4vDFF19g165d6N69O/r163fft8YpKSkIDQ3FoUOHcO7cOWltVaKa7HHqiXsZjUbk5ubi+vXrfHaiGis2NhYHDhyAu7s7tFotVq1a9UjD3BITE7F582ZoNBrY2tpi6dKlJrf008MJYgX/0pQ0M9/9vUajQf/+/eHh4YGIiAiEh4cjNzf3kYNxcXFBhw4d0Lp1a8TExOCvv/6C0WiEKIr8g0jVzr1l4l5yuRytWrXCiBEjUK9ePajV6geOHy65XlFRERISEhAZGSmNHS5vaQOWCapuHlYm7qdkWY+BAwfC3d0dcrlc+nwbjUbodDrExsbi8OHDCA8Pv++YSJYJqm5MqSc8PDxgbW1t8jwTJUljTk4OLl++jF9++YXPTlRtPaxMyGQy1K9fH127doWjoyO2bNmCW7dumfxZlsvl8PDwwODBg2FtbY1Vq1YhLS2Nc0xUskdOJEu2iaIo7auMP1gymUy65t1/BPnHkKqbijw0y+VyWFtbw8HBAXZ2dg/8A1bymS8qKkJ2djZycnIe2I2VZYKqm0dNJEvY29vD2dlZWtpDEATo9Xro9XpkZmYiJyfngZ97lgmqbiq7nrjfPUq6jGdmZqKgoIDPTlRtVbSeKPlcy2Qyk1+u3H2Nu3OVkhcsVHkeK5GsSvzFU3XDMkFUGssEUWksE0SlsUzULlzVnIiIiIiIiEzCRJKIiIiIiIhMwkSSiIiIiIiITMJEkoiIiIiIiEzCRJKIiIiIiIhMwkSSiIiIiIiITFLh5T+IiIiIiIiIALZIEhERERERkYmYSBIREREREZFJmEgSERERERGRSZhIEhERERERkUmYSBIREREREZFJmEgSERERERGRSZhIEhERERERkUmYSBIREREREZFJmEgSERERERGRSZhIEhERERERkUmYSBIREREREZFJmEgSERERERGRSZhIEhERERERkUmYSBIREREREZFJmEgSERERERGRSR47kQwODoYgCNL3QUFBEAQBTZo0Kff4PXv2QBAECIKAv/76677XIaqpyvssi6KIlStXonv37nB0dISNjQ1atWqFzz77DLm5uWWuERQUhClTplRRxERV4371RcmXVqtFx44dsWzZMhiNRum4KVOmICgoyAIRE5lXSZko+e/Dvnx8fAAAc+fOlf6fqLa4u45ITk6GVqtFr169yhyn0+nQqlUr+Pj4SM9QzCMsQ2GOi6rVakRFRSEsLAyBgYGl9q1evRpqtRoFBQXmuDVRtWMwGDB+/Hhs2LAB3bp1w9y5c2FjY4PDhw/j008/xZ9//om9e/fC3d3d0qESVTlvb298+eWXAICUlBT88ccfmDp1Kq5evYqvvvrKwtERVY3u3btj5cqVpba9+OKLCAwMxMsvvyxts7Ozq+rQiCzCzc0NX3/9NV5++WWsWLECkydPlvYtWLAAFy5cwLZt22Bra2vBKMksiWTjxo2h1+uxdu3aUolkQUEBNm/ejIEDB2Ljxo3muDVRtfPNN99gw4YNmDlzJr799ltp+8svv4wxY8Zg2LBhmDJlCnbu3GnBKIksQ6PRYMKECdL3r7zyCpo1a4Yff/wR8+bNg1KptGB0RFWjUaNGaNSoUaltr776Kho1alSqfBDVJS+++CL++OMPzJw5E4MGDYKzszNu3LiBzz77DCNGjMCgQYMsHWKdZ7Yxks8++yzWr19fqnvStm3bkJeXhzFjxpjrtkTVSn5+Pr799ls0bdpUanW52+DBgzF58mT8+++/OHHihAUiJKpebGxs0LlzZ+Tm5iIlJcXS4RARkYUIgoDFixcjMzMTM2fOBAC8/vrrUCgUWLRokYWjI8CMieT48eORkJCAkJAQaduaNWvQu3dvuLm5meu2RNXKkSNHcOfOHYwfPx4KRfkdACZNmgQA2L59e1WGRlRtXb9+HXK5HI6OjpYOhYiILKhFixaYOXMmgoODMW3aNPz777/4/PPP4eXlZenQCIAgiqJYmRcMCgpCamoqLly4gI4dO6J169b47bffkJGRAXd3dyxduhQNGjRAz5498eeff2LUqFGVeXuiamXhwoWYPn06Nm/ejGHDhpV7zJ07d6DVajFixAh2+aY6JSgoCElJSTh8+DAAIDU1Fb/88gsWLVqEwYMHY+vWrRaOkMhy7OzsMGrUKAQHB1s6FCKLys/PR8uWLXH9+nW0b98eoaGhkMvllg6LYOblP8aPH49NmzahqKgIf/31F+RyOYYPH27OWxJVK9nZ2QAAe3v7+x5Tsi8rK6tKYiKqTq5cuQJXV1e4urqiefPm+OGHHzBw4EAsW7bM0qEREVE1oFKpoNFoAAC9e/dmElmNmDWRHDduHDIzM7Fz506sXr0agwYNeuADNVFtU/J5L0koy1ORZJOotvLx8cGePXuwd+9eHDlyBImJidi+fTtcXFwsHRoREVUDCxcuREREBFq2bIlFixYhKirK0iHR/2fWRLJevXoICgrCggULcOjQIYwfP96ctyOqdpo3bw4AOHfu3H2PKdkXEBBQJTERVSe2trbo06cPevfujaeeeopj6ImISBIXF4c5c+Zg2LBh2L17N1QqFd544w1Lh0X/n1kTSaC4e+vhw4fh4OCAZ555xty3I6pWunbtCkdHR6xZswYGg6HcY/744w8A4DTWRERERHd58803AQCLFi1CvXr18N///he7d+/GunXrLBwZAVWQSI4aNQpz5szBzz//DJVKZe7bEVUrNjY2mDlzJiIjIzF79uwy+3fs2IHg4GA8/fTT6Ny5swUiJCIiIqp+Nm/ejK1bt+Kzzz5D/fr1ARQv/9G+fXvMmDGDc0tUA+WvR1CJNBoN5s6da+7bEFVbs2bNQkREBL7++mscP34cI0eOhLW1NY4cOYJVq1ahefPmWLFihaXDJCIiIqoWsrOzMW3aNLRt2xbTpk2TtstkMixevBidOnXC7Nmz8cMPP1gwSjJ7iyRRXSeXy7FhwwYsX74cBoMB//nPfzBt2jScOnUKc+bMQWhoKNzd3S0dJhEREVG18J///Ae3b9/GkiVLyszS2qFDB7z++uv4+eefcerUKQtFSIAZ1pEkIiIiIiKi2o0tkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSRUUPFATBnHE8FJe7pOqGZYKoNJYJotJYJohKY5moXdgiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJlFU5sWUSiWUSiWMRiMAoKCg4LGuJwgCbGxsoNfrKyM8ompDJpNBo9FAJit+l6PT6aTtAFBUVITCwkIYDAaLxUhkDiqVCmq1GnK5HIIgSJ/98giCAJlMBkEQpDLB+oBqOrlcDpVKBZVKBQDSM9PdBEGAXC6HKIoQRbHcY+6uP4qKilg2qMYr+Ztf8tkWRfGRPtcymQxyuVz63mAwlFuG6PFVWiJpa2sLf39/tGjRAhkZGTAYDNi9e/cDHxIeRCaTwcnJCUOGDEFiYmJlhUlkcXK5HD4+Pnj99dfh4OAAURQRFRUFmUwGW1tbyGQyXLp0CSdOnMDNmzf5cEC1hq2tLVq1aoWePXvCxcUFarUaUVFR9z1erVbD3t4eSqUSV69exfHjx3Hjxg0UFBTwJQvVSDKZDPXr10eHDh3QvHlzKJVKZGVllfk8q9VqaLVaGAwG5OfnIysrq8x1NBoNjEYjrl27hoiICERFRaGoqKgqfxyiSmVjY4N69erBzc0NRqMR2dnZuHTpEkRRNOk6Wq0WjRo1gkKhQFFREaKjo5GRkWHydejhKiWRVKvVaN26NSZOnIiRI0fCzs4Op06dgk6nw+7du02+niAI0Gg0GDlyJObOnYt69epVRphEFqdWq+Hv74/ffvsNbdq0KfXGrIQoisjIyMCOHTuwZMkShIeHP3brPpGlqdVqtGrVCi+88ALGjh0LBwcHk87Py8tDTEwMdu/ejfXr1+PChQvIzc3lgwHVKPXr10ezZs3w5ptvolu3blLLy6MSRRFnzpzBkiVLYDAYEBUVxZcsVCPZ2NjAy8sLI0eOxKRJk2BlZYWVK1fi559/RlJSUoWvY2dnh/bt2+P1119Ht27dcPz4cXz22We4cuUKMjMzzfgT1E2VMkayoKAAzZs3x9ChQ+Hq6gpra2u0aNECDRs2fKTriaIIuVyOoUOHMomkWsPd3R2///47IiIi0L59+3KTSKD4RYqTkxMmTJiAPXv2YMOGDSwHVOMVFBSgadOm6Nevn8lJJFD8kBEQEIDp06fjyJEjOHr0KDp27AhBEMwQLZF5GI1GvPTSS2jfvv1jJ5FAcX3h7++PUaNGwdnZmUkk1ViFhYV4+umn8eKLL8Lf3x++vr4YP3486tevb9J1NBoNnnrqKQwZMgROTk7o3bs35syZg7y8PDNFXrdV2mQ7ly9fxokTJ6TvlUolWrRo8UjXEgQB1tbW8PLyqqzwiCxKo9Fg8ODBGD9+vEnnqdVq9OjRA998842ZIiOqOmfOnMG+ffse+zpyuRxPPPEEdu/ejffee+++L2WIqpu4uDjMnDkTu3btqpRuqDqdDkeOHMGnn36KY8eOVUKERJZhMBiwc+dO/Pvvv9KwuAYNGuCHH36Ara1tha6hUqkwfPhwvPbaawCKG6aSkpLw9ddfP/JQO3qwSkskc3JycOfOHel7a2tr9O3bF05OTiZfy8rKCs2bN0fr1q0rKzwii7C1tUWfPn2wdu1aLFmypNS+Y8eOoXnz5vDy8pK+6tWrh6FDh2LPnj3Sm2UHBweMHTsWZ8+exbBhw/jQTLWCTqfDmTNn0KZNm1Jl4N4vT09PtGzZEq+//joOHjyIwsJC6Rp2dnaYOHEiFIpKnTeOyKxiYmLw3HPPwcPDAx4eHmU+840bN8aqVauk48+ePYtWrVrB29u73PIxZMgQHDlyxII/EVHluH79OpYuXYrg4GAUFRVBpVIhICAA7733XoXO79evH0aOHAkXFxcYjUZER0dj1qxZOHr0qJkjr7vMVvvK5XI0aNAAnTt3xs6dO00619HREQMHDjRTZERVx9vbG0OHDkX//v2lLng6nQ79+vXD8ePHUVRUVGaM17Zt27Bv3z5MmTIFs2bNgre3N5RKJZo3b45Zs2Zh165dyM/Pt8SPQ1RpRFFEYWEhkpKSHjqhWmJiIi5fvoyNGzdi3LhxmD17Ntzc3CCXy+Hl5YXRo0eXevAmqu4KCwtLvRS5m42NTam/8UVFRUhKSkJqairHBFOtZjQacfbsWfz1118IDAxE69at4eDggOnTp+Pvv//GmTNn7nuuj48PxowZgx49egAAMjMzERISgvXr13PGVjMy6zqSarUaU6dONekchUIBb29vDBgwwExREVWNkjdpffv2lZLI/Px8vPvuuzh69CgKCwvLfSgQRRG5ublYt24dFixYgOzsbADFZaNRo0bo2rVrlf4cROZUkQfjkuUPUlJSsGXLFvzyyy9SNyUHBwfMnDnzkcfkE1U395aJkiVAiOoCURRx4sQJfPfdd9ILFXt7ewQHB9+3i6tarcbbb7+NgQMHQhAE6PV6RERE4L///S+TSDMzayIpl8sRGBhoUgVvb2+PVq1aoVGjRmaMjMi85HI5AgIC0L9/f/j5+QEofqscGhqKjRs3VqivfmZmJg4dOoQ///wTOp1OmoTno48+gpubm7l/BKJqRxRFJCQkIDQ0FBkZGQCKl0FwdnbmwwIRUS2RnZ2Nw4cPS0OCZDIZmjdvjunTp5d7/MiRIxEUFCQNpzt37hyWLFmC2NjYqgq5zjJbIimKIgRBgLu7O55++ukKn+fu7o5nnnmG48CoRjMYDPD19UWHDh2kz3JmZiYWLVpU4XVR9Xo9Ll++jG3btiEnJwdAcatky5YtoVarzRY7UXVWVFSEhIQEXLhwAUBxXZOcnCwllkREVLOJoojY2FisWrVK6s6qUqkwffp0BAQElDq2UaNGmDRpEgICAiAIAhISErBr1y78888/fMFYBcySSIqiKC2irlQqMWrUKFhbWz/0PLVaDT8/PwQGBpojLKIqo1QqodVq4ezsDKD44ffatWvYt2+fSX/Y8vPzcfny5VLjjB0cHNCgQYNKj5mopjAajdJkVKIoQqfTcdkDIqJaRK/X4+rVq/jmm2+ktbRdXFwwf/582NnZASjOG9577z20b98eKpUKOp0OBw4cwJo1a6QX8GReZkkkjUaj1K9ZEAQEBgbC19f3oee5ubmhQ4cO0pp59xuITlTdeXp6IiAgQOqCmp6ejvXr1yMrK8vka92+fRt///030tPTARS3So4ZM6ZS4yWqKQRBgIODAxo3bgyguMuTp6dnhV5WEhFRzZGTk4ODBw/ijz/+kLb16NED48aNg1KpxJAhQ9CnTx+pS2toaCg2b96Mq1evWirkOsdsLZJ5eXnS2wCNRoN+/fo9cPFdmUwGX19fdOvWDUqlEgaDASkpKeYIj8is5HI5unbtiu7du8Pa2hqiKCIzMxOhoaGPdL3c3Fxcu3YNqampAIrLCmc1prpIEATUq1cPHTt2hIeHB4Di+ubOnTtQKpUWjo6IiCpTydCFpUuXSsmhjY0N3nzzTXTp0gWvvvoqvL29IZPJkJCQgO3bt+PQoUOVskYrVYzZEsn8/HxERkZK24YOHQp7e/v7nmNnZwc/Pz80b94cQHFXwAdN80tUXSkUCnh5eUkPukajEbm5uUhLS3uk65WUp+TkZGmbq6trpcRKVFMIggAPDw88/fTTGDVqFKysrAAAeXl52Lx5M7sxERHVQnq9HufOncMvv/wiJYitW7fGjBkz0LlzZ6jVauj1evz777/YtWtXqWclMj+zJpIhISHSL71t27bw9/e/b6ukp6cnWrduDWdnZ4iiiIyMDC4gSjWS0WjEnTt3pG6sOp2u1PemKmnhv7urBteRpJpOEAQolUq4uLjAzc3tgV8+Pj4ICgrClClTMGXKFHTs2BEymUxaEuSvv/5iIklEVEvpdDqsXr0aBw4ckLYNGjRIGtIQERGBjRs34sqVK5YKsc5SmOvCBQUFCAkJwfjx41GvXj1oNBr0798fZ8+elQbNlpDL5WjevDk6deoElUqFgoICREVF4dq1a+YKj8hsdDodMjMzpQdbnU6HtLS0R04kgeJWl+vXr0vfs9sG1XQymQyurq4YN24ccnNz73ucKIpwdnZG27Zt0bp1a7i4uEAQBIiiiKysLBw9epR1BRFRLSaKIlJTUzF//nwEBgbCyclJapjKysrC6tWrER4eXia/IPMzWyKp1+tx/fp1XLx4UZo8Z9CgQfj111+RmJhYanFdR0dHtGjRAs2aNZNaIw8fPiwtxE5U0xiNRml21rtnMX4cd1+jMq5HVNWsra2l2fbkcjnq16+Pjz/+2OTrlLT6Hzp0CEuWLKnQuqxERFRziaKIAwcOIDY2VppcBwASExMRGhrKLq0WYtZ1JLOzs7Fp0yZpWvamTZvC39+/zKQITZo0QZs2baDRaKDX6xEfH49//vnHXKERVTlBECr1Gg+auIqounJxcYG3t/cjnavX65GZmYm4uDicO3cO27Ztw6effoojR45wrTAiojrAx8dHmn+ihIuLC+rXrw9bW1sLRVW3ma1FEiiu+Hfv3o3k5GTUq1cPDg4OGDJkCCIiIqSueTKZTOqyBBQv+RETE4MrV66gffv25gyPyGyUSiVUKhWA4s+4jY0N5HL5I19PLpeXmqyqZKIRopokPj4ekZGR6NKlC4Dibt8ZGRkPTQSNRiMSEhIQFhaG3bt349ixY0hOTi7Vs4WIiGova2trzJw5E+7u7gCKG6wEQYBWq8XEiRMRFxeHsLAwvlisYmZNJI1GI27evImwsDAMGTIEgiBg2LBhWLJkCbKzs2EwGODh4YFWrVrBy8sLAJCQkIB//vmH3VqpxlKpVPDx8YGnpyeA4qmq69evD2dn5weOBbsfQRDg6OiIrl27StscHBwqLV6iqiKKopT86fV6REZG4r333pPWSL1XyVjIvLw8pKWlIT09nesLExHVMQqFAp07d8arr74qbbt16xbq1asHuVyOwYMHIyIiAvHx8YiPj7dgpHWPWRNJADAYDFixYgW6desGrVYLHx8fdOjQAfHx8cjJyUH79u3RqlUrWFtbo7CwENHR0di/f7+5wyIyG5lMhvz8fOTl5UnbVCoV6tWrh9jY2Ee6pkqlgouLi/R9Xl4eF2CnGq1kWZyIiAgkJSVZOhwiIqqGSpZ+Cg4OlrZlZmZi0qRJ+PXXX9GoUSPIZDK8/vrrSElJwW+//cZJd6qQ2QdaiaKIgwcPIjMzU9o2ZswYaLVaWFtbIygoCP7+/gCAjIwMXLx4Ebdu3TJ3WERmU1BQgK1bt2L79u0oLCyETCaDRqNBo0aNHul6VlZW8PDwkBJJo9GInTt3VmbIRERERNWORqPBe++9hwYNGkjbPvjgAxw5cgSffPIJbt++DaB4rOSIESPQu3dvS4VaJ1XJjB3p6enYsmWL1G+5c+fO8PX1RYsWLdCkSRNp7Ne1a9ewc+dOzkhJNV5cXBzOnDkjdbHw8PDAtGnTykw0VRHe3t547rnn4ObmBlEUkZaWhh9//LGyQyYiIiKqNqytrfHUU09h2rRp0rb9+/fjt99+g06nw9q1a/Hvv//izp07AICePXti7Nix8PHxsVDEdU+VTf24atUq6Rft6uqKoKAgPPfcc2jRogWsrKyQk5ODK1eu4PTp01UVEpHZ6HQ65ObmSuO5lEolfH19ERQUZNJ1BEFA/fr1MWzYMOl7a2trxMXFVXLERERERNWDXC5HQEAAfvnlF2lbTk4Opk6dKq0GAQBz5sxBRESE1Ag1ZMgQzJgxA2q1uspjrouqLJG8cuUKoqKipFbJUaNGYciQIdIak9evX8exY8ekZJOopgsLC8OWLVukNe60Wi3mzZuHhg0bVuh8pVKJJ598Eq+99pq09p5er8e+ffu4XhIRERHVWj4+PnjttddQv359AMVzrrz//vu4efNmqeNu376NhQsX4syZMwCKu8L27NkTzz77bFWHXCdVWSKZm5uL9evXSw/VjRo1QoMGDWBtbY2CggKcPn0ax48fr6pwiMwuNjYW+/btw6lTpwAUJ4YtWrTA6NGjK3S+jY0N2rVrhwEDBkChUEAURSQmJuKjjz5i928iIiKqlZycnNCrVy9MmDABQHESeejQISxbtqzcZZ927tyJPXv2SBO3NW/eHFOmTEHTpk2rNO66qEpXNd+5cyeSk5NhNBphbW0NhaJ40tjMzExER0ezux7VKkajEbdu3UJ4eLjUEm9ra4sPP/wQH374YZlFdUvIZDK0aNECn3zyCT7++GOpNVKn0yEyMlIaWE5ERERUmygUCnTv3h3vvvuutGZ2fn4+ZsyYcd/ln3Q6HX799Vfs27cPQHG32DZt2mDu3Lmc4d7MzL78x92io6MRGhqKQYMGleq7fOHCBZw6darUcglEtUF8fDz+/PNPaDQaTJgwQVo8d/r06ejbty/mzZuHlJQUKdGUyWTo0aMHhgwZgvbt20Or1QIoHhdw6NAhLFy4EDk5OZb8kYiIiIgqnUKhQIcOHTBy5Eg0btwYAJCVlYVvvvkGFy9efOC5cXFxWLNmDdzd3dG7d2/Y29ujS5cumDBhAv744w+uQWwmlZZICoIAQRCk/y9pbbybTqfDpk2b0L17dymR1Ol0OHv2LC5evFhuczVRTZabm4tTp04hLS0N58+fxzfffAMAcHNzg0ajwcKFC0utdyQIAlxcXODm5gYbGxsAQGpqKjZt2oRFixYhJiaG3VqpxqpIPUFExS8VS6hUKqncENVmXl5eGDx4sDSkJz8/H6GhoVi2bJk0NO5+DAYDDh8+DD8/PzRr1gze3t7w9PTEq6++ioiICISHh1fRT1G3VFotrtVq4eXlBaC4SVmj0cDOzq7MQtOXLl0qNdsSACQkJCAxMbHUNrlcLk3EQ1RTiaKI3NxcXLlyBXq9Hv369UOfPn0AFK8P2apVqween5eXh4iICKxbt+6hb+OIqruK1hNEdZlcLkezZs2k7z08PKBSqSwYEVHV6NSpE3r37i2tm52cnIyffvoJCQkJFTo/KysLe/bsgb+/P1599VUolUo0bdoUEydOxKlTp9hgZQaVlkjGxsbiwIEDcHd3h1arxapVq5CZmVnmuFu3bmH79u0YPXo07OzssHfvXpw6darMmwa9Xo+LFy9iw4YNGDp0qNRPmqgmMhgMiIuLwxdffIFdu3ahe/fu6Nev330/1ykpKQgNDcWhQ4dw7tw5LotDtUJF6wmiukyn02HNmjXw8PCAs7Mzfv31V+Tm5lo6LCKzEgQBV69eRWhoKDw9PSEIAv7++2+EhISYdJ3r169j69atCAgIQJs2bXD58mUcPnzYPEETBLGC6fnDulXIZDLUr18fXbt2haOjI7Zs2YJbt26Vyf7lcjnat2+P3r17w87ODocOHcLJkydx586dMscqlUq0b98eTz/9NObOnWvaT0ZkZo/a1ahkWY+BAwfC3d0dcrlc+uwbjUbodDrExsbi8OHDCA8Pv++YSL5Zo+qmsuqJR8UyQdXNo9QTgiDA1tYW48ePh5OTE1asWIGUlJQyvbkqgmWCqpsHlQmZTIaWLVuie/fukMlkOHbs2CO1JHp4eODJJ5+Ev78/rl+/jr/++gtGoxGiKLJMVLJKSyTvPk4URchkMmkCkXspFAoYjUZpDIDBYLjvL7bkOvzFU3XzuGNW7O3t4ezsLC3tIQgC9Ho99Ho9MjMzkZOT88DPPcsEVTeVWU88CpYJqm4etZ4oGU9cUjc8ajlhmaDq5mFlouSzLwgC5HI5ioqKTL6HXC6HXC6HTqeTylFJWWCZqFyVnkiaC3/xVN2wTBCVxjJBVBrLBFFpLBO1S5WuI0lEREREREQ1HxNJIiIiIiIiMgkTSSIiIiIiIjIJE0kiIiIiIiIyCRNJIiIiIiIiMgkTSSIiIiIiIjJJhZf/ICIiIiIiIgLYIklEREREREQmYiJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCZhIklEREREREQmYSJJREREREREJmEiSURERERERCaptEQyODgYgiCYfJ6Pjw/mzp1bWWEQVQv3loegoCC0bNmyzHH79u2DjY0N2rVrh/T0dAAsE1R7lVdPXL58Gf3794ednR20Wi0mTpyIlJSUUseEhIRAEATExMRUYbRE5nVveQgLC8Prr7+O9u3bQ6lU3veZKiYmBoIgICQkpIoiJaoad5cJo9GI4OBgDBkyBPXr14etrS1atmyJzz//HAUFBaXOY5mwHLZIElnI/v37MXjwYDRr1gx79+6FVqu1dEhEVSo+Ph7du3dHVFQUvvjiC8ycORM7duxA3759UVRUZOnwiKrUP//8g99++w2CIKBRo0aWDofIovLy8vD8888jJSUFr776Kr7//nsEBgZizpw5GDBgAERRtHSIBEBh6QCI6qKDBw9i8ODBaNq0KZNIqrO++OIL5Obm4tSpU2jQoAEAIDAwEH379kVwcDBefvllC0dIVHVee+01fPDBB7C2tsabb76Jq1evWjokIotRqVQ4evQounTpIm176aWX4OPjgzlz5mDfvn3o06ePBSMkwEwtkpMnT4aLiwt0Ol2Zff369UOzZs3McVuiGuHw4cMYOHAg/Pz8sHfvXjg7O1s6JCKL2LhxIwYNGiQlkQDQp08fNG3aFBs2bLBgZERVz93dHdbW1pYOg6haUKlUpZLIEsOHDwdQPCyCLM8sieTEiRORlpaGXbt2ldqemJiI/fv3Y8KECea4LVG1d/ToUTzzzDPw9fXFvn374OLiYumQiCzi1q1bSE5ORocOHcrsCwwMREREhAWiIiKi6iwxMREA+PxUTQiiGToZG41GNGzYEE899RTWrVsnbf/uu+/w7rvvIjo6Gr6+vpV9W6JqKygoCOfPn4dOp0ODBg1w4MABuLq6WjosIosJDw9Hx44d8ccff2DixIml9r3//vv49ttvUVBQACsrKwtFSGQ5b775Jn766SeOAyO6R9++fREWFoabN2/C0dHR0uHUeWZpkZTJZHjuueewdetWZGdnS9tXr16NLl26MImkOik3NxfZ2dlwd3eHg4ODpcMhsqj8/HwAKDdRVKvVpY4hIiL64osvsHfvXnz11VdMIqsJs83aOmnSJOTn52Pz5s0AgMjISJw6darMm2eiusLPzw9ff/019u/fj2effRYGg8HSIRFZTMlYsMLCwjL7SqZ253gxIiICgPXr1+Pjjz/G1KlT8dprr1k6HPr/zJZIBgQEoH379li1ahUAYNWqVVCpVBgzZoy5bklU7b3//vt4//33sXnzZrz00kvstkR1Vr169QAACQkJZfYlJCRAq9WyWysREWHPnj2YNGkSBg4ciMWLF1s6HLqLWZf/mDRpEmbMmIGEhASsWbMGAwcOhJOTkzlvSVTtff3110hPT8dvv/0GJycnLFiwwNIhEVU5Ly8vuLq6Ijw8vMy+sLAwtGnTpuqDIiKiaiU0NBTDhw9Hhw4dsGHDBigUXLmwOjFbiyQAPPvssxAEAW+//TauX7/O2VqJ/r8lS5Zg1KhR+N///ofPP//c0uEQWcTIkSOxfft2xMXFSdv27duHq1evYvTo0RaMjIiILO3y5csYOHAgfHx8sH37dg53qIbMMmvr3QYPHozt27fD0dERiYmJ7KpEdVJQUBBSU1Nx4cIFaVtRUREGDRqEPXv24KeffsLrr79uwQiJql5cXBzatm0LR0dHvP3228jJycG3334Lb29vnDx5kvUF1Sk3b97EypUrAQDbt29HaGgo5s2bBwBo2LAh55igOiU7OxstWrTArVu38MUXX8DLy6vU/saNG+PJJ5+0UHRUwuyJ5J9//okxY8bg5ZdfxpIlS8x5K6Jqq7xEEiieybVPnz4ICwvDypUrMX78eAtFSGQZFy9exIwZM3DkyBGoVCoMHDgQCxYsgLu7u6VDI6pSISEh6NmzZ7n7evTogZCQkKoNiMiCYmJiHrjKw+TJkxEcHFx1AVG5zJ5IbtmyBcOGDcOhQ4fQrVs3c96KiIiIiIiIqoDZE8lBgwbh8uXLiIqKgiAI5rwVERERERERVQGzTX20bt06nDt3Djt27MDChQuZRBIREREREdUSZmuRFAQBdnZ2GDt2LBYvXszpeomIiIiIiGoJs2V3XGidiIiIiIiodjLrOpJERERERERU+zCRJCIiIiIiIpMwkSQiIiIiIiKTVHiMpKVnXeWYS6puWCaISmOZICqNZYKoNJaJ2oUtkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBImkkRERERERGQSJpJERERERERkEiaSREREREREZBKFpQMgqs3kcjlkMhlEUQQA6PX6R7qOTCaDQqGAKIowGo0wGAyVGSaRxSkUCjg4OEAmk0EQBBgMBhgMBuTn50On00lliKguUiiKH9dkMhn0ej2MRqOFIyKqWnK5HLa2tpDL5TAajTAajZDJZJDJZDAajSgoKEBRURHriirGRJLITFQqFdzd3eHl5YWCggLk5+cjKirK5CTQysoK9erVg7e3N4qKihAfH4/ExEQzRU1UdQRBgEwmg7W1NRo1aoRXXnkFNjY2sLGxQXJyMlJTU7Fv3z5cuXIFWVlZTCipTpLL5XjiiScAAGq1Gjdu3EBKSsojv5gkqmlUKhWaNGmCIUOGwNnZGdnZ2cjKyoK9vT1sbW2Rl5eH48ePIyIiAmlpaXzRUoUEsYK1siAI5o7lgfjwQNXNg8qEQqGAj48PBg0ahHHjxsHZ2Rk///wzduzYgatXr1b4HiqVCgEBAXjjjTfw3HPPISYmBi+//DKio6Nx+/btyvgxiCqNKfWEXC6Ho6MjvLy88Oabb2LAgAHw8PCQWl4AoKioCMnJyTh58iQ2bNiAo0ePIi0tDXl5eeVek/UEVTeV8ezUrl07rFy5Eo0bN8apU6ewYsUK/Pvvv7h169ZDX0yyTFB1Y0qZEAQB9vb26Ny5M7777jv4+/tDJis7Ks9oNOLWrVtYtmwZVq1ahZs3b0Kn05V7TZaJysVEkugRPaxMDBw4EB999BG6dOkCURQRFxeHIUOG4OzZsxW+R4MGDTB16lR88sknAIr/WEZHR6Nbt25slaRqx5R6olGjRli8eDH69u1b4XOio6Px008/4bvvvit3P+sJqm4e99lJJpMhLCwMbdu2lR6gT506hZkzZyIkJOSh57NMUHVT0TKhUCjwxBNPIDg4GK1atarw9ZOSkrB8+XLMmzev3JeOLBOVi5PtEJlJXFwcrly5AgAwGAxITU01Kfnz8/PDO++8IyWRAKDT6bBmzRpkZmZWerxEVcXV1RVDhw41KYkEgMaNG2PYsGHo2rWrmSIjql6OHz+Odu3alWqFiY6ORkZGhuWCIjIzBwcHjBw5Ehs3bjQpiQQAd3d3vP7661i2bBl8fHzMEyBJmEgSVQGj0XjfbhbladmyJT7++GNMnz5d2paQkIAnnngCc+fORUFBgRmiJDI/lUqFwMBAzJ49W9omiiL++ecfjBs3Dj4+PvDy8kKDBg0wePBg/P7776W6cQcGBuKdd96Bg4ODJcInMjsbGxu8//77yMrKQmBgYJkWnPj4eGRnZ1soOiLza9CgAZ5++ulSiaBer8evv/4KLy+vMl8fffQRrl27JrU2Ojg4oH379ujTp4+FfoK6g5PtEFUz7dq1w8cff4yhQ4dK2yIjIzFs2DCTxlcSVTcKhQJ9+/bFF198AWdnZwBAfn4+ZsyYgd9++02aia9EfHw8du7cid69e+P9999H7969oVar0bNnTyxcuBCvvfYaX6pQraHRaDBkyBCMGTMG/fr1g0qlkvbpdDoolUoLRkdUNaytrdG2bVv0799f2padnY0XXngB27ZtQ2FhYZlz5s+fj8OHD+ODDz7AwIEDIQgCvLy8MGrUKGzevBlpaWlV+SPUKWyRJKpGOnfujK+++gqDBw+WujKFhYVh5MiRTCKpRpPL5Xjqqafw0ksvoWXLlgCAwsJCHDx4EL///nu5SxqIogiDwYB9+/Zh69atuHHjBgDAyckJTz/9NN566y0+XFOtYGdnh44dO2L48OHo379/qSQyOjoaSUlJnImSaj25XI7OnTtjzJgx8PDwAADk5eVh48aN2L59e7lJJFD8oiU0NBSbNm3C+fPnARQnpK1bt8ZLL73EesKMmEgSVRNdunTB119/jR49ekgzV+7Zswcvv/wyrly5wocIqtFEUYSTkxPq1asnvSTJyMjAjz/++NBu3waDAfv378fBgwelrkvW1tbw9fVluaBaQ6FQQKvVSn//dTodtm3bhvnz5yM/P5+ThFCtJ5fL4eHhgfr160tduvPy8hASEvLQ3ic6nQ5hYWEIDw+XyoparYaXlxfrCTNiIklUDTz11FP49ttv0alTJ+lN9KZNm/Dhhx/i4sWLJq89SVTdGI1GCIJQ6iE5JiYGR44cqdD5UVFROHPmDNLT0wEUz2ZpZWXFskG1Ql5eHk6ePIkff/wRx48fR3h4OEaPHo3XX38de/bsMWmMPVFNpdPpkJWVJY0BFkUReXl5Uivjw9y+fRu3b9+W6oWSc1lPmM9jj5FUqVQYOnQounTpgitXrmDbtm0VXt/OysoKzzzzDLp164aLFy9i06ZNuHPnzuOGRFSjlHRnbd++PaysrAAAa9aswcKFC3H+/HkuOk21gkqlgouLizQ2Mjs7G4cOHUJWVlaFzi8oKEBkZCQuXLiAHj16QKlUwsvLC7a2tsjNzTVn6ERmZzQacefOHezZswdRUVEwGo2IiopCXl4ePDw82KJCdYIoijh+/DgWLFiApKQkeHp6YsGCBdIM+A/j5OQEZ2dnyOVyAMUTV5UMpSDzeKxE0traGpMmTcLkyZPh5+eH+Ph42Nvb47vvvqtQ9v/MM8/glVdeQbt27ZCQkABXV1csW7YMycnJjxMWUY3RoUMHfPnllwgMDJRaIlesWIFffvkFZ86cQVFRkYUjJKoctra2cHZ2hr29PYDiN8UREREmdddLSEjA9evXpe7fLi4u0Gq1TCSpVjAajcjMzMTZs2fZjZXqrPT0dOzfvx/Xr1+HjY0NLly4UO56kOXp3r07unXrBkEQYDQakZKSggMHDpg54rrtsRJJKysrNGzYEPXr14erqytsbW3RpUsXrFy5EklJSQ8819raGr169UK7du3g6uoKlUqFJk2awNra+nFCIqoR5HI5hgwZgpdeegldu3aVuvsxiaTaytXVFQ0aNICdnR0MBgPu3LkjTZ5TUenp6YiNjUVBQQGsrKzg6uoKPz8/xMXFmSlqoqrHJJLquqysLJw7d86kcwICAtC7d280adIEAJCTk4NTp04hJCTEDBFSiccaI1lYWIiIiAjExsYCKB7U2qBBA7Ru3fqh53p5ecHPz096O33r1i2cO3eOi+xSradQKNC/f3+899576N+/v5RErlmzBj/++CMiIiLuOzMZUU2kVCrRunVrtGrVCiqVCqIooqCgwOS/9wUFBcjJyYFer4cgCNBqtRgwYECpGS6JiKhuUSqVGDBgADp37iwNEUpNTcWRI0c4ZM7MHiuRzM/Px9GjRxEWFoaMjAzIZDJ4e3ujd+/eD63Yu3btigYNGkCpVKKwsBBnzpxBSEgIMjMzHyckompNJpPBz88P7777Lp588klpVrLExET8/PPPOHfuHFsiqdaRy+Xw8/NDo0aNAACCIEAQBJM/6waDAXq9Xpr11crKCp07d2YiSURUR8nlcrRo0QJBQUFo2LAhgOIWzYiICISEhEjPWWQejz1r6+3bt3Ho0CFcuHABAKDVatGtWzfUr1//vufY29tjwIABqF+/PuRyOeLj43HixIkKD6YlqonkcjkaNGiAl19+GT179iy1Lzs7G0qlUnpAJqpN9Ho98vPzpZknDQYD8vLyKjzRTomCggJkZmZKLfYlM/rx5QsRUd3k7++PKVOmoE2bNlAqlcjLy0N4eDhWr16NS5cusau4mVXKU+upU6dw8uRJFBUVQalUomHDhujZs+d93wJ06NABLVu2hI2NDQwGA8LDwxEWFsbufFRrCYIAe3t7jB8/Hu+88460veTBukmTJpgwYQIaNWokzTZGVFvo9Xrcvn1bGjtfVFSEpKQk5OTkmHSdoqIiZGRkSEuAFBUVITIykokkEVEdpFar8cILL+DZZ5+Ft7c3gOKlolatWoXNmzcziawClZJIJiQk4MyZM4iJiQFQPP3uyJEjpfGPd5PL5RgxYgS8vLwgl8uRnp6O0NBQXLp0qTJCIaqWZDIZ3N3dMW3aNGlbZGQkIiIipOU9Jk+ejMGDB8PDw4NdMajWUavV0tiVkjUgTX1pIggClEplqevY2dmxvBAR1TEqlQpdu3bFiBEj4ObmBqD45eLRo0fx77//Wji6uqNSEkmdTofTp0/j4MGDMBqNsLKyQrNmzdCuXbsyx3p7eyMoKAh2dnYAgDNnzuDMmTPS4qNEtUXJODCg+AWKo6OjtO/27dsYOHAgJk+ejLi4OBiNRigUCrz33nt45plnyn0JQ1RTKRQK+Pj4SEMe1Go1vL29pXqgoqysrODs7Ax3d3cAxQ8S7du35xhJIqI6RKFQoHPnzli7di18fHwAFA8RioiIQFhYGFJSUiwbYB1SaQOyoqOjcfToUaSmpkqtLxMmTCjzpnjIkCHS2Eij0YgDBw6wNZJqJbVaDVtb2zLb09LS0KNHD0RHR+PKlSv45JNPkJaWBgBwdnbGSy+9hN69e7OLK9UaSqUScXFx0nIfBoMBRUVFpV6uVIRarYZarZbWFCsoKMCJEyekFkoiIqrdZDIZ2rRpgx07dsDFxQVAcV3w448/YsqUKVi7dq3U04vMr9ISyfz8fFy8eBGhoaEAiiv83r17SzMoAcWT7EyePFl6uL506RIiIiL45oBqHblcDm9vb/j5+ZXanpaWhi5duiAqKkraVtKXv2TG4o4dO2LcuHHo0KFDlcZMZC75+fkIDQ1FWFgYcnJyIAgCFAqFyesGKxQKWFlZQSaTwWAwIDExEZs3bzZ50h4iIqp5BEFAy5Ytcfz4calHi16vx2effYaffvoJV65c4XwrVaxSp4iMiorC5s2bkZ2dDZlMhnr16mHChAnS/s6dO0tjIwFg06ZNuHjxIoxGY2WGQWRxoihCp9OVmgQkJSUFU6dOxdWrV8sc/+677yIiIkI6fuTIkZg6dSo8PT2rLGYic0pMTMStW7eg1+uhUCjg5uZW5kXLwzg7O8PX1xf29vYQRRE5OTm4fPmymSImIqLqQhAENG/eHGfPnpXW3waKn5+WLl2KW7duWTC6uqtSE8mMjAxERETg/PnzAIrHr0yePBlarRYAMG3aNLi4uEAQBNy+fRvHjh2TZvEjqk2MRiNu3rwpPeQaDAYkJSUhMjKy3ONzcnLw0ksvITw8HEajEXK5HOPGjcPs2bNhY2NTlaETmUVubi7S0tKkmVrt7OzQsWNHk67h5eWFpk2bAih+C52WlsbFpomIajmZTIYWLVrg4sWLpba/+OKLCA4ORmpqqoUio0pftC4uLg6rVq2Svnd0dESvXr3QsmVLdO7cWXqLsGXLFk7bTrWaKIrS1NMGgwG5ubkPfOiNiorC119/jdOnTwMo7gr+zDPP4O23366SeInMqbCwELdu3cLt27cBFH++e/XqVeHurUqlEs2aNUPr1q0BFI+JuXr1KidqIyKqxRQKBdq1a4ezZ8+W2j5hwgSsWbOGQxssrNITyYyMDISFhSE6OhqCIMDZ2RnTpk3DG2+8IbVM3rlzB3///TcSExMr+/ZENdr27dvx559/SpOSNGzYEOPGjcPAgQMtHBnR41MoFFAqlQCKE0Nvb2+0b9++Quc2bNgQLVu2lCbokclkUKvV5gqViIgszMrKCk899RSOHj0Kmez/Upbx48fjr7/+Qn5+vgWjI8AMiaTBYEBcXBzWr18PoLhPc6tWrTB06FDpQ3Do0CFERkaioKCgsm9PVKMZjUYsXboUmzZtkiYladGiBT777DNpimuimkihUODGjRs4deqUtM3Z2RlffvnlQ2ddVSgUGDFiBAYNGiTNBJ6ZmYndu3dz6Q8iolrIxsYGffr0wdatW0v9nZ80aRI2bdrESXWqiUpPJIHiVslt27ZJfZY1Go207ldOTg7WrVsnLXdARKVlZGRg6dKlWLt2LYDiGWD9/f3xww8/lLucCFFNoNfrERkZiY0bN2L//v0Ailsl27Vrh++//x6urq7lnqfRaPDqq69i/Pjx8PDwAFA8cc+aNWuwe/duDo8gIqpl7OzsMHjwYCxbtgwODg4AihuqXnnlFWzcuJFJZDWiePghpisqKsKNGzdw6NAhjBgxotTC7NevX0d4eLi0DhgRlSaKIqKjo7F27Vq4u7tjyJAhsLa2RpcuXTBr1ix8/fXX0oQlRDVJfn4+jh07hiVLlqB58+aoV68ebGxsMG7cODRq1Ah79+7Fvn37UFhYCKVSiSeffBLdu3dHYGAgvL29IZPJkJeXh+PHj+Onn37iRDtERLWMk5MTRo8ejdmzZ8PNzQ1AcV7x448/4sKFC/D19ZXmn3gQg8GArKwspKenM/E0I7MkkkBxt6N169Zh8ODB0pgYnU6HdevWITk5mUt+UK139wuUknXzKkqv1+PkyZNYt24d/Pz8EBAQAEdHR0ycOBExMTH4/fffzRU2kVllZWXh2LFjWLp0KWbPng25XA5HR0d0794dzZs3x9ixY2E0GiGTyaDVauHi4gJbW1vIZDKIoogzZ85g6dKliI+Pr9DDBFFtUPIcBUCqV4hqG39/fzz33HN49tln0aBBA2m7QqHA4MGD0a1btwpfSxRF5Obm4vTp05g5c6Y5wiWYMZEsKirCyZMnS/3Bk8lkOHXqFMdGUp2g1Wrh5eUFoLh7qkajgZ2dXYWXvMnJyUFISAi8vb3x2WefQa1Ww9vbGxMnTsSff/5pztCJzCopKQlbt25Fz549pQcDtVqN+vXro379+vc9LzY2Frt27cKxY8eYRFKdYWdnB0dHR2meiYYNG0Kj0Vg4KqLKJZPJ0L59ewwYMACNGzcus69JkyYmX9NoNMLPzw83btzATz/9VFmh0l3MlkjKZDK4uLiUanm8ceMGoqOjodfrzXVbomojNjYWBw4cgLu7O7RaLVatWoXMzEyTrpGUlIRt27bB398fY8aMQXZ2Nv766y8YDAYzRU1kfjqdDpGRkfjyyy+xc+dONG3aFO3atYO/v3+pSRWKiooQGxuLixcv4sqVK7h8+TLCw8NNLkdENVlmZiZ+//13vPTSS7h58yYOHDjANbipVrp48SJOnz6NZs2awc7O7rGvZzQakZubi+vXr0MQBL6ANAOzJZL29vYYNWqU9AYtOzsbv/76KxISEtitleqEGzduYO3atYiLi4OjoyO2bNli8iRTRqMRcXFxWLJkCW7cuIGcnBwEBwdzymuq8XJycrBr1y6EhISgcePGaN26Ndq3bw+tVgu5XA6j0Yg7d+7gypUruHDhAq5du4a0tDS+RKE6Jy0tDYsXL0Z6ejri4+Nx8OBBJpJU6xiNRpw7dw7BwcFIS0uDu7s7lErlI+UMJUljTk4OLl++jN27d5shYgIAQaxgem5Kn3y1Wo1OnTrh999/l5qnd+7ciSlTpiAlJeWR3gjwLQJVNxUtEyV/0GQy2SP9QZTJZFCpVCgqKoIoihAEAUajkWWCqp3HHbul1Wrh4OAAlUoFvV6PrKwsZGRkVLgXC8sEVTeVNZ6xpP4wtR5hmaDq5mFlQqlUws7ODvb29lCpVI/0GS557srNzUVmZiYKCgqk67BMVK5Kb5GUy+Vo0KABJk2aJCWRhYWFCA4OfuQkkqgmK/nMP2pLvNFoLDWumGWIaqv09HSkp6dbOgyiaqek/mCPLqrtdDod7ty5w1m5a4hKX0fSxcUFAwcOxHPPPSdtu3XrFvbv388HYCIiIiIiolqgUlokBUGAWq2GVqtF9+7d8fbbb8PKygpA8ZuFY8eOITU1tTJuRURERERERBb2WInk3UsatG3bFlOmTMHAgQOl9Y4KCwtx6dIlLFu2rFKCJSIiIiIiIst75ERSEAT4+/tj9uzZGDRokDRNb8kg2oKCAhw8eBD9+/evnEiJiIiIiIioWnjkRLJJkyZ4++230bt3b9jb25faV1BQgHPnzuHtt99+7ACJiIiIiIioennkRFImk+HmzZu4c+cOXF1dIQgCdDodLl68iEWLFuHkyZOIjIyszFiJiIiIiIioGqjwOpJEREREREREgBmW/yAiIiIiIqLajYkkERERERERmYSJJBEREREREZmEiSQRERERERGZhIkkERERERERmYSJJBEREREREZmEiSQRERERERGZhIkkERERERERmYSJJBEREREREZmEiSQRERERERGZhIkkERERERERmYSJJBEREREREZmEiSQRERERERGZhIkkERERERERmYSJJBEREREREZmkUhLJ4OBgCIIgfb906VL06NED7u7usLKygq+vL55//nnExMSUOi8mJgaCICAkJKQywiCqVu4tF3fT6XQICAiAIAiYP39+qX0hISEQBKFMeSGq6e4tE1OmTIEgCGW+/P39S53HMkG1VXn1hNFoxC+//II2bdrA2toazs7O6NWrF86ePSsdwzJBtdW9ZaK8OqLkq2/fvtJxLBOWoTDHRSMiIuDr64shQ4bAyckJN27cwNKlS7F9+3acPXsWnp6e5rgtUY3xww8/IDY21tJhEFmclZUVfvvtt1LbNBqNhaIhsrwXXngBq1evxqRJk/Dmm28iNzcXERERSE5OtnRoRFVu5cqVZbaFh4dj4cKF6NevnwUioruZJZH8+eefy2wbNmwYOnTogD/++AOzZs0yx22JaoTk5GR89tln+OCDD/DJJ59YOhwii1IoFJgwYYKlwyCqFjZs2IAVK1Zg06ZNGD58uKXDIbK48uqHktbHZ5991gIR0d2qbIykj48PACAjI6OqbklULc2aNQvNmjXjwzPR/2cwGJCVlWXpMIgs7n//+x8CAwMxfPhwGI1G5ObmWjokomqlsLAQGzduRI8ePeDt7W3pcOo8syaSaWlpSE5ORnh4OJ5//nkAQO/evc15S6JqLSwsDCtWrMD3339/3/GTRHVJXl4eHBwcoNFooNVq8cYbbyAnJ8fSYRFVuaysLISFhaFjx4746KOPoNFoYGdnh0aNGmHDhg2WDo+oWvjnn3+QkZGB5557ztKhEABBFEXRXBdXq9UoLCwEADg7O2POnDl46623zHU7ompNFEV07twZjRs3xpo1axATEwNfX198++23mDlzpqXDI6pyH374IURRRLt27WA0GvHvv/9ixYoVeOqppxASEgKFwiyjL4iqpYiICLRr1w7Ozs5QKBSYM2cONBoNFi5ciJMnT+Kff/5B//79LR0mkUWNGjUK27dvR2JiIhwdHS0dTp1n1lp6586dKCgowOXLl7Fq1Sp20aA6LTg4GOfPn8dff/1l6VCIqoUvv/yy1Pfjxo1D06ZNMXv2bPz1118YN26chSIjqnolLfFpaWk4ceIEOnXqBAAYMmQIfH198fnnnzORpDotKysLO3bswDPPPMMkspowa9fWnj17YsCAAZgxYwb+/PNPfPrpp/jxxx/NeUuiaikrKwsffvgh3nvvPdSvX9/S4RBVW++88w5kMhn27t1r6VCIqpS1tTUAwNfXV0oiAcDOzg6DBw9GWFgY9Hq9pcIjsriNGzeioKCA3VqrkSqbbKdx48Zo27YtVq9eXVW3JKo25s+fj6KiIowdOxYxMTGIiYlBfHw8AODOnTuIiYlBUVGRhaMksrySdfPS09MtHQpRlSpZGs3d3b3MPjc3N+h0Ovbsojpt9erV0Gg0GDRokKVDof+vyhJJAMjPz0dmZmZV3pKoWoiNjcWdO3fQokUL+Pr6wtfXF926dQMAfPHFF/D19cWlS5csHCWR5WVnZyM1NRWurq6WDoWoSnl6esLDwwO3bt0qs+/27dtQq9Wwt7e3QGRElpeQkIADBw5g5MiRsLKysnQ49P9V+hhJvV6P7OxsODk5ldoeFhaG8+fPY/z48ZV9S6Jqb9q0aRg2bFipbcnJyXjllVcwZcoUDB06FL6+vpYJjsgCCgoKoNPpyjwYz5s3D6IociwY1Uljx47FwoULsWfPHvTt2xcAkJqaii1btqBXr16Qyar0/T9RtbFu3ToYjUZ2a61mKn3W1oyMDHh7e2Ps2LFo0aIFbG1tcf78eSxfvhxqtRonTpxAkyZNKvOWRDUSZ22luiwmJgZt27bFs88+C39/fwDArl27pJkpd+zYwYdmqnOSkpLQtm1b5OTkYMaMGdBoNFi8eDHi4uJw/PhxtG7d2tIhEllEhw4dkJCQgLi4ONYN1Uilt0ja2NjgxRdfxIEDB/DXX38hPz8fnp6eePbZZ/Hxxx/Dx8ensm9JREQ1jKOjIwYNGoQ9e/ZgxYoVMBgM8PPzwxdffIGZM2fyQYHqJHd3dxw5cgQzZ87Ed999B51OhyeffBKrVq1iEkl1VmRkJE6dOoUZM2awbqhmzLqOJBEREREREdU+TOuJiIiIiIjIJEwkiYiIiIiIyCRMJImIiIiIiMgkTCSJiIiIiIjIJEwkiYiIiIiIyCRMJImIiIiIiMgkTCSJiIiIiIjIJIqKHigIgjnjeCgud0nVDcsEUWksE0SlsUwQlcYyUbuwRZKIiIiIiIhMwkSSiIiIiIiITMJEkoiIiIiIiEzCRJKIiIiIiIhMwkSSiIiIiIiITMJEkoiIiIiIiEzCRJKIiIiIiIhMwkSSiIiIiIiITMJEkoiIiIiIiEzCRJKIiIiIiIhMwkSSiIiIiIiITMJEkoiIiIiIiEyiqOwLymQyqFQqqFQqyOVy6PX6Cp0nl8shCAL0ej2KiopQWFhY2aERVWtKpRJKpRIAYDQaodPpYDAYLBwVUeV7lHpCJit+76nT6VBUVFThuoWoJhMEAba2tlAqlZDJZBBFEUajEQUFBSgqKoLRaLR0iESVRhAEKBQKWFlZQS6XA8AjfcZlMpl0vk6nQ15eHp+nzKRSE0mZTAatVovWrVujXbt2cHNzQ2JiYoU+BE5OTrC1tUVsbCzOnTuH0NBQ5OXlVWZ4RNWWTCZD8+bN0aJFCyiVSiQmJiIyMhJxcXF8UKBaRS6XQ6vVom3btmjdunWF6gmZTAaNRgODwYCoqChEREQgKioKRUVFVRg5UdURBAFqtRouLi545pln4O/vD7VaDb1ej5ycHISFheH06dNISkpiQkm1ho2NDZo1a4aOHTvCy8sLOp0OOTk5Jl/H1tYWbm5uEEUR0dHR2Lp1K2JjY1lOzKDSEklBEODk5IQ2bdpg5MiRePXVVx/pOunp6dixYwfS09Nx5coVtkxSneDh4YFx48Zh4sSJ8PT0xIEDB7By5UocOHAAsbGxlg6PqFIIggCtVougoCC88cYb6NGjh0nni6KIM2fOYMmSJVJSybfMVJsIggAHBwc4Ojpi0qRJGDNmDOrXrw+NRiMdYzQakZmZiYsXL+Lvv//Gtm3bcPPmTT4vUY1mY2MDLy8vjBo1Ci+//DKcnZ0f+5qiKCI5ORnZ2dnYs2cP4uPjKyFSululjZEURREqlQodOnTAK6+88sjX0Wq16N+/P4YOHco/ilRnqFQqjBgxAt7e3pDJZOjVqxeeeuopFBQUWDo0okojiiIcHBzQuXNnk5NIoPgh29/fH6NGjYKzszOTSKp17O3t8c477+D69ev47LPP0LJly1JJJFDcQu/k5ISuXbti/vz5CA0NxaJFi6Tu30Q1UWFhITp16oSnn366UpJIoLjOcHV1xccff4w7d+5UyjWptEr9q5OQkIDly5dj1qxZj3wNvV6PK1eu4Msvv6zEyIiqt4ULF6J+/frS91lZWbh9+zYyMjIsFxSRGej1+kcetqDT6XDkyBF8+umnOHbsWCVHRmRZKpUKbdq0wZw5c0xKCh0dHREUFITJkyebMToi8zIYDFizZg0WLlyIqKioSrtuamoq3n77beTm5lbaNen/VPpkO0lJSZg/fz4WL14MOzu7Bx5rb2+P//73vxg8eDBUKhUMBgMOHTqESZMmQafTVXZoRNXSO++8gx49esDGxkbalpmZiczMTAtGRWQetra28PDwAFD84HD16lWMGTMG6enpDz23qKgIOTk5bKmnWql+/fr46quvSm27cOECfvzxRxw4cAC5ubkQRRENGjTAgAEDMGLECAQEBEAmk8HHxwdTpkzBjh07kJycbKGfgOjxGAwGrFq1Cn/99Zc0yZQpbGxs8Pzzz2PatGmws7NDdnY2du7cie3bt5spYqr0RBIo7r+flZWFrKysBx43f/58dO3aFSqVCkajEYcOHcKcOXNw69Ytc4RFVO20adMGc+fOhYODg6VDIaoS1tbW0Gq1AIrritTUVMTExDzShApEtUXLli3x+eefo3PnzgCKy8avv/6KuXPnIiUlpdQkIQkJCTh58iSOHz+Od955B/369YNKpYK/vz+mTp2Kb775ht2+qcYyGo3Iy8t7pJ4rPXv2xBNPPAE7OzsYDAbcuHEDP//8sxmipBIW61A/adIkDBo0CG5ubgCAixcvYu3atTh+/LilQiKqUvb29li7dq3Ucs+Kn+qCktn0gOIHhoyMDM6+SnVagwYNMGLECPTr1w+CIEAURSQmJuKTTz5BcnJymZkmRVGEwWDA5cuXERoaitu3byMpKQlHjx7FlStXOFaS6iRbW1v07NkTXbp0AQDk5OTg7NmzOHfunIUjq93M0iL5ME888QTeeustNG7cGIIgIDU1FZs3b8aGDRv4ME11xqefforGjRtDJpNBp9PhwoULaNSoUZmJFYhqC7lcDltbW6kF3mAw4Pbt20wkqU6zs7ND/fr1YW1tDaB40pHFixcjNTUVoije97z4+HisWbMGUVFRcHBwwK1bt7B3714ODaI6qW3btujQoQOcnJyg1+sRGRmJlStXciiEmVV5IqnRaLBgwQK0aNECCoUCOp0Oq1evxooVKzgmjOoEQRAQFBSE559/Xur/HxoaipiYGHh4eDCRpFpLLpfDzs6uVCKZkpICoHhsy93jhAVBkMZDPuhhmqimk8lksLKyAlDc2piamoo1a9Y89HNvMBgQHR2NW7duQSaTwWAwsIs41UnW1tYYNGgQ2rVrB0EQkJ2djQsXLuDEiROWDq3Wq/JEcu7cuejYsSOsra1hNBqxceNGbNiwgWvlUZ0gCALc3NzwzTffwNHREUDxxDpff/01unTpwhZ5qtUUCgWsra2hVqsBFD80y+VyTJ8+Hc888wzs7e2lYwVBQEpKCkJCQnD48GFcvXoVmZmZTCqp1tFoNPDy8gJQPDPxtWvXcPv27VLHKJVKyGQyFBUVlSoDOp2OLZBU57Vu3Rrt27eHi4sL9Ho9Ll68iG3btvHFShWo0kTyP//5D8aOHSs9LAiCgBYtWqBnz55IT09HZGQkHxKoVrOyssLs2bPRpk0bAMVvlBcsWIBjx46hbdu2lg2OyMxUKhXs7e2l1hcbGxuMHDkSCoUCDRo0gEJRukoqKipCixYtMGLECGRkZODMmTPYsWMHzpw588hLiBBVJwqFAq6urtLyT0VFRTh37hwKCwuhVqvh7e2NcePGoW3btlAoFLh16xa2bduGs2fPIjU1ld3Cqc5TqVQYOHAg/P39IZfLkZmZicuXL+PEiRPMKapAlSWSgYGBePHFF+Hh4QFBEAD83+LSkyZNQpMmTXDixAmcOXMG586d40MC1TpqtRoDBgzA2LFjpQfm/fv3Y/Xq1cjIyOAfPKr17Ozs4ObmJk0wpVAo0LRp0/ser1Kp4OvrC19fXwDFsxzb2toiKSkJ0dHRVRIzkTnZ2NjAw8NDWoC9ZAKqF154Ac2aNUOjRo3QsWNHeHt7QxAEZGVloVWrVoiKikJ0dDQuXLiAc+fOITY2Fnq93sI/DVHV8/Pzw1NPPQU3NzfodDpcvnwZhw8floZNkHlVSSIpCAK8vLyQnp4u/TEsoVQq0bRpU3h6eqJTp044duwYli9fjtDQUHbXoFpDoVDAz88Pb731ljRjZUpKChYtWoTY2Ngys/IR1TaCIKBBgwYICAgoszZYQUEBkpKSkJycjKysLCiVSlhbW0sP2Gq1GjKZDPXq1cOgQYOQmZmJFStW8OGZajRBENCoUSO0a9dO6qklk8nQvn17jB49Gt7e3mWWhnJwcEDXrl0RGBiItLQ0XL16Fdu2bcPq1auRmJhoiR+DyGIUCgUGDx6MJk2aQKVSITMzE+Hh4Th69CiHClWRKmuRvHz5MrZs2QJra2vY29sjLy8PdnZ20Gg0UKvVsLOzg7+/P7RaLYxGI3JycnD+/Hl+EKjGEwQBHh4eeP7559G9e3cAgF6vR3BwMPbs2cMHYaoTBEGARqOBq6urtE2n0yEmJgaHDh3ChQsXkJCQgDt37kClUsHGxgYNGjRAs2bN0L59ezRp0gR2dnZo3LgxXnjhBSiVSqxfvx4XLlxgPUE1UsnLlZYtW0ovV1QqFbp27SqNoQeKZ3E1GAyQyWQQBAEqlQoqlQr16tWDq6sr7O3tkZGRgRUrVrA+oTrF29sbzzzzjFSvxMTEIDw8HHFxcRaOrO6okkRSFEVERkZi+/btyMjIgJOTEzIyMqDVauHn54c2bdrA19cXVlZWcHNzw+DBg3H9+nVcvHiRDwhU42k0GvTu3RtTp06FXC6HwWDAiRMnsGDBAhQWFlo6PKIqIYoiYmNjcezYMXh4eMDGxgYxMTH4559/sGrVKsTHx5c5Ry6Xw8vLC7169cKAAQPQpUsXeHt7w9vbG88//zyys7Nx6dIl1hNUI4miiMLCQuTn50vbSpJEoHgM/bVr13D+/HlkZGRALpdDrVbDx8cHfn5+cHZ2hkKhQOvWrfHaa68hNTUVoaGhbJmkOkEQBPTp0wdNmjSBlZUVioqKcPz4cfZorGJV1iIpiiLCw8MRHh5earuHhwdGjRqFSZMmoU2bNlAqlbC1tUWbNm04ZoxqPCsrK7Rp0wYvvvgiNBoNRFHE7du3MW/ePCQlJVk6PKIqI4oiLl26hN9++01a6qak0s/Kyir3HIPBgNjYWAQHB+PkyZOYPn06Jk+eDKVSCTs7O7Ru3ZpJJNVYoigiMTERN2/eLLMvOzsbR44cwcaNG7Fjxw4kJSVBFEXY2dmhQ4cOGDRoELp3746mTZtCo9GgXbt2+P777/Hpp58iODi46n8Yoirm5OSEMWPGwMnJCQAQGxuL0NBQ3Lhxw8KR1S1VvvzHvRITE/H7778jPz8fs2bNgp+fH1QqFTw9PaHVapGcnGzpEIkeiUwmg6+vL0aOHImuXbsCAPLz87Fp0ybs3r3bwtERVT1RFHH9+nX8+uuvJp97/fp1nDhxAkFBQVI94eXlBRcXF9YTVGOJoljmpblOp8PevXsxYsSIMsfn5OQgJCQEISEh6NOnD2bMmIGnn34aMpkMLi4uGDBgAFauXMkXLFTrdenSBc2bN4eVlRUMBgP279+P8PBw9vSqYjJLBwAUP1wfPHgQ27dvB1DctaN58+bo1atXqYl5iGoKmUwGd3d3DB06FC+++CKA/2td+f777+Hk5FTmy9raGnK5XDrfxsZG2ufs7AxXV1c4OTlJxxDVJawnqDYSBKHU51cURSQlJWHWrFkPPffYsWP4999/pRcpcrkcDg4O0qzIRLWVSqXCG2+8ATc3NwiCgISEBOzfvx/Xrl2zdGh1jsVbJEtkZmYiMTEROp0OSqUSGo0G7777LrZu3cqlQKjG8fLywuuvv47XX39dWny9sLAQUVFR+OSTT8o9p3Xr1tBoNAAAR0dHDBkyBE2aNIEoirC2toajoyNu3LiBBQsW4Pr161X2sxBVF6wnqLaxs7MrNbFOfn4+du3ahatXrz703Ly8POzfvx8tW7bESy+9BLVajYCAALRr1w4HDhwwY9REltWxY0e0a9cOSqUSoihi165dOH/+PFsjLaDaJJI6nQ75+fkoLCyEUqmEwWBAWloaW1+oxrGxsUGPHj3Qs2fPUlO329jYYNCgQRW6hp2dHQIDAxEYGChtE0VRmu5948aNlR43UXXHeoJqE5VKhRYtWqBjx47SNoPBgISEhApfIyMjAwkJCdLLFQ8PD8yePRunTp2679hjoppMEAS8/fbb0Gq1EAQB6enp2LZtG8dGWki16NoKFD8k63Q6aepqo9GItLQ0yGTVJkSiCtHr9cjPz0dRUVGlXrdk+YR27dqx9YXqJNYTVJuUrB989+fXYDCYNBGbwWCAXq+XriGXy+Hm5gZbW9vKDZaommjWrBl69+4NhaK4LWzXrl24fPlyqdmPqepUmxZJo9EIhUIhLcqrUqnQtm1bPjBTjVNUVIStW7dKn+c2bdpU2nWPHTuGd999F6mpqZVyTaKahPUE1SZ6vR5///03tFotPvvsMyiVSunlSEXl5uYiMzOz1IQ9KpWKdQTVWjNmzJC6g2dkZGD58uWIjY21bFB1WKW8xpXL5Rg4cCA2bdqE9evXo0uXLiZfQ6vVwsvLS+qilJGRgblz53ItGKqRdDodNmzYgMDAQNja2kKj0cDe3v6BX/PmzZO6NMXFxeHDDz+Es7OztN/R0RF9+vThDJVUI7GeICrrzp07uHbtmrSAukqlQvfu3St8vpOTEzw9PaXWmYyMDHzyyScsE1Qr+fn5YdSoUVIL/M6dO3Hp0iUUFBRYOLK667FbJJVKJZ5//nm89dZbCAgIgE6ng5ubG0aMGIE7d+5U6Bq2trbo27cvhg8fDqD4ITw6OpqDxalGK+mGV9EKvaioSJqyvWSh6pycnErvIktU1VhPEJVPp9Ph1q1biIyMRKNGjWBjY4M+ffogICAAly5deuC5tra26NOnD4YNGyZd6/r16ywTVCvJ5XK8+eab0rqR2dnZWLp0KVvfLeyxWySVSiVSUlKQkpICmUwGKysrBAQE4M0335Rmq3wYb29vdOjQAY0bNwZQPLvlzZs3K/yAQURE1RfrCaL7y8/Pl7qzymQyeHp6YvDgwQ89z9vbGx07doSfnx+A4jIRExPDMkG1jiAI8PT0xOjRo6Vthw8fxsWLFzlTq4U9dotkfn4+wsPDERYWho4dO8LOzg7Ozs6YOnUq7ty5g+DgYOTk5JR7roODA3r06IEXX3wRvXr1krorFRQU4MaNG1wbjIioFmA9QXR/ubm5SElJgdFohEwmg7W1Nd5//33Y2tpi2bJliImJKXX8/cpEYWEhywTVSiqVCuPHj4eHhwcAICcnBz/88AMyMjIsGxg9fiJZsnju2bNncf36dTzxxBOQy+Xw9vbG+++/j169emHnzp04duyYNBhcpVLhySefRFBQEJ544gl4e3tLC+gmJiZi3bp1WL16tTQzHxER1VysJ4juLzExEZs2bYJGo8Hzzz8PQRCg1Wrx2muv4emnn8bevXvxzz//ID8/H506dbpvmfh/7d17cFTl/cfxzzmbe5bcSACTkAJJuAYh3KkBAVEKA7WDpdR2KAxakLFIy7RQxzrTav8oOlOl0NY6tUJ1FIMjttxhaAtUkVsS2lwNhEhCbiSQkOsmu3t+fzDuND9uWdglCb5f/3FyLg+T/c4+nzzPeZ7MzEy999571ATuK6Zp6oEHHtDSpUs970aePn1aJ0+e5NWfHsAnq7Z2dHTozJkz2rt3r2JiYpSYmOjpJERHR2vMmDGdViEzTVMxMTGKjY1VeHi454ORm5urP/3pT9q1a5cqKio6rUIG3O9sNpunFkzTZG883Ff4ngBurLm5WdnZ2aqtrVVhYaFeeeUVSVJsbKyio6OVlJSkefPmyeVy3bYmKisrqQncV8LDw/WDH/zAM4W7tbVVb731Fvuk9hA+CZKWZam0tFTbtm2Tw+HQsmXLlJSUJMMwZLfbZbfbNWTIkFveo7y8XAcOHNCuXbuum8YBfBUMHjzYs/dXZGSkIiMjCZO4b/A9AdyYZVlqbm5WcXGx/va3v2n69OmaM2eOAgMDZbPZNGDAAM+Uvv+PmsD9zDRNxcXFaeHChQoMDJQkNTQ06B//+Acj7z2Ez/aRbGlpUX5+vhobG1VcXKyUlBSNHDlS6enpGjx48HUd4o6ODl24cEG5ubkqLCxUUVGRsrOzVVFR4asmAb2GYRjavXu3Bg4cqJEjR+r48eP69NNPPau4AvcDvieAm3O5XCorK9OGDRt06NAhJScna9KkSRoxYoRn71RqAl8llmWpqalJ77//vgYOHCi32623335bNTU1jLz3EIbVxd+ENy9vm6apiIgIDRs2TOnp6Ro9erTsdrtnGobL5VJDQ4MKCgqUm5ur4uJi1dXV3fKvC3xg0NP4ekGDkJAQPfbYY0pOTlZOTo5OnTqlpqamm372qQn0NHxPAJ3d6fdEQECAkpKSlJ6erjFjxmjgwIEyTVP19fXUBHo1b2vCZrMpMjJSTz/9tNxut3bs2KGSkpI7/mxTE77llyD5vwIDAxUTEyO73e65h9vtVmNjo65cudLloWl+8ehpfB0kDcOQYRidPuu3+txTE+hp+J4AOvPF90R4eLji4uJks9nU0NCg+vp6agK91p3UhGmasiyr0/fDnaImfMvvQdJX+MWjp6EmgM6oCaAzagLojJq4v5jd3QAAAAAAQO9CkAQAAAAAeIUgCQAAAADwCkESAAAAAOAVgiQAAAAAwCsESQAAAACAV7q8/QcAAAAAABIjkgAAAAAALxEkAQAAAABeIUgCAAAAALxCkAQAAAAAeIUgCQAAAADwCkESAAAAAOAVgiQAAAAAwCsESQAAAACAVwiSAAAAAACvECQBAAAAAF4hSAIAAAAAvEKQBAAAAAB4hSAJAAAAAPAKQRIAAAAA4BWCJAAAAADAKz4Lklu2bJFhGJ2OZWZmasqUKYqKilLfvn318MMPa/fu3Z3O+de//iXDMFRaWuqrpgA9wo1qYvPmzRoxYoSCg4OVkJCgtWvXqrm5udM51AQAAAB6Or+NSG7atEmLFy9WbGysfvOb3+jFF19UQ0OD5s+fr48++shfjwV6rPXr12v16tVKS0vTxo0b9cQTT2jTpk1auHBhdzcNAAAA8EqAv268adMmTZw4UTt37vSMyixfvlwJCQnaunUrnWd8pVRWVuq3v/2tlixZor/+9a+e40OHDtXq1au1c+dOLViwoBtbCAAAAHSd30Ykr169qn79+nWa2hcRESG73a7Q0FB/PRbokY4dOyan06nvfve7nY5/+e9t27Z1R7MAAACAO+K3IDljxgzt27dPmzZtUmlpqQoLC/Xss8+qoaFBa9as8ddjgR7J4XBI0nV/RAkLC5MknT59+p63CQAAALhThmVZlj9uXFNTo+9973s6dOiQ51hsbKz+/ve/a+rUqf54JNBjZWVlafz48Xr55Zf1i1/8wnN8//79+sY3viG73a7GxsZubCEAAADQdX4Lkk1NTVq/fr2am5s1f/58NTY26rXXXtOlS5d09OhRpaSk+OOxQI81ZcoU5eXl6Xe/+51mzpypgoICrVq1ShUVFXK73XI6nd3dRAAAAKBL/BYk586dq4CAAO3cudNz7PLly0pNTdXs2bP1wQcf+OOxQI918eJFLV68WJ988okkyWazae3atTp8+LCKiopUX1/fvQ0EAAAAusgvq7aWlJRo3759evPNNzsdj4mJUUZGhqcjDXyVJCQk6N///reKi4tVVVWl1NRUDRgwQPHx8Ro6dGh3Nw8AAADoMr8EyerqakmSy+W67mcdHR1M4cNXWmpqqlJTUyVJ+fn5qqys1LJly7q3UQAAAIAX/LJqa0pKikzT1AcffKD/nTlbXl6uo0ePKj093R+PBXoVt9utdevWKSwsTM8880x3NwcAAADoMr+MSMbFxWn58uX685//rEceeUQLFy5UY2Oj/vCHP6i1tVXPP/+8Px4L9Ghr1qxRW1ubxo4dq46ODr333ns6ceKEtm7dqqSkpO5uHgAAANBlfltsx+l06o033tBbb72ls2fPSpImTpyoF198UTNnzvTHI4EebcuWLXr99dd19uxZmaapSZMm6YUXXqAeAAAA0Ov4LUgCAAAAAO5PfnlHEgAAAABw/yJIAgAAAAC8QpAEAAAAAHiFIAkAAAAA8ApBEgAAAADgFYIkAAAAAMArBEkAAAAAgFcCunqiYRj+bMdtsd0lehpqAgAAAF9VjEgCAAAAALxCkAQAAAAAeIUgCQAAAADwCkESAAAAAOAVgiQAAAAAwCsESQAAAACAVwiSAAAAAACvECQBAAAAAF4hSAIAAAAAvEKQBAAAAAB4hSAJAAAAAPAKQRIAAAAA4JUAfz8gODhYdrtdhmHI5XLJ6XTKNE2ZpinLstTW1qb29na53W5/NwXoMUzTVJ8+fRQUFCTLsuRwONTS0iKXy9XdTQMAAABuy29BMiAgQHa7XRMmTNB3vvMdhYSE6NKlS6qoqFB4eLgiIyPV3t6uEydOKCsrS9XV1QRK3PdsNptCQ0MVHx+vxYsXa/jw4XI6nTp9+rQOHTqksrIytbS0yOl0dndTAQAAgJsyLMuyunSiYXTphqZpKioqSuPGjdOPfvQjZWRkqG/fvjc81+12q6GhQXl5efr444+1c+dOffHFF3I4HNed28VmAvdMV2viy3PDwsKUnJysZ555RnPmzFFiYqKCgoIkSU1NTaqsrNT+/fu1fft2/ec//1FjY+MtRyipCQAAAHQXnwZJwzA0depUbdmyRampqV43pr6+XpmZmVq1atV1I5N0mtHTdDVI2mw2paWl6c0339SkSZNue75lWfrkk0+0ceNG7dix46ZhkpoAAABAd/HpYjsjR47U8uXL7yhESlJUVJRmzJihpUuX+rJZQLcaPHiwfvzjH3cpRErXAmpGRoa+//3va+zYsf5tHAAAAHAHfDYiGR4erieffFKvvvqqoqKiJEktLS3KzMzUu+++q4KCguuuSUpK0ty5c7Vw4UKNHDlSpmmqvb1dx48f17e//W3V1NR4zmX0BT3NrWrCMAwlJCRowYIFWrlypcaMGeP5mdPp1LFjx/T73/9en332mWw2mx566CGtWLFC48aNU1hYmKRrn/ni4mJt2bJFW7duVUVFRadnUBMAAADoLj4JkqZpau7cuVq/fr2mTZsmSbp69apeeeUVbdiwQS6X64adXsMwZJqmHnvsMa1du1azZ8+WJF2+fFlbt27Vz372M8+0PjrN6GluVxMLFizQz3/+c02ePFmGYciyLFVXV2vmzJk6e/as3G63Zwq3aZoKDQ3ViBEjtGHDBs2aNUvStfeI8/Pz9fLLLyszM7PTM6gJAAAAdBefTG01DKNTp9iyLDU1Nemzzz6T0+m8aYfXsiy5XC6VlJTo3Llznd4F+3KbEKA3MgxDERER6tu3rydEVlZWasmSJfr888/ldDo7vQfsdrvV3NysnJwcvf766zp8+LAsy5JpmgoPD1e/fv28WtwHAAAA8CefBcm2tjY1NjZ6jrlcLjU1NXXpeofDIYfD4elYO51O1dXVESTRa4WFhal///6eFYsbGxt18OBBnThx4pZb3DidTp08eVJZWVlqaGiQdG0v1ri4OEYgAQAA0GP4JKk5nU7Z7XbFxsZKuhYsQ0JCNGDAgC5dHxERoaioKAUGBkqSgoKClJiYqPb2dl80D7jnhg4dqvT0dEVFRcnlcunChQvaunVrpz+23MylS5eUnZ2t8+fPS5L69OmjYcOGed6dBAAAALqbz4b8cnJydODAAbW2tkq6tgLrunXrNHz48FteFx8fr0WLFnnej3S5XCorK9P777/PCAx6rYyMDGVkZMg0TZmmKbfbrbNnz3bpM+1yuVRcXOwJkqGhoRoyZIji4+P93WwAAACgSwJ8daPKykodOXJEM2fO1LRp0xQYGKjx48dr8+bNKi8v1759+3Tq1Cm1tLQoNDRUDz74oKZPn66UlBSlpaV5Ri/r6up08OBBZWVl+appwD1ls9kUExPjWb3YMAwZhqGOjo4u38OyLM8U2ICAACUnJ+u5557Tc889548mAwAAAF7xWZB0uVw6d+6c/vnPf2rChAkKDQ1VcHCwZs2apfb2dqWlpemLL75QW1ubgoODlZiYqNTUVEVGRspms0m6NkW2tLRUBw4cYForeq3AwEDV1taqurpaERERnkAYGxurqqqqLt0jMjJSkZGRnn8HBQUpISFBISEhamtr80u7AQAAgK7yWZC0LEs1NTU6ePCghgwZookTJyo1NVWmaSo4OFjjx4/X+PHjZVnWdatPWpal2tpa5ebmat++fcrOzmZaK3otl8ul7OxsZWdna9CgQQoICFCfPn2Unp6u3Nzc215vGIYGDBigfv36eY61tbUpPz+fugAAAECP4NNlUVtaWpSXl6ePP/5YdXV1N9078mbXnjlzRnv27NGlS5foMKPX6ujoUEFBgXJyclRfXy/DMBQbG6t58+YpJibmltcahqF+/fpp1KhRSkhI8Bxvb29XaWmpHA6Hv5sPAAAA3JbP99cIDQ3V0KFDlZqa6pmy+r9uNGXVMAz17dtXw4cPV0JCAiESvd7Vq1dVXl6uy5cvS5LCw8M1Y8YMzZ8//5b7QQYFBWnWrFmaPn26Z+sQ6do+ky0tLX5vNwAAANAVPpva+uV2H48++qhWrlypuLg4Sdc6wNXV1SovL1dra6uuXLmiyMhIhYaGqn///urXr5/CwsJkt9v10EMPqbW1VbW1tTp37pzq6+t91Tzgnuro6FBVVZXKysqUnJysgIAAxcbG6qc//anKyspUUVHhqQnDMBQWFqaEhAQNGjRITz31lCZMmHDLwAkAAAB0J58FySFDhmjFihVavny5+vTpI0lqampSYWGhdu/erXfffVdlZWVyOBwKDAxUfHy85s6dq3nz5ik9PV0DBgxQnz59NH/+fCUnJ+uXv/ylduzY4avmAfdcfn6+PvroI0VHRystLU3BwcEaPXq09uzZo7179+qdd97RxYsXZRiGBg8erCVLligjI0MRERHX3ctms93wOAAAANAdDKuL80hvNTpis9m0aNEivfDCC0pLS5N0bXGQgwcP6oknnrjltgc2m02rVq3SmjVrlJKSIulaAN25c6eWLl3quZbpruhpujpiOG7cOG3evFmTJk264XTvG3G5XLIsSzabTYZhqL6+Xtu3b9eKFSs851ATAAAA6C4+eUfya1/7mqZMmeIJgi6XS+fPn9e6detuu3eey+XShx9+qG3btqmpqUmSFBYWpsmTJ2v8+PG+aB7QrYqKivTSSy+pqKhIra2tcrlcNzzPsiy5XC61trbq7NmzOn/+vDo6OuR2u1VXV6fjx48rODj4HrceAAAAuN5dB8mwsDAtWLBA8+bNU0hIiCSpurpaGzduVGFhYZfuUVVVpZ07d2r37t3XGmWaSkhI0PPPP++ZJgv0Vs3NzTp8+LCWLl2q1atX6/jx46qrq5PD4ZBlWZ6FdGpra3X06FH96le/0muvvaaioiIFBQXJ5XKppqZGhw4dYtVWAAAA9Ah3/Y6k0+lUaGio7Ha7pGuL61RVVWnXrl1e3aeoqEjHjh3Tt771LQUHB8s0TcXExMjpdN5tE4Fu19raqlOnTunUqVN65513lJKSol//+tcaNWqUHA6Hjh07pj/+8Y8qKCiQw+HQokWLPAtWORwOVVVVqba2tpv/FwAAAMA1dx0k29vbVVJSorKyMj3wwANqaWlRaWmp6urqvLpPc3OzysvLVV1draSkJDkcDmVlZam1tfVumwj0KO3t7crPz9fChQtv+POgoCAlJiaqf//+kq7VRnFxsWfqNwAAANDdfPKOpGVZnoU/TNNUYGDgHd3HMAyZps+3tgR6leDgYA0aNEjx8fGSpMbGxi5PEwcAAADuhbsekQwMDNSIESOUmpoqSQoNDdXAgQMVERGhtra2Lt8nODhYcXFxnlGYkJAQPfzwwwoODua9MPRKhmFo6NChevzxxxUfH689e/bowIEDt71u9uzZmjBhgoKCgmRZlpxOp9xu9z1oMQAAANA1PgmSJSUlKi4u1uTJk2UYhhISErRy5Uq9+uqrXQqTgYGB+vrXv6758+d7RjPb2tp09OhRhYSEECTR65imqXHjxunZZ5/V4sWL5XK5NG3aNFVUVCg3N/em10VHR+vxxx/X2LFjJV1b1bi2tla5ubmy2Ww3XfEVAAAAuJfueh5pa2urjhw5osOHD3ve4YqJidFTTz2lp59+2rMIz818OfL4wx/+UNOnT5d0rfN84cIFvf3227p69erdNhG45wzDUHBwsOx2u2cxqqFDh+onP/nJTWsiJSVFL730kh599FGFhYVJkgoKCvSXv/xFeXl5hEgAAAD0GHc9ImlZlqqqqpSTk6OSkhI9+OCDstlsSkxM1Lp16zRr1izt3btXn3766XUbqKelpWnGjBmaPHmyUlJSPB3slpYWZWVlqbCwkE3X0Su5XC4VFhbqyJEjmjVrlmJiYhQeHq5vfvObioyM1P79+z01ERYWpjlz5uiRRx7R6NGjFR0dLUkqLS3V9u3btWPHDq+miQMAAAD+dtdBUpI6Ojp05swZ7d27VzExMUpMTPSEyejoaI0ZM+aGq7hGREQoNjZWERERnimtLS0tys7O1ocffkjnGb3alStXdOzYMe3fv19PPvmkDMNQ3759NWfOHKWnp3tqwmazqV+/foqLi1NQUJAMw1B7e7uOHDmi7du3q76+vnv/IwAAAMD/45MgaVmWSktLtW3bNjkcDi1btkxJSUkyDEN2u112u11Dhgy57X0aGxt17NgxvfHGGzp69CgLjKBXc7vdKiwsVGZmpsaOHasRI0Z0qSacTqeOHz+uXbt26dy5c/e41QAAAMDtGVYX544ahnHbc4KCgjRw4EBNnTpVKSkpGjlypNLT0zV48GDZbLbrzne73aqpqVFBQYHy8vJUXFysvLw8nTx58rp3I5niip6mKzUhSbGxsZo5c6ZGjRp105poamrSuXPndObMGX3++efKyspSTk6OKisrb3pfagIAAADdxadB8kumaSoiIkLDhg1Tenq6Ro8eLbvdLtM0ZVmWDMOQZVlqaWnR+fPnlZubq4KCAl28ePGmK7TSaUZP46ua6OjoUGVlpQoKCvTf//5X58+fV3Nzs9xu9y0/99QEAAAAuotfguT/CgwMVExMjOx2uydA/m+QvHr1qlpbW287jZVOM3oaX9REQECAHA6HLl++rKamJq+mc1MTAAAA6C5+D5K+QqcZPQ01AQAAgK+qu95HEgAAAADw1UKQBAAAAAB4hSAJAAAAAPAKQRIAAAAA4BWCJAAAAADAKwRJAAAAAIBXurz9BwAAAAAAEiOSAAAAAAAvESQBAAAAAF4hSAIAAAAAvEKQBAAAAAB4hSAJAAAAAPAKQRIAAAAA4BWCJAAAAADAKwRJAAAAAIBXCJIAAAAAAK/8H/pfUF1yB++lAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 25 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Функция для отображения шаблонов\n",
|
||
"def display_templates(templates):\n",
|
||
" n_templates = len(templates)\n",
|
||
" cols = 5 # Количество столбцов в сетке\n",
|
||
" rows = (n_templates + cols - 1) // cols # Количество строк для размещения всех символов\n",
|
||
"\n",
|
||
" fig, axes = plt.subplots(rows, cols, figsize=(10, 5))\n",
|
||
" fig.suptitle(\"Шаблоны символов\", fontsize=16)\n",
|
||
"\n",
|
||
" # Проходим по всем шаблонам\n",
|
||
" for i, (char, img) in enumerate(templates.items()):\n",
|
||
" row, col = divmod(i, cols)\n",
|
||
" axes[row, col].imshow(img, cmap='gray')\n",
|
||
" axes[row, col].set_title(f\"'{char}'\")\n",
|
||
" axes[row, col].axis('off')\n",
|
||
"\n",
|
||
" # Удаляем лишние оси\n",
|
||
" for j in range(i + 1, rows * cols):\n",
|
||
" row, col = divmod(j, cols)\n",
|
||
" axes[row, col].axis('off')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"# Пример использования\n",
|
||
"templates = generate_templates(whitelist) # Генерация шаблонов\n",
|
||
"display_templates(templates) # Отображение\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"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": "3.9.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|