Upload test.ipynb

test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove warning message\n",
+    "import os\n",
+    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
+    "\n",
+    "# required library\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "from local_utils import detect_lp\n",
+    "from os.path import splitext,basename\n",
+    "from keras.models import model_from_json\n",
+    "from keras.preprocessing.image import load_img, img_to_array\n",
+    "from keras.applications.mobilenet_v2 import preprocess_input\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model(path):\n",
+    "    try:\n",
+    "        path = splitext(path)[0]\n",
+    "        with open('%s.json' % path, 'r') as json_file:\n",
+    "            model_json = json_file.read()\n",
+    "        model = model_from_json(model_json, custom_objects={})\n",
+    "        model.load_weights('%s.h5' % path)\n",
+    "        print(\"Loading model successfully...\")\n",
+    "        return model\n",
+    "    except Exception as e:\n",
+    "        print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model successfully...\n"
+     ]
+    }
+   ],
+   "source": [
+    "wpod_net_path = \"wpod-net.json\"\n",
+    "wpod_net = load_model(wpod_net_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_image(image_path,resize=False):\n",
+    "    img = cv2.imread(image_path)\n",
+    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
+    "    img = img / 255\n",
+    "    if resize:\n",
+    "        img = cv2.resize(img, (224,224))\n",
+    "    return img\n",
+    "\n",
+    "def get_plate(image_path, Dmax=650, Dmin = 270):\n",
+    "    vehicle = preprocess_image(image_path)\n",
+    "    ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])\n",
+    "    side = int(ratio * Dmin)\n",
+    "    bound_dim = min(side, Dmax)\n",
+    "    _ , LpImg, _, cor = detect_lp(wpod_net, vehicle, bound_dim, lp_threshold=0.5)\n",
+    "    return vehicle, LpImg, cor\n",
+    "\n",
+    "test_image_path = \"Plate_examples/india_car_plate.jpg\"\n",
+    "vehicle, LpImg,cor = get_plate(test_image_path)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if (len(LpImg)): #check if there is at least one license image\n",
+    "    # Scales, calculates absolute values, and converts the result to 8-bit.\n",
+    "    plate_image = cv2.convertScaleAbs(LpImg[0], alpha=(255.0))\n",
+    "    \n",
+    "    # convert to grayscale and blur the image\n",
+    "    gray = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY)\n",
+    "    blur = cv2.GaussianBlur(gray,(7,7),0)\n",
+    "    \n",
+    "    # Applied inversed thresh_binary \n",
+    "    binary = cv2.threshold(blur, 180, 255,\n",
+    "                         cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]\n",
+    "    \n",
+    "    kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n",
+    "    thre_mor = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel3)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "# plt.savefig(\"threshding.png\", dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detect 10 letters...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 720x432 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sort_contours() function to grab the contour of each digit from left to right\n",
+    "def sort_contours(cnts,reverse = False):\n",
+    "    i = 0\n",
+    "    boundingBoxes = [cv2.boundingRect(c) for c in cnts]\n",
+    "    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n",
+    "                                        key=lambda b: b[1][i], reverse=reverse))\n",
+    "    return cnts\n",
+    "\n",
+    "cont, _  = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
+    "\n",
+    "# creat a copy version \"test_roi\" of plat_image to draw bounding box\n",
+    "test_roi = plate_image.copy()\n",
+    "\n",
+    "# Initialize a list which will be used to append charater image\n",
+    "crop_characters = []\n",
+    "\n",
+    "# define standard width and height of character\n",
+    "digit_w, digit_h = 30, 60\n",
+    "\n",
+    "for c in sort_contours(cont):\n",
+    "    (x, y, w, h) = cv2.boundingRect(c)\n",
+    "    ratio = h/w\n",
+    "    if 1<=ratio<=3.5: # Only select contour with defined ratio\n",
+    "        if h/plate_image.shape[0]>=0.5: # Select contour which has the height larger than 50% of the plate\n",
+    "            # Draw bounding box arroung digit number\n",
+    "            cv2.rectangle(test_roi, (x, y), (x + w, y + h), (0, 255,0), 2)\n",
+    "\n",
+    "            # Sperate number and gibe prediction\n",
+    "            curr_num = thre_mor[y:y+h,x:x+w]\n",
+    "            curr_num = cv2.resize(curr_num, dsize=(digit_w, digit_h))\n",
+    "            _, curr_num = cv2.threshold(curr_num, 220, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
+    "            crop_characters.append(curr_num)\n",
+    "\n",
+    "print(\"Detect {} letters...\".format(len(crop_characters)))\n",
+    "fig = plt.figure(figsize=(10,6))\n",
+    "#plt.axis(False)\n",
+    "#plt.imshow(test_roi)\n",
+    "#plt.savefig('grab_digit_contour.png',dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] Model loaded successfully...\n",
+      "[INFO] Labels loaded successfully...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load model architecture, weight and labels\n",
+    "json_file = open('MobileNets_character_recognition.json', 'r')\n",
+    "loaded_model_json = json_file.read()\n",
+    "json_file.close()\n",
+    "model = model_from_json(loaded_model_json)\n",
+    "model.load_weights(\"License_character_recognition_weight.h5\")\n",
+    "print(\"[INFO] Model loaded successfully...\")\n",
+    "\n",
+    "labels = LabelEncoder()\n",
+    "labels.classes_ = np.load('license_character_classes.npy')\n",
+    "print(\"[INFO] Labels loaded successfully...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pre-processing input images and pedict with model\n",
+    "def predict_from_model(image,model,labels):\n",
+    "    image = cv2.resize(image,(80,80))\n",
+    "    image = np.stack((image,)*3, axis=-1)\n",
+    "    prediction = labels.inverse_transform([np.argmax(model.predict(image[np.newaxis,:]))])\n",
+    "    return prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MH12DE1433\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x216 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(15,3))\n",
+    "cols = len(crop_characters)\n",
+    "grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig)\n",
+    "\n",
+    "final_string = ''\n",
+    "for i,character in enumerate(crop_characters):\n",
+    "    #fig.add_subplot(grid[i])\n",
+    "    title = np.array2string(predict_from_model(character,model,labels))\n",
+    "    #plt.title('{}'.format(title.strip(\"'[]\"),fontsize=20))\n",
+    "    final_string+=title.strip(\"'[]\")\n",
+    "    #plt.axis(False)\n",
+    "    #plt.imshow(character,cmap='gray')\n",
+    "\n",
+    "print(final_string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 85  57  76]\n",
+      "  [ 93  65  84]\n",
+      "  [ 95  68  84]\n",
+      "  ...\n",
+      "  [173 151 145]\n",
+      "  [170 148 143]\n",
+      "  [169 147 142]]\n",
+      "\n",
+      " [[ 85  59  75]\n",
+      "  [ 94  69  83]\n",
+      "  [ 99  74  88]\n",
+      "  ...\n",
+      "  [173 151 145]\n",
+      "  [171 149 144]\n",
+      "  [170 148 143]]\n",
+      "\n",
+      " [[113  92 101]\n",
+      "  [119  98 106]\n",
+      "  [122 101 109]\n",
+      "  ...\n",
+      "  [174 152 146]\n",
+      "  [172 150 145]\n",
+      "  [171 149 144]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[204 216 228]\n",
+      "  [201 213 223]\n",
+      "  [206 218 228]\n",
+      "  ...\n",
+      "  [102  76  62]\n",
+      "  [ 94  68  54]\n",
+      "  [ 92  66  52]]\n",
+      "\n",
+      " [[206 217 231]\n",
+      "  [200 211 225]\n",
+      "  [205 216 230]\n",
+      "  ...\n",
+      "  [ 99  73  61]\n",
+      "  [ 92  68  56]\n",
+      "  [ 93  69  57]]\n",
+      "\n",
+      " [[216 226 243]\n",
+      "  [227 237 254]\n",
+      "  [221 232 246]\n",
+      "  ...\n",
+      "  [ 92  66  54]\n",
+      "  [ 86  62  50]\n",
+      "  [ 91  67  55]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "img = cv2.imread(\"C:/Users/JomerJuan/Documents/Deep Learning/Plate Number Recognition/Plate_examples/germany_car_plate.jpg\")\n",
+    "\n",
+    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
+    "print(img)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6e8d6bc3219a43fccaa16aa1e841da92ab3f69dc51828f7269e34e3f0779a8af"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.3 ('Plate_Number_Recognition': 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": "3.8.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}