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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
}
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