def initialize():
wpod_net_path = "model/wpod-net.json"
wpod_net = CarModel.load_model(wpod_net_path)
# Load model architecture, weight and labels
json_file = open('model/MobileNets_character_recognition.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("model/License_character_recognition_weight.h5")
print("[INFO] Model loaded successfully...")
labels = LabelEncoder()
labels.classes_ = np.load('model/license_character_classes.npy')
print("[INFO] Labels loaded successfully...")
return wpod_net, model, labels
import cv2 import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from local_utils import detect_lp from os.path import splitext,basename from keras.models import model_from_json from keras.preprocessing.image import load_img, img_to_array from keras.applications.mobilenet_v2 import preprocess_input from sklearn.preprocessing import LabelEncoder import glob from model import CarModel from helpers import CarHelpers wpod_net_path = "model/wpod-net.json" wpod_net = CarModel.load_model(wpod_net_path) test_image_path = "Plate_examples/2.jpg" vehicle, LpImg,cor = CarHelpers.get_plate(test_image_path, wpod_net) fig = plt.figure(figsize=(12,6)) grid = gridspec.GridSpec(ncols=2,nrows=1,figure=fig) fig.add_subplot(grid[0]) plt.axis(False) plt.imshow(vehicle) grid = gridspec.GridSpec(ncols=2,nrows=1,figure=fig) fig.add_subplot(grid[1]) plt.axis(False) plt.imshow(LpImg[0]) if (len(LpImg)): #check if there is at least one license image
