alpr.py

import os

import cv2
import numpy as np
import tensorflow.compat.v1 as tf
from numpy.linalg import norm
from local_utils import detect_lp
from os.path import splitext
from tensorflow.python.keras.backend import set_session
from tensorflow.keras.models import model_from_json
from tensorflow.compat.v1 import ConfigProto


class DetectLicensePlate:
    def __init__(self):
        tf.compat.v1.disable_eager_execution()
        #config = ConfigProto()
        #config.gpu_options.allow_growth = False
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.433)
        self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
        #self.sess = tf.Session(config=config)
        self.graph = tf.get_default_graph()
        set_session(self.sess)

        self.wpod_net_path ="wpod-net.json"  # model path
        self.wpod_net = self.load_model(self.wpod_net_path)

    def load_model(self, path):
        try:
            path = splitext(path)[0]
            with open('%s.json' % path, 'r') as json_file:
                model_json = json_file.read()
            model = model_from_json(model_json, custom_objects={})
            model.load_weights('%s.h5' % path)
            print("Loading model successfully...")
            self.graph = tf.get_default_graph()
            return model
        except Exception as e:
            print(e)

    def preprocess_image(self, image_path, resize=False):
        img = image_path
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = img / 255
        if resize:
            img = cv2.resize(img, (224, 224))
        return img

    def get_plate(self, image_path, Dmax=608, Dmin=608):
        vehicle = self.preprocess_image(image_path)
        ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
        side = int(ratio * Dmin)
        bound_dim = min(side, Dmax)
        _, plates, _, cor = detect_lp(self.graph,self.sess,self.wpod_net, vehicle, bound_dim, lp_threshold=0.5)
        return vehicle, plates, cor





def alpr(frame,license_plate):

    plate_image = frame.copy()

    try:
        vehicle, plates, cor = license_plate.get_plate(frame)
        return plates[0]

    except Exception as e:
        print(str(e))