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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect COLAB with Google Drive"
   ]
  },
  {

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import cv2
import numpy as np
import matplotlib.pyplot as plt
from local_utils import detect_lp
from os.path import splitext, basename
from keras.models import model_from_json
import glob
%matplotlib.inline

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def load_model(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...")
        return model
    except Exception as e:

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

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# Create a list of image paths 
image_paths = glob.glob("Plate_examples/*.jpg")
print("Found %i images..."%(len(image_paths)))

# Visualize data in subplot 
fig = plt.figure(figsize=(12,8))
cols = 5
rows = 4
fig_list = []
for i in range(cols*rows):

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def get_plate(image_path, Dmax=608, Dmin=256):
    vehicle = preprocess_image(image_path)
    ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
    side = int(ratio * Dmin)
    bound_dim = min(side, Dmax)
    _ , LpImg, _, cor = detect_lp(wpod_net, vehicle, bound_dim, lp_threshold=0.5)
    return LpImg, cor

# Obtain plate image and its coordinates from an image
test_image = image_paths[0]

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def draw_box(image_path, cor, thickness=3): 
    pts=[]  
    x_coordinates=cor[0][0]
    y_coordinates=cor[0][1]
    # store the top-left, top-right, bottom-left, bottom-right 
    # of the plate license respectively
    for i in range(4):
        pts.append([int(x_coordinates[i]),int(y_coordinates[i])])
    
    pts = np.array(pts, np.int32)

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# Viualize all obtained plate images 
fig = plt.figure(figsize=(12,6))
cols = 5
rows = 4
fig_list = []

for i in range(cols*rows):
    fig_list.append(fig.add_subplot(rows,cols,i+1))
    title = splitext(basename(image_paths[i]))[0]
    fig_list[-1].set_title(title)

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# Extract mutiple plate license in one image 

multiple_plates_image = "Plate_examples/multiple_plates.png"
LpImg,cor = get_plate(multiple_plates_image)

print("Detect %i plate(s) in"%len(LpImg),splitext(basename(multiple_plates_image))[0])

# Visualize the original image
plt.figure(figsize=(10,5))
plt.axis(False)