#import libraries
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import win32api
import cv2
import obd
import time

from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from utils import label_map_util
from utils import visualization_utils as vis_util

# model yang akan di download
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path ke frozen detection graph. Ini adalah model yang sebenarnya digunakan untuk object detection
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

# List dari string yang digunakan untuk menambahkan label yang benar untuk tiap box
PATH_TO_LABELS = os.path.join('C:/Tensorflow/models/research/object_detection/data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# Untuk download model jika belum ada dan mengekstraknya
if not os.path.exists(MODEL_NAME + '/frozen_inference_graph.pb'):
	#print ('Downloading the model')
	opener = urllib.request.URLopener()
	opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
	tar_file = tarfile.open(MODEL_FILE)
	for file in tar_file.getmembers():
	  file_name = os.path.basename(file.name)
	  if 'frozen_inference_graph.pb' in file_name:
	    tar_file.extract(file, os.getcwd())

    
detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')
    
#%%

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)


#%%


def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)
  
#%%
  
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

# Ukuran hasil gambar, dalam inches.
IMAGE_SIZE = (12, 8)


#%%
# mendapatkan kecepatan mobil
connection = obd.OBD()

cmd = obd.commands.SPEED

response = connection.query(cmd)

if response.value.to('mph') >= 10:
cap = cv2.VideoCapture(0) 
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    ret = True
    x = 0
    while(ret):
      ret,image_np = cap.read()  
      
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')

      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
# Class 1 adalah person dan Class 77 adalah cell phone
      if 1 and 77 in classes:
          x = x + 1
      if x == 10:
          win32api.Beep(100, 10)
          win32api.MessageBox(0, 'Stop playing phone !', 'WARNING!!!')

      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)

      
      cv2.imshow('image',cv2.resize(image_np,(1280,960)))
      if cv2.waitKey(25) & 0xFF == ord('q'):
          cv2.destroyAllWindows()
          cap.release()
          break
  
  
  
