#!/usr/bin/python
# -*- coding: utf-8 -*-
# ----------------------------------------------
# --- Author         : Ahmet Ozlu
# --- Mail           : ahmetozlu93@gmail.com
# --- Date           : 27th January 2018
# ----------------------------------------------

# Imports
import argparse
import numpy as np
import os
import tensorflow as tf
import cv2
import numpy as np
import matplotlib.image as mpimg

from PIL import Image

# Object detection imports
from utils import label_map_util
from utils import visualization_utils as vis_util


# Variables
total_passed_vehicle = 0  # using it to count vehicles

# By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 3

FLAGS = None

# Detection
def object_detection_function():

    # Load a (frozen) Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    # Loading label map
    # Label maps map indices to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here I use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
    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)

    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:

            # Definite input and output Tensors for detection_graph
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

            # Each box represents a part of the image where a particular object was detected.
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

            # Each score represent how level of confidence for each of the objects.
            # Score is shown on the result image, together with the class label.
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')

            # for all the frames that are extracted from input video
            
            image = mpimg.imread(FLAGS.pic)

            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
            image_np_expanded = np.expand_dims(image, axis=0)

            # Actual detection.
            (boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores,
                        detection_classes, num_detections],
                        feed_dict={image_tensor: image_np_expanded})

            #print('Box shape:{0},value {1}'.format(boxes.shape,boxes))

            image.setflags(write=1)

            vis_util.runMutipyClassify(
                image,
                FLAGS.pic,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index)

            mpimg.imsave(os.path.join('./output_images', os.path.basename(FLAGS.pic)), image)

def parse_args(check=True):
    parser = argparse.ArgumentParser()

    parser.add_argument(
        '--pic',
        type=str,
        default='/home/yu/Mix/pic/test.jpg'
    )

    parser.add_argument(
      '--model_dir',
      type=str,
      default='/home/yu/Mix/',
      help="""\
      Path to classify_image_graph_def.pb,
      imagenet_synset_to_human_label_map.txt, and
      imagenet_2012_challenge_label_map_proto.pbtxt.\
      """
    )

    FLAGS, unparsed = parser.parse_known_args()
    return FLAGS, unparsed

if __name__ == '__main__':

    FLAGS, unparsed = parse_args()
    object_detection_function()