# -*- coding: utf-8 -*-
"""
Created on Tue Oct 18 17:56:01 2018

@author: inovakomerfaruk
"""



import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

import time

import cv2
from collections import defaultdict
from io import StringIO
# from matplotlib import pyplot as plt
from PIL import Image

sys.path.append("..")
from object_detection.utils import ops as utils_ops


from utils import label_map_util

from utils import visualization_utils as vis_util

from datetime import datetime


MODEL_NAME = 'TrainedModels/RailSurfaceDefects'

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

PATH_TO_LABELS = os.path.join('data', 'RailSurfaceDefects/RailSurfaceDefectsLabel.pbtxt')

NUM_CLASSES = 2


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='')


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
  # print(im_width,im_height)
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

PATH_TO_TEST_IMAGES_DIR = 'test_images/RailSurfaceDefectsImages'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 6) ]

IMAGE_SIZE = (12, 8)


def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: np.expand_dims(image, 0)})

      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.uint8)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

i=1




for image_path in TEST_IMAGE_PATHS:

    start = datetime.now()

    image = Image.open(image_path)
    image_np = load_image_into_numpy_array(image)
    (im_width, im_height) = image.size
    image_np_expanded = np.expand_dims(image_np, axis=0)
    output_dict = run_inference_for_single_image(image_np, detection_graph)

    # BestAccuracy = round(output_dict['detection_scores'][0] * 100, 2)
    # print("Best Accuracy: {}".format(BestAccuracy))

    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=2)

    # plt.figure(figsize=IMAGE_SIZE)
    # plt.imshow(image_np)
    img = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (250, 950))
    end = datetime.now()
    AnalysisTime = round((end-start).total_seconds(), 2)
    print("Analysis Time: {} ms".format(AnalysisTime))

    # cv2.putText(img, "Analysis Time: {} ms".format(AnalysisTime), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), 2)
    cv2.imshow('ImageOutput'+str(i), img)

    i = i+1

cv2.waitKey(0)
cv2.destroyAllWindows()