# -*- coding: utf-8 -*-
"""
Created on Tue Sep  4 12:33:12 2018

@author: jgs808
"""

# In[1]:

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

# In[2]:

from collections import defaultdict
from io import StringIO
import matplotlib.pyplot as plt
import cv2
sys.path.append('')

# In[3]:

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# In[4]:

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_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# In[5]:

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())
    
# In[6]:
    
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='')
    
# In[7]:

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)

# In[8]:

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)
  
# In[9]:
  
cap = cv2.VideoCapture(0)

width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)   # float
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # float

# In[10]:

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        while True:
            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})
            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=5)
            
            """has_already_print_sthg = False
            for i_box in range(1):
                class_name = category_index[np.squeeze(classes)[i_box]]['name']
                if (class_name == 'person'):
                    if not has_already_print_sthg:
                        print('Test')
                        has_already_print_sthg = True"""
            for i in classes[0]:
                print(category_index[i]['name'])
            
            ymin = int((boxes[0][0][0]*height))
            xmin = int((boxes[0][0][1]*width))
            ymax = int((boxes[0][0][2]*height))
            xmax = int((boxes[0][0][3]*width))
            
            box = np.array(image_np[ymin:ymax,xmin:xmax])
            
            cv2.imshow('Object detection', box)
            if cv2.waitKey(25) & 0xFF == ord('q'):
                cv2.destroyAllWindows()
                break
            
# In[11]:
