from distutils.version import StrictVersion
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from PIL import Image
import cv2 as cv
from flask_sqlalchemy import SQLAlchemy
#please change the path to the folder 'tensorflow'


TENSORFLOW_DIR = "/anaconda2/lib/python2.7/site-packages/tensorflow"



#---------------------------------------------
MR_DIR = TENSORFLOW_DIR + "/models/research"
OD_DIR = MR_DIR + "/object_detection"
# This is needed since the notebook is stored in the object_detection folder.
sys.path.insert(0,OD_DIR)
sys.path.insert(1,MR_DIR)
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
	raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
# This is needed to display the images.
from utils import label_map_util
from utils import visualization_utils as vis_util

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
#DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = os.path.join('res/object_detection',MODEL_NAME + '/frozen_inference_graph.pb')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'res/object_detection/mscoco_label_map.pbtxt'

NUM_CLASSES = 90


class Object_Detection():
	def __init__(self,socket,ap):
		self.socketio = socket
		self.app = ap
		#load model
		self.detection_graph = tf.Graph()
		with self.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='')

		# load the label
		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)
		self.category_index = label_map_util.create_category_index(categories)

		#alert objects
		self.alert_objects = [1,2]
		with open('static/label.txt') as f:
			self.labels = eval(f.read())
		print("\nObject Detection is Ready!\n")



	def load_image_into_numpy_array(self,image):
		(im_width, im_height) = image.size
		return np.array(image.getdata()).reshape(
			(im_height, im_width, 3)).astype(np.uint8)

	def run_inference_for_single_image(self,image, graph):
		with graph.as_default():
			with tf.Session() as sess:
				# Get handles to input and output tensors
				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:
					# The following processing is only for single image
					detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
					detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
					# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
					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)
					# Follow the convention by adding back the batch dimension
					tensor_dict['detection_masks'] = tf.expand_dims(
						detection_masks_reframed, 0)
				image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

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

				# all outputs are float32 numpy arrays, so convert types as appropriate
				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


	def detect(self,img):
		print("in object detection")
		image = Image.fromarray(img)
		# the array based representation of the image will be used later in order to prepare the
		# result image with boxes and labels on it.
		image_np = self.load_image_into_numpy_array(image)
		# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
		image_np_expanded = np.expand_dims(image_np, axis=0)
		# Actual detection.
		output_dict = self.run_inference_for_single_image(image_np, self.detection_graph)
		# Visualization of the results of a detection.
		vis_util.visualize_boxes_and_labels_on_image_array(
			image_np,
			output_dict['detection_boxes'],
			output_dict['detection_classes'],
			output_dict['detection_scores'],
			self.category_index,
			instance_masks=output_dict.get('detection_masks'),
			use_normalized_coordinates=True,
			line_thickness=5)

		#temp = []
		#for i in output_dict['detection_classes']:
		#	if i in self.alert_objects and i not in temp:
		#		temp.append(i)
		#		info = "WARNING: Object %s appeared!"%self.labels[str(i)]
		#		with self.app.app_context():
		#			self.socketio.emit('message',info,namespace='/warning')
		#		print('\nmsg\n')
		return image_np




95d11c24927db36b75861187f742943863048d13
<mask token>


class Object_Detection:
    <mask token>

    def load_image_into_numpy_array(self, image):
        im_width, im_height = image.size
        return np.array(image.getdata()).reshape((im_height, im_width, 3)
            ).astype(np.uint8)
    <mask token>
    <mask token>

<mask token>
sys.path.insert(0, OD_DIR)
sys.path.insert(1, MR_DIR)
<mask token>
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
    raise ImportError(
        'Please upgrade your TensorFlow installation to v1.9.* or later!')
<mask token>


class Object_Detection:

    def __init__(self, socket, ap):
        self.socketio = socket
        self.app = ap
        self.detection_graph = tf.Graph()
        with self.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)
        self.category_index = label_map_util.create_category_index(categories)
        self.alert_objects = [1, 2]
        with open('static/label.txt') as f:
            self.labels = eval(f.read())
        print('\nObject Detection is Ready!\n')

    def load_image_into_numpy_array(self, image):
        im_width, im_height = image.size
        return np.array(image.getdata()).reshape((im_height, im_width, 3)
            ).astype(np.uint8)

    def run_inference_for_single_image(self, 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

    def detect(self, img):
        print('in object detection')
        image = Image.fromarray(img)
        image_np = self.load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = self.run_inference_for_single_image(image_np, self.
            detection_graph)
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], self.category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=5)
        return image_np

<mask token>
TENSORFLOW_DIR = '/anaconda2/lib/python2.7/site-packages/tensorflow'
MR_DIR = TENSORFLOW_DIR + '/models/research'
OD_DIR = MR_DIR + '/object_detection'
sys.path.insert(0, OD_DIR)
sys.path.insert(1, MR_DIR)
<mask token>
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
    raise ImportError(
        'Please upgrade your TensorFlow installation to v1.9.* or later!')
<mask token>
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
PATH_TO_FROZEN_GRAPH = os.path.join('res/object_detection', MODEL_NAME +
    '/frozen_inference_graph.pb')
PATH_TO_LABELS = 'res/object_detection/mscoco_label_map.pbtxt'
NUM_CLASSES = 90


class Object_Detection:

    def __init__(self, socket, ap):
        self.socketio = socket
        self.app = ap
        self.detection_graph = tf.Graph()
        with self.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)
        self.category_index = label_map_util.create_category_index(categories)
        self.alert_objects = [1, 2]
        with open('static/label.txt') as f:
            self.labels = eval(f.read())
        print('\nObject Detection is Ready!\n')

    def load_image_into_numpy_array(self, image):
        im_width, im_height = image.size
        return np.array(image.getdata()).reshape((im_height, im_width, 3)
            ).astype(np.uint8)

    def run_inference_for_single_image(self, 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

    def detect(self, img):
        print('in object detection')
        image = Image.fromarray(img)
        image_np = self.load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = self.run_inference_for_single_image(image_np, self.
            detection_graph)
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], self.category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=5)
        return image_np

from distutils.version import StrictVersion
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from PIL import Image
import cv2 as cv
from flask_sqlalchemy import SQLAlchemy
TENSORFLOW_DIR = '/anaconda2/lib/python2.7/site-packages/tensorflow'
MR_DIR = TENSORFLOW_DIR + '/models/research'
OD_DIR = MR_DIR + '/object_detection'
sys.path.insert(0, OD_DIR)
sys.path.insert(1, MR_DIR)
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
    raise ImportError(
        'Please upgrade your TensorFlow installation to v1.9.* or later!')
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
PATH_TO_FROZEN_GRAPH = os.path.join('res/object_detection', MODEL_NAME +
    '/frozen_inference_graph.pb')
PATH_TO_LABELS = 'res/object_detection/mscoco_label_map.pbtxt'
NUM_CLASSES = 90


class Object_Detection:

    def __init__(self, socket, ap):
        self.socketio = socket
        self.app = ap
        self.detection_graph = tf.Graph()
        with self.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)
        self.category_index = label_map_util.create_category_index(categories)
        self.alert_objects = [1, 2]
        with open('static/label.txt') as f:
            self.labels = eval(f.read())
        print('\nObject Detection is Ready!\n')

    def load_image_into_numpy_array(self, image):
        im_width, im_height = image.size
        return np.array(image.getdata()).reshape((im_height, im_width, 3)
            ).astype(np.uint8)

    def run_inference_for_single_image(self, 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

    def detect(self, img):
        print('in object detection')
        image = Image.fromarray(img)
        image_np = self.load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = self.run_inference_for_single_image(image_np, self.
            detection_graph)
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], self.category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=5)
        return image_np

from distutils.version import StrictVersion
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from PIL import Image
import cv2 as cv
from flask_sqlalchemy import SQLAlchemy
#please change the path to the folder 'tensorflow'


TENSORFLOW_DIR = "/anaconda2/lib/python2.7/site-packages/tensorflow"



#---------------------------------------------
MR_DIR = TENSORFLOW_DIR + "/models/research"
OD_DIR = MR_DIR + "/object_detection"
# This is needed since the notebook is stored in the object_detection folder.
sys.path.insert(0,OD_DIR)
sys.path.insert(1,MR_DIR)
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
	raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
# This is needed to display the images.
from utils import label_map_util
from utils import visualization_utils as vis_util

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
#DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = os.path.join('res/object_detection',MODEL_NAME + '/frozen_inference_graph.pb')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'res/object_detection/mscoco_label_map.pbtxt'

NUM_CLASSES = 90


class Object_Detection():
	def __init__(self,socket,ap):
		self.socketio = socket
		self.app = ap
		#load model
		self.detection_graph = tf.Graph()
		with self.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='')

		# load the label
		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)
		self.category_index = label_map_util.create_category_index(categories)

		#alert objects
		self.alert_objects = [1,2]
		with open('static/label.txt') as f:
			self.labels = eval(f.read())
		print("\nObject Detection is Ready!\n")



	def load_image_into_numpy_array(self,image):
		(im_width, im_height) = image.size
		return np.array(image.getdata()).reshape(
			(im_height, im_width, 3)).astype(np.uint8)

	def run_inference_for_single_image(self,image, graph):
		with graph.as_default():
			with tf.Session() as sess:
				# Get handles to input and output tensors
				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:
					# The following processing is only for single image
					detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
					detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
					# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
					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)
					# Follow the convention by adding back the batch dimension
					tensor_dict['detection_masks'] = tf.expand_dims(
						detection_masks_reframed, 0)
				image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

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

				# all outputs are float32 numpy arrays, so convert types as appropriate
				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


	def detect(self,img):
		print("in object detection")
		image = Image.fromarray(img)
		# the array based representation of the image will be used later in order to prepare the
		# result image with boxes and labels on it.
		image_np = self.load_image_into_numpy_array(image)
		# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
		image_np_expanded = np.expand_dims(image_np, axis=0)
		# Actual detection.
		output_dict = self.run_inference_for_single_image(image_np, self.detection_graph)
		# Visualization of the results of a detection.
		vis_util.visualize_boxes_and_labels_on_image_array(
			image_np,
			output_dict['detection_boxes'],
			output_dict['detection_classes'],
			output_dict['detection_scores'],
			self.category_index,
			instance_masks=output_dict.get('detection_masks'),
			use_normalized_coordinates=True,
			line_thickness=5)

		#temp = []
		#for i in output_dict['detection_classes']:
		#	if i in self.alert_objects and i not in temp:
		#		temp.append(i)
		#		info = "WARNING: Object %s appeared!"%self.labels[str(i)]
		#		with self.app.app_context():
		#			self.socketio.emit('message',info,namespace='/warning')
		#		print('\nmsg\n')
		return image_np



