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import numpy as np
import os
import six.moves.urllib as urllib
import tarfile
import tensorflow as tf
import json
if tf.__version__ != '1.4.0':
raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')
# ENV SETUP ### CWH: remove matplot display and manually add paths to references
# Object detection imports
from object_detection.utils import label_map_util ### CWH: Add object_detection path
# Model Preparation
# 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('object_detection/data', 'mscoco_label_map.pbtxt') ### CWH: Add object_detection path
# Download Model
opener = urllib.request.URLopener()
tar_file =
for file in tar_file.getmembers():
file_name = os.path.basename(
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
# 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 =
tf.import_graph_def(od_graph_def, name='')
# Loading label map
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)
# Helper code
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)
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')
# added to put object in JSON
class Object(object):
def __init__(self):"webrtcHacks TensorFlow Object Detection REST API"
def toJSON(self):
return json.dumps(self.__dict__)
def get_objects(image, threshold=0.5):
image_np = 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.
(boxes, scores, classes, num) =
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
classes = np.squeeze(classes).astype(np.int32)
scores = np.squeeze(scores)
boxes = np.squeeze(boxes)
obj_above_thresh = sum(n > threshold for n in scores)
print("detected %s objects in image above a %s score" % (obj_above_thresh, threshold))
output = []
# Add some metadata to the output
item = Object()
item.version = "0.0.1"
item.numObjects = obj_above_thresh
item.threshold = threshold
for c in range(0, len(classes)):
class_name = category_index[classes[c]]['name']
if scores[c] >= threshold: # only return confidences equal or greater than the threshold
print(" object %s - score: %s, coordinates: %s" % (class_name, scores[c], boxes[c]))
item = Object() = 'Object'
item.class_name = class_name
item.score = float(scores[c])
item.y = float(boxes[c][0])
item.x = float(boxes[c][1])
item.height = float(boxes[c][2])
item.width = float(boxes[c][3])
outputJson = json.dumps([ob.__dict__ for ob in output])
return outputJson