Streaming Object Detection Video - Tensorflow Object Detection API Tutorial

Welcome to part 2 of the TensorFlow Object Detection API tutorial. In this tutorial, we're going to cover how to adapt the sample code from the API's github repo to apply object detection to streaming video from our webcam.

To begin, we're going to modify the notebook first by converting it to a .py file. If you want to keep it in a notebook, that's fine too. To convert, you can go to file > save as > python file. Once that's done, you're going to want to comment out the get_ipython().magic('matplotlib inline') line.

Next, we're going to bring in the Python Open CV wrapper:

If you do not have OpenCV installed, you will need to grab it. See the OpenCV introduction for instructions.

import cv2

cap = cv2.VideoCapture(0)

This will prepare the cap variable to access your webcam.

Next, you're going to replace the following code:

    for image_path in TEST_IMAGE_PATHS:
      image =
      # 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 = load_image_into_numpy_array(image)


    while True:
      ret, image_np =

Finally, replace the following:



      cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
      if cv2.waitKey(25) & 0xFF == ord('q'):

That's it! Full code:

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 matplotlib import pyplot as plt
from PIL import Image

import cv2
cap = cv2.VideoCapture(1)

# This is needed since the notebook is stored in the object_detection folder.

# ## Object detection imports
# Here are the imports from the object detection module.

# In[3]:

from utils import label_map_util

from utils import visualization_utils as vis_util

# # Model preparation 

# ## Variables
# Any model exported using the `` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo]( for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

# In[4]:

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'

# 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')


# ## Download Model

# In[5]:

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.

# In[6]:

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

# 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)

# ## Helper code

# 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)

# # Detection

# In[9]:

# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

# In[10]:

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while True:
      ret, image_np =
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      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.
      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.
      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')
      # Actual detection.
      (boxes, scores, classes, num_detections) =
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.

      cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
      if cv2.waitKey(25) & 0xFF == ord('q'):

There's certainly some more cleaning of the code that we could do, like getting rid of the matplotlib imports, and the old image data, feel free to clean things up if you like.

You should have a streaming webcam feed that is also being labeled. Some objects that you can test with: Yourself, a cellphone, or a bottle of water. All of those should work.

In the next tutorial, we're going to cover how we can add our own custom objects to be tracked.

The next tutorial:

  • Introduction and Use - Tensorflow Object Detection API Tutorial
  • Streaming Object Detection Video - Tensorflow Object Detection API Tutorial
  • Tracking Custom Objects Intro - Tensorflow Object Detection API Tutorial
  • Creating TFRecords - Tensorflow Object Detection API Tutorial
  • Training Custom Object Detector - Tensorflow Object Detection API Tutorial
  • Testing Custom Object Detector - Tensorflow Object Detection API Tutorial