TFODApply.py

# ## Apply module
# 
# Load a public image from Open Images v4, save locally, and display.

# In[4]:


# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg
image_url = "https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg"  #@param
downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True)


# Pick an object detection module and apply on the downloaded image. Modules:
# * **FasterRCNN+InceptionResNet V2**: high accuracy,
# * **ssd+mobilenet V2**: small and fast.

# In[5]:


module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1" #@param ["https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1", "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"]

detector = hub.load(module_handle).signatures['default']


# In[6]:


def load_img(path):
  img = tf.io.read_file(path)
  img = tf.image.decode_jpeg(img, channels=3)
  return img


# In[7]:


def run_detector(detector, path):
  img = load_img(path)

  converted_img  = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]
  start_time = time.time()
  result = detector(converted_img)
  end_time = time.time()

  result = {key:value.numpy() for key,value in result.items()}

  print("Found %d objects." % len(result["detection_scores"]))
  print("Inference time: ", end_time-start_time)

  image_with_boxes = draw_boxes(
      img.numpy(), result["detection_boxes"],
      result["detection_class_entities"], result["detection_scores"])

  display_image(image_with_boxes)


# In[8]:


run_detector(detector, downloaded_image_path)