run_inference_for_single_image(image, graph) - Tensorflow, object detection

Question

In reference to object_detection_tutorial.ipynb. I am wondering if its possible to run for all the images in a directory.

Rather than writing a for loop and running a "run_inference_for_single_image(image, graph)". Is there a way to run the inference on all the images in a directory or run the inference on multiple images. link

  for f in files:
    if f.lower().endswith(('.png', '.jpg', '.jpeg')):
      image_path = files_dir + '/' + f
       .... // Read image etc.
      output_dict = run_inference_for_single_image(image_np, detection_graph)

This will create tf.session each time and i think its computationally expensive. Please correct me if i am wrong.

Answer 1

As you know, 'run_inference_for_single_image' method create each time. If you wanna inference for multiple images, you should change code like,

• Method Call

  images = []
  for f in files:
    if f.lower().endswith(('.png', '.jpg', '.jpeg')):
      image_path = files_dir + '/' + f
      image =  .... // Read image etc.
      images.append(image)
      output_dicts = run_inference_for_multiple_images(images, detection_graph)
  

• run_inference_for_multiple_images

  def run_inference_for_multiple_images(images, grapg):
    with graph.as_default():
      with tf.Session() as sess:
        output_dicts = []
  
        for index, image in enumerate(images):
          ... same as inferencing for single image
  
           output_dicts.append(output_dict)
  
     return output_dicts
  

This code will be performed without creating tf.session each time but once.

Answer 2

I found this tutorial from google - creating-object-detection-application-tensorflow. After looking into its github page --> object_detection_app --> app.py we only need to run detect_objects(image_path) function every single time we want to detect an object.