run_inference_for_single_image(image, graph) - Tensorflow, object detection - tensorflow

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.

Answer 3

It is possible to run inference on batch of images depending on computational power of GPU and size of the images.
step 1: stacking all the test images in one array:
for image_path in glob.glob(PATH_TO_TEST_IMAGES_DIR + '/*.jpg'):
image_np = io.imread(image_path) #
image_array.append(image_np)
image_array = np.array(image_array)
step 2: run inference on batches: (higher batch size might cause out of memory issues)
BATCH_SIZE = 5
for i in range(0, image_array.shape[0],BATCH_SIZE):
output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image_array[i:i+BATCH_SIZE]})
print("number of images inferenced = ", i+BATCH_SIZE)
output_dict_array.append(output_dict)
make sure dimensions of image_tensor and image_array match. In this example image_array is (?, height, width, 3)
some tips:
You would want to load the graph only once as it takes few seconds to load.
I observed that using skimage.io.imread() or cv2.imread() is pretty fast in loading images. These functions directly load images as numpy arrays.
skimage or opencv for saving images are faster than matplotlib.