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

Answer 1

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:

1. You would want to load the graph only once as it takes few seconds to load.
2. I observed that using skimage.io.imread() or cv2.imread() is pretty fast in loading images. These functions directly load images as numpy arrays.
3. skimage or opencv for saving images are faster than matplotlib.