b3ef7ae9d3c1f6cfc9a202255ed2aa6b9d2aa791,research/delf/delf/python/examples/extract_features.py,,main,#Any#,65

Before Change

    tf.io.gfile.makedirs(cmd_args.output_dir)

  // Tell TensorFlow that the model will be built into the default Graph.
  with tf.Graph().as_default():
    with tf.compat.v1.Session() as sess:
      init_op = tf.compat.v1.global_variables_initializer()
      sess.run(init_op)

      extractor_fn = extractor.MakeExtractor(sess, config)

      start = time.clock()
      for i in range(num_images):
        // Write to log-info once in a while.
        if i == 0:
          print("Starting to extract DELF features from images...")
        elif i % _STATUS_CHECK_ITERATIONS == 0:
          elapsed = (time.clock() - start)
          print(
              f"Processing image {i} out of {num_images}, last "
              f"{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds"
              )
          start = time.clock()

        // If descriptor already exists, skip its computation.
        out_desc_filename = os.path.splitext(os.path.basename(
            image_paths[i]))[0] + _DELF_EXT
        out_desc_fullpath = os.path.join(cmd_args.output_dir, out_desc_filename)
        if tf.io.gfile.exists(out_desc_fullpath):
          print(f"Skipping {image_paths[i]}")
          continue

        im = np.array(utils.RgbLoader(image_paths[i]))

        // Extract and save features.
        extracted_features = extractor_fn(im)
        locations_out = extracted_features["local_features"]["locations"]
        descriptors_out = extracted_features["local_features"]["descriptors"]
        feature_scales_out = extracted_features["local_features"]["scales"]
        attention_out = extracted_features["local_features"]["attention"]

        feature_io.WriteToFile(out_desc_fullpath, locations_out,
                               feature_scales_out, descriptors_out,
                               attention_out)


if __name__ == "__main__":
  parser = argparse.ArgumentParser()
  parser.register("type", "bool", lambda v: v.lower() == "true")
  parser.add_argument(

After Change

  extractor_fn = extractor.MakeExtractor(config)

  start = time.time()
  for i in range(num_images):
    // Report progress once in a while.
    if i == 0:
      print("Starting to extract DELF features from images...")
    elif i % _STATUS_CHECK_ITERATIONS == 0:
      elapsed = (time.time() - start)
      print(
          f"Processing image {i} out of {num_images}, last "
          f"{_STATUS_CHECK_ITERATIONS} images took {elapsed} seconds"
      )
      start = time.time()

    // If descriptor already exists, skip its computation.
    out_desc_filename = os.path.splitext(os.path.basename(
        image_paths[i]))[0] + _DELF_EXT
    out_desc_fullpath = os.path.join(cmd_args.output_dir, out_desc_filename)
    if tf.io.gfile.exists(out_desc_fullpath):
      print(f"Skipping {image_paths[i]}")
      continue

    im = np.array(utils.RgbLoader(image_paths[i]))

    // Extract and save features.
    extracted_features = extractor_fn(im)
    locations_out = extracted_features["local_features"]["locations"]
    descriptors_out = extracted_features["local_features"]["descriptors"]
    feature_scales_out = extracted_features["local_features"]["scales"]
    attention_out = extracted_features["local_features"]["attention"]

    feature_io.WriteToFile(out_desc_fullpath, locations_out, feature_scales_out,
                           descriptors_out, attention_out)


if __name__ == "__main__":
  parser = argparse.ArgumentParser()
  parser.register("type", "bool", lambda v: v.lower() == "true")
  parser.add_argument(

Instances