# Copyright 2020 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Extracts DELG features for images from Revisited Oxford/Paris datasets.

Note that query images are cropped before feature extraction, as required by the
evaluation protocols of these datasets.

The types of extracted features (local and/or global) depend on the input
DelfConfig.

The program checks if features already exist, and skips computation for those.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time

from absl import app
from absl import flags
import numpy as np
import tensorflow as tf

from google.protobuf import text_format
from delf import delf_config_pb2
from delf import datum_io
from delf import feature_io
from delf import utils
from delf.python.detect_to_retrieve import dataset
from delf import extractor

FLAGS = flags.FLAGS

flags.DEFINE_string(
    'delf_config_path', '/tmp/delf_config_example.pbtxt',
    'Path to DelfConfig proto text file with configuration to be used for DELG '
    'extraction. Local features are extracted if use_local_features is True; '
    'global features are extracted if use_global_features is True.')
flags.DEFINE_string(
    'dataset_file_path', '/tmp/gnd_roxford5k.mat',
    'Dataset file for Revisited Oxford or Paris dataset, in .mat format.')
flags.DEFINE_string(
    'images_dir', '/tmp/images',
    'Directory where dataset images are located, all in .jpg format.')
flags.DEFINE_enum('image_set', 'query', ['query', 'index'],
                  'Whether to extract features from query or index images.')
flags.DEFINE_string(
    'output_features_dir', '/tmp/features',
    "Directory where DELG features will be written to. Each image's features "
    'will be written to files with same name but different extension: the '
    'global feature is written to a file with extension .delg_global and the '
    'local features are written to a file with extension .delg_local.')

# Extensions.
_DELG_GLOBAL_EXTENSION = '.delg_global'
_DELG_LOCAL_EXTENSION = '.delg_local'
_IMAGE_EXTENSION = '.jpg'

# Pace to report extraction log.
_STATUS_CHECK_ITERATIONS = 50


def main(argv):
  if len(argv) > 1:
    raise RuntimeError('Too many command-line arguments.')

  # Read list of images from dataset file.
  print('Reading list of images from dataset file...')
  query_list, index_list, ground_truth = dataset.ReadDatasetFile(
      FLAGS.dataset_file_path)
  if FLAGS.image_set == 'query':
    image_list = query_list
  else:
    image_list = index_list
  num_images = len(image_list)
  print('done! Found %d images' % num_images)

  # Parse DelfConfig proto.
  config = delf_config_pb2.DelfConfig()
  with tf.io.gfile.GFile(FLAGS.delf_config_path, 'r') as f:
    text_format.Parse(f.read(), config)

  # Create output directory if necessary.
  if not tf.io.gfile.exists(FLAGS.output_features_dir):
    tf.io.gfile.makedirs(FLAGS.output_features_dir)

  extractor_fn = extractor.MakeExtractor(config)

  start = time.time()
  for i in range(num_images):
    if i == 0:
      print('Starting to extract features...')
    elif i % _STATUS_CHECK_ITERATIONS == 0:
      elapsed = (time.time() - start)
      print('Processing image %d out of %d, last %d '
            'images took %f seconds' %
            (i, num_images, _STATUS_CHECK_ITERATIONS, elapsed))
      start = time.time()

    image_name = image_list[i]
    input_image_filename = os.path.join(FLAGS.images_dir,
                                        image_name + _IMAGE_EXTENSION)

    # Compose output file name and decide if image should be skipped.
    should_skip_global = True
    should_skip_local = True
    if config.use_global_features:
      output_global_feature_filename = os.path.join(
          FLAGS.output_features_dir, image_name + _DELG_GLOBAL_EXTENSION)
      if not tf.io.gfile.exists(output_global_feature_filename):
        should_skip_global = False
    if config.use_local_features:
      output_local_feature_filename = os.path.join(
          FLAGS.output_features_dir, image_name + _DELG_LOCAL_EXTENSION)
      if not tf.io.gfile.exists(output_local_feature_filename):
        should_skip_local = False
    if should_skip_global and should_skip_local:
      print('Skipping %s' % image_name)
      continue

    pil_im = utils.RgbLoader(input_image_filename)
    resize_factor = 1.0
    if FLAGS.image_set == 'query':
      # Crop query image according to bounding box.
      original_image_size = max(pil_im.size)
      bbox = [int(round(b)) for b in ground_truth[i]['bbx']]
      pil_im = pil_im.crop(bbox)
      cropped_image_size = max(pil_im.size)
      resize_factor = cropped_image_size / original_image_size

    im = np.array(pil_im)

    # Extract and save features.
    extracted_features = extractor_fn(im, resize_factor)
    if config.use_global_features:
      global_descriptor = extracted_features['global_descriptor']
      datum_io.WriteToFile(global_descriptor, output_global_feature_filename)
    if config.use_local_features:
      locations = extracted_features['local_features']['locations']
      descriptors = extracted_features['local_features']['descriptors']
      feature_scales = extracted_features['local_features']['scales']
      attention = extracted_features['local_features']['attention']
      feature_io.WriteToFile(output_local_feature_filename, locations,
                             feature_scales, descriptors, attention)


if __name__ == '__main__':
  app.run(main)
