"""Utility methods for computing the performance metrics."""
from matching import Matching
from rectangle import Rectangle


def safe_divide(numerator, denominator):
  """Computes the safe division to avoid the divide by zero problem."""
  if denominator == 0:
    return 0
  return numerator / denominator


def compute_statistics_given_rectangle_matches(groundtruth_rects_matched, rects_matched):
  """Computes the staticstics given the groundtruth_rects and rects matches.

  Args:
      image_id: the image_id referring to the image to be evaluated.
      groundtruth_rects_matched: the groundtruth_rects_matched represents
        a list of integers returned from the Matching class instance to
        indicate the matched rectangle indices from rects for each of the
        groundtruth_rects.
      rects_matched: the rects_matched represents a list of integers returned
        from the Matching class instance to indicate the matched rectangle
        indices from groundtruth_rects for each of the rects.
  Returns:
      A dictionary holding the computed statistics as well as the inputs.
  """
  # Calculate the total_positives, true_positives, and false_positives.
  total_positives = len(groundtruth_rects_matched)
  true_positives = sum(item is not None for item in groundtruth_rects_matched)
  false_positives = sum(item is None for item in rects_matched)
  return {'groundtruth_rects_matched': groundtruth_rects_matched,
          'rects_matched': rects_matched,
          'total_positives': total_positives,
          'true_positives': true_positives,
          'false_positives': false_positives}


def compute_precision_recall_given_image_statistics_list(
    iou_threshold, image_statistics_list):
  """Computes the precision recall numbers given iou_threshold and statistics.

  Args:
      iou_threshold: the iou_threshold under which the statistics are computed.
      image_statistics_list: a list of the statistics computed and returned
      by the compute_statistics_given_rectangle_matches method for a list of
      images.
  Returns:
      A dictionary holding the precision, recall as well as the inputs.
  """
  total_positives = 0
  true_positives = 0
  false_positives = 0
  for statistics in image_statistics_list:
    total_positives += statistics['total_positives']
    true_positives += statistics['true_positives']
    false_positives += statistics['false_positives']
  precision = safe_divide(true_positives, true_positives + false_positives)
  recall = safe_divide(true_positives, total_positives)
  return {'iou_threshold': iou_threshold,
          'precision': precision,
          'recall': recall,
          'image_statistics_list': image_statistics_list}


def compute_average_precision_recall_given_precision_recall_dict(
    precision_recall_dict):
  """Computes the average precision (AP) and average recall (AR).

  Args:
      precision_recall_dict: the precision_recall_dict holds the dictionary of
      precision and recall information returned by the
      compute_precision_recall_given_image_statistics_list method, which is
      calcualted under a range of iou_thresholds, where the iou_threshold is
      the key.
  Returns:
      average_precision, average_recall.
  """
  precision = 0
  recall = 0
  for _, value in precision_recall_dict.items():
    precision += value['precision']
    recall += value['recall']
  average_precision = safe_divide(precision, len(precision_recall_dict))
  average_recall = safe_divide(recall, len(precision_recall_dict))
  return average_precision, average_recall


def convert_to_rectangle_list(coordinates):
  """Converts the coordinates in a list to the Rectangle list."""
  rectangle_list = []
  number_of_rects = int(len(coordinates) / 4)
  for i in range(number_of_rects):
    rectangle_list.append(Rectangle(
        coordinates[4 * i], coordinates[4 * i + 1], coordinates[4 * i + 2],
        coordinates[4 * i + 3]))
  return rectangle_list


def compute_average_precision_recall(
    groundtruth_coordinates, coordinates, iou_threshold):
  """Computes the average precision (AP) and average recall (AR).

  Args:
      groundtruth_info_dict: the groundtruth_info_dict holds all the groundtruth
        information for an evaluation dataset. The format of this groundtruth_info_dict is
        as follows:
        {'image_id_0':
         [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_N0,ymin_N0,xmax_N0,ymax_N0],
         ...,
         'image_id_M':
         [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_NM,ymin_NM,xmax_NM,ymax_NM]},
        where
          image_id_* is an image_id that has the groundtruth rectangles labeled.
          xmin_*,ymin_*,xmax_*,ymax_* is the top-left and bottom-right corners
            of one groundtruth rectangle.

      test_info_dict: the test_info_dict holds all the test information for an
        evaluation dataset.
         The format of this test_info_dict is the same
        as the above groundtruth_info_dict.

      iou_threshold_range: the IOU threshold range to compute the average
        precision (AP) and average recall (AR). For example:
        iou_threshold_range = [0.50:0.05:0.95]
  Returns:
      average_precision, average_recall, as well as the precision_recall_dict,
      where precision_recall_dict holds the full precision/recall information
      for each of the iou_threshold in the iou_threshold_range.
  Raises:
      ValueError: if the input groundtruth_info_dict and test_info_dict show
      inconsistent information.
  """

  # Start to build up the Matching instances for each of the image_id_*, which
  # is to hold the IOU computation between the rectangle pairs for the same
  # image_id_*.
  matchings = {}
  if (len(groundtruth_coordinates) % 4 != 0) or (len(coordinates) % 4 != 0):
    raise ValueError('groundtruth_info_dict and test_info_dict should hold '
                     'only 4 * N numbers.')

  groundtruth_rects = convert_to_rectangle_list(groundtruth_coordinates)
  rects = convert_to_rectangle_list(coordinates)
  matching = Matching(groundtruth_rects, rects)

  image_statistics_list = []
  groundtruth_rects_matched, rects_matched = (
      matching.matching_by_greedy_assignment(iou_threshold))

  image_statistics = compute_statistics_given_rectangle_matches(
      groundtruth_rects_matched, rects_matched)
  image_statistics_list.append(image_statistics)

  # Compute the precision and recall under this iou_threshold.
  precision_recall = compute_precision_recall_given_image_statistics_list(
      iou_threshold, image_statistics_list)

  # Compute the average_precision and average_recall.
  #average_precision, average_recall = (
  #    compute_average_precision_recall_given_precision_recall_dict(
  #        precision_recall_dict))

  return precision_recall

"""Utility methods for computing the performance metrics."""
from matching import Matching
from rectangle import Rectangle


def safe_divide(numerator, denominator):
  """Computes the safe division to avoid the divide by zero problem."""
  if denominator == 0:
    return 0
  return numerator / denominator


def compute_statistics_given_rectangle_matches(groundtruth_rects_matched, rects_matched):
  """Computes the staticstics given the groundtruth_rects and rects matches.

  Args:
      image_id: the image_id referring to the image to be evaluated.
      groundtruth_rects_matched: the groundtruth_rects_matched represents
        a list of integers returned from the Matching class instance to
        indicate the matched rectangle indices from rects for each of the
        groundtruth_rects.
      rects_matched: the rects_matched represents a list of integers returned
        from the Matching class instance to indicate the matched rectangle
        indices from groundtruth_rects for each of the rects.
  Returns:
      A dictionary holding the computed statistics as well as the inputs.
  """
  # Calculate the total_positives, true_positives, and false_positives.
  total_positives = len(groundtruth_rects_matched)
  true_positives = sum(item is not None for item in groundtruth_rects_matched)
  false_positives = sum(item is None for item in rects_matched)
  return {'groundtruth_rects_matched': groundtruth_rects_matched,
          'rects_matched': rects_matched,
          'total_positives': total_positives,
          'true_positives': true_positives,
          'false_positives': false_positives}


def compute_precision_recall_given_image_statistics_list(
    iou_threshold, image_statistics_list):
  """Computes the precision recall numbers given iou_threshold and statistics.

  Args:
      iou_threshold: the iou_threshold under which the statistics are computed.
      image_statistics_list: a list of the statistics computed and returned
      by the compute_statistics_given_rectangle_matches method for a list of
      images.
  Returns:
      A dictionary holding the precision, recall as well as the inputs.
  """
  total_positives = 0
  true_positives = 0
  false_positives = 0
  for statistics in image_statistics_list:
    total_positives += statistics['total_positives']
    true_positives += statistics['true_positives']
    false_positives += statistics['false_positives']
  precision = safe_divide(true_positives, true_positives + false_positives)
  recall = safe_divide(true_positives, total_positives)
  return {'iou_threshold': iou_threshold,
          'precision': precision,
          'recall': recall,
          'image_statistics_list': image_statistics_list}


def compute_average_precision_recall_given_precision_recall_dict(
    precision_recall_dict):
  """Computes the average precision (AP) and average recall (AR).

  Args:
      precision_recall_dict: the precision_recall_dict holds the dictionary of
      precision and recall information returned by the
      compute_precision_recall_given_image_statistics_list method, which is
      calcualted under a range of iou_thresholds, where the iou_threshold is
      the key.
  Returns:
      average_precision, average_recall.
  """
  precision = 0
  recall = 0
  for _, value in precision_recall_dict.items():
    precision += value['precision']
    recall += value['recall']
  average_precision = safe_divide(precision, len(precision_recall_dict))
  average_recall = safe_divide(recall, len(precision_recall_dict))
  return average_precision, average_recall


def convert_to_rectangle_list(coordinates):
  """Converts the coordinates in a list to the Rectangle list."""
  rectangle_list = []
  number_of_rects = int(len(coordinates) / 4)
  for i in range(number_of_rects):
    rectangle_list.append(Rectangle(
        coordinates[4 * i], coordinates[4 * i + 1], coordinates[4 * i + 2],
        coordinates[4 * i + 3]))
  return rectangle_list


def compute_average_precision_recall(
    groundtruth_coordinates, coordinates, iou_threshold):
  """Computes the average precision (AP) and average recall (AR).

  Args:
      groundtruth_info_dict: the groundtruth_info_dict holds all the groundtruth
        information for an evaluation dataset. The format of this groundtruth_info_dict is
        as follows:
        {'image_id_0':
         [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_N0,ymin_N0,xmax_N0,ymax_N0],
         ...,
         'image_id_M':
         [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_NM,ymin_NM,xmax_NM,ymax_NM]},
        where
          image_id_* is an image_id that has the groundtruth rectangles labeled.
          xmin_*,ymin_*,xmax_*,ymax_* is the top-left and bottom-right corners
            of one groundtruth rectangle.

      test_info_dict: the test_info_dict holds all the test information for an
        evaluation dataset.
         The format of this test_info_dict is the same
        as the above groundtruth_info_dict.

      iou_threshold_range: the IOU threshold range to compute the average
        precision (AP) and average recall (AR). For example:
        iou_threshold_range = [0.50:0.05:0.95]
  Returns:
      average_precision, average_recall, as well as the precision_recall_dict,
      where precision_recall_dict holds the full precision/recall information
      for each of the iou_threshold in the iou_threshold_range.
  Raises:
      ValueError: if the input groundtruth_info_dict and test_info_dict show
      inconsistent information.
  """

  # Start to build up the Matching instances for each of the image_id_*, which
  # is to hold the IOU computation between the rectangle pairs for the same
  # image_id_*.
  matchings = {}
  if (len(groundtruth_coordinates) % 4 != 0) or (len(coordinates) % 4 != 0):
    raise ValueError('groundtruth_info_dict and test_info_dict should hold '
                     'only 4 * N numbers.')

  groundtruth_rects = convert_to_rectangle_list(groundtruth_coordinates)
  rects = convert_to_rectangle_list(coordinates)
  matching = Matching(groundtruth_rects, rects)

  image_statistics_list = []
  groundtruth_rects_matched, rects_matched = (
      matching.matching_by_greedy_assignment(iou_threshold))

  image_statistics = compute_statistics_given_rectangle_matches(
      groundtruth_rects_matched, rects_matched)
  image_statistics_list.append(image_statistics)

  # Compute the precision and recall under this iou_threshold.
  precision_recall = compute_precision_recall_given_image_statistics_list(
      iou_threshold, image_statistics_list)

  # Compute the average_precision and average_recall.
  #average_precision, average_recall = (
  #    compute_average_precision_recall_given_precision_recall_dict(
  #        precision_recall_dict))

  return precision_recall

Utility methods for computing the performance metrics. Computes the safe division to avoid the divide by zero problem. Computes the staticstics given the groundtruth_rects and rects matches.    Args:       image_id: the image_id referring to the image to be evaluated.       groundtruth_rects_matched: the groundtruth_rects_matched represents         a list of integers returned from the Matching class instance to         indicate the matched rectangle indices from rects for each of the         groundtruth_rects.       rects_matched: the rects_matched represents a list of integers returned         from the Matching class instance to indicate the matched rectangle         indices from groundtruth_rects for each of the rects.   Returns:       A dictionary holding the computed statistics as well as the inputs. # Calculate the total_positives, true_positives, and false_positives. Computes the precision recall numbers given iou_threshold and statistics.    Args:       iou_threshold: the iou_threshold under which the statistics are computed.       image_statistics_list: a list of the statistics computed and returned       by the compute_statistics_given_rectangle_matches method for a list of       images.   Returns:       A dictionary holding the precision, recall as well as the inputs. Computes the average precision (AP) and average recall (AR).    Args:       precision_recall_dict: the precision_recall_dict holds the dictionary of       precision and recall information returned by the       compute_precision_recall_given_image_statistics_list method, which is       calcualted under a range of iou_thresholds, where the iou_threshold is       the key.   Returns:       average_precision, average_recall. Converts the coordinates in a list to the Rectangle list. Computes the average precision (AP) and average recall (AR).    Args:       groundtruth_info_dict: the groundtruth_info_dict holds all the groundtruth         information for an evaluation dataset. The format of this groundtruth_info_dict is         as follows:         {'image_id_0':          [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_N0,ymin_N0,xmax_N0,ymax_N0],          ...,          'image_id_M':          [xmin_0,ymin_0,xmax_0,ymax_0,...,xmin_NM,ymin_NM,xmax_NM,ymax_NM]},         where           image_id_* is an image_id that has the groundtruth rectangles labeled.           xmin_*,ymin_*,xmax_*,ymax_* is the top-left and bottom-right corners             of one groundtruth rectangle.        test_info_dict: the test_info_dict holds all the test information for an         evaluation dataset.          The format of this test_info_dict is the same         as the above groundtruth_info_dict.        iou_threshold_range: the IOU threshold range to compute the average         precision (AP) and average recall (AR). For example:         iou_threshold_range = [0.50:0.05:0.95]   Returns:       average_precision, average_recall, as well as the precision_recall_dict,       where precision_recall_dict holds the full precision/recall information       for each of the iou_threshold in the iou_threshold_range.   Raises:       ValueError: if the input groundtruth_info_dict and test_info_dict show       inconsistent information. # Start to build up the Matching instances for each of the image_id_*, which # is to hold the IOU computation between the rectangle pairs for the same # image_id_*. # Compute the precision and recall under this iou_threshold. # Compute the average_precision and average_recall. #average_precision, average_recall = ( #    compute_average_precision_recall_given_precision_recall_dict( #        precision_recall_dict))