<filename>research/object_detection/metrics/oid_od_challenge_evaluation_utils.py
# Copyright 2018 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.
# ==============================================================================
r"""Converts data from CSV to the OpenImagesDetectionChallengeEvaluator format.
"""

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

from research.object_detection.core import standard_fields


def build_groundtruth_boxes_dictionary(data, class_label_map):
    """Builds a groundtruth dictionary from groundtruth data in CSV file.

  Args:
    data: Pandas DataFrame with the groundtruth data for a single image.
    class_label_map: Class labelmap from string label name to an integer.

  Returns:
    A dictionary with keys suitable for passing to
    OpenImagesDetectionChallengeEvaluator.add_single_ground_truth_image_info:
        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
          of shape [num_boxes] containing 1-indexed groundtruth classes for the
          boxes.
        standard_fields.InputDataFields.verified_labels: integer 1D numpy array
          containing all classes for which labels are verified.
        standard_fields.InputDataFields.groundtruth_group_of: Optional length
          M numpy boolean array denoting whether a groundtruth box contains a
          group of instances.
  """
    data_boxes = data[data.ConfidenceImageLabel.isnull()]
    data_labels = data[data.XMin.isnull()]

    return {
        standard_fields.InputDataFields.groundtruth_boxes:
            data_boxes[['YMin', 'XMin', 'YMax', 'XMax']].values,
        standard_fields.InputDataFields.groundtruth_classes:
            data_boxes['LabelName'].map(lambda x: class_label_map[x]).values,
        standard_fields.InputDataFields.groundtruth_group_of:
            data_boxes['IsGroupOf'].values.astype(int),
        standard_fields.InputDataFields.groundtruth_image_classes:
            data_labels['LabelName'].map(lambda x: class_label_map[x]).values,
    }


def build_predictions_dictionary(data, class_label_map):
    """Builds a predictions dictionary from predictions data in CSV file.

  Args:
    data: Pandas DataFrame with the predictions data for a single image.
    class_label_map: Class labelmap from string label name to an integer.

  Returns:
    Dictionary with keys suitable for passing to
    OpenImagesDetectionChallengeEvaluator.add_single_detected_image_info:
        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.

  """
    return {
        standard_fields.DetectionResultFields.detection_boxes:
            data[['YMin', 'XMin', 'YMax', 'XMax']].values,
        standard_fields.DetectionResultFields.detection_classes:
            data['LabelName'].map(lambda x: class_label_map[x]).values,
        standard_fields.DetectionResultFields.detection_scores:
            data['Score'].values
    }

<filename>research/object_detection/metrics/oid_od_challenge_evaluation_utils.py
# Copyright 2018 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.
# ==============================================================================
r"""Converts data from CSV to the OpenImagesDetectionChallengeEvaluator format.
"""

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

from research.object_detection.core import standard_fields


def build_groundtruth_boxes_dictionary(data, class_label_map):
    """Builds a groundtruth dictionary from groundtruth data in CSV file.

  Args:
    data: Pandas DataFrame with the groundtruth data for a single image.
    class_label_map: Class labelmap from string label name to an integer.

  Returns:
    A dictionary with keys suitable for passing to
    OpenImagesDetectionChallengeEvaluator.add_single_ground_truth_image_info:
        standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array
          of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of
          the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.InputDataFields.groundtruth_classes: integer numpy array
          of shape [num_boxes] containing 1-indexed groundtruth classes for the
          boxes.
        standard_fields.InputDataFields.verified_labels: integer 1D numpy array
          containing all classes for which labels are verified.
        standard_fields.InputDataFields.groundtruth_group_of: Optional length
          M numpy boolean array denoting whether a groundtruth box contains a
          group of instances.
  """
    data_boxes = data[data.ConfidenceImageLabel.isnull()]
    data_labels = data[data.XMin.isnull()]

    return {
        standard_fields.InputDataFields.groundtruth_boxes:
            data_boxes[['YMin', 'XMin', 'YMax', 'XMax']].values,
        standard_fields.InputDataFields.groundtruth_classes:
            data_boxes['LabelName'].map(lambda x: class_label_map[x]).values,
        standard_fields.InputDataFields.groundtruth_group_of:
            data_boxes['IsGroupOf'].values.astype(int),
        standard_fields.InputDataFields.groundtruth_image_classes:
            data_labels['LabelName'].map(lambda x: class_label_map[x]).values,
    }


def build_predictions_dictionary(data, class_label_map):
    """Builds a predictions dictionary from predictions data in CSV file.

  Args:
    data: Pandas DataFrame with the predictions data for a single image.
    class_label_map: Class labelmap from string label name to an integer.

  Returns:
    Dictionary with keys suitable for passing to
    OpenImagesDetectionChallengeEvaluator.add_single_detected_image_info:
        standard_fields.DetectionResultFields.detection_boxes: float32 numpy
          array of shape [num_boxes, 4] containing `num_boxes` detection boxes
          of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.

  """
    return {
        standard_fields.DetectionResultFields.detection_boxes:
            data[['YMin', 'XMin', 'YMax', 'XMax']].values,
        standard_fields.DetectionResultFields.detection_classes:
            data['LabelName'].map(lambda x: class_label_map[x]).values,
        standard_fields.DetectionResultFields.detection_scores:
            data['Score'].values
    }

# Copyright 2018 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. # ============================================================================== Converts data from CSV to the OpenImagesDetectionChallengeEvaluator format. Builds a groundtruth dictionary from groundtruth data in CSV file.    Args:     data: Pandas DataFrame with the groundtruth data for a single image.     class_label_map: Class labelmap from string label name to an integer.    Returns:     A dictionary with keys suitable for passing to     OpenImagesDetectionChallengeEvaluator.add_single_ground_truth_image_info:         standard_fields.InputDataFields.groundtruth_boxes: float32 numpy array           of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of           the format [ymin, xmin, ymax, xmax] in absolute image coordinates.         standard_fields.InputDataFields.groundtruth_classes: integer numpy array           of shape [num_boxes] containing 1-indexed groundtruth classes for the           boxes.         standard_fields.InputDataFields.verified_labels: integer 1D numpy array           containing all classes for which labels are verified.         standard_fields.InputDataFields.groundtruth_group_of: Optional length           M numpy boolean array denoting whether a groundtruth box contains a           group of instances. Builds a predictions dictionary from predictions data in CSV file.    Args:     data: Pandas DataFrame with the predictions data for a single image.     class_label_map: Class labelmap from string label name to an integer.    Returns:     Dictionary with keys suitable for passing to     OpenImagesDetectionChallengeEvaluator.add_single_detected_image_info:         standard_fields.DetectionResultFields.detection_boxes: float32 numpy           array of shape [num_boxes, 4] containing `num_boxes` detection boxes           of the format [ymin, xmin, ymax, xmax] in absolute image coordinates.         standard_fields.DetectionResultFields.detection_scores: float32 numpy           array of shape [num_boxes] containing detection scores for the boxes.         standard_fields.DetectionResultFields.detection_classes: integer numpy           array of shape [num_boxes] containing 1-indexed detection classes for           the boxes.