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| # Copyright 2017 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. | |
| # ============================================================================== | |
| """Tensorflow Example proto parser for data loading. | |
| A parser to decode data containing serialized tensorflow.Example | |
| protos into materialized tensors (numpy arrays). | |
| """ | |
| import numpy as np | |
| from object_detection.core import data_parser | |
| from object_detection.core import standard_fields as fields | |
| class FloatParser(data_parser.DataToNumpyParser): | |
| """Tensorflow Example float parser.""" | |
| def __init__(self, field_name): | |
| self.field_name = field_name | |
| def parse(self, tf_example): | |
| return np.array( | |
| tf_example.features.feature[self.field_name].float_list.value, | |
| dtype=np.float).transpose() if tf_example.features.feature[ | |
| self.field_name].HasField("float_list") else None | |
| class StringParser(data_parser.DataToNumpyParser): | |
| """Tensorflow Example string parser.""" | |
| def __init__(self, field_name): | |
| self.field_name = field_name | |
| def parse(self, tf_example): | |
| return b"".join(tf_example.features.feature[ | |
| self.field_name].bytes_list.value) if tf_example.features.feature[ | |
| self.field_name].HasField("bytes_list") else None | |
| class Int64Parser(data_parser.DataToNumpyParser): | |
| """Tensorflow Example int64 parser.""" | |
| def __init__(self, field_name): | |
| self.field_name = field_name | |
| def parse(self, tf_example): | |
| return np.array( | |
| tf_example.features.feature[self.field_name].int64_list.value, | |
| dtype=np.int64).transpose() if tf_example.features.feature[ | |
| self.field_name].HasField("int64_list") else None | |
| class BoundingBoxParser(data_parser.DataToNumpyParser): | |
| """Tensorflow Example bounding box parser.""" | |
| def __init__(self, xmin_field_name, ymin_field_name, xmax_field_name, | |
| ymax_field_name): | |
| self.field_names = [ | |
| ymin_field_name, xmin_field_name, ymax_field_name, xmax_field_name | |
| ] | |
| def parse(self, tf_example): | |
| result = [] | |
| parsed = True | |
| for field_name in self.field_names: | |
| result.append(tf_example.features.feature[field_name].float_list.value) | |
| parsed &= ( | |
| tf_example.features.feature[field_name].HasField("float_list")) | |
| return np.array(result).transpose() if parsed else None | |
| class TfExampleDetectionAndGTParser(data_parser.DataToNumpyParser): | |
| """Tensorflow Example proto parser.""" | |
| def __init__(self): | |
| self.items_to_handlers = { | |
| fields.DetectionResultFields.key: | |
| StringParser(fields.TfExampleFields.source_id), | |
| # Object ground truth boxes and classes. | |
| fields.InputDataFields.groundtruth_boxes: (BoundingBoxParser( | |
| fields.TfExampleFields.object_bbox_xmin, | |
| fields.TfExampleFields.object_bbox_ymin, | |
| fields.TfExampleFields.object_bbox_xmax, | |
| fields.TfExampleFields.object_bbox_ymax)), | |
| fields.InputDataFields.groundtruth_classes: ( | |
| Int64Parser(fields.TfExampleFields.object_class_label)), | |
| # Object detections. | |
| fields.DetectionResultFields.detection_boxes: (BoundingBoxParser( | |
| fields.TfExampleFields.detection_bbox_xmin, | |
| fields.TfExampleFields.detection_bbox_ymin, | |
| fields.TfExampleFields.detection_bbox_xmax, | |
| fields.TfExampleFields.detection_bbox_ymax)), | |
| fields.DetectionResultFields.detection_classes: ( | |
| Int64Parser(fields.TfExampleFields.detection_class_label)), | |
| fields.DetectionResultFields.detection_scores: ( | |
| FloatParser(fields.TfExampleFields.detection_score)), | |
| } | |
| self.optional_items_to_handlers = { | |
| fields.InputDataFields.groundtruth_difficult: | |
| Int64Parser(fields.TfExampleFields.object_difficult), | |
| fields.InputDataFields.groundtruth_group_of: | |
| Int64Parser(fields.TfExampleFields.object_group_of), | |
| fields.InputDataFields.groundtruth_image_classes: | |
| Int64Parser(fields.TfExampleFields.image_class_label), | |
| } | |
| def parse(self, tf_example): | |
| """Parses tensorflow example and returns a tensor dictionary. | |
| Args: | |
| tf_example: a tf.Example object. | |
| Returns: | |
| A dictionary of the following numpy arrays: | |
| fields.DetectionResultFields.source_id - string containing original image | |
| id. | |
| fields.InputDataFields.groundtruth_boxes - a numpy array containing | |
| groundtruth boxes. | |
| fields.InputDataFields.groundtruth_classes - a numpy array containing | |
| groundtruth classes. | |
| fields.InputDataFields.groundtruth_group_of - a numpy array containing | |
| groundtruth group of flag (optional, None if not specified). | |
| fields.InputDataFields.groundtruth_difficult - a numpy array containing | |
| groundtruth difficult flag (optional, None if not specified). | |
| fields.InputDataFields.groundtruth_image_classes - a numpy array | |
| containing groundtruth image-level labels. | |
| fields.DetectionResultFields.detection_boxes - a numpy array containing | |
| detection boxes. | |
| fields.DetectionResultFields.detection_classes - a numpy array containing | |
| detection class labels. | |
| fields.DetectionResultFields.detection_scores - a numpy array containing | |
| detection scores. | |
| Returns None if tf.Example was not parsed or non-optional fields were not | |
| found. | |
| """ | |
| results_dict = {} | |
| parsed = True | |
| for key, parser in self.items_to_handlers.items(): | |
| results_dict[key] = parser.parse(tf_example) | |
| parsed &= (results_dict[key] is not None) | |
| for key, parser in self.optional_items_to_handlers.items(): | |
| results_dict[key] = parser.parse(tf_example) | |
| return results_dict if parsed else None | |