As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1.types.detection

# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
import proto  # type: ignore

from google.cloud.automl_v1.types import geometry


__protobuf__ = proto.module(
    package="google.cloud.automl.v1",
    manifest={
        "ImageObjectDetectionAnnotation",
        "BoundingBoxMetricsEntry",
        "ImageObjectDetectionEvaluationMetrics",
    },
)


[docs]class ImageObjectDetectionAnnotation(proto.Message): r"""Annotation details for image object detection. Attributes: bounding_box (google.cloud.automl_v1.types.BoundingPoly): Output only. The rectangle representing the object location. score (float): Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence. """ bounding_box = proto.Field(proto.MESSAGE, number=1, message=geometry.BoundingPoly,) score = proto.Field(proto.FLOAT, number=2,)
[docs]class BoundingBoxMetricsEntry(proto.Message): r"""Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds. Attributes: iou_threshold (float): Output only. The intersection-over-union threshold value used to compute this metrics entry. mean_average_precision (float): Output only. The mean average precision, most often close to au_prc. confidence_metrics_entries (Sequence[google.cloud.automl_v1.types.BoundingBoxMetricsEntry.ConfidenceMetricsEntry]): Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them. """
[docs] class ConfidenceMetricsEntry(proto.Message): r"""Metrics for a single confidence threshold. Attributes: confidence_threshold (float): Output only. The confidence threshold value used to compute the metrics. recall (float): Output only. Recall under the given confidence threshold. precision (float): Output only. Precision under the given confidence threshold. f1_score (float): Output only. The harmonic mean of recall and precision. """ confidence_threshold = proto.Field(proto.FLOAT, number=1,) recall = proto.Field(proto.FLOAT, number=2,) precision = proto.Field(proto.FLOAT, number=3,) f1_score = proto.Field(proto.FLOAT, number=4,)
iou_threshold = proto.Field(proto.FLOAT, number=1,) mean_average_precision = proto.Field(proto.FLOAT, number=2,) confidence_metrics_entries = proto.RepeatedField( proto.MESSAGE, number=3, message=ConfidenceMetricsEntry, )
[docs]class ImageObjectDetectionEvaluationMetrics(proto.Message): r"""Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes. Attributes: evaluated_bounding_box_count (int): Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had. bounding_box_metrics_entries (Sequence[google.cloud.automl_v1.types.BoundingBoxMetricsEntry]): Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. bounding_box_mean_average_precision (float): Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries. """ evaluated_bounding_box_count = proto.Field(proto.INT32, number=1,) bounding_box_metrics_entries = proto.RepeatedField( proto.MESSAGE, number=2, message="BoundingBoxMetricsEntry", ) bounding_box_mean_average_precision = proto.Field(proto.FLOAT, number=3,)
__all__ = tuple(sorted(__protobuf__.manifest))