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))