-
Notifications
You must be signed in to change notification settings - Fork 444
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'develop' into vsaltykovx/add_mmdetection_input_paramete…
…rs_validation
- Loading branch information
Showing
142 changed files
with
18,574 additions
and
68 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,169 @@ | ||
| """ | ||
| Dataset utils for OTE Anomaly | ||
| """ | ||
|
|
||
| # Copyright (C) 2021 Intel Corporation | ||
| # | ||
| # 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. | ||
|
|
||
| from typing import Tuple | ||
|
|
||
| from ote_sdk.entities.annotation import AnnotationSceneEntity, AnnotationSceneKind | ||
| from ote_sdk.entities.dataset_item import DatasetItemEntity | ||
| from ote_sdk.entities.datasets import DatasetEntity | ||
| from ote_sdk.entities.resultset import ResultSetEntity | ||
| from ote_sdk.entities.shapes.rectangle import Rectangle | ||
|
|
||
|
|
||
| def split_local_global_dataset(dataset) -> Tuple[DatasetEntity, DatasetEntity]: | ||
| """Split a dataset into globally and locally annotated items.""" | ||
| globally_annotated = [] | ||
| locally_annotated = [] | ||
| for gt_item in dataset: | ||
|
|
||
| annotations = gt_item.get_annotations() | ||
| global_annotations = [annotation for annotation in annotations if Rectangle.is_full_box(annotation.shape)] | ||
| local_annotations = [annotation for annotation in annotations if not Rectangle.is_full_box(annotation.shape)] | ||
|
|
||
| if not any(label.is_anomalous for label in gt_item.get_shapes_labels()): | ||
| # normal images get added to both datasets | ||
| globally_annotated.append(gt_item) | ||
| locally_annotated.append(gt_item) | ||
| else: # image is abnormal | ||
| globally_annotated.append( | ||
| DatasetItemEntity( | ||
| media=gt_item.media, | ||
| annotation_scene=AnnotationSceneEntity(global_annotations, kind=AnnotationSceneKind.ANNOTATION), | ||
| metadata=gt_item.metadata, | ||
| subset=gt_item.subset, | ||
| ignored_labels=gt_item.ignored_labels, | ||
| ) | ||
| ) | ||
| # add locally annotated dataset items | ||
| if len(local_annotations) > 0: | ||
| locally_annotated.append( | ||
| DatasetItemEntity( | ||
| media=gt_item.media, | ||
| annotation_scene=AnnotationSceneEntity(local_annotations, kind=AnnotationSceneKind.ANNOTATION), | ||
| metadata=gt_item.metadata, | ||
| subset=gt_item.subset, | ||
| ignored_labels=gt_item.ignored_labels, | ||
| ) | ||
| ) | ||
| global_gt_dataset = DatasetEntity(globally_annotated, purpose=dataset.purpose) | ||
| local_gt_dataset = DatasetEntity(locally_annotated, purpose=dataset.purpose) | ||
| return global_gt_dataset, local_gt_dataset | ||
|
|
||
|
|
||
| def split_local_global_resultset(resultset) -> Tuple[ResultSetEntity, ResultSetEntity]: | ||
| """Split resultset based on the type of available annotations.""" | ||
| # splits the dataset | ||
| globally_annotated = [] | ||
| locally_annotated = [] | ||
| globally_predicted = [] | ||
| locally_predicted = [] | ||
| for gt_item, pred_item in zip(resultset.ground_truth_dataset, resultset.prediction_dataset): | ||
|
|
||
| annotations = gt_item.get_annotations() | ||
| global_annotations = [annotation for annotation in annotations if Rectangle.is_full_box(annotation.shape)] | ||
| local_annotations = [annotation for annotation in annotations if not Rectangle.is_full_box(annotation.shape)] | ||
|
|
||
| predictions = gt_item.get_annotations() | ||
| global_predictions = [predictions for predictions in predictions if Rectangle.is_full_box(predictions.shape)] | ||
| local_predictions = [predictions for predictions in predictions if not Rectangle.is_full_box(predictions.shape)] | ||
|
|
||
| if not any(label.is_anomalous for label in gt_item.get_shapes_labels()): | ||
| # normal images get added to both datasets | ||
| globally_annotated.append(gt_item) | ||
| locally_annotated.append(gt_item) | ||
| globally_predicted.append( | ||
| DatasetItemEntity( | ||
| media=pred_item.media, | ||
| annotation_scene=AnnotationSceneEntity(global_predictions, kind=AnnotationSceneKind.PREDICTION), | ||
| metadata=pred_item.metadata, | ||
| subset=pred_item.subset, | ||
| ignored_labels=pred_item.ignored_labels, | ||
| ) | ||
| ) | ||
| locally_predicted.append( | ||
| DatasetItemEntity( | ||
| media=pred_item.media, | ||
| annotation_scene=AnnotationSceneEntity(local_predictions, kind=AnnotationSceneKind.PREDICTION), | ||
| metadata=pred_item.metadata, | ||
| subset=pred_item.subset, | ||
| ignored_labels=pred_item.ignored_labels, | ||
| ) | ||
| ) | ||
| else: # image is abnormal | ||
| globally_annotated.append( | ||
| DatasetItemEntity( | ||
| media=gt_item.media, | ||
| annotation_scene=AnnotationSceneEntity(global_annotations, kind=AnnotationSceneKind.ANNOTATION), | ||
| metadata=gt_item.metadata, | ||
| subset=gt_item.subset, | ||
| ignored_labels=gt_item.ignored_labels, | ||
| ) | ||
| ) | ||
| globally_predicted.append( | ||
| DatasetItemEntity( | ||
| media=pred_item.media, | ||
| annotation_scene=AnnotationSceneEntity(global_predictions, kind=AnnotationSceneKind.PREDICTION), | ||
| metadata=pred_item.metadata, | ||
| subset=pred_item.subset, | ||
| ignored_labels=pred_item.ignored_labels, | ||
| ) | ||
| ) | ||
| # add locally annotated dataset items | ||
| if len(local_annotations) > 0: | ||
| locally_annotated.append( | ||
| DatasetItemEntity( | ||
| media=gt_item.media, | ||
| annotation_scene=AnnotationSceneEntity(local_annotations, kind=AnnotationSceneKind.ANNOTATION), | ||
| metadata=gt_item.metadata, | ||
| subset=gt_item.subset, | ||
| ignored_labels=gt_item.ignored_labels, | ||
| ) | ||
| ) | ||
| locally_predicted.append( | ||
| DatasetItemEntity( | ||
| media=pred_item.media, | ||
| annotation_scene=AnnotationSceneEntity(local_predictions, kind=AnnotationSceneKind.PREDICTION), | ||
| metadata=pred_item.metadata, | ||
| subset=pred_item.subset, | ||
| ignored_labels=pred_item.ignored_labels, | ||
| ) | ||
| ) | ||
|
|
||
| global_resultset = ResultSetEntity( | ||
| model=resultset.model, | ||
| ground_truth_dataset=DatasetEntity(globally_annotated, purpose=resultset.ground_truth_dataset.purpose), | ||
| prediction_dataset=DatasetEntity(globally_predicted, purpose=resultset.prediction_dataset.purpose), | ||
| purpose=resultset.purpose, | ||
| ) | ||
| local_resultset = ResultSetEntity( | ||
| model=resultset.model, | ||
| ground_truth_dataset=DatasetEntity(locally_annotated, purpose=resultset.ground_truth_dataset.purpose), | ||
| prediction_dataset=DatasetEntity(locally_predicted, purpose=resultset.prediction_dataset.purpose), | ||
| purpose=resultset.purpose, | ||
| ) | ||
|
|
||
| return global_resultset, local_resultset | ||
|
|
||
|
|
||
| def contains_anomalous_images(dataset: DatasetEntity) -> bool: | ||
| """Find the number of local annotations in a resultset.""" | ||
| for item in dataset: | ||
| labels = item.get_shapes_labels() | ||
| if any(label.is_anomalous for label in labels): | ||
| return True | ||
| return False |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Submodule deep-object-reid
deleted from
d5a9f2
Oops, something went wrong.