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Picsellia CV Engine

coco converter

Picsellia CV Engine

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core.services.model.evaluator.utils.coco_converter¶

`coco_converter` ¶

Functions:

Name	Description
`save_json`
`get_asset`
`flatten_segmentation`
`compute_bbox_and_area_from_polygon`
`extract_prediction_list`
`build_annotation`
`generate_coco_predictions`
`extract_asset_annotations`
`build_gt_annotation`
`generate_coco_ground_truth`
`create_coco_files_from_experiment`

`save_json(coco_data, output_path)` ¶

`get_asset(id, connexion, dataset_version_id)` ¶

`flatten_segmentation(segmentation)` ¶

`compute_bbox_and_area_from_polygon(flattened_polygon)` ¶

`extract_prediction_list(evaluation_info, inference_type)` ¶

`build_annotation(pred, image_id, label_name, category_id, annotation_id, inference_type)` ¶

`generate_coco_predictions(evaluations, image_name_map, label_name_map, categories, gt_coco_path, inference_type)` ¶

`extract_asset_annotations(asset, inference_type)` ¶

`build_gt_annotation(ann, image_id, label_name, category_id, annotation_id, inference_type)` ¶

`generate_coco_ground_truth(assets, image_name_map, label_name_map, categories, inference_type)` ¶

`create_coco_files_from_experiment(experiment, assets, gt_coco_path, pred_coco_path, inference_type)` ¶