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| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # 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 evaluate | |
| import datasets | |
| import motmetrics as mm | |
| import numpy as np | |
| _CITATION = """\ | |
| @InProceedings{huggingface:module, | |
| title = {A great new module}, | |
| authors={huggingface, Inc.}, | |
| year={2020} | |
| }\ | |
| @article{milan2016mot16, | |
| title={MOT16: A benchmark for multi-object tracking}, | |
| author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad}, | |
| journal={arXiv preprint arXiv:1603.00831}, | |
| year={2016} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The MOT Metrics module is designed to evaluate multi-object tracking (MOT) | |
| algorithms by computing various metrics based on predicted and ground truth bounding | |
| boxes. It serves as a crucial tool in assessing the performance of MOT systems, | |
| aiding in the iterative improvement of tracking algorithms.""" | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using certain scores | |
| Args: | |
| predictions: list of predictions to score. Each predictions | |
| should be a string with tokens separated by spaces. | |
| references: list of reference for each prediction. Each | |
| reference should be a string with tokens separated by spaces. | |
| max_iou (`float`, *optional*): | |
| If specified, this is the minimum Intersection over Union (IoU) threshold to consider a detection as a true positive. | |
| Default is 0.5. | |
| """ | |
| class MotMetrics(evaluate.Metric): | |
| """TODO: Short description of my evaluation module.""" | |
| def _info(self): | |
| # TODO: Specifies the evaluate.EvaluationModuleInfo object | |
| return evaluate.MetricInfo( | |
| # This is the description that will appear on the modules page. | |
| module_type="metric", | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| # This defines the format of each prediction and reference | |
| features=datasets.Features({ | |
| "predictions": datasets.Sequence( | |
| datasets.Sequence(datasets.Value("float")) | |
| ), | |
| "references": datasets.Sequence( | |
| datasets.Sequence(datasets.Value("float")) | |
| ) | |
| }), | |
| # Additional links to the codebase or references | |
| codebase_urls=["http://github.com/path/to/codebase/of/new_module"], | |
| reference_urls=["http://path.to.reference.url/new_module"] | |
| ) | |
| def _download_and_prepare(self, dl_manager): | |
| """Optional: download external resources useful to compute the scores""" | |
| # TODO: Download external resources if needed | |
| pass | |
| def _compute(self, payload, max_iou: float = 0.5, debug: bool = False): | |
| """Returns the scores""" | |
| # TODO: Compute the different scores of the module | |
| return calculate_from_payload(payload, max_iou, debug) | |
| #return calculate(predictions, references, max_iou) | |
| def calculate(predictions, references, max_iou: float = 0.5): | |
| """Returns the scores""" | |
| try: | |
| np_predictions = np.array(predictions) | |
| except: | |
| raise ValueError("The predictions should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]") | |
| try: | |
| np_references = np.array(references) | |
| except: | |
| raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]") | |
| if np_predictions.shape[1] != 7: | |
| raise ValueError("The predictions should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]") | |
| if np_references.shape[1] != 6: | |
| raise ValueError("The references should be a list of np.arrays in the format [frame number, object id, bb_left, bb_top, bb_width, bb_height]") | |
| if np_predictions[:, 0].min() <= 0: | |
| raise ValueError("The frame number in the predictions should be a positive integer") | |
| if np_references[:, 0].min() <= 0: | |
| raise ValueError("The frame number in the references should be a positive integer") | |
| num_frames = int(max(np_references[:, 0].max(), np_predictions[:, 0].max())) | |
| acc = mm.MOTAccumulator(auto_id=True) | |
| for i in range(1, num_frames+1): | |
| preds = np_predictions[np_predictions[:, 0] == i, 1:6] | |
| refs = np_references[np_references[:, 0] == i, 1:6] | |
| C = mm.distances.iou_matrix(refs[:,1:], preds[:,1:], max_iou = max_iou) | |
| acc.update(refs[:,0].astype('int').tolist(), preds[:,0].astype('int').tolist(), C) | |
| mh = mm.metrics.create() | |
| summary = mh.compute(acc).to_dict() | |
| for key in summary: | |
| summary[key] = summary[key][0] | |
| return summary | |
| def calculate_from_payload(payload: dict, max_iou: float = 0.5, debug: bool = False): | |
| if not isinstance(payload, dict): | |
| try: | |
| payload = payload.to_dict() | |
| except Exception as e: | |
| raise ValueError( | |
| "The payload should be a dictionary or a compatible object" | |
| ) from e | |
| gt_field_name = payload['gt_field_name'] | |
| models = payload['models'] | |
| sequence_list = payload['sequence_list'] | |
| if debug: | |
| print("gt_field_name: ", gt_field_name) | |
| print("models: ", models) | |
| print("sequence_list: ", sequence_list) | |
| output = {} | |
| for sequence in sequence_list: | |
| output[sequence] = {} | |
| frames = payload['sequences'][sequence][gt_field_name] | |
| formated_references = [] | |
| for frame_id, frame in enumerate(frames): | |
| for detection in frame: | |
| id = detection['index'] | |
| x, y, w, h = detection['bounding_box'] | |
| formated_references.append([frame_id+1, id, x, y, w, h]) | |
| for model in models: | |
| frames = payload['sequences'][sequence][model] | |
| formated_predictions = [] | |
| for frame_id, frame in enumerate(frames): | |
| for detection in frame: | |
| id = detection['index'] | |
| x, y, w, h = detection['bounding_box'] | |
| confidence = detection['confidence'] | |
| confidence = 1 #TODO: remove this line | |
| formated_predictions.append([frame_id+1, id, x, y, w, h, confidence]) | |
| if debug: | |
| print("sequence/model: ", sequence, model) | |
| print("formated_predictions: ", formated_predictions) | |
| print("formated_references: ", formated_references) | |
| if len(formated_predictions) == 0: | |
| output[sequence][model] = "Model had no predictions." | |
| elif len(formated_references) == 0: | |
| output[sequence][model] = "No ground truth." | |
| else: | |
| output[sequence][model] = calculate(formated_predictions, formated_references, max_iou=max_iou) | |
| return output | |