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jd}+t|*D ]6\}},|)|,k}-|- r|(|-  |+|< nd|+|< q|+ ||d < q| }.dd t| D }/|.|/fS )aR  Calculate the Mean Average Precision (mAP) of detected objects.

    Code altered from https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/utils.py#L271.
    Background class (0 index) is excluded.

    Args:
        pred_boxes: a tensor list of predicted bounding boxes.
        pred_labels: a tensor list of predicted labels.
        pred_scores: a tensor list of predicted labels' scores.
        gt_boxes: a tensor list of ground truth bounding boxes.
        gt_labels: a tensor list of ground truth labels.
        n_classes: the number of classes.
        threshold: count as a positive if the overlap is greater than the threshold.

    Returns:
        mean average precision (mAP), list of average precisions for each class.

    Examples:
        >>> boxes, labels, scores = torch.tensor([[100, 50, 150, 100.]]), torch.tensor([1]), torch.tensor([.7])
        >>> gt_boxes, gt_labels = torch.tensor([[100, 50, 150, 100.]]), torch.tensor([1])
        >>> mean_average_precision([boxes], [labels], [scores], [gt_boxes], [gt_labels], 2)
        (tensor(1.), {1: 1.0})
    r   )dim)devicedtyper   )r   r   T)r   Z
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   r   r   r   Z	gt_imagesilabelsZ	_gt_boxesZ
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_gt_imagesZpred_imagesZ_pred_boxesZ_pred_labelsZ_pred_scoresZ_pred_imagesZaverage_precisionsr   Zgt_class_imagesZgt_class_boxesZgt_class_boxes_detectedZpred_class_imagesZpred_class_boxesZpred_class_scoresZn_class_detectionsZsort_indZgt_positivesZfalse_positivesdZthis_detection_boxZ
this_imageZobject_boxesoverlapsZmax_overlapindZoriginal_indZcumul_gt_positivesZcumul_false_positivesZcumul_precisionZcumul_recallZrecall_thresholdsZ
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
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r2   )r   )typingr   r   r!   Zmean_iour   ZTensorintfloatdictr2   r   r   r   r   <module>   s   
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