o
    oi#                     @   s   d dl mZmZmZ d dlZd dlmZmZmZm	Z	 ddl
mZ 	ddee dee d	ee d
ee dee dededeeeeef f fddZdS )    )DictListTupleN)Tensorconcatenatetensorzeros   )mean_iou_bbox      ?
pred_boxespred_labelspred_scoresgt_boxes	gt_labels	n_classes	thresholdreturnc           0      C   s  t | t |  krt |  krt |  krt |ks"t tg }t|D ]\}}	||g|	d  q(t|d}
t|d}t||
jtj	d}|d|
d  kr_|dksbt tg }t|D ]\}}	||g|	d  qht| d}t|d}t|d}t||jtj	d}|d|d  kr|d  kr|dkst tt
|d |j|jd}td|D ]%}|||k }|
||k }t
|dtj|jd}|||k }|||k }|||k }|d}|dkrqtj|ddd\}}|| }|| }t
|f|j|jd}t
|f|j|jd}t|D ]i}|| d}|| } ||| k }!|!ddkrAd||< q t||!}"tj|"ddd\}#}$tt|d|jtj	d|| k |$ }%|# |kr||% dkr~d||< d||%< q d||< q d||< q tj|dd}&tj|dd}'|&|&|' d  }(|&|
d })tjdd	d
d }*t
t |*|
j|
jd}+t|*D ]\}},|)|,k}-|- r|(|-  |+|< qd|+|< q|+ ||d < q| }.dd t| D }/|.|/fS )aS  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   )devicedtyper	   )r   r   T)dim
descending)r   g|=g?g?)startendstepg        c                 S   s   i | ]\}}|d  t |qS )r	   )float).0cv r   Y/home/ubuntu/.local/lib/python3.10/site-packages/kornia/metrics/mean_average_precision.py
<dictcomp>   s    z*mean_average_precision.<locals>.<dictcomp>)lenAssertionError	enumerateextendsizer   r   r   torchlongr   r   rangeuint8sort	unsqueezer
   maxsqueezeitemcumsumarangetolistanymean)0r   r   r   r   r   r   r   	gt_imagesilabels	_gt_boxes
_gt_labels
_gt_imagespred_images_pred_boxes_pred_labels_pred_scores_pred_imagesaverage_precisionsr   gt_class_imagesgt_class_boxesgt_class_boxes_detectedpred_class_imagespred_class_boxespred_class_scoresn_class_detectionssort_indgt_positivesfalse_positivesdthis_detection_box
this_imageobject_boxesoverlapsmax_overlapindoriginal_indcumul_gt_positivescumul_false_positivescumul_precisioncumul_recallrecall_thresholds
precisionstrecalls_above_tmean_apap_dictr   r   r    mean_average_precision   s   :"

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4



r]   )r   )typingr   r   r   r'   kornia.corer   r   r   r   mean_iour
   intr   r]   r   r   r   r    <module>   s,   
