import numpy as np


def nms(bboxes, scores, nms_thresh):
    """"Pure Python NMS."""
    x1 = bboxes[:, 0]  #xmin
    y1 = bboxes[:, 1]  #ymin
    x2 = bboxes[:, 2]  #xmax
    y2 = bboxes[:, 3]  #ymax

    areas = (x2 - x1) * (y2 - y1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        # compute iou
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(1e-10, xx2 - xx1)
        h = np.maximum(1e-10, yy2 - yy1)
        inter = w * h

        iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-14)
        #reserve all the boundingbox whose ovr less than thresh
        inds = np.where(iou <= nms_thresh)[0]
        order = order[inds + 1]

    return keep


def multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh):
    # nms
    keep = nms(bboxes, scores, nms_thresh)

    scores = scores[keep]
    labels = labels[keep]
    bboxes = bboxes[keep]

    return scores, labels, bboxes


def multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes):
    # nms
    keep = np.zeros(len(bboxes), dtype=np.int32)
    for i in range(num_classes):
        inds = np.where(labels == i)[0]
        if len(inds) == 0:
            continue
        c_bboxes = bboxes[inds]
        c_scores = scores[inds]
        c_keep = nms(c_bboxes, c_scores, nms_thresh)
        keep[inds[c_keep]] = 1

    keep = np.where(keep > 0)
    scores = scores[keep]
    labels = labels[keep]
    bboxes = bboxes[keep]

    return scores, labels, bboxes


def multiclass_nms(scores, labels, bboxes, nms_thresh, num_classes, class_agnostic=False):
    if class_agnostic:
        return multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh)
    else:
        return multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes)


##--------------------------------------------torch---------------------------------------
# import torch

# def nms(bboxes, scores, nms_thresh):
#     """"Pure PyTorch NMS."""
#     x1 = bboxes[:, 0]  #xmin
#     y1 = bboxes[:, 1]  #ymin
#     x2 = bboxes[:, 2]  #xmax
#     y2 = bboxes[:, 3]  #ymax

#     areas = (x2 - x1) * (y2 - y1)
#     _, order = scores.sort(descending=True)

#     keep = []
#     while order.numel() > 0:
#         i = order[0]
#         keep.append(i.item())
#         # compute iou
#         xx1 = torch.max(x1[i], x1[order[1:]])
#         yy1 = torch.max(y1[i], y1[order[1:]])
#         xx2 = torch.min(x2[i], x2[order[1:]])
#         yy2 = torch.min(y2[i], y2[order[1:]])

#         w = torch.clamp(xx2 - xx1, min=1e-10)
#         h = torch.clamp(yy2 - yy1, min=1e-10)
#         inter = w * h

#         iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-14)
#         #reserve all the boundingbox whose ovr less than thresh
#         inds = torch.where(iou <= nms_thresh)[0]
#         order = order[inds + 1]

#     return keep


# def multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh):
#     # nms
#     keep = nms(bboxes, scores, nms_thresh)

#     scores = scores[keep]
#     labels = labels[keep]
#     bboxes = bboxes[keep]

#     return scores, labels, bboxes


# def multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes):
#     # nms
#     keep = torch.zeros(len(bboxes), dtype=torch.int32)
#     for i in range(num_classes):
#         inds = torch.where(labels == i)[0]
#         if len(inds) == 0:
#             continue
#         c_bboxes = bboxes[inds]
#         c_scores = scores[inds]
#         c_keep = nms(c_bboxes, c_scores, nms_thresh)
#         keep[inds[c_keep]] = 1

#     keep = torch.where(keep > 0)
#     scores = scores[keep]
#     labels = labels[keep]
#     bboxes = bboxes[keep]

#     return scores, labels, bboxes


# def multiclass_nms(scores, labels, bboxes, nms_thresh, num_classes, class_agnostic=False):
#     if class_agnostic:
#         return multiclass_nms_class_agnostic(scores, labels, bboxes, nms_thresh)
#     else:
#         return multiclass_nms_class_aware(scores, labels, bboxes, nms_thresh, num_classes)