# --------------------------------------------------------
# Flow-Guided Feature Aggregation
# Copyright (c) 2017 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Modified by Yuwen Xiong
# --------------------------------------------------------
# Based on:
# py-faster-rcnn
# Copyright (c) 2016 by Contributors
# Licence under The MIT License
# https://github.com/rbgirshick/py-faster-rcnn
# --------------------------------------------------------

import numpy as np

from cpu_nms import cpu_nms
from gpu_nms import gpu_nms

def py_nms_wrapper(thresh):
    def _nms(dets):
        return nms(dets, thresh)
    return _nms


def cpu_nms_wrapper(thresh):
    def _nms(dets):
        return cpu_nms(dets, thresh)
    return _nms


def gpu_nms_wrapper(thresh, device_id):
    def _nms(dets):
        return gpu_nms(dets, thresh, device_id)
    return _nms


def nms(dets, thresh):
    """
    greedily select boxes with high confidence and overlap with current maximum <= thresh
    rule out overlap >= thresh
    :param dets: [[x1, y1, x2, y2 score]]
    :param thresh: retain overlap < thresh
    :return: indexes to keep
    """
    if dets.shape[0] == 0:
        return []

    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]

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

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        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(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]

    return keep
