# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from libs.configs import cfgs
import numpy as np
from libs.box_utils.rbbox_overlaps import rbbx_overlaps
from libs.box_utils import bbox_transform

from libs.box_utils.coordinate_convert import coordinate_present_convert


def refinebox_target_layer(gt_boxes_r, gt_smooth_label, anchors, pos_threshold, neg_threshold, gpu_id=0):

    anchor_states = np.zeros((anchors.shape[0],))
    labels = np.zeros((anchors.shape[0], cfgs.CLASS_NUM))
    if gt_boxes_r.shape[0]:
        # [N, M]

        if cfgs.ANGLE_RANGE == 180:
            anchors_ = coordinate_present_convert(anchors, 1)

            overlaps = rbbx_overlaps(np.ascontiguousarray(anchors_, dtype=np.float32),
                                     np.ascontiguousarray(gt_boxes_r[:, :-1], dtype=np.float32), gpu_id)
        else:
            overlaps = rbbx_overlaps(np.ascontiguousarray(anchors, dtype=np.float32),
                                     np.ascontiguousarray(gt_boxes_r[:, :-1], dtype=np.float32), gpu_id)

        # overlaps = np.clip(overlaps, 0.0, 1.0)

        argmax_overlaps_inds = np.argmax(overlaps, axis=1)
        max_overlaps = overlaps[np.arange(overlaps.shape[0]), argmax_overlaps_inds]

        # compute box regression targets
        target_boxes = gt_boxes_r[argmax_overlaps_inds]
        target_smooth_label = gt_smooth_label[argmax_overlaps_inds]

        if cfgs.USE_ANGLE_COND:
            delta_theta = np.abs(target_boxes[:, -2] - anchors[:, -1])
            theta_indices = delta_theta < 15
            positive_indices = (max_overlaps >= pos_threshold) & theta_indices
            ignore_indices = (max_overlaps > neg_threshold) & (max_overlaps < pos_threshold)

        else:
            positive_indices = max_overlaps >= pos_threshold
            ignore_indices = (max_overlaps > neg_threshold) & ~positive_indices
        anchor_states[ignore_indices] = -1
        anchor_states[positive_indices] = 1

        # compute target class labels
        labels[positive_indices, target_boxes[positive_indices, -1].astype(int) - 1] = 1
    else:
        # no annotations? then everything is background
        target_boxes = np.zeros((anchors.shape[0], gt_boxes_r.shape[1]))
        target_smooth_label = np.zeros((anchors.shape[0], 90))

    if cfgs.ANGLE_RANGE == 180:
        target_boxes = coordinate_present_convert(target_boxes, mode=-1)

    target_delta = bbox_transform.rbbox_transform(ex_rois=anchors, gt_rois=target_boxes,
                                                  scale_factors=cfgs.ANCHOR_SCALE_FACTORS)

    return np.array(labels, np.float32), np.array(target_delta, np.float32), \
           np.array(anchor_states, np.float32), np.array(target_boxes, np.float32), \
           np.array(target_smooth_label, np.float32)




