# -*-coding: utf-8 -*-

from __future__ import absolute_import, division, print_function

import tensorflow as tf
import tensorflow.contrib.slim as slim

from alpharotate.libs.models.detectors.single_stage_base_network import DetectionNetworkBase
from alpharotate.libs.models.losses.losses_csl import LossCSL
from alpharotate.libs.utils import bbox_transform
from alpharotate.libs.utils import nms_rotate
from alpharotate.libs.utils.coordinate_convert import coordinate_present_convert
from alpharotate.libs.models.samplers.csl.anchor_sampler_csl import AnchorSamplerCSL


class DetectionNetworkCSL(DetectionNetworkBase):

    def __init__(self, cfgs, is_training):
        super(DetectionNetworkCSL, self).__init__(cfgs, is_training)
        self.anchor_sampler_csl = AnchorSamplerCSL(cfgs)
        self.losses = LossCSL(self.cfgs)
        self.coding_len = cfgs.ANGLE_RANGE // cfgs.OMEGA

    def rpn_reg_net(self, inputs, scope_list, reuse_flag, level):
        rpn_conv2d_3x3 = inputs
        for i in range(self.cfgs.NUM_SUBNET_CONV):
            rpn_conv2d_3x3 = slim.conv2d(inputs=rpn_conv2d_3x3,
                                         num_outputs=self.cfgs.FPN_CHANNEL,
                                         kernel_size=[3, 3],
                                         weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
                                         biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
                                         stride=1,
                                         activation_fn=None if self.cfgs.USE_GN else tf.nn.relu,
                                         trainable=self.is_training,
                                         scope='{}_{}'.format(scope_list[1], i),
                                         reuse=reuse_flag)
            if self.cfgs.USE_GN:
                rpn_conv2d_3x3 = tf.contrib.layers.group_norm(rpn_conv2d_3x3)
                rpn_conv2d_3x3 = tf.nn.relu(rpn_conv2d_3x3)

        rpn_delta_boxes = slim.conv2d(rpn_conv2d_3x3,
                                      num_outputs=5 * self.num_anchors_per_location,
                                      kernel_size=[3, 3],
                                      stride=1,
                                      weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
                                      biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
                                      scope=scope_list[3],
                                      activation_fn=None,
                                      trainable=self.is_training,
                                      reuse=reuse_flag)

        rpn_angle_cls = slim.conv2d(rpn_conv2d_3x3,
                                    num_outputs=self.coding_len * self.num_anchors_per_location,
                                    kernel_size=[3, 3],
                                    stride=1,
                                    weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
                                    biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
                                    scope=scope_list[4],
                                    trainable=self.is_training,
                                    activation_fn=None,
                                    reuse=reuse_flag)

        rpn_delta_boxes = tf.reshape(rpn_delta_boxes, [-1, 5],
                                     name='rpn_{}_regression_reshape'.format(level))
        rpn_angle_cls = tf.reshape(rpn_angle_cls, [-1, self.coding_len],
                                   name='rpn_{}_angle_cls_reshape'.format(level))
        return rpn_delta_boxes, rpn_angle_cls

    def rpn_net(self, feature_pyramid, name):

        rpn_delta_boxes_list = []
        rpn_scores_list = []
        rpn_probs_list = []
        rpn_angle_cls_list = []
        with tf.variable_scope(name):
            with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(self.cfgs.WEIGHT_DECAY)):
                for level in self.cfgs.LEVEL:

                    if self.cfgs.SHARE_NET:
                        reuse_flag = None if level == self.cfgs.LEVEL[0] else True
                        scope_list = ['conv2d_3x3_cls', 'conv2d_3x3_reg', 'rpn_classification',
                                      'rpn_regression', 'rpn_angle_cls']
                    else:
                        reuse_flag = None
                        scope_list = ['conv2d_3x3_cls_' + level, 'conv2d_3x3_reg_' + level,
                                      'rpn_classification_' + level, 'rpn_regression_' + level,
                                      'rpn_angle_cls_' + level]

                    rpn_box_scores, rpn_box_probs = self.rpn_cls_net(feature_pyramid[level], scope_list, reuse_flag, level)
                    rpn_delta_boxes, rpn_angle_cls = self.rpn_reg_net(feature_pyramid[level], scope_list, reuse_flag, level)

                    rpn_scores_list.append(rpn_box_scores)
                    rpn_probs_list.append(rpn_box_probs)
                    rpn_delta_boxes_list.append(rpn_delta_boxes)
                    rpn_angle_cls_list.append(rpn_angle_cls)

            return rpn_delta_boxes_list, rpn_scores_list, rpn_probs_list, rpn_angle_cls_list

    def build_whole_detection_network(self, input_img_batch, gtboxes_batch_h=None, gtboxes_batch_r=None,
                                      gt_smooth_label=None, gpu_id=0):

        if self.is_training:
            gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
            gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)

            gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
            gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)

            gt_smooth_label = tf.reshape(gt_smooth_label, [-1, self.coding_len])
            gt_smooth_label = tf.cast(gt_smooth_label, tf.float32)

        if self.cfgs.USE_GN:
            input_img_batch = tf.reshape(input_img_batch, [1, self.cfgs.IMG_SHORT_SIDE_LEN,
                                                           self.cfgs.IMG_MAX_LENGTH, 3])

        # 1. build backbone
        feature_pyramid = self.build_backbone(input_img_batch)

        # 2. build rpn
        rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list, rpn_angle_cls_list = self.rpn_net(feature_pyramid, 'rpn_net')
        rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
        rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
        rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
        rpn_angle_cls = tf.concat(rpn_angle_cls_list, axis=0)

        # 3. generate anchors
        anchor_list = self.make_anchors(feature_pyramid)
        anchors = tf.concat(anchor_list, axis=0)

        # 4. build loss
        if self.is_training:
            with tf.variable_scope('build_loss'):
                labels, target_delta, anchor_states, target_boxes, target_encode_label = tf.py_func(
                    func=self.anchor_sampler_csl.anchor_target_layer,
                    inp=[gtboxes_batch_h, gtboxes_batch_r,
                         gt_smooth_label, anchors, gpu_id],
                    Tout=[tf.float32, tf.float32, tf.float32,
                          tf.float32, tf.float32])

                if self.method == 'H':
                    self.add_anchor_img_smry(input_img_batch, anchors, anchor_states, 0)
                else:
                    self.add_anchor_img_smry(input_img_batch, anchors, anchor_states, 1)

                cls_loss = self.losses.focal_loss(labels, rpn_cls_score, anchor_states)

                if self.cfgs.REG_LOSS_MODE == 0:
                    reg_loss = self.losses.iou_smooth_l1_loss_log(target_delta, rpn_box_pred, anchor_states,
                                                                  target_boxes, anchors)
                elif self.cfgs.REG_LOSS_MODE == 1:
                    reg_loss = self.losses.iou_smooth_l1_loss_exp(target_delta, rpn_box_pred, anchor_states,
                                                                  target_boxes, anchors, alpha=self.cfgs.ALPHA,
                                                                  beta=self.cfgs.BETA)
                else:
                    reg_loss = self.losses.smooth_l1_loss(target_delta, rpn_box_pred, anchor_states)

                angle_cls_loss = self.losses.angle_focal_loss(target_encode_label, rpn_angle_cls, anchor_states)

                self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
                self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
                self.losses_dict['angle_cls_loss'] = angle_cls_loss * self.cfgs.ANGLE_WEIGHT

        # 5. postprocess
        with tf.variable_scope('postprocess_detctions'):
            boxes, scores, category, boxes_angle = self.postprocess_detctions(rpn_bbox_pred=rpn_box_pred,
                                                                              rpn_cls_prob=rpn_cls_prob,
                                                                              rpn_angle_prob=tf.sigmoid(rpn_angle_cls),
                                                                              anchors=anchors,
                                                                              gpu_id=gpu_id)
            boxes = tf.stop_gradient(boxes)
            scores = tf.stop_gradient(scores)
            category = tf.stop_gradient(category)
            boxes_angle = tf.stop_gradient(boxes_angle)

        if self.is_training:
            return boxes, scores, category, boxes_angle, self.losses_dict
        else:
            return boxes_angle, scores, category

    def postprocess_detctions(self, rpn_bbox_pred, rpn_cls_prob, rpn_angle_prob, anchors, gpu_id):

        return_boxes_pred = []
        return_boxes_pred_angle = []
        return_scores = []
        return_labels = []
        for j in range(0, self.cfgs.CLASS_NUM):
            scores = rpn_cls_prob[:, j]
            if self.is_training:
                indices = tf.reshape(tf.where(tf.greater(scores, self.cfgs.VIS_SCORE)), [-1, ])
            else:
                indices = tf.reshape(tf.where(tf.greater(scores, self.cfgs.FILTERED_SCORE)), [-1, ])

            anchors_ = tf.gather(anchors, indices)
            rpn_bbox_pred_ = tf.gather(rpn_bbox_pred, indices)
            scores = tf.gather(scores, indices)
            rpn_angle_prob_ = tf.gather(rpn_angle_prob, indices)
            angle_cls = tf.cast(tf.argmax(rpn_angle_prob_, axis=1), tf.float32)

            if self.cfgs.METHOD == 'H':
                x_c = (anchors_[:, 2] + anchors_[:, 0]) / 2
                y_c = (anchors_[:, 3] + anchors_[:, 1]) / 2
                h = anchors_[:, 2] - anchors_[:, 0] + 1
                w = anchors_[:, 3] - anchors_[:, 1] + 1
                theta = -90 * tf.ones_like(x_c)
                anchors_ = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))

            if self.cfgs.ANGLE_RANGE == 180:
                anchors_ = tf.py_func(coordinate_present_convert,
                                      inp=[anchors_, -1],
                                      Tout=[tf.float32])
                anchors_ = tf.reshape(anchors_, [-1, 5])

            boxes_pred = bbox_transform.rbbox_transform_inv(boxes=anchors_, deltas=rpn_bbox_pred_)

            boxes_pred = tf.reshape(boxes_pred, [-1, 5])
            angle_cls = (tf.reshape(angle_cls, [-1, ]) * -1 - 0.5) * self.cfgs.OMEGA

            x, y, w, h, theta = tf.unstack(boxes_pred, axis=1)
            boxes_pred_angle = tf.transpose(tf.stack([x, y, w, h, angle_cls]))

            if self.cfgs.ANGLE_RANGE == 180:
                # _, _, _, _, theta = tf.unstack(boxes_pred, axis=1)
                # indx = tf.reshape(tf.where(tf.logical_and(tf.less(theta, 0), tf.greater_equal(theta, -180))), [-1, ])
                # boxes_pred = tf.gather(boxes_pred, indx)
                # scores = tf.gather(scores, indx)

                boxes_pred = tf.py_func(coordinate_present_convert,
                                        inp=[boxes_pred, 1],
                                        Tout=[tf.float32])
                boxes_pred = tf.reshape(boxes_pred, [-1, 5])

                boxes_pred_angle = tf.py_func(coordinate_present_convert,
                                              inp=[boxes_pred_angle, 1],
                                              Tout=[tf.float32])
                boxes_pred_angle = tf.reshape(boxes_pred_angle, [-1, 5])

            max_output_size = 4000 if 'DOTA' in self.cfgs.NET_NAME else 200
            nms_indices = nms_rotate.nms_rotate(decode_boxes=boxes_pred_angle,
                                                scores=scores,
                                                iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
                                                max_output_size=100 if self.is_training else max_output_size,
                                                use_gpu=True,
                                                gpu_id=gpu_id)

            tmp_boxes_pred = tf.reshape(tf.gather(boxes_pred, nms_indices), [-1, 5])
            tmp_boxes_pred_angle = tf.reshape(tf.gather(boxes_pred_angle, nms_indices), [-1, 5])
            tmp_scores = tf.reshape(tf.gather(scores, nms_indices), [-1, ])

            return_boxes_pred.append(tmp_boxes_pred)
            return_boxes_pred_angle.append(tmp_boxes_pred_angle)
            return_scores.append(tmp_scores)
            return_labels.append(tf.ones_like(tmp_scores) * (j + 1))

        return_boxes_pred = tf.concat(return_boxes_pred, axis=0)
        return_boxes_pred_angle = tf.concat(return_boxes_pred_angle, axis=0)
        return_scores = tf.concat(return_scores, axis=0)
        return_labels = tf.concat(return_labels, axis=0)

        return return_boxes_pred, return_scores, return_labels, return_boxes_pred_angle
