# Copyright (c) SenseTime. All Rights Reserved.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np
import torch.nn.functional as F
import torch

from DFAT.core.config import cfg
from DFAT.utils.anchor import Anchors
from DFAT.tracker.base_tracker import SiameseTracker
from DFAT.utils.bbox import corner2center

class SiamRPNTracker(SiameseTracker):
    def __init__(self, model):
        super(SiamRPNTracker, self).__init__()
        self.score_size = (cfg.TRACK.INSTANCE_SIZE - cfg.TRACK.EXEMPLAR_SIZE) // \
            cfg.ANCHOR.STRIDE + 1 + cfg.TRACK.BASE_SIZE
        self.anchor_num = len(cfg.ANCHOR.RATIOS) * len(cfg.ANCHOR.SCALES)
        hanning = np.hanning(self.score_size)
        window = np.outer(hanning, hanning)
        if cfg.ANCHOR.TYPE:
            self.window = np.tile(window.flatten(), self.anchor_num)
            self.anchors = self.generate_anchor(self.score_size)
            self.cls_out_channels = 2
        else:
            self.cls_out_channels = cfg.TRAIN.Cls_out_channels
            self.window = window.flatten()
            self.points = self.generate_points(cfg.POINT.STRIDE, self.score_size)
        self.model = model
        self.model.eval()


    def update(self, img, bbox):
        # calculate z crop size
        w_z = self.size[0] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        h_z = self.size[1] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        s_z = round(np.sqrt(w_z * h_z))

        self.channel_average_rgb = np.mean(img[0], axis=(0, 1))
        self.channel_average_ir = np.mean(img[1], axis=(0, 1))

        # get crop
        z_crop = []
        z_crop_rgb = self.get_subwindow(img[0], self.center_pos,
                                        cfg.TRACK.EXEMPLAR_SIZE,
                                        s_z, self.channel_average_rgb)
        z_crop_ir = self.get_subwindow(img[1], self.center_pos,
                                       cfg.TRACK.EXEMPLAR_SIZE,
                                       s_z, self.channel_average_ir)
        #z_crop = [z_crop_rgb, z_crop_ir]
        m_r = torch.mean(z_crop_rgb)
        m_i = torch.mean(z_crop_ir)
        dis = m_r - m_i
        z_crop = [z_crop_rgb, z_crop_ir + dis]
        # z_crop = [z_crop_rgb, z_crop_ir]
        self.model.templateupdate(z_crop)

    def init(self, img, bbox):
        """
        args:
            img(np.ndarray): BGR image
            bbox: (x, y, w, h) bbox
        """
        self.center_pos = np.array([bbox[0]+(bbox[2]-1)/2,
                                    bbox[1]+(bbox[3]-1)/2])
        self.size = np.array([bbox[2], bbox[3]])

        # calculate z crop size
        w_z = self.size[0] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        h_z = self.size[1] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        s_z = round(np.sqrt(w_z * h_z))

        # calculate channle average
        self.channel_average_rgb = np.mean(img[0], axis=(0, 1))
        self.channel_average_ir = np.mean(img[1], axis=(0, 1))

        # get crop
        z_crop = []
        z_crop_rgb = self.get_subwindow(img[0], self.center_pos,
                                    cfg.TRACK.EXEMPLAR_SIZE,
                                    s_z, self.channel_average_rgb)
        z_crop_ir = self.get_subwindow(img[1], self.center_pos,
                                    cfg.TRACK.EXEMPLAR_SIZE,
                                    s_z, self.channel_average_ir)
        m_r = torch.mean(z_crop_rgb)
        m_i = torch.mean(z_crop_ir)
        dis = m_r - m_i
        z_crop = [z_crop_rgb, z_crop_ir + dis]
        # z_crop = [z_crop_rgb, z_crop_ir]
        self.model.template(z_crop)
        #self.model.set_prebbox(bbox)


    def generate_anchor(self, score_size):
        anchors = Anchors(cfg.ANCHOR.STRIDE,
                          cfg.ANCHOR.RATIOS,
                          cfg.ANCHOR.SCALES)
        anchor = anchors.anchors
        x1, y1, x2, y2 = anchor[:, 0], anchor[:, 1], anchor[:, 2], anchor[:, 3]
        anchor = np.stack([(x1+x2)*0.5, (y1+y2)*0.5, x2-x1, y2-y1], 1)
        total_stride = anchors.stride
        anchor_num = anchor.shape[0]
        anchor = np.tile(anchor, score_size * score_size).reshape((-1, 4))
        ori = - (score_size // 2) * total_stride
        xx, yy = np.meshgrid([ori + total_stride * dx for dx in range(score_size)],
                             [ori + total_stride * dy for dy in range(score_size)])
        xx, yy = np.tile(xx.flatten(), (anchor_num, 1)).flatten(), \
            np.tile(yy.flatten(), (anchor_num, 1)).flatten()
        anchor[:, 0], anchor[:, 1] = xx.astype(np.float32), yy.astype(np.float32)
        return anchor

    def generate_points(self, stride, size):
        ori = - (size // 2) * stride
        x, y = np.meshgrid([ori + stride * dx for dx in np.arange(0, size)],
                           [ori + stride * dy for dy in np.arange(0, size)])
        points = np.zeros((size * size, 2), dtype=np.float32)
        points[:, 0], points[:, 1] = x.astype(np.float32).flatten(), y.astype(np.float32).flatten()

        return points

    def _convert_bbox_anchor(self, delta, anchor):
        delta = delta.permute(1, 2, 3, 0).contiguous().view(4, -1)
        delta = delta.data.cpu().numpy()

        delta[0, :] = delta[0, :] * anchor[:, 2] + anchor[:, 0]
        delta[1, :] = delta[1, :] * anchor[:, 3] + anchor[:, 1]
        delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
        delta[3, :] = np.exp(delta[3, :]) * anchor[:, 3]
        return delta

    def _convert_bbox_point(self, delta, point):
        delta = delta.permute(1, 2, 3, 0).contiguous().view(4, -1)
        delta = delta.detach().cpu().numpy()

        delta[0, :] = point[:, 0] - delta[0, :]
        delta[1, :] = point[:, 1] - delta[1, :]
        delta[2, :] = point[:, 0] + delta[2, :]
        delta[3, :] = point[:, 1] + delta[3, :]
        delta[0, :], delta[1, :], delta[2, :], delta[3, :] = corner2center(delta)
        return delta

    def _convert_bbox2(self, delta, anchor):
        # delta = delta.permute(1, 2, 3, 0).contiguous().view(4, -1)
        delta = delta.contiguous().view(4, -1)
        delta = delta.data.cpu().numpy()

        delta[0, :] = delta[0, :] * anchor[:, 2] + anchor[:, 0]
        delta[1, :] = delta[1, :] * anchor[:, 3] + anchor[:, 1]
        delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
        delta[3, :] = np.exp(delta[3, :]) * anchor[:, 3]
        return delta

    # def _convert_score(self, score):
    #     score = score.permute(1, 2, 3, 0).contiguous().view(2, -1).permute(1, 0)
    #     score = F.softmax(score, dim=1).data[:, 1].cpu().numpy()
    #     #score = F.softmax(score, dim=1).data[:, 1]
    #     return score

    def _convert_score(self, score):
        if self.cls_out_channels == 1:
            score = score.permute(1, 2, 3, 0).contiguous().view(-1)
            score = score.sigmoid().detach().cpu().numpy()
        else:
            score = score.permute(1, 2, 3, 0).contiguous().view(self.cls_out_channels, -1).permute(1, 0)
            score = score.softmax(1).detach()[:, 1].cpu().numpy()
        return score

    
    def _convert_score2(self, score):
        # score = score.permute(1, 2, 3, 0).contiguous().view(2, -1).permute(1, 0)
        score = F.softmax(score).cpu().numpy()
        #score = F.softmax(score, dim=1).data[:, 1]
        return score

    def _bbox_clip(self, cx, cy, width, height, boundary):
        cx = max(0, min(cx, boundary[1]))
        cy = max(0, min(cy, boundary[0]))
        width = max(10, min(width, boundary[1]))
        height = max(10, min(height, boundary[0]))
        return cx, cy, width, height



    def track(self, img):
        """
        args:
            img(np.ndarray): BGR image
        return:
            bbox(list):[x, y, width, height]
        """
        w_z = self.size[0] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        h_z = self.size[1] + cfg.TRACK.CONTEXT_AMOUNT * np.sum(self.size)
        s_z = np.sqrt(w_z * h_z)
        scale_z = cfg.TRACK.EXEMPLAR_SIZE / s_z
        s_x = s_z * (cfg.TRACK.INSTANCE_SIZE / cfg.TRACK.EXEMPLAR_SIZE)
        x_crop = self.get_subwindow(img, self.center_pos,
                                    cfg.TRACK.INSTANCE_SIZE,
                                    round(s_x), self.channel_average)

        outputs = self.model.track(x_crop)

        score = self._convert_score(outputs['cls'])
        pred_bbox = self._convert_bbox(outputs['loc'], self.anchors)

        score_nz = np.roll(score, -np.mod(np.argmax(score),np.square(self.score_size)))
        # score_nz = score_nz.reshape(5, self.score_size, self.score_size)

        def change(r):
            return np.maximum(r, 1. / r)

        def sz(w, h):
            pad = (w + h) * 0.5
            return np.sqrt((w + pad) * (h + pad))

        # scale penalty
        s_c = change(sz(pred_bbox[2, :], pred_bbox[3, :]) /
                     (sz(self.size[0]*scale_z, self.size[1]*scale_z)))

        # aspect ratio penalty
        r_c = change((self.size[0]/self.size[1]) /
                     (pred_bbox[2, :]/pred_bbox[3, :]))
        penalty = np.exp(-(r_c * s_c - 1) * cfg.TRACK.PENALTY_K)
        pscore = penalty * score

        # window penalty
        pscore = pscore * (1 - cfg.TRACK.WINDOW_INFLUENCE) + \
            self.window * cfg.TRACK.WINDOW_INFLUENCE
        best_idx = np.argmax(pscore)

        bbox = pred_bbox[:, best_idx] / scale_z
        lr = penalty[best_idx] * score[best_idx] * cfg.TRACK.LR

        cx = bbox[0] + self.center_pos[0]
        cy = bbox[1] + self.center_pos[1]

        # smooth bbox
        width = self.size[0] * (1 - lr) + bbox[2] * lr
        height = self.size[1] * (1 - lr) + bbox[3] * lr

        # clip boundary
        cx, cy, width, height = self._bbox_clip(cx, cy, width,
                                                height, img.shape[:2])

        # udpate state
        self.center_pos = np.array([cx, cy])
        self.size = np.array([width, height])

        bbox = [cx - width / 2,
                cy - height / 2,
                width,
                height]
        best_score = score[best_idx]
        return {
                'bbox': bbox,
                'best_score': best_score,
                'score_nz': score_nz
               }


