import math

from lib.models.artrack import build_artrack
from lib.test.tracker.basetracker import BaseTracker
import torch

from lib.test.tracker.vis_utils import gen_visualization
from lib.test.utils.hann import hann2d
from lib.train.data.processing_utils import sample_target
# for debug
import cv2
import os

from lib.test.tracker.data_utils import Preprocessor
from lib.utils.box_ops import clip_box
from lib.utils.ce_utils import generate_mask_cond
import random

class RandomErasing(object):
    def __init__(self, EPSILON=0.5, sl=0.02, sh=0.33, r1=0.3, mean=[0.4914, 0.4822, 0.4465]):
        self.EPSILON = EPSILON
        self.mean = mean
        self.sl = sl
        self.sh = sh
        self.r1 = r1

    def __call__(self, img):

        if random.uniform(0, 1) > self.EPSILON:
            return img

        for attempt in range(100):
            print(img.size())
            area = img.size()[1] * img.size()[2]

            target_area = random.uniform(self.sl, self.sh) * area
            aspect_ratio = random.uniform(self.r1, 1 / self.r1)

            h = int(round(math.sqrt(target_area * aspect_ratio)))
            w = int(round(math.sqrt(target_area / aspect_ratio)))

            if w < img.size()[2] and h < img.size()[1]:
                x1 = random.randint(0, img.size()[1] - h)
                y1 = random.randint(0, img.size()[2] - w)
                if img.size()[0] == 3:
                    # img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
                    # img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
                    # img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
                    # img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w))
                else:
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
                    # img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w))
                return img

        return img


class ARTrack(BaseTracker):
    def __init__(self, params, dataset_name):
        super(ARTrack, self).__init__(params)
        network = build_artrack(params.cfg, training=False)
        print(self.params.checkpoint)
        network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True)
        self.cfg = params.cfg
        self.bins = self.cfg.MODEL.BINS
        self.network = network.cuda()
        self.network.eval()
        self.preprocessor = Preprocessor()
        self.state = None
        self.range = self.cfg.MODEL.RANGE

        self.feat_sz = self.cfg.TEST.SEARCH_SIZE // self.cfg.MODEL.BACKBONE.STRIDE
        # motion constrain
        self.output_window = hann2d(torch.tensor([self.feat_sz, self.feat_sz]).long(), centered=True).cuda()

        # for debug
        self.debug = params.debug
        self.use_visdom = params.debug
        self.frame_id = 0
        self.erase = RandomErasing()
        if self.debug:
            if not self.use_visdom:
                self.save_dir = "debug"
                if not os.path.exists(self.save_dir):
                    os.makedirs(self.save_dir)
            else:
                # self.add_hook()
                self._init_visdom(None, 1)
        # for save boxes from all queries
        self.save_all_boxes = params.save_all_boxes
        self.z_dict1 = {}

    def initialize(self, image, info: dict):
        # forward the template once

        z_patch_arr, resize_factor, z_amask_arr = sample_target(image, info['init_bbox'], self.params.template_factor,
                                                    output_sz=self.params.template_size)#output_sz=self.params.template_size
        self.z_patch_arr = z_patch_arr
        template = self.preprocessor.process(z_patch_arr, z_amask_arr)
        with torch.no_grad():
            self.z_dict1 = template

        self.box_mask_z = None
        #if self.cfg.MODEL.BACKBONE.CE_LOC:
        #    template_bbox = self.transform_bbox_to_crop(info['init_bbox'], resize_factor,
        #                                                template.tensors.device).squeeze(1)
        #    self.box_mask_z = generate_mask_cond(self.cfg, 1, template.tensors.device, template_bbox)

        # save states
        self.state = info['init_bbox']
        self.frame_id = 0
        if self.save_all_boxes:
            '''save all predicted boxes'''
            all_boxes_save = info['init_bbox'] * self.cfg.MODEL.NUM_OBJECT_QUERIES
            return {"all_boxes": all_boxes_save}

    def track(self, image, info: dict = None):
        magic_num = (self.range - 1) * 0.5
        H, W, _ = image.shape
        self.frame_id += 1
        x_patch_arr, resize_factor, x_amask_arr = sample_target(image, self.state, self.params.search_factor,
                                                                output_sz=self.params.search_size)  # (x1, y1, w, h)
        search = self.preprocessor.process(x_patch_arr, x_amask_arr)


        with torch.no_grad():
            x_dict = search
            # merge the template and the search
            # run the transformer
            out_dict = self.network.forward(
                template=self.z_dict1.tensors, search=x_dict.tensors)

        # add hann windows
        # pred_score_map = out_dict['score_map']
        # response = self.output_window * pred_score_map
        # pred_boxes = self.network.box_head.cal_bbox(response, out_dict['size_map'], out_dict['offset_map'])
        # pred_boxes = pred_boxes.view(-1, 4)

        pred_boxes = out_dict['seqs'][:, 0:4] / (self.bins - 1) - magic_num
        pred_boxes = pred_boxes.view(-1, 4).mean(dim=0)
        pred_new = pred_boxes
        pred_new[2] = pred_boxes[2] - pred_boxes[0]
        pred_new[3] = pred_boxes[3] - pred_boxes[1]
        pred_new[0] = pred_boxes[0] + pred_boxes[2]/2
        pred_new[1] = pred_boxes[1] + pred_boxes[3]/2

        pred_boxes = (pred_new * self.params.search_size / resize_factor).tolist()

        # Baseline: Take the mean of all pred boxes as the final result
        #pred_box = (pred_boxes.mean(
        #    dim=0) * self.params.search_size / resize_factor).tolist()  # (cx, cy, w, h) [0,1]
        # get the final box result
        self.state = clip_box(self.map_box_back(pred_boxes, resize_factor), H, W, margin=10)

        # for debug
        if self.debug:
            if not self.use_visdom:
                x1, y1, w, h = self.state
                image_BGR = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
                cv2.rectangle(image_BGR, (int(x1),int(y1)), (int(x1+w),int(y1+h)), color=(0,0,255), thickness=2)
                save_path = os.path.join(self.save_dir, "%04d.jpg" % self.frame_id)
                cv2.imwrite(save_path, image_BGR)
            else:
                self.visdom.register((image, info['gt_bbox'].tolist(), self.state), 'Tracking', 1, 'Tracking')

                self.visdom.register(torch.from_numpy(x_patch_arr).permute(2, 0, 1), 'image', 1, 'search_region')
                self.visdom.register(torch.from_numpy(self.z_patch_arr).permute(2, 0, 1), 'image', 1, 'template')
                self.visdom.register(pred_score_map.view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map')
                self.visdom.register((pred_score_map * self.output_window).view(self.feat_sz, self.feat_sz), 'heatmap', 1, 'score_map_hann')

                if 'removed_indexes_s' in out_dict and out_dict['removed_indexes_s']:
                    removed_indexes_s = out_dict['removed_indexes_s']
                    removed_indexes_s = [removed_indexes_s_i.cpu().numpy() for removed_indexes_s_i in removed_indexes_s]
                    masked_search = gen_visualization(x_patch_arr, removed_indexes_s)
                    self.visdom.register(torch.from_numpy(masked_search).permute(2, 0, 1), 'image', 1, 'masked_search')

                while self.pause_mode:
                    if self.step:
                        self.step = False
                        break

        if self.save_all_boxes:
            '''save all predictions'''
            all_boxes = self.map_box_back_batch(pred_boxes * self.params.search_size / resize_factor, resize_factor)
            all_boxes_save = all_boxes.view(-1).tolist()  # (4N, )
            return {"target_bbox": self.state,
                    "all_boxes": all_boxes_save}
        else:
            return {"target_bbox": self.state}

    def map_box_back(self, pred_box: list, resize_factor: float):
        cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3]
        cx, cy, w, h = pred_box
        half_side = 0.5 * self.params.search_size / resize_factor
        cx_real = cx + (cx_prev - half_side)
        cy_real = cy + (cy_prev - half_side)
        #cx_real = cx + cx_prev
        #cy_real = cy + cy_prev
        return [cx_real - 0.5 * w, cy_real - 0.5 * h, w, h]

    def map_box_back_batch(self, pred_box: torch.Tensor, resize_factor: float):
        cx_prev, cy_prev = self.state[0] + 0.5 * self.state[2], self.state[1] + 0.5 * self.state[3]
        cx, cy, w, h = pred_box.unbind(-1) # (N,4) --> (N,)
        half_side = 0.5 * self.params.search_size / resize_factor
        cx_real = cx + (cx_prev - half_side)
        cy_real = cy + (cy_prev - half_side)
        return torch.stack([cx_real - 0.5 * w, cy_real - 0.5 * h, w, h], dim=-1)

    def add_hook(self):
        conv_features, enc_attn_weights, dec_attn_weights = [], [], []

        for i in range(12):
            self.network.backbone.blocks[i].attn.register_forward_hook(
                # lambda self, input, output: enc_attn_weights.append(output[1])
                lambda self, input, output: enc_attn_weights.append(output[1])
            )

        self.enc_attn_weights = enc_attn_weights


def get_tracker_class():
    return ARTrack