# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
#   
# Licensed under the Apache License, Version 2.0 (the "License");   
# you may not use this file except in compliance with the License.  
# You may obtain a copy of the License at   
#   
#     http://www.apache.org/licenses/LICENSE-2.0    
#   
# Unless required by applicable law or agreed to in writing, software   
# distributed under the License is distributed on an "AS IS" BASIS, 
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
# See the License for the specific language governing permissions and   
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import copy
import numpy as np
import paddle
import paddle.nn.functional as F
from ppdet.modeling.bbox_utils import bbox_iou_np_expand
from .map_utils import ap_per_class
from .metrics import Metric

from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)

__all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric']


def read_mot_results(filename, is_gt=False, is_ignore=False):
    valid_labels = {1}
    ignore_labels = {2, 7, 8, 12}
    results_dict = dict()
    if os.path.isfile(filename):
        with open(filename, 'r') as f:
            for line in f.readlines():
                linelist = line.split(',')
                if len(linelist) < 7:
                    continue
                fid = int(linelist[0])
                if fid < 1:
                    continue
                results_dict.setdefault(fid, list())

                box_size = float(linelist[4]) * float(linelist[5])

                if is_gt:
                    if 'MOT16-' in filename or 'MOT17-' in filename:
                        label = int(float(linelist[7]))
                        mark = int(float(linelist[6]))
                        if mark == 0 or label not in valid_labels:
                            continue
                    score = 1
                elif is_ignore:
                    if 'MOT16-' in filename or 'MOT17-' in filename:
                        label = int(float(linelist[7]))
                        vis_ratio = float(linelist[8])
                        if label not in ignore_labels and vis_ratio >= 0:
                            continue
                    else:
                        continue
                    score = 1
                else:
                    score = float(linelist[6])

                tlwh = tuple(map(float, linelist[2:6]))
                target_id = int(linelist[1])

                results_dict[fid].append((tlwh, target_id, score))
    return results_dict


"""
labels={'ped', ...			    % 1
        'person_on_vhcl', ...	% 2
        'car', ...				% 3
        'bicycle', ...			% 4
        'mbike', ...			% 5
        'non_mot_vhcl', ...		% 6
        'static_person', ...	% 7
        'distractor', ...		% 8
        'occluder', ...			% 9
        'occluder_on_grnd', ...	% 10
        'occluder_full', ...	% 11
        'reflection', ...		% 12
        'crowd' ...			    % 13
};
"""


def unzip_objs(objs):
    if len(objs) > 0:
        tlwhs, ids, scores = zip(*objs)
    else:
        tlwhs, ids, scores = [], [], []
    tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
    return tlwhs, ids, scores


class MOTEvaluator(object):
    def __init__(self, data_root, seq_name, data_type):
        self.data_root = data_root
        self.seq_name = seq_name
        self.data_type = data_type

        self.load_annotations()
        self.reset_accumulator()

    def load_annotations(self):
        assert self.data_type == 'mot'
        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',
                                   'gt.txt')
        self.gt_frame_dict = read_mot_results(gt_filename, is_gt=True)
        self.gt_ignore_frame_dict = read_mot_results(
            gt_filename, is_ignore=True)

    def reset_accumulator(self):
        import motmetrics as mm
        mm.lap.default_solver = 'lap'
        self.acc = mm.MOTAccumulator(auto_id=True)

    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
        import motmetrics as mm
        mm.lap.default_solver = 'lap'
        # results
        trk_tlwhs = np.copy(trk_tlwhs)
        trk_ids = np.copy(trk_ids)

        # gts
        gt_objs = self.gt_frame_dict.get(frame_id, [])
        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]

        # ignore boxes
        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
        ignore_tlwhs = unzip_objs(ignore_objs)[0]

        # remove ignored results
        keep = np.ones(len(trk_tlwhs), dtype=bool)
        iou_distance = mm.distances.iou_matrix(
            ignore_tlwhs, trk_tlwhs, max_iou=0.5)
        if len(iou_distance) > 0:
            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
            match_ious = iou_distance[match_is, match_js]

            match_js = np.asarray(match_js, dtype=int)
            match_js = match_js[np.logical_not(np.isnan(match_ious))]
            keep[match_js] = False
            trk_tlwhs = trk_tlwhs[keep]
            trk_ids = trk_ids[keep]

        # get distance matrix
        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)

        # acc
        self.acc.update(gt_ids, trk_ids, iou_distance)

        if rtn_events and iou_distance.size > 0 and hasattr(self.acc,
                                                            'last_mot_events'):
            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics
        else:
            events = None
        return events

    def eval_file(self, filename):
        self.reset_accumulator()

        result_frame_dict = read_mot_results(filename, is_gt=False)
        frames = sorted(list(set(result_frame_dict.keys())))
        for frame_id in frames:
            trk_objs = result_frame_dict.get(frame_id, [])
            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)

        return self.acc

    @staticmethod
    def get_summary(accs,
                    names,
                    metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1',
                             'precision', 'recall')):
        import motmetrics as mm
        mm.lap.default_solver = 'lap'
        names = copy.deepcopy(names)
        if metrics is None:
            metrics = mm.metrics.motchallenge_metrics
        metrics = copy.deepcopy(metrics)

        mh = mm.metrics.create()
        summary = mh.compute_many(
            accs, metrics=metrics, names=names, generate_overall=True)
        return summary

    @staticmethod
    def save_summary(summary, filename):
        import pandas as pd
        writer = pd.ExcelWriter(filename)
        summary.to_excel(writer)
        writer.save()


class MOTMetric(Metric):
    def __init__(self, save_summary=False):
        self.save_summary = save_summary
        self.MOTEvaluator = MOTEvaluator
        self.result_root = None
        self.reset()

    def reset(self):
        self.accs = []
        self.seqs = []

    def update(self, data_root, seq, data_type, result_root, result_filename):
        evaluator = self.MOTEvaluator(data_root, seq, data_type)
        self.accs.append(evaluator.eval_file(result_filename))
        self.seqs.append(seq)
        self.result_root = result_root

    def accumulate(self):
        import motmetrics as mm
        import openpyxl
        metrics = mm.metrics.motchallenge_metrics
        mh = mm.metrics.create()
        summary = self.MOTEvaluator.get_summary(self.accs, self.seqs, metrics)
        self.strsummary = mm.io.render_summary(
            summary,
            formatters=mh.formatters,
            namemap=mm.io.motchallenge_metric_names)
        if self.save_summary:
            self.MOTEvaluator.save_summary(
                summary, os.path.join(self.result_root, 'summary.xlsx'))

    def log(self):
        print(self.strsummary)

    def get_results(self):
        return self.strsummary


class JDEDetMetric(Metric):
    # Note this detection AP metric is different from COCOMetric or VOCMetric,
    # and the bboxes coordinates are not scaled to the original image
    def __init__(self, overlap_thresh=0.5):
        self.overlap_thresh = overlap_thresh
        self.reset()

    def reset(self):
        self.AP_accum = np.zeros(1)
        self.AP_accum_count = np.zeros(1)

    def update(self, inputs, outputs):
        bboxes = outputs['bbox'][:, 2:].numpy()
        scores = outputs['bbox'][:, 1].numpy()
        labels = outputs['bbox'][:, 0].numpy()
        bbox_lengths = outputs['bbox_num'].numpy()
        if bboxes.shape[0] == 1 and bboxes.sum() == 0.0:
            return

        gt_boxes = inputs['gt_bbox'].numpy()[0]
        gt_labels = inputs['gt_class'].numpy()[0]
        if gt_labels.shape[0] == 0:
            return

        correct = []
        detected = []
        for i in range(bboxes.shape[0]):
            obj_pred = 0
            pred_bbox = bboxes[i].reshape(1, 4)
            # Compute iou with target boxes
            iou = bbox_iou_np_expand(pred_bbox, gt_boxes, x1y1x2y2=True)[0]
            # Extract index of largest overlap
            best_i = np.argmax(iou)
            # If overlap exceeds threshold and classification is correct mark as correct
            if iou[best_i] > self.overlap_thresh and obj_pred == gt_labels[
                    best_i] and best_i not in detected:
                correct.append(1)
                detected.append(best_i)
            else:
                correct.append(0)

        # Compute Average Precision (AP) per class
        target_cls = list(gt_labels.T[0])
        AP, AP_class, R, P = ap_per_class(
            tp=correct,
            conf=scores,
            pred_cls=np.zeros_like(scores),
            target_cls=target_cls)
        self.AP_accum_count += np.bincount(AP_class, minlength=1)
        self.AP_accum += np.bincount(AP_class, minlength=1, weights=AP)

    def accumulate(self):
        logger.info("Accumulating evaluatation results...")
        self.map_stat = self.AP_accum[0] / (self.AP_accum_count[0] + 1E-16)

    def log(self):
        map_stat = 100. * self.map_stat
        logger.info("mAP({:.2f}) = {:.2f}%".format(self.overlap_thresh,
                                                   map_stat))

    def get_results(self):
        return self.map_stat