# ------------------------------------------------------------------------
# HOTR official code : hotr/data/evaluators/hico_eval.py
# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
# Modified from QPIC (https://github.com/hitachi-rd-cv/qpic)
# Copyright (c) Hitachi, Ltd. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import numpy as np
from collections import defaultdict

class HICOEvaluator():
    def __init__(self, preds, gts, rare_triplets, non_rare_triplets, correct_mat):
        self.overlap_iou = 0.5
        self.max_hois = 100

        self.rare_triplets = rare_triplets
        self.non_rare_triplets = non_rare_triplets

        self.fp = defaultdict(list)
        self.tp = defaultdict(list)
        self.score = defaultdict(list)
        self.sum_gts = defaultdict(lambda: 0)
        self.gt_triplets = []

        self.preds = []
        for img_preds in preds:
            img_preds = {k: v.to('cpu').numpy() for k, v in img_preds.items() if k != 'hoi_recognition_time'}
            bboxes = [{'bbox': bbox, 'category_id': label} for bbox, label in zip(img_preds['boxes'], img_preds['labels'])]
            hoi_scores = img_preds['verb_scores']
            verb_labels = np.tile(np.arange(hoi_scores.shape[1]), (hoi_scores.shape[0], 1))
            subject_ids = np.tile(img_preds['sub_ids'], (hoi_scores.shape[1], 1)).T
            object_ids = np.tile(img_preds['obj_ids'], (hoi_scores.shape[1], 1)).T

            hoi_scores = hoi_scores.ravel()
            verb_labels = verb_labels.ravel()
            subject_ids = subject_ids.ravel()
            object_ids = object_ids.ravel()

            if len(subject_ids) > 0:
                object_labels = np.array([bboxes[object_id]['category_id'] for object_id in object_ids])
                masks = correct_mat[verb_labels, object_labels]
                hoi_scores *= masks

                hois = [{'subject_id': subject_id, 'object_id': object_id, 'category_id': category_id, 'score': score} for
                        subject_id, object_id, category_id, score in zip(subject_ids, object_ids, verb_labels, hoi_scores)]
                hois.sort(key=lambda k: (k.get('score', 0)), reverse=True)
                hois = hois[:self.max_hois]
            else:
                hois = []

            self.preds.append({
                'predictions': bboxes,
                'hoi_prediction': hois
            })

        self.gts = []
        for img_gts in gts:
            img_gts = {k: v.to('cpu').numpy() for k, v in img_gts.items() if k != 'id'}
            self.gts.append({
                'annotations': [{'bbox': bbox, 'category_id': label} for bbox, label in zip(img_gts['boxes'], img_gts['labels'])],
                'hoi_annotation': [{'subject_id': hoi[0], 'object_id': hoi[1], 'category_id': hoi[2]} for hoi in img_gts['hois']]
            })
            for hoi in self.gts[-1]['hoi_annotation']:
                triplet = (self.gts[-1]['annotations'][hoi['subject_id']]['category_id'],
                           self.gts[-1]['annotations'][hoi['object_id']]['category_id'],
                           hoi['category_id'])

                if triplet not in self.gt_triplets:
                    self.gt_triplets.append(triplet)

                self.sum_gts[triplet] += 1

    def evaluate(self):
        for img_id, (img_preds, img_gts) in enumerate(zip(self.preds, self.gts)):
            print(f"Evaluating Score Matrix... : [{(img_id+1):>4}/{len(self.gts):<4}]" ,flush=True, end="\r")
            pred_bboxes = img_preds['predictions']
            gt_bboxes = img_gts['annotations']
            pred_hois = img_preds['hoi_prediction']
            gt_hois = img_gts['hoi_annotation']
            if len(gt_bboxes) != 0:
                bbox_pairs, bbox_overlaps = self.compute_iou_mat(gt_bboxes, pred_bboxes)
                self.compute_fptp(pred_hois, gt_hois, bbox_pairs, pred_bboxes, bbox_overlaps)
            else:
                for pred_hoi in pred_hois:
                    triplet = [pred_bboxes[pred_hoi['subject_id']]['category_id'],
                               pred_bboxes[pred_hoi['object_id']]['category_id'], pred_hoi['category_id']]
                    if triplet not in self.gt_triplets:
                        continue
                    self.tp[triplet].append(0)
                    self.fp[triplet].append(1)
                    self.score[triplet].append(pred_hoi['score'])
        print(f"[stats] Score Matrix Generation completed!!          ")
        map = self.compute_map()
        return map

    def compute_map(self):
        ap = defaultdict(lambda: 0)
        rare_ap = defaultdict(lambda: 0)
        non_rare_ap = defaultdict(lambda: 0)
        max_recall = defaultdict(lambda: 0)
        for triplet in self.gt_triplets:
            sum_gts = self.sum_gts[triplet]
            if sum_gts == 0:
                continue

            tp = np.array((self.tp[triplet]))
            fp = np.array((self.fp[triplet]))
            if len(tp) == 0:
                ap[triplet] = 0
                max_recall[triplet] = 0
                if triplet in self.rare_triplets:
                    rare_ap[triplet] = 0
                elif triplet in self.non_rare_triplets:
                    non_rare_ap[triplet] = 0
                else:
                    print('Warning: triplet {} is neither in rare triplets nor in non-rare triplets'.format(triplet))
                continue

            score = np.array(self.score[triplet])
            sort_inds = np.argsort(-score)
            fp = fp[sort_inds]
            tp = tp[sort_inds]
            fp = np.cumsum(fp)
            tp = np.cumsum(tp)
            rec = tp / sum_gts
            prec = tp / (fp + tp)
            ap[triplet] = self.voc_ap(rec, prec)
            max_recall[triplet] = np.amax(rec)
            if triplet in self.rare_triplets:
                rare_ap[triplet] = ap[triplet]
            elif triplet in self.non_rare_triplets:
                non_rare_ap[triplet] = ap[triplet]
            else:
                print('Warning: triplet {} is neither in rare triplets nor in non-rare triplets'.format(triplet))
        m_ap = np.mean(list(ap.values())) * 100 # percentage
        m_ap_rare = np.mean(list(rare_ap.values())) * 100 # percentage
        m_ap_non_rare = np.mean(list(non_rare_ap.values())) * 100 # percentage
        m_max_recall = np.mean(list(max_recall.values()))

        return {'mAP': m_ap, 'mAP rare': m_ap_rare, 'mAP non-rare': m_ap_non_rare, 'mean max recall': m_max_recall}

    def voc_ap(self, rec, prec):
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
        return ap

    def compute_fptp(self, pred_hois, gt_hois, match_pairs, pred_bboxes, bbox_overlaps):
        pos_pred_ids = match_pairs.keys()
        vis_tag = np.zeros(len(gt_hois))
        pred_hois.sort(key=lambda k: (k.get('score', 0)), reverse=True)
        if len(pred_hois) != 0:
            for pred_hoi in pred_hois:
                is_match = 0
                if len(match_pairs) != 0 and pred_hoi['subject_id'] in pos_pred_ids and pred_hoi['object_id'] in pos_pred_ids:
                    pred_sub_ids = match_pairs[pred_hoi['subject_id']]
                    pred_obj_ids = match_pairs[pred_hoi['object_id']]
                    pred_sub_overlaps = bbox_overlaps[pred_hoi['subject_id']]
                    pred_obj_overlaps = bbox_overlaps[pred_hoi['object_id']]
                    pred_category_id = pred_hoi['category_id']
                    max_overlap = 0
                    max_gt_hoi = 0
                    for gt_hoi in gt_hois:
                        if gt_hoi['subject_id'] in pred_sub_ids and gt_hoi['object_id'] in pred_obj_ids \
                           and pred_category_id == gt_hoi['category_id']:
                            is_match = 1
                            min_overlap_gt = min(pred_sub_overlaps[pred_sub_ids.index(gt_hoi['subject_id'])],
                                                 pred_obj_overlaps[pred_obj_ids.index(gt_hoi['object_id'])])
                            if min_overlap_gt > max_overlap:
                                max_overlap = min_overlap_gt
                                max_gt_hoi = gt_hoi
                triplet = (pred_bboxes[pred_hoi['subject_id']]['category_id'], pred_bboxes[pred_hoi['object_id']]['category_id'],
                           pred_hoi['category_id'])
                if triplet not in self.gt_triplets:
                    continue
                if is_match == 1 and vis_tag[gt_hois.index(max_gt_hoi)] == 0:
                    self.fp[triplet].append(0)
                    self.tp[triplet].append(1)
                    vis_tag[gt_hois.index(max_gt_hoi)] =1
                else:
                    self.fp[triplet].append(1)
                    self.tp[triplet].append(0)
                self.score[triplet].append(pred_hoi['score'])

    def compute_iou_mat(self, bbox_list1, bbox_list2):
        iou_mat = np.zeros((len(bbox_list1), len(bbox_list2)))
        if len(bbox_list1) == 0 or len(bbox_list2) == 0:
            return {}
        for i, bbox1 in enumerate(bbox_list1):
            for j, bbox2 in enumerate(bbox_list2):
                iou_i = self.compute_IOU(bbox1, bbox2)
                iou_mat[i, j] = iou_i

        iou_mat_ov=iou_mat.copy()
        iou_mat[iou_mat>=self.overlap_iou] = 1
        iou_mat[iou_mat<self.overlap_iou] = 0

        match_pairs = np.nonzero(iou_mat)
        match_pairs_dict = {}
        match_pair_overlaps = {}
        if iou_mat.max() > 0:
            for i, pred_id in enumerate(match_pairs[1]):
                if pred_id not in match_pairs_dict.keys():
                    match_pairs_dict[pred_id] = []
                    match_pair_overlaps[pred_id]=[]
                match_pairs_dict[pred_id].append(match_pairs[0][i])
                match_pair_overlaps[pred_id].append(iou_mat_ov[match_pairs[0][i],pred_id])
        return match_pairs_dict, match_pair_overlaps

    def compute_IOU(self, bbox1, bbox2):
        if isinstance(bbox1['category_id'], str):
            bbox1['category_id'] = int(bbox1['category_id'].replace('\n', ''))
        if isinstance(bbox2['category_id'], str):
            bbox2['category_id'] = int(bbox2['category_id'].replace('\n', ''))
        if bbox1['category_id'] == bbox2['category_id']:
            rec1 = bbox1['bbox']
            rec2 = bbox2['bbox']
            # computing area of each rectangles
            S_rec1 = (rec1[2] - rec1[0]+1) * (rec1[3] - rec1[1]+1)
            S_rec2 = (rec2[2] - rec2[0]+1) * (rec2[3] - rec2[1]+1)

            # computing the sum_area
            sum_area = S_rec1 + S_rec2

            # find the each edge of intersect rectangle
            left_line = max(rec1[1], rec2[1])
            right_line = min(rec1[3], rec2[3])
            top_line = max(rec1[0], rec2[0])
            bottom_line = min(rec1[2], rec2[2])
            # judge if there is an intersect
            if left_line >= right_line or top_line >= bottom_line:
                return 0
            else:
                intersect = (right_line - left_line+1) * (bottom_line - top_line+1)
                return intersect / (sum_area - intersect)
        else:
            return 0