Deteval.py

# 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.

import json
import numpy as np
import scipy.io as io
import Polygon as plg
from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area


def get_socre_A(gt_dir, pred_dict):
    allInputs = 1

    def input_reading_mod(pred_dict):
        """This helper reads input from txt files"""
        det = []
        n = len(pred_dict)
        for i in range(n):
            points = pred_dict[i]['points']
            text = pred_dict[i]['texts']
            point = ",".join(map(str, points.reshape(-1, )))
            det.append([point, text])
        return det

    def gt_reading_mod(gt_dict):
        """This helper reads groundtruths from mat files"""
        gt = []
        n = len(gt_dict)
        for i in range(n):
            points = gt_dict[i]['points'].tolist()
            h = len(points)
            text = gt_dict[i]['text']
            xx = [
                np.array(
                    ['x:'], dtype='<U2'), 0, np.array(
                        ['y:'], dtype='<U2'), 0, np.array(
                            ['#'], dtype='<U1'), np.array(
                                ['#'], dtype='<U1')
            ]
            t_x, t_y = [], []
            for j in range(h):
                t_x.append(points[j][0])
                t_y.append(points[j][1])
            xx[1] = np.array([t_x], dtype='int16')
            xx[3] = np.array([t_y], dtype='int16')
            if text != "":
                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
                xx[5] = np.array(['c'], dtype='<U1')
            gt.append(xx)
        return gt

    def detection_filtering(detections, groundtruths, threshold=0.5):
        for gt_id, gt in enumerate(groundtruths):
            if (gt[5] == '#') and (gt[1].shape[1] > 1):
                gt_x = list(map(int, np.squeeze(gt[1])))
                gt_y = list(map(int, np.squeeze(gt[3])))
                for det_id, detection in enumerate(detections):
                    detection_orig = detection
                    detection = [float(x) for x in detection[0].split(',')]
                    detection = list(map(int, detection))
                    det_x = detection[0::2]
                    det_y = detection[1::2]
                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
                    if det_gt_iou > threshold:
                        detections[det_id] = []

                detections[:] = [item for item in detections if item != []]
        return detections

    def sigma_calculation(det_x, det_y, gt_x, gt_y):
        """
        sigma = inter_area / gt_area
        """
        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
                         area(gt_x, gt_y)), 2)

    def tau_calculation(det_x, det_y, gt_x, gt_y):
        if area(det_x, det_y) == 0.0:
            return 0
        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
                         area(det_x, det_y)), 2)

    ##############################Initialization###################################
    # global_sigma = []
    # global_tau = []
    # global_pred_str = []
    # global_gt_str = []
    ###############################################################################

    for input_id in range(allInputs):
        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
                and (input_id != 'Deteval_result_non_curved.txt'):
            detections = input_reading_mod(pred_dict)
            groundtruths = gt_reading_mod(gt_dir)
            detections = detection_filtering(
                detections,
                groundtruths)  # filters detections overlapping with DC area
            dc_id = []
            for i in range(len(groundtruths)):
                if groundtruths[i][5] == '#':
                    dc_id.append(i)
            cnt = 0
            for a in dc_id:
                num = a - cnt
                del groundtruths[num]
                cnt += 1

            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
            local_tau_table = np.zeros((len(groundtruths), len(detections)))
            local_pred_str = {}
            local_gt_str = {}

            for gt_id, gt in enumerate(groundtruths):
                if len(detections) > 0:
                    for det_id, detection in enumerate(detections):
                        detection_orig = detection
                        detection = [float(x) for x in detection[0].split(',')]
                        detection = list(map(int, detection))
                        pred_seq_str = detection_orig[1].strip()
                        det_x = detection[0::2]
                        det_y = detection[1::2]
                        gt_x = list(map(int, np.squeeze(gt[1])))
                        gt_y = list(map(int, np.squeeze(gt[3])))
                        gt_seq_str = str(gt[4].tolist()[0])

                        local_sigma_table[gt_id, det_id] = sigma_calculation(
                            det_x, det_y, gt_x, gt_y)
                        local_tau_table[gt_id, det_id] = tau_calculation(
                            det_x, det_y, gt_x, gt_y)
                        local_pred_str[det_id] = pred_seq_str
                        local_gt_str[gt_id] = gt_seq_str

            global_sigma = local_sigma_table
            global_tau = local_tau_table
            global_pred_str = local_pred_str
            global_gt_str = local_gt_str

    single_data = {}
    single_data['sigma'] = global_sigma
    single_data['global_tau'] = global_tau
    single_data['global_pred_str'] = global_pred_str
    single_data['global_gt_str'] = global_gt_str
    return single_data


def get_socre_B(gt_dir, img_id, pred_dict):
    allInputs = 1

    def input_reading_mod(pred_dict):
        """This helper reads input from txt files"""
        det = []
        n = len(pred_dict)
        for i in range(n):
            points = pred_dict[i]['points']
            text = pred_dict[i]['texts']
            point = ",".join(map(str, points.reshape(-1, )))
            det.append([point, text])
        return det

    def gt_reading_mod(gt_dir, gt_id):
        gt = io.loadmat('%s/poly_gt_img%s.mat' % (gt_dir, gt_id))
        gt = gt['polygt']
        return gt

    def detection_filtering(detections, groundtruths, threshold=0.5):
        for gt_id, gt in enumerate(groundtruths):
            if (gt[5] == '#') and (gt[1].shape[1] > 1):
                gt_x = list(map(int, np.squeeze(gt[1])))
                gt_y = list(map(int, np.squeeze(gt[3])))
                for det_id, detection in enumerate(detections):
                    detection_orig = detection
                    detection = [float(x) for x in detection[0].split(',')]
                    detection = list(map(int, detection))
                    det_x = detection[0::2]
                    det_y = detection[1::2]
                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
                    if det_gt_iou > threshold:
                        detections[det_id] = []

                detections[:] = [item for item in detections if item != []]
        return detections

    def sigma_calculation(det_x, det_y, gt_x, gt_y):
        """
        sigma = inter_area / gt_area
        """
        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
                         area(gt_x, gt_y)), 2)

    def tau_calculation(det_x, det_y, gt_x, gt_y):
        if area(det_x, det_y) == 0.0:
            return 0
        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
                         area(det_x, det_y)), 2)

    ##############################Initialization###################################
    # global_sigma = []
    # global_tau = []
    # global_pred_str = []
    # global_gt_str = []
    ###############################################################################

    for input_id in range(allInputs):
        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
                and (input_id != 'Deteval_result_non_curved.txt'):
            detections = input_reading_mod(pred_dict)
            groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
            detections = detection_filtering(
                detections,
                groundtruths)  # filters detections overlapping with DC area
            dc_id = []
            for i in range(len(groundtruths)):
                if groundtruths[i][5] == '#':
                    dc_id.append(i)
            cnt = 0
            for a in dc_id:
                num = a - cnt
                del groundtruths[num]
                cnt += 1

            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
            local_tau_table = np.zeros((len(groundtruths), len(detections)))
            local_pred_str = {}
            local_gt_str = {}

            for gt_id, gt in enumerate(groundtruths):
                if len(detections) > 0:
                    for det_id, detection in enumerate(detections):
                        detection_orig = detection
                        detection = [float(x) for x in detection[0].split(',')]
                        detection = list(map(int, detection))
                        pred_seq_str = detection_orig[1].strip()
                        det_x = detection[0::2]
                        det_y = detection[1::2]
                        gt_x = list(map(int, np.squeeze(gt[1])))
                        gt_y = list(map(int, np.squeeze(gt[3])))
                        gt_seq_str = str(gt[4].tolist()[0])

                        local_sigma_table[gt_id, det_id] = sigma_calculation(
                            det_x, det_y, gt_x, gt_y)
                        local_tau_table[gt_id, det_id] = tau_calculation(
                            det_x, det_y, gt_x, gt_y)
                        local_pred_str[det_id] = pred_seq_str
                        local_gt_str[gt_id] = gt_seq_str

            global_sigma = local_sigma_table
            global_tau = local_tau_table
            global_pred_str = local_pred_str
            global_gt_str = local_gt_str

    single_data = {}
    single_data['sigma'] = global_sigma
    single_data['global_tau'] = global_tau
    single_data['global_pred_str'] = global_pred_str
    single_data['global_gt_str'] = global_gt_str
    return single_data


def get_score_C(gt_label, text, pred_bboxes):
    """
    get score for CentripetalText (CT) prediction.
    """

    def gt_reading_mod(gt_label, text):
        """This helper reads groundtruths from mat files"""
        groundtruths = []
        nbox = len(gt_label)
        for i in range(nbox):
            label = {"transcription": text[i][0], "points": gt_label[i].numpy()}
            groundtruths.append(label)

        return groundtruths

    def get_union(pD, pG):
        areaA = pD.area()
        areaB = pG.area()
        return areaA + areaB - get_intersection(pD, pG)

    def get_intersection(pD, pG):
        pInt = pD & pG
        if len(pInt) == 0:
            return 0
        return pInt.area()

    def detection_filtering(detections, groundtruths, threshold=0.5):
        for gt in groundtruths:
            point_num = gt['points'].shape[1] // 2
            if gt['transcription'] == '###' and (point_num > 1):
                gt_p = np.array(gt['points']).reshape(point_num,
                                                      2).astype('int32')
                gt_p = plg.Polygon(gt_p)

                for det_id, detection in enumerate(detections):
                    det_y = detection[0::2]
                    det_x = detection[1::2]

                    det_p = np.concatenate((np.array(det_x), np.array(det_y)))
                    det_p = det_p.reshape(2, -1).transpose()
                    det_p = plg.Polygon(det_p)

                    try:
                        det_gt_iou = get_intersection(det_p,
                                                      gt_p) / det_p.area()
                    except:
                        print(det_x, det_y, gt_p)
                    if det_gt_iou > threshold:
                        detections[det_id] = []

                detections[:] = [item for item in detections if item != []]
        return detections

    def sigma_calculation(det_p, gt_p):
        """
        sigma = inter_area / gt_area
        """
        if gt_p.area() == 0.:
            return 0
        return get_intersection(det_p, gt_p) / gt_p.area()

    def tau_calculation(det_p, gt_p):
        """
        tau = inter_area / det_area
        """
        if det_p.area() == 0.:
            return 0
        return get_intersection(det_p, gt_p) / det_p.area()

    detections = []

    for item in pred_bboxes:
        detections.append(item[:, ::-1].reshape(-1))

    groundtruths = gt_reading_mod(gt_label, text)

    detections = detection_filtering(
        detections, groundtruths)  # filters detections overlapping with DC area

    for idx in range(len(groundtruths) - 1, -1, -1):
        #NOTE: source code use 'orin' to indicate '#', here we use 'anno',
        # which may cause slight drop in fscore, about 0.12
        if groundtruths[idx]['transcription'] == '###':
            groundtruths.pop(idx)

    local_sigma_table = np.zeros((len(groundtruths), len(detections)))
    local_tau_table = np.zeros((len(groundtruths), len(detections)))

    for gt_id, gt in enumerate(groundtruths):
        if len(detections) > 0:
            for det_id, detection in enumerate(detections):
                point_num = gt['points'].shape[1] // 2

                gt_p = np.array(gt['points']).reshape(point_num,
                                                      2).astype('int32')
                gt_p = plg.Polygon(gt_p)

                det_y = detection[0::2]
                det_x = detection[1::2]

                det_p = np.concatenate((np.array(det_x), np.array(det_y)))

                det_p = det_p.reshape(2, -1).transpose()
                det_p = plg.Polygon(det_p)

                local_sigma_table[gt_id, det_id] = sigma_calculation(det_p,
                                                                     gt_p)
                local_tau_table[gt_id, det_id] = tau_calculation(det_p, gt_p)

    data = {}
    data['sigma'] = local_sigma_table
    data['global_tau'] = local_tau_table
    data['global_pred_str'] = ''
    data['global_gt_str'] = ''
    return data


def combine_results(all_data, rec_flag=True):
    tr = 0.7
    tp = 0.6
    fsc_k = 0.8
    k = 2
    global_sigma = []
    global_tau = []
    global_pred_str = []
    global_gt_str = []

    for data in all_data:
        global_sigma.append(data['sigma'])
        global_tau.append(data['global_tau'])
        global_pred_str.append(data['global_pred_str'])
        global_gt_str.append(data['global_gt_str'])

    global_accumulative_recall = 0
    global_accumulative_precision = 0
    total_num_gt = 0
    total_num_det = 0
    hit_str_count = 0
    hit_count = 0

    def one_to_one(local_sigma_table, local_tau_table,
                   local_accumulative_recall, local_accumulative_precision,
                   global_accumulative_recall, global_accumulative_precision,
                   gt_flag, det_flag, idy, rec_flag):
        hit_str_num = 0
        for gt_id in range(num_gt):
            gt_matching_qualified_sigma_candidates = np.where(
                local_sigma_table[gt_id, :] > tr)
            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
                0].shape[0]
            gt_matching_qualified_tau_candidates = np.where(
                local_tau_table[gt_id, :] > tp)
            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
                0].shape[0]

            det_matching_qualified_sigma_candidates = np.where(
                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
                > tr)
            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
                0].shape[0]
            det_matching_qualified_tau_candidates = np.where(
                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
                tp)
            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
                0].shape[0]

            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
                    (det_matching_num_qualified_sigma_candidates == 1) and (
                    det_matching_num_qualified_tau_candidates == 1):
                global_accumulative_recall = global_accumulative_recall + 1.0
                global_accumulative_precision = global_accumulative_precision + 1.0
                local_accumulative_recall = local_accumulative_recall + 1.0
                local_accumulative_precision = local_accumulative_precision + 1.0

                gt_flag[0, gt_id] = 1
                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
                # recg start
                if rec_flag:
                    gt_str_cur = global_gt_str[idy][gt_id]
                    pred_str_cur = global_pred_str[idy][matched_det_id[0]
                                                        .tolist()[0]]
                    if pred_str_cur == gt_str_cur:
                        hit_str_num += 1
                    else:
                        if pred_str_cur.lower() == gt_str_cur.lower():
                            hit_str_num += 1
                # recg end
                det_flag[0, matched_det_id] = 1
        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num

    def one_to_many(local_sigma_table, local_tau_table,
                    local_accumulative_recall, local_accumulative_precision,
                    global_accumulative_recall, global_accumulative_precision,
                    gt_flag, det_flag, idy, rec_flag):
        hit_str_num = 0
        for gt_id in range(num_gt):
            # skip the following if the groundtruth was matched
            if gt_flag[0, gt_id] > 0:
                continue

            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]

            if num_non_zero_in_sigma >= k:
                ####search for all detections that overlaps with this groundtruth
                qualified_tau_candidates = np.where((local_tau_table[
                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
                num_qualified_tau_candidates = qualified_tau_candidates[
                    0].shape[0]

                if num_qualified_tau_candidates == 1:
                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
                            and
                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
                         tr)):
                        # became an one-to-one case
                        global_accumulative_recall = global_accumulative_recall + 1.0
                        global_accumulative_precision = global_accumulative_precision + 1.0
                        local_accumulative_recall = local_accumulative_recall + 1.0
                        local_accumulative_precision = local_accumulative_precision + 1.0

                        gt_flag[0, gt_id] = 1
                        det_flag[0, qualified_tau_candidates] = 1
                        # recg start
                        if rec_flag:
                            gt_str_cur = global_gt_str[idy][gt_id]
                            pred_str_cur = global_pred_str[idy][
                                qualified_tau_candidates[0].tolist()[0]]
                            if pred_str_cur == gt_str_cur:
                                hit_str_num += 1
                            else:
                                if pred_str_cur.lower() == gt_str_cur.lower():
                                    hit_str_num += 1
                        # recg end
                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
                      >= tr):
                    gt_flag[0, gt_id] = 1
                    det_flag[0, qualified_tau_candidates] = 1
                    # recg start
                    if rec_flag:
                        gt_str_cur = global_gt_str[idy][gt_id]
                        pred_str_cur = global_pred_str[idy][
                            qualified_tau_candidates[0].tolist()[0]]
                        if pred_str_cur == gt_str_cur:
                            hit_str_num += 1
                        else:
                            if pred_str_cur.lower() == gt_str_cur.lower():
                                hit_str_num += 1
                    # recg end

                    global_accumulative_recall = global_accumulative_recall + fsc_k
                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k

                    local_accumulative_recall = local_accumulative_recall + fsc_k
                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k

        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num

    def many_to_one(local_sigma_table, local_tau_table,
                    local_accumulative_recall, local_accumulative_precision,
                    global_accumulative_recall, global_accumulative_precision,
                    gt_flag, det_flag, idy, rec_flag):
        hit_str_num = 0
        for det_id in range(num_det):
            # skip the following if the detection was matched
            if det_flag[0, det_id] > 0:
                continue

            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]

            if num_non_zero_in_tau >= k:
                ####search for all detections that overlaps with this groundtruth
                qualified_sigma_candidates = np.where((
                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
                num_qualified_sigma_candidates = qualified_sigma_candidates[
                    0].shape[0]

                if num_qualified_sigma_candidates == 1:
                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
                         tp) and
                        (local_sigma_table[qualified_sigma_candidates, det_id]
                         >= tr)):
                        # became an one-to-one case
                        global_accumulative_recall = global_accumulative_recall + 1.0
                        global_accumulative_precision = global_accumulative_precision + 1.0
                        local_accumulative_recall = local_accumulative_recall + 1.0
                        local_accumulative_precision = local_accumulative_precision + 1.0

                        gt_flag[0, qualified_sigma_candidates] = 1
                        det_flag[0, det_id] = 1
                        # recg start
                        if rec_flag:
                            pred_str_cur = global_pred_str[idy][det_id]
                            gt_len = len(qualified_sigma_candidates[0])
                            for idx in range(gt_len):
                                ele_gt_id = qualified_sigma_candidates[
                                    0].tolist()[idx]
                                if ele_gt_id not in global_gt_str[idy]:
                                    continue
                                gt_str_cur = global_gt_str[idy][ele_gt_id]
                                if pred_str_cur == gt_str_cur:
                                    hit_str_num += 1
                                    break
                                else:
                                    if pred_str_cur.lower() == gt_str_cur.lower(
                                    ):
                                        hit_str_num += 1
                                    break
                        # recg end
                elif (np.sum(local_tau_table[qualified_sigma_candidates,
                                             det_id]) >= tp):
                    det_flag[0, det_id] = 1
                    gt_flag[0, qualified_sigma_candidates] = 1
                    # recg start
                    if rec_flag:
                        pred_str_cur = global_pred_str[idy][det_id]
                        gt_len = len(qualified_sigma_candidates[0])
                        for idx in range(gt_len):
                            ele_gt_id = qualified_sigma_candidates[0].tolist()[
                                idx]
                            if ele_gt_id not in global_gt_str[idy]:
                                continue
                            gt_str_cur = global_gt_str[idy][ele_gt_id]
                            if pred_str_cur == gt_str_cur:
                                hit_str_num += 1
                                break
                            else:
                                if pred_str_cur.lower() == gt_str_cur.lower():
                                    hit_str_num += 1
                                    break
                    # recg end

                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
                    global_accumulative_precision = global_accumulative_precision + fsc_k

                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
                    local_accumulative_precision = local_accumulative_precision + fsc_k
        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num

    for idx in range(len(global_sigma)):
        local_sigma_table = np.array(global_sigma[idx])
        local_tau_table = global_tau[idx]

        num_gt = local_sigma_table.shape[0]
        num_det = local_sigma_table.shape[1]

        total_num_gt = total_num_gt + num_gt
        total_num_det = total_num_det + num_det

        local_accumulative_recall = 0
        local_accumulative_precision = 0
        gt_flag = np.zeros((1, num_gt))
        det_flag = np.zeros((1, num_det))

        #######first check for one-to-one case##########
        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
                                                    local_accumulative_recall, local_accumulative_precision,
                                                    global_accumulative_recall, global_accumulative_precision,
                                                    gt_flag, det_flag, idx, rec_flag)

        hit_str_count += hit_str_num
        #######then check for one-to-many case##########
        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
                                                     local_accumulative_recall, local_accumulative_precision,
                                                     global_accumulative_recall, global_accumulative_precision,
                                                     gt_flag, det_flag, idx, rec_flag)
        hit_str_count += hit_str_num
        #######then check for many-to-one case##########
        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
                                                     local_accumulative_recall, local_accumulative_precision,
                                                     global_accumulative_recall, global_accumulative_precision,
                                                     gt_flag, det_flag, idx, rec_flag)
        hit_str_count += hit_str_num

    try:
        recall = global_accumulative_recall / total_num_gt
    except ZeroDivisionError:
        recall = 0

    try:
        precision = global_accumulative_precision / total_num_det
    except ZeroDivisionError:
        precision = 0

    try:
        f_score = 2 * precision * recall / (precision + recall)
    except ZeroDivisionError:
        f_score = 0

    try:
        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
    except ZeroDivisionError:
        seqerr = 1

    try:
        recall_e2e = float(hit_str_count) / total_num_gt
    except ZeroDivisionError:
        recall_e2e = 0

    try:
        precision_e2e = float(hit_str_count) / total_num_det
    except ZeroDivisionError:
        precision_e2e = 0

    try:
        f_score_e2e = 2 * precision_e2e * recall_e2e / (
            precision_e2e + recall_e2e)
    except ZeroDivisionError:
        f_score_e2e = 0

    final = {
        'total_num_gt': total_num_gt,
        'total_num_det': total_num_det,
        'global_accumulative_recall': global_accumulative_recall,
        'hit_str_count': hit_str_count,
        'recall': recall,
        'precision': precision,
        'f_score': f_score,
        'seqerr': seqerr,
        'recall_e2e': recall_e2e,
        'precision_e2e': precision_e2e,
        'f_score_e2e': f_score_e2e
    }
    return final