avism/data/aviseval/metrics/avisa.py

import os
import numpy as np
from scipy.optimize import linear_sum_assignment
from ._base_metric import _BaseMetric
from .. import _timing


class avisA(_BaseMetric):
    def __init__(self, config=None):
        super().__init__()
        self.plottable = True
        self.array_labels = np.arange(0.05, 0.99, 0.05)
        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']
        self.float_array_fields = ['AssA', 'AssRe', 'AssPr', 'SegA']
        self.float_fields = ['SegA(0)']
        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields
        self.summary_fields = self.float_array_fields + self.float_fields

    @_timing.time
    def eval_sequence(self, data):
        """Calculates the AssA and SegA metrics for one sequence"""

        # Initialise results
        res = {}
        for field in self.float_array_fields + self.integer_array_fields:
            res[field] = np.zeros((len(self.array_labels)), dtype=float)
        for field in self.float_fields:
            res[field] = 0

        # Return result quickly if tracker or gt sequence is empty
        if data['num_tracker_dets'] == 0:
            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=float)
            res['SegA'] = np.ones((len(self.array_labels)), dtype=float)
            res['SegA(0)'] = 1.0
            return res
        if data['num_gt_dets'] == 0:
            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=float)
            res['SegA'] = np.ones((len(self.array_labels)), dtype=float)
            res['SegA(0)'] = 1.0
            return res

        # Variables counting global association
        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))
        gt_id_count = np.zeros((data['num_gt_ids'], 1))
        tracker_id_count = np.zeros((1, data['num_tracker_ids']))

        # First loop through each timestep and accumulate global track information.
        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
            # Count the potential matches between ids in each timestep
            # These are normalised, weighted by the match similarity.
            similarity = data['similarity_scores'][t]
            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity
            sim_iou = np.zeros_like(similarity)
            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps
            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou

            # Calculate the total number of dets for each gt_id and tracker_id.
            gt_id_count[gt_ids_t] += 1
            tracker_id_count[0, tracker_ids_t] += 1

        # Calculate overall jaccard alignment score (before unique matching) between IDs
        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)
        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]

        # Calculate scores for each timestep
        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):
            # Deal with the case that there are no gt_det/tracker_det in a timestep.
            if len(gt_ids_t) == 0:
                for a, alpha in enumerate(self.array_labels):
                    res['HOTA_FP'][a] += len(tracker_ids_t)
                continue
            if len(tracker_ids_t) == 0:
                for a, alpha in enumerate(self.array_labels):
                    res['HOTA_FN'][a] += len(gt_ids_t)
                continue

            # Get matching scores between pairs of dets for optimizing HOTA
            similarity = data['similarity_scores'][t]
            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity

            # Hungarian algorithm to find best matches: 找出最优跟踪路线
            match_rows, match_cols = linear_sum_assignment(-score_mat)

            # Calculate and accumulate basic statistics
            for a, alpha in enumerate(self.array_labels):
                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps
                alpha_match_rows = match_rows[actually_matched_mask]
                alpha_match_cols = match_cols[actually_matched_mask]
                num_matches = len(alpha_match_rows)
                res['HOTA_TP'][a] += num_matches
                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches
                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches
                if num_matches > 0:
                    res['SegA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])
                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1

        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
        for a, alpha in enumerate(self.array_labels):
            matches_count = matches_counts[a]
            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)
            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])
            ass_re = matches_count / np.maximum(1, gt_id_count)
            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])
            ass_pr = matches_count / np.maximum(1, tracker_id_count)
            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])

        # Calculate final scores
        res['SegA'] = np.maximum(1e-10, res['SegA']) / np.maximum(1e-10, res['HOTA_TP'])
        res = self._compute_final_fields(res)
        return res

    def combine_sequences(self, all_res):
        """Combines metrics across all sequences"""
        res = {}
        for field in self.integer_array_fields:
            res[field] = self._combine_sum(all_res, field)
        for field in ['AssRe', 'AssPr', 'AssA']:
            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
        sega_weighted_sum = sum([all_res[k]['SegA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
        res['SegA'] = np.maximum(1e-10, sega_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
        res = self._compute_final_fields(res)
        return res

    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
        """Combines metrics across all classes by averaging over the class values.
        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
        """
        res = {}
        for field in self.integer_array_fields:
            if ignore_empty_classes:
                res[field] = self._combine_sum(
                    {k: v for k, v in all_res.items()
                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)
            else:
                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)

        for field in self.float_fields + self.float_array_fields:
            if ignore_empty_classes:
                res[field] = np.mean([v[field] for v in all_res.values() if
                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],
                                     axis=0)
            else:
                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
        return res

    def combine_classes_det_averaged(self, all_res):
        """Combines metrics across all classes by averaging over the detection values"""
        res = {}
        for field in self.integer_array_fields:
            res[field] = self._combine_sum(all_res, field)
        for field in ['AssRe', 'AssPr', 'AssA']:
            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')
        sega_weighted_sum = sum([all_res[k]['SegA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])
        res['SegA'] = np.maximum(1e-10, sega_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])
        res = self._compute_final_fields(res)
        return res

    @staticmethod
    def _compute_final_fields(res):
        """Calculate sub-metric ('field') values which only depend on other sub-metric values.
        This function is used both for both per-sequence calculation, and in combining values across sequences.
        """
        res['SegA(0)'] = res['SegA'][0]
        return res

    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
        """Create plot of results"""

        # Only loaded when run to reduce minimum requirements
        from matplotlib import pyplot as plt

        res = table_res['COMBINED_SEQ']
        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm']
        for name, style in zip(self.float_array_fields, styles_to_plot):
            plt.plot(self.array_labels, res[name], style)
        plt.xlabel('alpha')
        plt.ylabel('score')
        plt.title(tracker + ' - ' + cls)
        plt.axis([0, 1, 0, 1])
        legend = []
        for name in self.float_array_fields:
            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']
        plt.legend(legend, loc='lower left')
        out_file = os.path.join(output_folder, cls + '_plot.pdf')
        os.makedirs(os.path.dirname(out_file), exist_ok=True)
        plt.savefig(out_file)
        plt.savefig(out_file.replace('.pdf', '.png'))
        plt.clf()