# Copyright (c) 2019 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 from __future__ import unicode_literals import sys import numpy as np import logging logger = logging.getLogger(__name__) __all__ = ['bbox_area', 'jaccard_overlap', 'DetectionMAP'] def bbox_area(bbox, is_bbox_normalized): """ Calculate area of a bounding box """ norm = 1. - float(is_bbox_normalized) width = bbox[2] - bbox[0] + norm height = bbox[3] - bbox[1] + norm return width * height def jaccard_overlap(pred, gt, is_bbox_normalized=False): """ Calculate jaccard overlap ratio between two bounding box """ if pred[0] >= gt[2] or pred[2] <= gt[0] or \ pred[1] >= gt[3] or pred[3] <= gt[1]: return 0. inter_xmin = max(pred[0], gt[0]) inter_ymin = max(pred[1], gt[1]) inter_xmax = min(pred[2], gt[2]) inter_ymax = min(pred[3], gt[3]) inter_size = bbox_area([inter_xmin, inter_ymin, inter_xmax, inter_ymax], is_bbox_normalized) pred_size = bbox_area(pred, is_bbox_normalized) gt_size = bbox_area(gt, is_bbox_normalized) overlap = float(inter_size) / (pred_size + gt_size - inter_size) return overlap class DetectionMAP(object): """ Calculate detection mean average precision. Currently support two types: 11point and integral Args: class_num (int): the class number. overlap_thresh (float): The threshold of overlap ratio between prediction bounding box and ground truth bounding box for deciding true/false positive. Default 0.5. map_type (str): calculation method of mean average precision, currently support '11point' and 'integral'. Default '11point'. is_bbox_normalized (bool): whther bounding boxes is normalized to range[0, 1]. Default False. evaluate_difficult (bool): whether to evaluate difficult bounding boxes. Default False. """ def __init__(self, class_num, overlap_thresh=0.5, map_type='11point', is_bbox_normalized=False, evaluate_difficult=False): self.class_num = class_num self.overlap_thresh = overlap_thresh assert map_type in ['11point', 'integral'], \ "map_type currently only support '11point' "\ "and 'integral'" self.map_type = map_type self.is_bbox_normalized = is_bbox_normalized self.evaluate_difficult = evaluate_difficult self.reset() def update(self, bbox, gt_box, gt_label, difficult=None): """ Update metric statics from given prediction and ground truth infomations. """ if difficult is None: difficult = np.zeros_like(gt_label) # record class gt count for gtl, diff in zip(gt_label, difficult): if self.evaluate_difficult or int(diff) == 0: self.class_gt_counts[int(np.array(gtl))] += 1 # record class score positive visited = [False] * len(gt_label) score_idx = np.argsort(-bbox[:, 1]) for b in bbox[score_idx, :]: label, score, xmin, ymin, xmax, ymax = b.tolist() pred = [xmin, ymin, xmax, ymax] max_idx = -1 max_overlap = -1.0 for i, gl in enumerate(gt_label): if int(gl) == int(label): overlap = jaccard_overlap(pred, gt_box[i], self.is_bbox_normalized) if overlap > max_overlap: max_overlap = overlap max_idx = i if max_overlap > self.overlap_thresh: if self.evaluate_difficult or \ int(np.array(difficult[max_idx])) == 0: if not visited[max_idx]: self.class_score_poss[int(label)].append([score, 1.0]) visited[max_idx] = True else: self.class_score_poss[int(label)].append([score, 0.0]) else: self.class_score_poss[int(label)].append([score, 0.0]) def reset(self): """ Reset metric statics """ self.class_score_poss = [[] for _ in range(self.class_num)] self.class_gt_counts = [0] * self.class_num self.mAP = None def accumulate(self): """ Accumulate metric results and calculate mAP """ mAP = 0. valid_cnt = 0 for score_pos, count in zip(self.class_score_poss, self.class_gt_counts): if count == 0: continue if len(score_pos) == 0: valid_cnt += 1 continue accum_tp_list, accum_fp_list = \ self._get_tp_fp_accum(score_pos) precision = [] recall = [] for ac_tp, ac_fp in zip(accum_tp_list, accum_fp_list): precision.append(float(ac_tp) / (ac_tp + ac_fp)) recall.append(float(ac_tp) / count) if self.map_type == '11point': max_precisions = [0.] * 11 start_idx = len(precision) - 1 for j in range(10, -1, -1): for i in range(start_idx, -1, -1): if recall[i] < float(j) / 10.: start_idx = i if j > 0: max_precisions[j - 1] = max_precisions[j] break else: if max_precisions[j] < precision[i]: max_precisions[j] = precision[i] mAP += sum(max_precisions) / 11. valid_cnt += 1 elif self.map_type == 'integral': import math ap = 0. prev_recall = 0. for i in range(len(precision)): recall_gap = math.fabs(recall[i] - prev_recall) if recall_gap > 1e-6: ap += precision[i] * recall_gap prev_recall = recall[i] mAP += ap valid_cnt += 1 else: logger.error("Unspported mAP type {}".format(self.map_type)) sys.exit(1) self.mAP = mAP / float(valid_cnt) if valid_cnt > 0 else mAP def get_map(self): """ Get mAP result """ if self.mAP is None: logger.error("mAP is not calculated.") return self.mAP def _get_tp_fp_accum(self, score_pos_list): """ Calculate accumulating true/false positive results from [score, pos] records """ sorted_list = sorted(score_pos_list, key=lambda s: s[0], reverse=True) accum_tp = 0 accum_fp = 0 accum_tp_list = [] accum_fp_list = [] for (score, pos) in sorted_list: accum_tp += int(pos) accum_tp_list.append(accum_tp) accum_fp += 1 - int(pos) accum_fp_list.append(accum_fp) return accum_tp_list, accum_fp_list