forked from PulseFocusPlatform/PulseFocusPlatform
396 lines
14 KiB
Python
396 lines
14 KiB
Python
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import os
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import sys
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import numpy as np
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import itertools
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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__all__ = [
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'draw_pr_curve',
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'bbox_area',
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'jaccard_overlap',
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'prune_zero_padding',
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'DetectionMAP',
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'ap_per_class',
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'compute_ap',
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]
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def draw_pr_curve(precision,
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recall,
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iou=0.5,
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out_dir='pr_curve',
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file_name='precision_recall_curve.jpg'):
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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output_path = os.path.join(out_dir, file_name)
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try:
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import matplotlib.pyplot as plt
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except Exception as e:
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logger.error('Matplotlib not found, plaese install matplotlib.'
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'for example: `pip install matplotlib`.')
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raise e
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plt.cla()
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plt.figure('P-R Curve')
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plt.title('Precision/Recall Curve(IoU={})'.format(iou))
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plt.xlabel('Recall')
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plt.ylabel('Precision')
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plt.grid(True)
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plt.plot(recall, precision)
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plt.savefig(output_path)
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def bbox_area(bbox, is_bbox_normalized):
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"""
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Calculate area of a bounding box
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"""
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norm = 1. - float(is_bbox_normalized)
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width = bbox[2] - bbox[0] + norm
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height = bbox[3] - bbox[1] + norm
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return width * height
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def jaccard_overlap(pred, gt, is_bbox_normalized=False):
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"""
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Calculate jaccard overlap ratio between two bounding box
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"""
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if pred[0] >= gt[2] or pred[2] <= gt[0] or \
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pred[1] >= gt[3] or pred[3] <= gt[1]:
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return 0.
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inter_xmin = max(pred[0], gt[0])
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inter_ymin = max(pred[1], gt[1])
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inter_xmax = min(pred[2], gt[2])
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inter_ymax = min(pred[3], gt[3])
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inter_size = bbox_area([inter_xmin, inter_ymin, inter_xmax, inter_ymax],
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is_bbox_normalized)
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pred_size = bbox_area(pred, is_bbox_normalized)
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gt_size = bbox_area(gt, is_bbox_normalized)
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overlap = float(inter_size) / (pred_size + gt_size - inter_size)
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return overlap
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def prune_zero_padding(gt_box, gt_label, difficult=None):
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valid_cnt = 0
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for i in range(len(gt_box)):
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if gt_box[i, 0] == 0 and gt_box[i, 1] == 0 and \
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gt_box[i, 2] == 0 and gt_box[i, 3] == 0:
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break
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valid_cnt += 1
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return (gt_box[:valid_cnt], gt_label[:valid_cnt], difficult[:valid_cnt]
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if difficult is not None else None)
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class DetectionMAP(object):
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"""
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Calculate detection mean average precision.
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Currently support two types: 11point and integral
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Args:
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class_num (int): The class number.
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overlap_thresh (float): The threshold of overlap
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ratio between prediction bounding box and
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ground truth bounding box for deciding
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true/false positive. Default 0.5.
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map_type (str): Calculation method of mean average
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precision, currently support '11point' and
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'integral'. Default '11point'.
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is_bbox_normalized (bool): Whether bounding boxes
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is normalized to range[0, 1]. Default False.
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evaluate_difficult (bool): Whether to evaluate
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difficult bounding boxes. Default False.
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catid2name (dict): Mapping between category id and category name.
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classwise (bool): Whether per-category AP and draw
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P-R Curve or not.
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"""
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def __init__(self,
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class_num,
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overlap_thresh=0.5,
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map_type='11point',
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is_bbox_normalized=False,
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evaluate_difficult=False,
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catid2name=None,
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classwise=False):
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self.class_num = class_num
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self.overlap_thresh = overlap_thresh
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assert map_type in ['11point', 'integral'], \
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"map_type currently only support '11point' "\
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"and 'integral'"
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self.map_type = map_type
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self.is_bbox_normalized = is_bbox_normalized
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self.evaluate_difficult = evaluate_difficult
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self.classwise = classwise
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self.classes = []
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for cname in catid2name.values():
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self.classes.append(cname)
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self.reset()
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def update(self, bbox, score, label, gt_box, gt_label, difficult=None):
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"""
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Update metric statics from given prediction and ground
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truth infomations.
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"""
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if difficult is None:
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difficult = np.zeros_like(gt_label)
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# record class gt count
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for gtl, diff in zip(gt_label, difficult):
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if self.evaluate_difficult or int(diff) == 0:
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self.class_gt_counts[int(np.array(gtl))] += 1
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# record class score positive
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visited = [False] * len(gt_label)
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for b, s, l in zip(bbox, score, label):
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xmin, ymin, xmax, ymax = b.tolist()
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pred = [xmin, ymin, xmax, ymax]
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max_idx = -1
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max_overlap = -1.0
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for i, gl in enumerate(gt_label):
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if int(gl) == int(l):
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overlap = jaccard_overlap(pred, gt_box[i],
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self.is_bbox_normalized)
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if overlap > max_overlap:
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max_overlap = overlap
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max_idx = i
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if max_overlap > self.overlap_thresh:
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if self.evaluate_difficult or \
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int(np.array(difficult[max_idx])) == 0:
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if not visited[max_idx]:
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self.class_score_poss[int(l)].append([s, 1.0])
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visited[max_idx] = True
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else:
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self.class_score_poss[int(l)].append([s, 0.0])
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else:
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self.class_score_poss[int(l)].append([s, 0.0])
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def reset(self):
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"""
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Reset metric statics
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"""
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self.class_score_poss = [[] for _ in range(self.class_num)]
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self.class_gt_counts = [0] * self.class_num
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self.mAP = None
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def accumulate(self):
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"""
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Accumulate metric results and calculate mAP
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"""
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mAP = 0.
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valid_cnt = 0
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eval_results = []
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for score_pos, count in zip(self.class_score_poss,
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self.class_gt_counts):
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if count == 0: continue
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if len(score_pos) == 0:
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valid_cnt += 1
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continue
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accum_tp_list, accum_fp_list = \
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self._get_tp_fp_accum(score_pos)
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precision = []
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recall = []
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for ac_tp, ac_fp in zip(accum_tp_list, accum_fp_list):
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precision.append(float(ac_tp) / (ac_tp + ac_fp))
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recall.append(float(ac_tp) / count)
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one_class_ap = 0.0
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if self.map_type == '11point':
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max_precisions = [0.] * 11
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start_idx = len(precision) - 1
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for j in range(10, -1, -1):
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for i in range(start_idx, -1, -1):
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if recall[i] < float(j) / 10.:
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start_idx = i
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if j > 0:
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max_precisions[j - 1] = max_precisions[j]
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break
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else:
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if max_precisions[j] < precision[i]:
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max_precisions[j] = precision[i]
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one_class_ap = sum(max_precisions) / 11.
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mAP += one_class_ap
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valid_cnt += 1
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elif self.map_type == 'integral':
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import math
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prev_recall = 0.
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for i in range(len(precision)):
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recall_gap = math.fabs(recall[i] - prev_recall)
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if recall_gap > 1e-6:
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one_class_ap += precision[i] * recall_gap
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prev_recall = recall[i]
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mAP += one_class_ap
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valid_cnt += 1
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else:
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logger.error("Unspported mAP type {}".format(self.map_type))
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sys.exit(1)
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eval_results.append({
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'class': self.classes[valid_cnt - 1],
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'ap': one_class_ap,
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'precision': precision,
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'recall': recall,
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})
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self.eval_results = eval_results
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self.mAP = mAP / float(valid_cnt) if valid_cnt > 0 else mAP
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def get_map(self):
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"""
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Get mAP result
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"""
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if self.mAP is None:
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logger.error("mAP is not calculated.")
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if self.classwise:
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# Compute per-category AP and PR curve
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try:
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from terminaltables import AsciiTable
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except Exception as e:
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logger.error(
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'terminaltables not found, plaese install terminaltables. '
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'for example: `pip install terminaltables`.')
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raise e
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results_per_category = []
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for eval_result in self.eval_results:
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results_per_category.append(
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(str(eval_result['class']),
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'{:0.3f}'.format(float(eval_result['ap']))))
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draw_pr_curve(
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eval_result['precision'],
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eval_result['recall'],
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out_dir='voc_pr_curve',
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file_name='{}_precision_recall_curve.jpg'.format(
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eval_result['class']))
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num_columns = min(6, len(results_per_category) * 2)
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results_flatten = list(itertools.chain(*results_per_category))
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headers = ['category', 'AP'] * (num_columns // 2)
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results_2d = itertools.zip_longest(
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*[results_flatten[i::num_columns] for i in range(num_columns)])
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table_data = [headers]
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table_data += [result for result in results_2d]
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table = AsciiTable(table_data)
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logger.info('Per-category of VOC AP: \n{}'.format(table.table))
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logger.info(
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"per-category PR curve has output to voc_pr_curve folder.")
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return self.mAP
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def _get_tp_fp_accum(self, score_pos_list):
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"""
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Calculate accumulating true/false positive results from
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[score, pos] records
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"""
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sorted_list = sorted(score_pos_list, key=lambda s: s[0], reverse=True)
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accum_tp = 0
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accum_fp = 0
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accum_tp_list = []
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accum_fp_list = []
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for (score, pos) in sorted_list:
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accum_tp += int(pos)
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accum_tp_list.append(accum_tp)
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accum_fp += 1 - int(pos)
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accum_fp_list.append(accum_fp)
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return accum_tp_list, accum_fp_list
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def ap_per_class(tp, conf, pred_cls, target_cls):
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"""
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Computes the average precision, given the recall and precision curves.
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Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
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Args:
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tp (list): True positives.
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conf (list): Objectness value from 0-1.
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pred_cls (list): Predicted object classes.
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target_cls (list): Target object classes.
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"""
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tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(
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pred_cls), np.array(target_cls)
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# Sort by objectness
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i = np.argsort(-conf)
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tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
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# Find unique classes
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unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))
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# Create Precision-Recall curve and compute AP for each class
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ap, p, r = [], [], []
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for c in unique_classes:
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i = pred_cls == c
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n_gt = sum(target_cls == c) # Number of ground truth objects
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n_p = sum(i) # Number of predicted objects
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if (n_p == 0) and (n_gt == 0):
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continue
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elif (n_p == 0) or (n_gt == 0):
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ap.append(0)
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r.append(0)
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p.append(0)
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else:
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# Accumulate FPs and TPs
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fpc = np.cumsum(1 - tp[i])
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tpc = np.cumsum(tp[i])
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# Recall
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recall_curve = tpc / (n_gt + 1e-16)
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r.append(tpc[-1] / (n_gt + 1e-16))
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# Precision
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precision_curve = tpc / (tpc + fpc)
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p.append(tpc[-1] / (tpc[-1] + fpc[-1]))
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# AP from recall-precision curve
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ap.append(compute_ap(recall_curve, precision_curve))
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return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(
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p)
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def compute_ap(recall, precision):
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"""
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|
Computes the average precision, given the recall and precision curves.
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|
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
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Args:
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|
recall (list): The recall curve.
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|
precision (list): The precision curve.
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|
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Returns:
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|
The average precision as computed in py-faster-rcnn.
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"""
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# correct AP calculation
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||
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|
# first append sentinel values at the end
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mrec = np.concatenate(([0.], recall, [1.]))
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mpre = np.concatenate(([0.], precision, [0.]))
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|
|
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# compute the precision envelope
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||
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for i in range(mpre.size - 1, 0, -1):
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mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
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|
|
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||
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# to calculate area under PR curve, look for points
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||
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|
# where X axis (recall) changes value
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||
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|
i = np.where(mrec[1:] != mrec[:-1])[0]
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|
|
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||
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|
# and sum (\Delta recall) * prec
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||
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|
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
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|
return ap
|