217 lines
7.0 KiB
Python
217 lines
7.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 31 16:25:43 2025
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@author: wqg
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"""
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import numpy as np
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from scipy.spatial.distance import cdist
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def get_topk_percent(data, k):
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"""
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获取数据中最大的 k% 的元素
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"""
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# 将数据转换为 NumPy 数组
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if isinstance(data, list):
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data = np.array(data)
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percentile = np.percentile(data, 100-k)
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top_k_percent = data[data >= percentile]
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return top_k_percent
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def cluster(data, thresh=0.15):
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# data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
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# data = np.array([0.1, 0.13, 0.2, 0.3])
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# data = np.array([0.1])
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if isinstance(data, list):
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data = np.array(data)
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data1 = np.sort(data)
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cluter, Cluters, = [data1[0]], []
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for i in range(1, len(data1)):
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if data1[i] - data1[i-1]< thresh:
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cluter.append(data1[i])
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else:
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Cluters.append(cluter)
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cluter = [data1[i]]
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Cluters.append(cluter)
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clt_center = []
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for clt in Cluters:
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## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中
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# if len(clt)>=3:
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# clt_center.append(np.mean(clt))
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clt_center.append(np.mean(clt))
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# print(clt_center)
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return clt_center
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def calsiml(feat1, feat2, topkp=75, cluth=0.15):
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'''轨迹样本和标准特征集样本相似度的选择策略'''
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matrix = 1 - cdist(feat1, feat2, 'cosine')
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simi_max = []
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for i in range(len(matrix)):
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sim = np.mean(get_topk_percent(matrix[i, :], topkp))
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simi_max.append(sim)
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cltc_max = cluster(simi_max, cluth)
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Simi = max(cltc_max)
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## cltc_max为空属于编程考虑不周，应予以排查解决
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# if len(cltc_max):
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# Simi = max(cltc_max)
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# else:
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# Simi = 0 #不应该走到该处
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return Simi
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def calsimi_vs_stdfeat_new(event, stdfeat):
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'''事件与标准库的对比策略
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该比对策略是否可以拓展到事件与事件的比对？
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'''
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front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(event.front_boxes)):
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front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
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front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
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back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(event.back_boxes)):
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back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
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back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
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front_simi, back_simi = None, None
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if len(front_feats):
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front_simi = calsiml(front_feats, stdfeat)
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if len(back_feats):
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back_simi = calsiml(back_feats, stdfeat)
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'''前后摄相似度融合策略'''
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if len(front_feats) and len(back_feats):
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diff_simi = abs(front_simi - back_simi)
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if diff_simi>0.15:
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Similar = max([front_simi, back_simi])
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else:
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Similar = (front_simi+back_simi)/2
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elif len(front_feats) and len(back_feats)==0:
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Similar = front_simi
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elif len(front_feats)==0 and len(back_feats):
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Similar = back_simi
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else:
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Similar = None # 在event.front_feats和event.back_feats同时为空时
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return Similar, front_simi, back_simi
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def calsimi_vs_stdfeat(event, stdfeat):
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evtfeat = event.feats_compose
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if isinstance(event.feats_select, list):
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if len(event.feats_select) and len(event.feats_select[0]):
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evtfeat = event.feats_select[0]
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else:
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return None, None, None
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else:
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evtfeat = event.feats_select
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if len(evtfeat)==0 or len(stdfeat)==0:
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return None, None, None
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evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
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stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
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matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
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matrix[matrix < 0] = 0
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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return simi_mean, simi_max, simi_mfeat[0,0]
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def calsimi_vs_evts(evta, evtb, simType=1):
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if simType==1:
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if len(evta.feats_compose) and len(evtb.feats_compose):
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feata = evta.feats_compose
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featb = evtb.feats_compose
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matrix = 1 - cdist(feata, featb, 'cosine')
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similar = np.mean(matrix)
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else:
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similar = None
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return similar
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if simType==2:
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if len(evta.feats_compose) and len(evtb.feats_compose):
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feata = evta.feats_compose
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featb = evtb.feats_compose
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matrix = 1 - cdist(feata, featb, 'cosine')
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similar = np.max(matrix)
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else:
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similar = None
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return similar
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if simType==3:
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if len(evta.feats_compose) and len(evtb.feats_compose):
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feata = evta.feats_compose
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featb = evtb.feats_compose
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similar = calsiml(feata, featb)
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else:
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similar = None
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return similar
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##1. the front feats of evta, evtb
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fr_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(evta.front_feats)):
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fr_feata = np.concatenate((fr_feata, evta.front_feats[i]), axis=0)
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fr_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(evtb.front_feats)):
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fr_featb = np.concatenate((fr_featb, evtb.front_feats[i]), axis=0)
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##2. the back feats of evta, evtb
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bk_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(evta.back_feats)):
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bk_feata = np.concatenate((bk_feata, evta.back_feats[i]), axis=0)
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bk_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
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for i in range(len(evtb.back_feats)):
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bk_featb = np.concatenate((bk_featb, evtb.back_feats[i]), axis=0)
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front_simi, back_simi = None, None
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if len(fr_feata) and len(fr_featb):
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front_simi = calsiml(fr_feata, fr_featb)
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if len(bk_feata) and len(bk_featb):
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back_simi = calsiml(bk_feata, bk_featb)
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'''前后摄相似度融合策略'''
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if front_simi is not None and back_simi is not None:
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diff_simi = abs(front_simi - back_simi)
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if diff_simi>0.15:
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similar = max([front_simi, back_simi])
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else:
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similar = (front_simi+back_simi)/2
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elif front_simi is not None and back_simi is None:
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similar = front_simi
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elif front_simi is None and back_simi is not None:
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similar = back_simi
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else:
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similar = None # 在event.front_feats和event.back_feats同时为空时
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return similar
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