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回传数据解析，兼容v5和v10

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jiajie555
2025-04-18 14:41:53 +08:00
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 31 16:25:43 2025
@author: wqg
"""
import numpy as np
from scipy.spatial.distance import cdist
def get_topk_percent(data, k):
"""
获取数据中最大的 k% 的元素
"""
# 将数据转换为 NumPy 数组
if isinstance(data, list):
data = np.array(data)
percentile = np.percentile(data, 100-k)
top_k_percent = data[data >= percentile]
return top_k_percent
def cluster(data, thresh=0.15):
# data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
# data = np.array([0.1, 0.13, 0.2, 0.3])
# data = np.array([0.1])
if isinstance(data, list):
data = np.array(data)
data1 = np.sort(data)
cluter, Cluters, = [data1[0]], []
for i in range(1, len(data1)):
if data1[i] - data1[i-1]< thresh:
cluter.append(data1[i])
else:
Cluters.append(cluter)
cluter = [data1[i]]
Cluters.append(cluter)
clt_center = []
for clt in Cluters:
## 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
# if len(clt)>=3:
# clt_center.append(np.mean(clt))
clt_center.append(np.mean(clt))
# print(clt_center)
return clt_center
def calsiml(feat1, feat2, topkp=75, cluth=0.15):
'''轨迹样本和标准特征集样本相似度的选择策略'''
matrix = 1 - cdist(feat1, feat2, 'cosine')
simi_max = []
for i in range(len(matrix)):
sim = np.mean(get_topk_percent(matrix[i, :], topkp))
simi_max.append(sim)
cltc_max = cluster(simi_max, cluth)
Simi = max(cltc_max)
## cltc_max为空属于编程考虑不周，应予以排查解决
# if len(cltc_max):
# Simi = max(cltc_max)
# else:
# Simi = 0 #不应该走到该处
return Simi
def calsimi_vs_stdfeat_new(event, stdfeat):
'''事件与标准库的对比策略
该比对策略是否可以拓展到事件与事件的比对？
'''
front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(event.front_boxes)):
front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(event.back_boxes)):
back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
front_simi, back_simi = None, None
if len(front_feats):
front_simi = calsiml(front_feats, stdfeat)
if len(back_feats):
back_simi = calsiml(back_feats, stdfeat)
'''前后摄相似度融合策略'''
if len(front_feats) and len(back_feats):
diff_simi = abs(front_simi - back_simi)
if diff_simi>0.15:
Similar = max([front_simi, back_simi])
else:
Similar = (front_simi+back_simi)/2
elif len(front_feats) and len(back_feats)==0:
Similar = front_simi
elif len(front_feats)==0 and len(back_feats):
Similar = back_simi
else:
Similar = None # 在event.front_feats和event.back_feats同时为空时
return Similar, front_simi, back_simi
def calsimi_vs_stdfeat(event, stdfeat):
evtfeat = event.feats_compose
if isinstance(event.feats_select, list):
if len(event.feats_select) and len(event.feats_select[0]):
evtfeat = event.feats_select[0]
else:
return None, None, None
else:
evtfeat = event.feats_select
if len(evtfeat)==0 or len(stdfeat)==0:
return None, None, None
evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
matrix[matrix < 0] = 0
simi_mean = np.mean(matrix)
simi_max = np.max(matrix)
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
return simi_mean, simi_max, simi_mfeat[0,0]
def calsimi_vs_evts(evta, evtb, simType=1):
if simType==1:
if len(evta.feats_compose) and len(evtb.feats_compose):
feata = evta.feats_compose
featb = evtb.feats_compose
matrix = 1 - cdist(feata, featb, 'cosine')
similar = np.mean(matrix)
else:
similar = None
return similar
if simType==2:
if len(evta.feats_compose) and len(evtb.feats_compose):
feata = evta.feats_compose
featb = evtb.feats_compose
matrix = 1 - cdist(feata, featb, 'cosine')
similar = np.max(matrix)
else:
similar = None
return similar
if simType==3:
if len(evta.feats_compose) and len(evtb.feats_compose):
feata = evta.feats_compose
featb = evtb.feats_compose
similar = calsiml(feata, featb)
else:
similar = None
return similar
##1. the front feats of evta, evtb
fr_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(evta.front_feats)):
fr_feata = np.concatenate((fr_feata, evta.front_feats[i]), axis=0)
fr_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(evtb.front_feats)):
fr_featb = np.concatenate((fr_featb, evtb.front_feats[i]), axis=0)
##2. the back feats of evta, evtb
bk_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(evta.back_feats)):
bk_feata = np.concatenate((bk_feata, evta.back_feats[i]), axis=0)
bk_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
for i in range(len(evtb.back_feats)):
bk_featb = np.concatenate((bk_featb, evtb.back_feats[i]), axis=0)
front_simi, back_simi = None, None
if len(fr_feata) and len(fr_featb):
front_simi = calsiml(fr_feata, fr_featb)
if len(bk_feata) and len(bk_featb):
back_simi = calsiml(bk_feata, bk_featb)
'''前后摄相似度融合策略'''
if front_simi is not None and back_simi is not None:
diff_simi = abs(front_simi - back_simi)
if diff_simi>0.15:
similar = max([front_simi, back_simi])
else:
similar = (front_simi+back_simi)/2
elif front_simi is not None and back_simi is None:
similar = front_simi
elif front_simi is None and back_simi is not None:
similar = back_simi
else:
similar = None # 在event.front_feats和event.back_feats同时为空时
return similar