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增加了单帧入侵判断及yoloV10

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2025-04-11 17:02:39 +08:00
parent 798c596acc
commit e044c85a04
197 changed files with 1863 additions and 997 deletions
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contrast/event_test.py
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@ -9,17 +9,19 @@ import cv2
import json
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from matplotlib import rcParams
from matplotlib.font_manager import FontProperties
from scipy.spatial.distance import cdist
from utils.event import ShoppingEvent, save_data
from utils.calsimi import calsimi_vs_stdfeat_new, get_topk_percent, cluster
from utils.tools import get_evtList
import pickle
rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文
rcParams['axes.unicode_minus'] = False # 正确显示负号
'''*********** USearch ***********'''
def read_usearch():
stdFeaturePath = r"D:\contrast\stdlib\v11_test.json"
@ -35,13 +37,12 @@ def read_usearch():
return stdlib
def get_eventlist():
def get_eventlist_errortxt(evtpaths):
'''
读取一次测试中的错误事件
'''
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
text1 = "one2n_Error.txt"
text2 = "one2SN_Error.txt"
'''
text1 = "one_2_Small_n_Error.txt"
text2 = "one_2_Big_N_Error.txt"
events = []
text = (text1, text2)
for txt in text:
@ -53,16 +54,16 @@ def get_eventlist():
if line:
fpath=os.path.join(evtpaths, line)
events.append(fpath)
events = list(set(events))
return events
def single_event():
events = get_eventlist()
def save_eventdata():
evtpaths = r"/home/wqg/dataset/test_dataset/performence_dataset/"
events = get_eventlist_errortxt(evtpaths)
'''定义当前事件存储地址及生成相应文件件'''
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event"
@ -74,121 +75,148 @@ def single_event():
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)
# 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]
# 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])
# 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)
# 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)
# 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))
# 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)
# # print(clt_center)
return clt_center
# return clt_center
def calc_simil(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？
'''
# 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？
# '''
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)
# 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
# ## 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
# return Simi
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)
# 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)
# 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)
if len(front_feats):
front_simi = calsiml(front_feats, stdfeat)
if len(back_feats):
back_simi = calsiml(back_feats, stdfeat)
# 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
'''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
def simi_matrix():
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event"
evtpaths = r"/home/wqg/dataset/pipeline/contrast/single_event_V10/evtobjs/"
stdlib = read_usearch()
events = get_eventlist()
for evtpath in events:
evtname = os.path.basename(evtpath)
_, barcode = evtname.split("_")
stdfeatPath = r"/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/"
resultPath = r"/home/wqg/dataset/performence_dataset/result/"
evt_paths, bcdSet = get_evtList(evtpaths)
## read std features
stdDict={}
evtDict = {}
for barcode in bcdSet:
stdpath = os.path.join(stdfeatPath, f"{barcode}.json")
if not os.path.isfile(stdpath):
continue
# 生成事件与相应标准特征集
event = ShoppingEvent(evtpath)
stdfeat = stdlib[barcode]
with open(stdpath, 'r', encoding='utf-8') as f:
stddata = json.load(f)
feat = np.array(stddata["value"])
stdDict[barcode] = feat
for evtpath in evt_paths:
barcode = Path(evtpath).stem.split("_")[-1]
if barcode not in stdDict.keys():
continue
Similar = calc_simil(event, stdfeat)
# try:
# with open(evtpath, 'rb') as f:
# evtdata = pickle.load(f)
# except Exception as e:
# print(evtname)
with open(evtpath, 'rb') as f:
event = pickle.load(f)
stdfeat = stdDict[barcode]
Similar = calsimi_vs_stdfeat_new(event, stdfeat)
# 构造 boxes 子图存储路径
subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg")
@ -217,9 +245,9 @@ def simi_matrix():
evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0)
imgpaths = event.back_imgpaths
assert len(boxes)==len(evtfeat), f"Please check the Event: {evtname}"
assert len(boxes)==len(evtfeat), f"Please check the Event: {event.evtname}"
if len(boxes)==0: continue
print(evtname)
print(event.evtname)
matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
simi_1d = matrix.flatten()
@ -309,8 +337,8 @@ def simi_matrix():
mean_diff = abs(mean_values[1]-mean_values[0])
ax[0, 1].set_title(f"mean diff: {mean_diff:.3f}")
if len(max_values)==2:
max_values = abs(max_values[1]-max_values[0])
ax[0, 2].set_title(f"max diff: {max_values:.3f}")
max_diff = abs(max_values[1]-max_values[0])
ax[0, 2].set_title(f"max diff: {max_diff:.3f}")
try:
fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16)
except Exception as e:
@ -319,19 +347,14 @@ def simi_matrix():
pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png")
plt.savefig(pltpath)
pltpath1 = os.path.join(histpath, f"{evtname}_.png")
pltpath1 = os.path.join(histpath, f"{event.evtname}_.png")
plt.savefig(pltpath1)
plt.close()
def main():
simi_matrix()