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回传数据解析，兼容v5和v10

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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 18:56:18 2024
@author: ym
"""
import os
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"
stdBarcode = []
stdlib = {}
with open(stdFeaturePath, 'r', encoding='utf-8') as f:
data = json.load(f)
for dic in data['total']:
barcode = dic['key']
feature = np.array(dic['value'])
stdBarcode.append(barcode)
stdlib[barcode] = feature
return stdlib
def get_eventlist_errortxt(evtpaths):
'''
读取一次测试中的错误事件
'''
text1 = "one_2_Small_n_Error.txt"
text2 = "one_2_Big_N_Error.txt"
events = []
text = (text1, text2)
for txt in text:
txtfile = os.path.join(evtpaths, txt)
with open(txtfile, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip()
if line:
fpath=os.path.join(evtpaths, line)
events.append(fpath)
events = list(set(events))
return events
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"
for evtpath in events:
event = ShoppingEvent(evtpath)
save_data(event, resultPath)
print(event.evtname)
# 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 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)
# ## 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
# 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)
# 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
def simi_matrix():
evtpaths = r"/home/wqg/dataset/pipeline/contrast/single_event_V10/evtobjs/"
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
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
# 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")
if not os.path.exists(subimgpath):
os.makedirs(subimgpath)
histpath = os.path.join(resultPath, "simi_hist")
if not os.path.exists(histpath):
os.makedirs(histpath)
mean_values, max_values = [], []
cameras = ('front', 'back')
fig, ax = plt.subplots(2, 3, figsize=(16, 9), dpi=100)
kpercent = 25
for camera in cameras:
boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
evtfeat = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容
if camera == 'front':
for i in range(len(event.front_boxes)):
boxes = np.concatenate((boxes, event.front_boxes[i]), axis=0)
evtfeat = np.concatenate((evtfeat, event.front_feats[i]), axis=0)
imgpaths = event.front_imgpaths
else:
for i in range(len(event.back_boxes)):
boxes = np.concatenate((boxes, event.back_boxes[i]), axis=0)
evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0)
imgpaths = event.back_imgpaths
assert len(boxes)==len(evtfeat), f"Please check the Event: {event.evtname}"
if len(boxes)==0: continue
print(event.evtname)
matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
simi_1d = matrix.flatten()
simi_mean = np.mean(matrix, axis=1)
# simi_max = np.max(matrix, axis=1)
'''以相似度矩阵每一行最大的 k% 的相似度做均值计算'''
simi_max = []
for i in range(len(matrix)):
sim = np.mean(get_topk_percent(matrix[i, :], kpercent))
simi_max.append(sim)
mean_values.append(np.mean(matrix))
max_values.append(np.mean(simi_max))
diff_max_mean = np.mean(simi_max) - np.mean(matrix)
'''相似度统计特性图示'''
k =0
if camera == 'front': k = 1
'''********************* 相似度全体数据 *********************'''
ax[k, 0].hist(simi_1d, bins=60, range=(-0.2, 1), edgecolor='black')
ax[k, 0].set_xlim([-0.2, 1])
ax[k, 0].set_title(camera)
_, y_max = ax[k, 0].get_ylim() # 获取y轴范围
'''相似度变动范围'''
ax[k, 0].text(-0.1, 0.15*y_max, f"rng:{max(simi_1d)-min(simi_1d):.3f}", fontsize=18, color='b')
'''********************* 均值********************************'''
ax[k, 1].hist(simi_mean, bins=24, range=(-0.2, 1), edgecolor='black')
ax[k, 1].set_xlim([-0.2, 1])
ax[k, 1].set_title("mean")
_, y_max = ax[k, 1].get_ylim() # 获取y轴范围
'''相似度变动范围'''
ax[k, 1].text(-0.1, 0.15*y_max, f"rng:{max(simi_mean)-min(simi_mean):.3f}", fontsize=18, color='b')
'''********************* 最大值 ******************************'''
ax[k, 2].hist(simi_max, bins=24, range=(-0.2, 1), edgecolor='black')
ax[k, 2].set_xlim([-0.2, 1])
ax[k, 2].set_title("max")
_, y_max = ax[k, 2].get_ylim() # 获取y轴范围
'''相似度变动范围'''
ax[k, 2].text(-0.1, 0.15*y_max, f"rng:{max(simi_max)-min(simi_max):.3f}", fontsize=18, color='b')
'''绘制聚类中心'''
cltc_mean = cluster(simi_mean)
for value in cltc_mean:
ax[k, 1].axvline(x=value, color='m', linestyle='--', linewidth=3)
cltc_max = cluster(simi_max)
for value in cltc_max:
ax[k, 2].axvline(x=value, color='m', linestyle='--', linewidth=3)
'''绘制相似度均值与最大值均值'''
ax[k, 1].axvline(x=np.mean(matrix), color='r', linestyle='-', linewidth=3)
ax[k, 2].axvline(x=np.mean(simi_max), color='g', linestyle='-', linewidth=3)
'''绘制相似度最大值均值 - 均值'''
_, y_max = ax[k, 2].get_ylim() # 获取y轴范围
ax[k, 2].text(-0.1, 0.05*y_max, f"g-r={diff_max_mean:.3f}", fontsize=18, color='m')
plt.show()
# for i, box in enumerate(boxes):
# x1, y1, x2, y2, tid, score, cls, fid, bid = box
# imgpath = imgpaths[int(fid-1)]
# image = cv2.imread(imgpath)
# subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
# camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
# subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID})_{simi_mean[i]:.3f}.png"
# imgpairs.append((subimgName, subimg))
# spath = os.path.join(subimgpath, subimgName)
# cv2.imwrite(spath, subimg)
# oldname = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
# oldpath = os.path.join(subimgpath, oldname)
# if os.path.exists(oldpath):
# os.remove(oldpath)
if len(mean_values)==2:
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_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:
print(e)
print(f"Similar: {Similar}")
pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png")
plt.savefig(pltpath)
pltpath1 = os.path.join(histpath, f"{event.evtname}_.png")
plt.savefig(pltpath1)
plt.close()
def main():
simi_matrix()
if __name__ == "__main__":
main()
# cluster()