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王庆刚
2024-12-31 16:45:04 +08:00
parent dac3b3f2b6
commit 7e13e0f5b4
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contrast/event_test.py
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@ -6,10 +6,39 @@ Created on Mon Dec 16 18:56:18 2024
"""
import os
import cv2
import json
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.font_manager import FontProperties
from scipy.spatial.distance import cdist
from utils.event import ShoppingEvent, save_data
def main():
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():
'''
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"
@ -24,9 +53,16 @@ def main():
if line:
fpath=os.path.join(evtpaths, line)
events.append(fpath)
events = list(set(events))
return events
def single_event():
events = get_eventlist()
'''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"
@ -35,6 +71,291 @@ def main():
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 calc_simil(event, stdfeat):
def calsiml(feat1, feat2):
'''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, :], 75))
simi_max.append(sim)
cltc_max = cluster(simi_max)
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():
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"
stdlib = read_usearch()
events = get_eventlist()
for evtpath in events:
evtname = os.path.basename(evtpath)
_, barcode = evtname.split("_")
# 生成事件与相应标准特征集
event = ShoppingEvent(evtpath)
stdfeat = stdlib[barcode]
Similar = calc_simil(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: {evtname}"
if len(boxes)==0: continue
print(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_values = abs(max_values[1]-max_values[0])
ax[0, 2].set_title(f"max diff: {max_values:.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"{evtname}_.png")
plt.savefig(pltpath1)
plt.close()
def main():
simi_matrix()
@ -42,3 +363,14 @@ def main():
if __name__ == "__main__":
main()
# cluster()