如何计算每一帧中行人与车辆的距离？
How to calculate the distance between pedestrians and vehicles in each frame?
我有来自无人机的数据。第一个 table 是每帧行人的数据。行人数据有行人Id, frame, x_est, y_est, v_abs。
第二个 table 是车辆的数据。车辆的数据有车辆 ID、车架、x_est、y_est、vel_est。
例如，在第 1 帧中，我有 39 个行人和两辆车。我想创建一个包含以下信息的新 table：
第一列是每一帧中每个行人和每辆车之间的距离。
比如我有39个行人和2个车：
d1 = sqrt((ped1_x - veh1_x)^2 + (ped1_y - veh1_y)^2)
d2 = sqrt((ped1_x - veh2_x)^2 + (ped1_y - veh2_y)^2)
d3 = sqrt((ped2_x - veh1_x)^2 + (ped2_y - veh1_y)^2)
d4 = sqrt((ped2_x - veh2_x)^2 + (ped2_y - veh2_y)^2)
等等
- 第二列和第三列是行人和车辆的关联速度。
例如，如果我得到 d1，那么我必须包括 ped1 的速度和 veh1 的速度。
如果我得到 d2，那么我必须包括 ped1 的速度和 veh2 的速度等等。
我有116帧。我想在 python 或 Matlab 中编写代码来完成这些任务。我尝试 python 使用以下代码但没有成功，因为车辆数据有 182 行，行人数据有 3950 行：
if peds["frame"] == vehs["frame"]:
distance = math.sqrt(((peds.x_est-vehs.x_est)**2)+((peds.y_est - vehs.y_est))**2)
我正在考虑添加一个 for 循环来遍历每一帧。 如何修改代码以循环遍历每一帧并计算这些对象之间的距离？
这是数据的一个例子。
行人数据：
id
0
1个
2个
3个
4个
5个
6个
7
8个
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
帧
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1个
1
x_est
15.31251243
14.62957291
14.81940554
16.261254
14.25065235
13.71913744
12.77037156
11.82149333
11.02452266
10.4550769
9.962500442
10.56845947
11.17672903
11.70758495
14.28816743
13.41605196
11.6316746
11.4797624
13.3395301
10.98651306
11.89763531
12.46714593
13.90898049
11.17611058
9.20275126
11.10086732
11.66968305
12.88437259
13.37708455
14.17485782
14.81943565
15.65388549
17.7046208
18.76703333
19.22188971
19.03219028
19.63930168
19.26095788
20.54963754
21.87812196
y_est
24.04146967
23.85122822
22.59973819
21.46111998
22.25845431
22.52372129
22.37086056
22.82695733
22.7892778
22.78941678
21.42243054
21.49884121
21.46045752
21.53683577
19.86642817
19.75351002
19.29791125
17.24875682
16.56578255
14.97104209
10.11358571
9.733765266
9.165258769
8.102246321
8.216836276
6.659910277
6.774266283
2.865368769
3.05553263
4.459266668
4.193704362
4.420605884
4.26981189
3.547987191
4.042100815
4.876447865
5.294238544
6.090777216
3.966160063
4.762697865
v_abs
2.459007157
2.654334571
3.315403455
3.389573803
3.378566929
3.045539512
3.23011785
2.925099475
2.584998721
2.901642056
2.811892448
2.151019342
1.96347414
1.500567927
3.540985451
2.709992115
3.267972565
3.395063149
2.721779676
4.012212099
0.880854234
0.813933137
0.704372621
0.912089788
0.549592663
3.007799428
1.978963898
3.44757396
3.15737162
3.529382782
3.5556166
3.409764593
1.170765247
1.580709745
1.085228781
0.922279132
0.802698916
1.875894301
0.804975425
1.205954878
车辆数据：
id
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
900
901
帧
1个
1个
2个
2个
3个
3个
4个
4个
5个
5个
6个
6个
7
7
8个
8个
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
x_est
13.12181538
20.79676544
13.01830532
20.73447182
12.92056089
20.65301595
12.77984823
20.52422202
12.67147093
20.45207468
12.56504216
20.34050999
12.45704283
20.23392863
12.35122477
20.13000728
12.24825411
20.03070371
12.15111691
19.9359101
12.04685347
19.8497257
11.93870377
19.7593045
11.82507185
19.66695714
11.70785362
19.57813133
11.60090539
19.48509113
11.48174963
19.39305883
11.36790853
19.29876475
11.26066404
19.21938896
11.14538066
19.12658484
11.02149472
19.02832259
y_est
12.48631346
12.04945225
12.50757522
12.09243053
12.53150136
12.14119099
12.56295128
12.20178773
12.59034906
12.22451609
12.60945482
12.2800529
12.61885415
12.31947563
12.62842376
12.35799492
12.64016339
12.40403046
12.66696466
12.44343686
12.67398026
12.4828893
12.69217314
12.52761456
12.70404418
12.56747969
12.71201387
12.60893079
12.7352435
12.65130145
12.75948092
12.68935761
12.77039761
12.72362537
12.78182668
12.76864139
12.80477996
12.80719663
12.82141401
12.8529026
vel_est
2.607251494
2.480379041
2.607160714
2.479543211
2.60660445
2.478939563
2.60984436
2.482517229
2.610124888
2.479412431
2.610034708
2.481993315
2.609925021
2.483303075
2.609504867
2.484305008
2.608686875
2.485197239
2.607293512
2.485028991
2.606477634
2.483509058
2.60665034
2.483007857
2.607657856
2.482442343
2.609280114
2.481348408
2.609374438
2.481088229
2.612094232
2.480242567
2.613246915
2.479486909
2.612946522
2.476581726
2.614972124
2.475903808
2.618889831
2.47716006
提前致谢
用文字思考问题。对于每个行人条目，对于每个汽车条目，如果帧号匹配，则计算它们之间的距离，并添加一个新行。没什么。
ped_id = "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39"
ped_frame = "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1"
ped_x_est = "15.31251243 14.62957291 14.81940554 16.261254 14.25065235 13.71913744 12.77037156 11.82149333 11.02452266 10.4550769 9.962500442 10.56845947 11.17672903 11.70758495 14.28816743 13.41605196 11.6316746 11.4797624 13.3395301 10.98651306 11.89763531 12.46714593 13.90898049 11.17611058 9.20275126 11.10086732 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 17.7046208 18.76703333 19.22188971 19.03219028 19.63930168 19.26095788 20.54963754 21.87812196"
ped_y_est = "24.04146967 23.85122822 22.59973819 21.46111998 22.25845431 22.52372129 22.37086056 22.82695733 22.7892778 22.78941678 21.42243054 21.49884121 21.46045752 21.53683577 19.86642817 19.75351002 19.29791125 17.24875682 16.56578255 14.97104209 10.11358571 9.733765266 9.165258769 8.102246321 8.216836276 6.659910277 6.774266283 2.865368769 3.05553263 4.459266668 4.193704362 4.420605884 4.26981189 3.547987191 4.042100815 4.876447865 5.294238544 6.090777216 3.966160063 4.762697865"
ped_v_abs = "2.459007157 2.654334571 3.315403455 3.389573803 3.378566929 3.045539512 3.23011785 2.925099475 2.584998721 2.901642056 2.811892448 2.151019342 1.96347414 1.500567927 3.540985451 2.709992115 3.267972565 3.395063149 2.721779676 4.012212099 0.880854234 0.813933137 0.704372621 0.912089788 0.549592663 3.007799428 1.978963898 3.44757396 3.15737162 3.529382782 3.5556166 3.409764593 1.170765247 1.580709745 1.085228781 0.922279132 0.802698916 1.875894301 0.804975425 1.205954878"
veh_id = "900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901"
veh_frame = "1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20"
veh_x_est = "13.12181538 20.79676544 13.01830532 20.73447182 12.92056089 20.65301595 12.77984823 20.52422202 12.67147093 20.45207468 12.56504216 20.34050999 12.45704283 20.23392863 12.35122477 20.13000728 12.24825411 20.03070371 12.15111691 19.9359101 12.04685347 19.8497257 11.93870377 19.7593045 11.82507185 19.66695714 11.70785362 19.57813133 11.60090539 19.48509113 11.48174963 19.39305883 11.36790853 19.29876475 11.26066404 19.21938896 11.14538066 19.12658484 11.02149472 19.02832259"
veh_y_est = "12.48631346 12.04945225 12.50757522 12.09243053 12.53150136 12.14119099 12.56295128 12.20178773 12.59034906 12.22451609 12.60945482 12.2800529 12.61885415 12.31947563 12.62842376 12.35799492 12.64016339 12.40403046 12.66696466 12.44343686 12.67398026 12.4828893 12.69217314 12.52761456 12.70404418 12.56747969 12.71201387 12.60893079 12.7352435 12.65130145 12.75948092 12.68935761 12.77039761 12.72362537 12.78182668 12.76864139 12.80477996 12.80719663 12.82141401 12.8529026"
veh_vel_est = "2.607251494 2.480379041 2.607160714 2.479543211 2.60660445 2.478939563 2.60984436 2.482517229 2.610124888 2.479412431 2.610034708 2.481993315 2.609925021 2.483303075 2.609504867 2.484305008 2.608686875 2.485197239 2.607293512 2.485028991 2.606477634 2.483509058 2.60665034 2.483007857 2.607657856 2.482442343 2.609280114 2.481348408 2.609374438 2.481088229 2.612094232 2.480242567 2.613246915 2.479486909 2.612946522 2.476581726 2.614972124 2.475903808 2.618889831 2.47716006"
import math
import pandas as pd
def convert(s,cvt):
return [cvt(k) for k in s.split()]
peds = list(zip(
convert(ped_id,int),
convert(ped_frame,int),
convert(ped_x_est,float),
convert(ped_y_est,float),
convert(ped_v_abs,float)
))
cars = list(zip(
convert(veh_id,int),
convert(veh_frame,int),
convert(veh_x_est,float),
convert(veh_y_est,float),
convert(veh_vel_est,float)
))
newdata = []
for ped in peds:
for car in cars:
if ped[1] != car[1]:
continue
dist = math.sqrt((ped[2]-car[2])**2 + (ped[3]-car[3])**2)
newdata.append( (ped[0], car[0], dist, ped[4], car[4]) )
df = pd.DataFrame( newdata, columns=("ped id", "car id", "distance", "ped vel", "car vel"))
print(df)
输出：
ped id car id distance ped vel car vel
0 0 900 11.760986 2.459007 2.607251
1 0 901 13.186566 2.459007 2.480379
2 1 900 11.464494 2.654335 2.607251
3 1 901 13.316012 2.654335 2.480379
4 2 900 10.254910 3.315403 2.607251
.. ... ... ... ... ...
75 37 901 6.153415 1.875894 2.480379
76 38 900 11.303343 0.804975 2.607251
77 38 901 8.087069 0.804975 2.480379
78 39 900 11.675921 1.205955 2.607251
79 39 901 7.366554 1.205955 2.480379
[80 rows x 5 columns]
我有来自无人机的数据。第一个 table 是每帧行人的数据。行人数据有行人Id, frame, x_est, y_est, v_abs。 第二个 table 是车辆的数据。车辆的数据有车辆 ID、车架、x_est、y_est、vel_est。 例如，在第 1 帧中，我有 39 个行人和两辆车。我想创建一个包含以下信息的新 table：
第一列是每一帧中每个行人和每辆车之间的距离。 比如我有39个行人和2个车：
d1 = sqrt((ped1_x - veh1_x)^2 + (ped1_y - veh1_y)^2)
d2 = sqrt((ped1_x - veh2_x)^2 + (ped1_y - veh2_y)^2)
d3 = sqrt((ped2_x - veh1_x)^2 + (ped2_y - veh1_y)^2)
d4 = sqrt((ped2_x - veh2_x)^2 + (ped2_y - veh2_y)^2)
等等
- 第二列和第三列是行人和车辆的关联速度。 例如，如果我得到 d1，那么我必须包括 ped1 的速度和 veh1 的速度。 如果我得到 d2，那么我必须包括 ped1 的速度和 veh2 的速度等等。
我有116帧。我想在 python 或 Matlab 中编写代码来完成这些任务。我尝试 python 使用以下代码但没有成功，因为车辆数据有 182 行，行人数据有 3950 行：
if peds["frame"] == vehs["frame"]:
distance = math.sqrt(((peds.x_est-vehs.x_est)**2)+((peds.y_est - vehs.y_est))**2)
我正在考虑添加一个 for 循环来遍历每一帧。 如何修改代码以循环遍历每一帧并计算这些对象之间的距离？
这是数据的一个例子。
行人数据： id 0 1个 2个 3个 4个 5个 6个 7 8个 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
帧 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1个 1
x_est 15.31251243 14.62957291 14.81940554 16.261254 14.25065235 13.71913744 12.77037156 11.82149333 11.02452266 10.4550769 9.962500442 10.56845947 11.17672903 11.70758495 14.28816743 13.41605196 11.6316746 11.4797624 13.3395301 10.98651306 11.89763531 12.46714593 13.90898049 11.17611058 9.20275126 11.10086732 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 17.7046208 18.76703333 19.22188971 19.03219028 19.63930168 19.26095788 20.54963754 21.87812196
y_est 24.04146967 23.85122822 22.59973819 21.46111998 22.25845431 22.52372129 22.37086056 22.82695733 22.7892778 22.78941678 21.42243054 21.49884121 21.46045752 21.53683577 19.86642817 19.75351002 19.29791125 17.24875682 16.56578255 14.97104209 10.11358571 9.733765266 9.165258769 8.102246321 8.216836276 6.659910277 6.774266283 2.865368769 3.05553263 4.459266668 4.193704362 4.420605884 4.26981189 3.547987191 4.042100815 4.876447865 5.294238544 6.090777216 3.966160063 4.762697865
v_abs 2.459007157 2.654334571 3.315403455 3.389573803 3.378566929 3.045539512 3.23011785 2.925099475 2.584998721 2.901642056 2.811892448 2.151019342 1.96347414 1.500567927 3.540985451 2.709992115 3.267972565 3.395063149 2.721779676 4.012212099 0.880854234 0.813933137 0.704372621 0.912089788 0.549592663 3.007799428 1.978963898 3.44757396 3.15737162 3.529382782 3.5556166 3.409764593 1.170765247 1.580709745 1.085228781 0.922279132 0.802698916 1.875894301 0.804975425 1.205954878
车辆数据：
id 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901
帧 1个 1个 2个 2个 3个 3个 4个 4个 5个 5个 6个 6个 7 7 8个 8个 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20
x_est 13.12181538 20.79676544 13.01830532 20.73447182 12.92056089 20.65301595 12.77984823 20.52422202 12.67147093 20.45207468 12.56504216 20.34050999 12.45704283 20.23392863 12.35122477 20.13000728 12.24825411 20.03070371 12.15111691 19.9359101 12.04685347 19.8497257 11.93870377 19.7593045 11.82507185 19.66695714 11.70785362 19.57813133 11.60090539 19.48509113 11.48174963 19.39305883 11.36790853 19.29876475 11.26066404 19.21938896 11.14538066 19.12658484 11.02149472 19.02832259
y_est 12.48631346 12.04945225 12.50757522 12.09243053 12.53150136 12.14119099 12.56295128 12.20178773 12.59034906 12.22451609 12.60945482 12.2800529 12.61885415 12.31947563 12.62842376 12.35799492 12.64016339 12.40403046 12.66696466 12.44343686 12.67398026 12.4828893 12.69217314 12.52761456 12.70404418 12.56747969 12.71201387 12.60893079 12.7352435 12.65130145 12.75948092 12.68935761 12.77039761 12.72362537 12.78182668 12.76864139 12.80477996 12.80719663 12.82141401 12.8529026
vel_est 2.607251494 2.480379041 2.607160714 2.479543211 2.60660445 2.478939563 2.60984436 2.482517229 2.610124888 2.479412431 2.610034708 2.481993315 2.609925021 2.483303075 2.609504867 2.484305008 2.608686875 2.485197239 2.607293512 2.485028991 2.606477634 2.483509058 2.60665034 2.483007857 2.607657856 2.482442343 2.609280114 2.481348408 2.609374438 2.481088229 2.612094232 2.480242567 2.613246915 2.479486909 2.612946522 2.476581726 2.614972124 2.475903808 2.618889831 2.47716006
提前致谢
用文字思考问题。对于每个行人条目，对于每个汽车条目，如果帧号匹配，则计算它们之间的距离，并添加一个新行。没什么。
ped_id = "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39"
ped_frame = "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1"
ped_x_est = "15.31251243 14.62957291 14.81940554 16.261254 14.25065235 13.71913744 12.77037156 11.82149333 11.02452266 10.4550769 9.962500442 10.56845947 11.17672903 11.70758495 14.28816743 13.41605196 11.6316746 11.4797624 13.3395301 10.98651306 11.89763531 12.46714593 13.90898049 11.17611058 9.20275126 11.10086732 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 17.7046208 18.76703333 19.22188971 19.03219028 19.63930168 19.26095788 20.54963754 21.87812196"
ped_y_est = "24.04146967 23.85122822 22.59973819 21.46111998 22.25845431 22.52372129 22.37086056 22.82695733 22.7892778 22.78941678 21.42243054 21.49884121 21.46045752 21.53683577 19.86642817 19.75351002 19.29791125 17.24875682 16.56578255 14.97104209 10.11358571 9.733765266 9.165258769 8.102246321 8.216836276 6.659910277 6.774266283 2.865368769 3.05553263 4.459266668 4.193704362 4.420605884 4.26981189 3.547987191 4.042100815 4.876447865 5.294238544 6.090777216 3.966160063 4.762697865"
ped_v_abs = "2.459007157 2.654334571 3.315403455 3.389573803 3.378566929 3.045539512 3.23011785 2.925099475 2.584998721 2.901642056 2.811892448 2.151019342 1.96347414 1.500567927 3.540985451 2.709992115 3.267972565 3.395063149 2.721779676 4.012212099 0.880854234 0.813933137 0.704372621 0.912089788 0.549592663 3.007799428 1.978963898 3.44757396 3.15737162 3.529382782 3.5556166 3.409764593 1.170765247 1.580709745 1.085228781 0.922279132 0.802698916 1.875894301 0.804975425 1.205954878"
veh_id = "900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901"
veh_frame = "1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20"
veh_x_est = "13.12181538 20.79676544 13.01830532 20.73447182 12.92056089 20.65301595 12.77984823 20.52422202 12.67147093 20.45207468 12.56504216 20.34050999 12.45704283 20.23392863 12.35122477 20.13000728 12.24825411 20.03070371 12.15111691 19.9359101 12.04685347 19.8497257 11.93870377 19.7593045 11.82507185 19.66695714 11.70785362 19.57813133 11.60090539 19.48509113 11.48174963 19.39305883 11.36790853 19.29876475 11.26066404 19.21938896 11.14538066 19.12658484 11.02149472 19.02832259"
veh_y_est = "12.48631346 12.04945225 12.50757522 12.09243053 12.53150136 12.14119099 12.56295128 12.20178773 12.59034906 12.22451609 12.60945482 12.2800529 12.61885415 12.31947563 12.62842376 12.35799492 12.64016339 12.40403046 12.66696466 12.44343686 12.67398026 12.4828893 12.69217314 12.52761456 12.70404418 12.56747969 12.71201387 12.60893079 12.7352435 12.65130145 12.75948092 12.68935761 12.77039761 12.72362537 12.78182668 12.76864139 12.80477996 12.80719663 12.82141401 12.8529026"
veh_vel_est = "2.607251494 2.480379041 2.607160714 2.479543211 2.60660445 2.478939563 2.60984436 2.482517229 2.610124888 2.479412431 2.610034708 2.481993315 2.609925021 2.483303075 2.609504867 2.484305008 2.608686875 2.485197239 2.607293512 2.485028991 2.606477634 2.483509058 2.60665034 2.483007857 2.607657856 2.482442343 2.609280114 2.481348408 2.609374438 2.481088229 2.612094232 2.480242567 2.613246915 2.479486909 2.612946522 2.476581726 2.614972124 2.475903808 2.618889831 2.47716006"
import math
import pandas as pd
def convert(s,cvt):
return [cvt(k) for k in s.split()]
peds = list(zip(
convert(ped_id,int),
convert(ped_frame,int),
convert(ped_x_est,float),
convert(ped_y_est,float),
convert(ped_v_abs,float)
))
cars = list(zip(
convert(veh_id,int),
convert(veh_frame,int),
convert(veh_x_est,float),
convert(veh_y_est,float),
convert(veh_vel_est,float)
))
newdata = []
for ped in peds:
for car in cars:
if ped[1] != car[1]:
continue
dist = math.sqrt((ped[2]-car[2])**2 + (ped[3]-car[3])**2)
newdata.append( (ped[0], car[0], dist, ped[4], car[4]) )
df = pd.DataFrame( newdata, columns=("ped id", "car id", "distance", "ped vel", "car vel"))
print(df)
输出：
ped id car id distance ped vel car vel
0 0 900 11.760986 2.459007 2.607251
1 0 901 13.186566 2.459007 2.480379
2 1 900 11.464494 2.654335 2.607251
3 1 901 13.316012 2.654335 2.480379
4 2 900 10.254910 3.315403 2.607251
.. ... ... ... ... ...
75 37 901 6.153415 1.875894 2.480379
76 38 900 11.303343 0.804975 2.607251
77 38 901 8.087069 0.804975 2.480379
78 39 900 11.675921 1.205955 2.607251
79 39 901 7.366554 1.205955 2.480379
[80 rows x 5 columns]