我有來自無人機的資料。第一個表包含每一幀中的行人資料。行人資料有行人Id、frame、x_est、y_est、v_abs。第二個表是車輛的資料。車輛的資料有車輛 ID、框架、x_est、y_est、vel_est。例如，在第 1 幀中，我有 39 個行人和兩輛車。我想創建一個包含以下資訊的新表：
第一列是每幀中每個行人和每輛車之間的距離。例如，我有 39 個行人和 2 個車輛：
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車輛的相關速度。例如，如果我得到 d1，那么我必須包括 ped1 的速度和 veh1 的速度。如果我得到 d2，那么我必須包括 ped1 的速度和 veh2 的速度等等。
我有 116 幀。我想用 python 或 Matlab 撰寫代碼來完成這些任務。我用下面的代碼嘗試了 python 但沒有作業，因為車輛的資料有 182 行，而行人的資料有 3950：
if peds["frame"] == vehs["frame"]:
distance = math.sqrt(((peds.x_est-vehs.x_est)**2) ((peds.y_est - vehs.y_est))**2)
我正在考慮添加一個 for 回圈來遍歷每一幀。如何修改代碼以遍歷每一幀并計算這些物件之間的距離？
這是資料的一個例子。
行人資料： 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 3 3 3 3 3 30 31 3 3 3 3
框架 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
x_est 15.31251243 14.62957291 14.81940554 14.25065235 16.261254 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 11.10086732 9.20275126 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 18.76703333 17.7046208 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 22.78941678 22.7892778 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
車輛資料：
編號 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
框架 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 20 19
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
Thanks in advance
uj5u.com熱心網友回復：
只需用文字思考問題。對于每個行人條目，對于每個汽車條目，如果幀號匹配，則計算它們之間的距離，并添加新行。沒什么。
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)
輸出：
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]
轉載請註明出處，本文鏈接：https://www.uj5u.com/gongcheng/366346.html
標籤：python pandas dataframe matlab computer-vision