-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathcalculate_obso.py
178 lines (143 loc) · 7.61 KB
/
calculate_obso.py
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
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
import Metrica_IO as mio
import Metrica_Viz as mviz
import Metrica_Velocities as mvel
import Metrica_PitchControl as mpc
import Metrica_EPV as mepv
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
import pdb
import warnings
import re
import argparse
warnings.simplefilter('ignore')
import third_party as thp
import obso_player as obs
# create parser
parser = argparse.ArgumentParser()
parser.add_argument('--id', type=int, default=2, help='game id')
parser.add_argument('--data', type=str, default='metrica', help='dataset')
args = parser.parse_args()
# select game number
game_id = args.id
if args.data == 'metrica':
# set up initial path to data
DATADIR = './metrica-sample-data/data/'
# read in the event data
events = mio.read_event_data(DATADIR,game_id)
# read in tracking data
tracking_home = mio.tracking_data(DATADIR,game_id,'Home')
tracking_away = mio.tracking_data(DATADIR,game_id,'Away')
# Convert positions from metrica units to meters (note change in Metrica's coordinate system since the last lesson)
tracking_home = mio.to_metric_coordinates(tracking_home)
tracking_away = mio.to_metric_coordinates(tracking_away)
events = mio.to_metric_coordinates(events)
# reverse direction of play in the second half so that home team is always attacking from right->left
tracking_home,tracking_away,events = mio.to_single_playing_direction(tracking_home,tracking_away,events)
# Calculate player velocities
tracking_home = mvel.calc_player_velocities(tracking_home,smoothing=True)
tracking_away = mvel.calc_player_velocities(tracking_away,smoothing=True)
# event data convert spadl to Metrica
Metrica_df = obs.convert_Metrica_for_event(sample_spadl)
# check 'Home' team in tracking and event data
Metrica_df = obs.check_home_away_event(Metrica_df, tracking_home, tracking_away)
# delete last event because this event is 'time up' event
Metrica_df = Metrica_df[:-1]
elif args.data == 'jleague':
# set folder and file name
Jdatafolder = "../JLeagueData"
FMfolder = "/Data_2019FM/"
Jdata_FM = Jdatafolder + FMfolder
event_data_name = "/play.csv"
player_data_name = "/player.csv"
game_date = os.listdir(path=Jdata_FM)
# set event data
sample_game_data = pd.read_csv(Jdata_FM+game_date[game_id]+event_data_name, encoding="shift_jis")
sample_spadl = thp.convert_J2spadl(sample_game_data)
# set tracking data
tracking_home = pd.read_csv(Jdata_FM+game_date[game_id]+'/home_tracking.csv')
tracking_away = pd.read_csv(Jdata_FM+game_date[game_id]+'/away_tracking.csv')
tracking_home = tracking_home.drop(columns='Unnamed: 0')
tracking_away = tracking_away.drop(columns='Unnamed: 0')
# preprocessing player position
entry_home_df = tracking_home.loc[0].isnull()
entry_away_df = tracking_away.loc[0].isnull()
home_column = tracking_home.columns
away_column = tracking_away.columns
home_player_num = [s[:-2] for s in home_column if re.match('Home_\d*_x', s)]
away_player_num = [s[:-2] for s in away_column if re.match('Away_\d*_x', s)]
# replace nan
for player in home_player_num:
if entry_home_df[player+'_x']:
tracking_home[player+'_x'] = tracking_home[player+'_x'].fillna(method='ffill')
tracking_home[player+'_y'] = tracking_home[player+'_y'].fillna(method='ffill')
else:
tracking_home[player+'_x'] = tracking_home[player+'_x'].fillna(method='bfill')
tracking_home[player+'_y'] = tracking_home[player+'_y'].fillna(method='bfill')
for player in away_player_num:
if entry_away_df[player+'_x']:
tracking_away[player+'_x'] = tracking_away[player+'_x'].fillna(method='ffill')
tracking_away[player+'_y'] = tracking_away[player+'_y'].fillna(method='ffill')
else:
tracking_away[player+'_x'] = tracking_away[player+'_x'].fillna(method='bfill')
tracking_away[player+'_y'] = tracking_away[player+'_y'].fillna(method='bfill')
# data interpolation in ball position in tracking data
tracking_home['ball_x'] = tracking_home['ball_x'].interpolate()
tracking_home['ball_y'] = tracking_home['ball_y'].interpolate()
tracking_away['ball_x'] = tracking_away['ball_x'].interpolate()
tracking_away['ball_y'] = tracking_away['ball_y'].interpolate()
# check nan ball position x and y in tracking data
tracking_home['ball_x'] = tracking_home['ball_x'].fillna(method='bfill')
tracking_home['ball_y'] = tracking_home['ball_y'].fillna(method='bfill')
tracking_away['ball_x'] = tracking_away['ball_x'].fillna(method='bfill')
tracking_away['ball_y'] = tracking_away['ball_y'].fillna(method='bfill')
# event data convert spadl to Metrica
Metrica_df = obs.convert_Metrica_for_event(sample_spadl)
# check 'Home' team in tracking and event data
Metrica_df = obs.check_home_away_event(Metrica_df, tracking_home, tracking_away)
# delete last event because this event is 'time up' event
Metrica_df = Metrica_df[:-1]
# filter:Savitzky-Golay
tracking_home = mvel.calc_player_velocities(tracking_home, smoothing=True)
tracking_away = mvel.calc_player_velocities(tracking_away, smoothing=True)
# set parameter
params = mpc.default_model_params()
GK_numbers = [mio.find_goalkeeper(tracking_home), mio.find_goalkeeper(tracking_away)]
# load control and transition model
EPV = mepv.load_EPV_grid('EPV_grid.csv')
EPV = EPV / np.max(EPV)
Trans_df = pd.read_csv('Transition_gauss.csv', header=None)
Trans = np.array((Trans_df))
Trans = Trans / np.max(Trans)
# set OBSO data
obso = np.zeros((len(Metrica_df), 32, 50))
for event_num, frame in tqdm(enumerate(Metrica_df['Start Frame'])):
if Metrica_df['Team'].loc[event_num]=='Home':
# check attack direction 1st half or 2nd half
if Metrica_df.loc[event_num]['Period']==1:
direction = mio.find_playing_direction(tracking_home[tracking_home['Period']==1], 'Home')
elif Metrica_df.loc[event_num]['Period']==2:
direction = mio.find_playing_direction(tracking_home[tracking_home['Period']==2], 'Home')
PPCF, _, _, _ = mpc.generate_pitch_control_for_event(event_num, Metrica_df, tracking_home, tracking_away, params, GK_numbers, offsides=True)
elif Metrica_df['Team'].loc[event_num]=='Away':
# check attack direction 1st half or 2nd half
if Metrica_df.loc[event_num]['Period']==1:
direction = mio.find_playing_direction(tracking_away[tracking_away['Period']==1], 'Away')
elif Metrica_df.loc[event_num]['Period']==2:
direction = mio.find_playing_direction(tracking_away[tracking_away['Period']==2], 'Away')
PPCF, _, _, _ = mpc.generate_pitch_control_for_event(event_num, Metrica_df, tracking_home, tracking_away, params, GK_numbers, offsides=True)
else:
obso[event_num] = np.zeros((32, 50))
continue
obso[event_num], _ = obs.calc_obso(PPCF, Trans, EPV, tracking_home.loc[frame], attack_direction=direction)
home_obso, away_obso = obs.calc_player_evaluate_match(obso, Metrica_df, tracking_home, tracking_away)
# calculate onball obso
home_onball_obso, away_onball_obso = obs.calc_onball_obso(Metrica_df, tracking_home, tracking_away, home_obso, away_obso)
# remove offside player
home_obso, away_obso = obs.remove_offside_obso(Metrica_df, tracking_home, tracking_away, home_obso, away_obso)
# save obso in home and away
home_obso.to_pickle(Jdata_FM+game_date[game_id]+'/home_obso.pkl')
away_obso.to_pickle(Jdata_FM+game_date[game_id]+'/away_obso.pkl')
home_onball_obso.to_pickle(Jdata_FM+game_date[game_id]+'/home_onball_obso.pkl')
away_onball_obso.to_pickle(Jdata_FM+game_date[game_id]+'/away_onball_obso.pkl')