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split_interaction.py
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# INT2: Interactive Trajectory Prediction at Intersections
# Published at ICCV 2023
# Written by Zhijie Yan
# All Rights Reserved
import os
import numpy as np
from math import sqrt
import pickle
from utils.interaction_utils import *
import warnings
import argparse
warnings.filterwarnings("ignore")
from p_tqdm import p_map
def parse_config():
parser = argparse.ArgumentParser(description='INT2 Dataset Interaction Filter Visualization.')
parser.add_argument('--interaction_scenario_path', '-s', type=str, default='int2_dataset_example/interaction_scenario/complete_scenario/8/010213355106-010213364106.pickle',
help='The scenario path to be visualized')
parser.add_argument('--output_dir', type=str, default='int2_dataset_example/interaction_scenario/split_scenario', help='')
args = parser.parse_args()
return args
def split_interaction(interaction_scenario_path, out_dir):
hdmap_id = interaction_scenario_path.split('/')[-2]
output_dir = os.path.join(out_dir, hdmap_id)
os.makedirs(output_dir, exist_ok=True)
with open(interaction_scenario_path, 'rb+') as f:
interaciton_scenario_info = pickle.load(f)
state = interaciton_scenario_info['AGENT_INFO']['state']
position_x = state['position_x']
position_y = state['position_y']
velocity_x = state['velocity_x']
velocity_y = state['velocity_y']
INTERACTION_INFO = interaciton_scenario_info['INTERACTION_INFO']
interaction_pair_info = INTERACTION_INFO['interaction_pair_info']
interaction_info_new = {}
n = 0
inter_pairs_num = len(interaction_pair_info.keys())
interaction_complete_list = []
for i in range(0, inter_pairs_num - 1, 1):
relation_set = set()
relation_set.add(interaction_pair_info[i]['influencer_id'])
relation_set.add(interaction_pair_info[i]['reactor_id'])
coexistence_time = [int(x) for x in interaction_pair_info[i]['coexistence_time']]
interaction_time = [int(x) for x in interaction_pair_info[i]['interaction_time']]
for j in range(i + 1, inter_pairs_num, 1):
now_interaction_info = interaction_pair_info[j]
now_i_id = now_interaction_info['influencer_id']
now_r_id = now_interaction_info['reactor_id']
now_co_time = [int(x) for x in now_interaction_info['coexistence_time']]
now_inter_time = [int(x) for x in now_interaction_info['interaction_time']]
if (now_i_id in relation_set) and (now_r_id in relation_set):
continue
if (now_i_id in relation_set) or (now_r_id in relation_set):
coexistence_time_new = [x for x in now_co_time if x in coexistence_time]
interaction_time_new = [x for x in now_inter_time if x in interaction_time]
if len(coexistence_time_new) > scenario_min_len and len(interaction_time_new) > 0:
relation_set.add(now_i_id)
relation_set.add(now_r_id)
coexistence_time = coexistence_time_new
interaction_time = interaction_time_new
relation_list = list(sorted(relation_set))
if relation_list in interaction_complete_list:
continue
interaction_complete_list.append(relation_list)
interaction_info_new[n] = {
'relation_list' : relation_list,
'coexistence_time': coexistence_time,
'interaction_time': interaction_time
}
n += 1
need2delete_indices = []
for i in range(0, len(interaction_complete_list) - 1, 1):
for j in range(i + 1, len(interaction_complete_list), 1):
inner_list = [x for x in interaction_complete_list[i] if x in interaction_complete_list[j]]
if len(inner_list) == len(interaction_complete_list[i]):
need2delete_indices.append(i)
break
elif len(inner_list) == len(interaction_complete_list[j]):
need2delete_indices.append(j)
break
else:
pass
need2delete_indices = list(set(need2delete_indices))
for key in need2delete_indices:
interaction_info_new.pop(key)
result = {}
tmp = 0
for key, value in interaction_info_new.items():
coexistence_start = value['coexistence_time'][0]
coexistence_end = value['coexistence_time'][-1]
interaction_time = np.array(value['interaction_time'])
interaction_start = interaction_time[0]
interaction_end = interaction_time[-1]
interaction_len = interaction_end - interaction_start + 1
if interaction_len > 91:
max_coverage = 0
best_start = 0
best_end = 0
for start_frame in interaction_time:
end_frame = start_frame + 90
coverage_valid = np.logical_and(interaction_time >= start_frame, interaction_time <= end_frame)
coverage_num = coverage_valid.sum()
if coverage_num > max_coverage:
max_coverage = coverage_num
best_start = start_frame
best_end = interaction_time[coverage_valid][-1]
interaction_start = best_start
interaction_end = best_end
start_r = max(interaction_start - 20, coexistence_start)
end_r = start_r + 90
if end_r < interaction_end:
diff = interaction_end - end_r
end_r += diff
start_r += diff
if end_r > coexistence_end:
diff = end_r - coexistence_end
end_r -= diff
start_r -= diff
result[tmp] = {
'interested_agents': value['relation_list'],
'split_time_91': [start_r, end_r],
}
tmp += 1
output_path = os.path.join(output_dir, interaction_scenario_path.split('/')[-1])
with open(output_path, 'wb+') as f:
f.write(pickle.dumps(result))
# print(output_path)
def main():
args = parse_config()
assert args.interaction_scenario_path != None
assert args.output_dir != None
split_interaction(args.interaction_scenario_path, args.output_dir)
def multi_process():
data_dir = 'int2_dataset/interaction_scenario/complete_scenario'
output_dir = 'int2_dataset/interaction_scenario/split_scenario'
dirs = [os.path.join(data_dir, f) for f in os.listdir(data_dir)]
for d_dir in dirs:
print(f'process in {d_dir}')
file_list = [os.path.join(d_dir, f) for f in os.listdir(os.path.join(d_dir))]
p_map(split_interaction, file_list, [output_dir] * len(file_list), num_cpus=0.2)
if __name__ == "__main__":
main()
# multi_process()