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gamma_compute_accuracy.py
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gamma_compute_accuracy.py
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from __future__ import print_function
import numpy as np
import copy
import math
import glob
all_traj_total_gamma_ade = 0.0
all_traj_total_gamma_fde = 0.0
trajectory_count = 0.0
agent_id_list = []
M = [[55.8303947300378, -21.4969860492286, 1119.51180638000], [-38.7841290361716, 31.3801203371366, 694.848288076000], [0.0412819851646424, 0.0501909553699786, 1]]
M = np.array(M)
def transform_world_to_pixel(world_x_list, world_y_list):
uv_list = []
for i in range(0, len(world_x_list)):
uv = np.dot(M, np.array([world_x_list[i],world_y_list[i], 1.0]))
uv /= uv[2]
uv_list.append(uv)
return uv_list
def divide_trajectories(agent_id):
global trajectory_segment
global content
global prediction_frame_count
frame_start = -1
frame_end = -1
frame_tmp = 1
for line in content:
if "begin to predict at frame:" in line:
frame_tmp = int(line.split(' ')[5])
elif "there are" not in line:
if int(line.split(' ')[0]) == agent_id:
if frame_start == -1:
frame_start = frame_tmp
frame_end = frame_tmp
if frame_start != -1 and frame_end != -1:
while frame_start + prediction_frame_count <= frame_end:
trajectory_segment[agent_id].append(frame_start)
trajectory_segment[agent_id].append(frame_start + prediction_frame_count)
frame_start += 1 #prediction_frame_count #1
def compute_accuracy(agent_trajectories, agent_id):
traj_num = int(len(agent_trajectories) / 2)
global prediction_frame_count
total_gamma_ade = 0.0
total_gamma_fde = 0.0
start_a_new_traj = False
prediction_frame_count_itr = 0
gamma_x_list = []
gamma_y_list = []
gamma_all_x_list = []
gamma_all_y_list = []
ori_x_list = []
ori_y_list = []
ori_all_x_list = []
ori_all_y_list = []
for line in content:
if "begin to predict at frame:" in line and int(line.split(' ')[5]) > agent_trajectories[(traj_num-1)*2+1]:
if gamma_x_list != []:
gamma_all_x_list.append(gamma_x_list)
gamma_all_y_list.append(gamma_y_list)
ori_all_x_list.append(ori_x_list)
ori_all_y_list.append(ori_y_list)
gamma_x_list = []
gamma_y_list = []
ori_x_list = []
ori_y_list = []
break
elif "begin to predict at frame:" in line and int(line.split(' ')[5]) >= agent_trajectories[0]:
frame_tmp = int(line.split(' ')[5])
for i in range(traj_num):
if frame_tmp == agent_trajectories[i*2]:
start_a_new_traj = True
prediction_frame_count_itr = 0
if gamma_x_list != []:
gamma_all_x_list.append(gamma_x_list)
gamma_all_y_list.append(gamma_y_list)
gamma_x_list = []
gamma_y_list = []
ori_all_x_list.append(ori_x_list)
ori_all_y_list.append(ori_y_list)
ori_x_list = []
ori_y_list = []
break
else:
start_a_new_traj = False
prediction_frame_count_itr = 0
elif start_a_new_traj and "there are" not in line:
if agent_id == int(line.split(' ')[0]) and prediction_frame_count_itr < prediction_frame_count:
if prediction_frame_count_itr == prediction_frame_count - 1:
total_gamma_fde += dist(float(line.split(' ')[2]), float(line.split(' ')[3]), float(line.split(' ')[4]), float(line.split(' ')[5]))
gamma_x_list.append(float(line.split(' ')[2]))
gamma_y_list.append(float(line.split(' ')[3]))
ori_x_list.append(float(line.split(' ')[4]))
ori_y_list.append(float(line.split(' ')[5]))
total_gamma_ade += dist(float(line.split(' ')[2]), float(line.split(' ')[3]), float(line.split(' ')[4]), float(line.split(' ')[5]))
gamma_x_list.append(float(line.split(' ')[2]))
gamma_y_list.append(float(line.split(' ')[3]))
ori_x_list.append(float(line.split(' ')[4]))
ori_y_list.append(float(line.split(' ')[5]))
prediction_frame_count_itr += 1
if output_file_for_visualization:
for i in range(len(gamma_all_x_list)):
output_dir = "pos_in_image_space/"
file_name = "frame_"+str(agent_trajectories[0])+"_agent_"+str(agent_id)+"_"+str(i)+"_gamma.txt"
output_file = open(output_dir+file_name, "w")
output_file2 = open(output_dir+file_name[:-9]+"ori.txt", "w")
uv_list = transform_world_to_pixel(gamma_all_x_list[i], gamma_all_y_list[i])
start_frame = agent_trajectories[2*i]
for j in range(len(uv_list)):
print(agent_id, start_frame+j, uv_list[j][0], uv_list[j][1], file = output_file)
uv_list = transform_world_to_pixel(ori_all_x_list[i], ori_all_y_list[i])
start_frame = agent_trajectories[2*i]
for j in range(len(uv_list)):
print(agent_id, start_frame+j, uv_list[j][0], uv_list[j][1], file = output_file2)
output_file.close()
output_file2.close()
return traj_num, total_gamma_ade, total_gamma_fde
def dist(x1,y1,x2,y2):
return math.sqrt((x1-x2)**2+(y1-y2)**2)
total_agent_num = 450
filename = "gamma_output.txt"
file_handler = open(filename,'r')
content = file_handler.read().splitlines()
prediction_duration = 4.8
prediction_frame_count = int(prediction_duration * 2.5)
trajectory_segment = [copy.deepcopy([]) for i in range(450)]
output_file_for_visualization = False
for i in range(0, total_agent_num):
divide_trajectories(i)
for i in range(0, total_agent_num):
if trajectory_segment[i] != []:
traj_num, total_gamma_ade, total_gamma_fde = compute_accuracy(trajectory_segment[i], i)
all_traj_total_gamma_ade += total_gamma_ade
all_traj_total_gamma_fde += total_gamma_fde
trajectory_count += float(traj_num)
print ("total number of trajectories: ", trajectory_count)
print ("ade: ", all_traj_total_gamma_ade / trajectory_count / prediction_frame_count)
print ("fde: ", all_traj_total_gamma_fde / trajectory_count)
print("finished")