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evaluate_sim_experiments.py
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import os
import numpy as np
import matplotlib.pyplot as plt
import subprocess
from tqdm import tqdm
def overall_rmse(rmse_list, T=200):
return np.sqrt((1 / (len(rmse_list) * T)) * np.sum(T * np.array(rmse_list)**2))
gtpath = "/workspace/raid/krb/boreas/robotica_sims"
sequences = sorted([f for f in os.listdir(gtpath)])
N = len(sequences)
# print("EVALUATING DISCRETELIO...")
# resultpath = "/home/krb/ASRL/temp/steam_icp/boreas_velodyne/discretelio/"
T = 200
# slow = []
# medium = []
# fast = []
# slow_failures = 0
# medium_failures = 0
# fast_failures = 0
# for i, seq in tqdm(enumerate(sequences)):
# try:
# gt_seq_path = os.path.join(gtpath, seq, "applanix", "lidar_poses_tum.txt")
# result_seq = f"{seq}_tum.txt"
# result_seq_path = os.path.join(resultpath, result_seq)
# arg = f"evo_ape tum {gt_seq_path} {result_seq_path} -a"
# # print(arg)
# with open("/tmp/evo_result.txt", "w") as f:
# p = subprocess.Popen(arg.split(), stdout=f)
# p.wait()
# # print(p.returncode)
# with open("/tmp/evo_result.txt") as f:
# line = [line for line in f.readlines() if "rmse" in line]
# rmse = float(line[0].split()[1])
# # print(rmse)
# if "slow" in seq:
# slow.append(rmse)
# if "medium" in seq:
# medium.append(rmse)
# if "fast" in seq:
# fast.append(rmse)
# except Exception as e:
# print(e)
# if "slow" in seq:
# slow_failures += 1
# if "medium" in seq:
# medium_failures += 1
# if "fast" in seq:
# fast_failures += 1
# print(f"SLOW - mean RMSE: {np.mean(slow)}")
# print(f"SLOW - overall RMSE: {overall_rmse(slow, T)}")
# print(f"SLOW - failure rate: {100.0 * slow_failures / 20.0} %")
# print(f"MEDIUM - mean RMSE: {np.mean(medium)}")
# print(f"MEDIUM - overall RMSE: {overall_rmse(medium, T)}")
# print(f"MEDIUM - failure rate: {100.0 * medium_failures / 20.0} %")
# print(f"FAST - mean RMSE: {np.mean(fast)}")
# print(f"FAST - overall RMSE: {overall_rmse(fast, T)}")
# print(f"FAST - failure rate: {100.0 * fast_failures / 20.0} %")
# print("EVALUATING SINGERLIO...")
# resultpath = "/home/krb/ASRL/temp/steam_icp/boreas_velodyne/singerlio/"
# slow = []
# medium = []
# fast = []
# slow_failures = 0
# medium_failures = 0
# fast_failures = 0
# for i, seq in tqdm(enumerate(sequences)):
# try:
# gt_seq_path = os.path.join(gtpath, seq, "applanix", "lidar_poses_tum.txt")
# result_seq = f"{seq}_tum.txt"
# result_seq_path = os.path.join(resultpath, result_seq)
# arg = f"evo_ape tum {gt_seq_path} {result_seq_path} -a"
# # print(arg)
# with open("/tmp/evo_result.txt", "w") as f:
# p = subprocess.Popen(arg.split(), stdout=f)
# p.wait()
# # print(p.returncode)
# with open("/tmp/evo_result.txt") as f:
# line = [line for line in f.readlines() if "rmse" in line]
# rmse = float(line[0].split()[1])
# # print(rmse)
# if "slow" in seq:
# slow.append(rmse)
# if "medium" in seq:
# medium.append(rmse)
# if "fast" in seq:
# fast.append(rmse)
# except Exception as e:
# print(e)
# if "slow" in seq:
# slow_failures += 1
# if "medium" in seq:
# medium_failures += 1
# if "fast" in seq:
# fast_failures += 1
# print(f"SLOW - mean RMSE: {np.mean(slow)}")
# print(f"SLOW - overall RMSE: {overall_rmse(slow, T)}")
# print(f"SLOW - failure rate: {100.0 * slow_failures / 20.0} %")
# print(f"MEDIUM - mean RMSE: {np.mean(medium)}")
# print(f"MEDIUM - overall RMSE: {overall_rmse(medium, T)}")
# print(f"MEDIUM - failure rate: {100.0 * medium_failures / 20.0} %")
# print(f"FAST - mean RMSE: {np.mean(fast)}")
# print(f"FAST - overall RMSE: {overall_rmse(fast, T)}")
# print(f"FAST - failure rate: {100.0 * fast_failures / 20.0} %")
print("EVALUATING SINGERLOG...")
resultpath = "/home/krb/ASRL/temp/steam_icp/boreas_velodyne/singerlog/"
slow = []
medium = []
fast = []
slow_failures = 0
medium_failures = 0
fast_failures = 0
for i, seq in tqdm(enumerate(sequences)):
try:
gt_seq_path = os.path.join(gtpath, seq, "applanix", "lidar_poses_tum.txt")
result_seq = f"{seq}_tum.txt"
result_seq_path = os.path.join(resultpath, result_seq)
arg = f"evo_ape tum {gt_seq_path} {result_seq_path}"
# print(arg)
with open("/tmp/evo_result.txt", "w") as f:
p = subprocess.Popen(arg.split(), stdout=f)
p.wait()
# print(p.returncode)
with open("/tmp/evo_result.txt") as f:
line = [line for line in f.readlines() if "rmse" in line]
rmse = float(line[0].split()[1])
# print(rmse)
if "slow" in seq:
slow.append(rmse)
if "medium" in seq:
medium.append(rmse)
if "fast" in seq:
fast.append(rmse)
except Exception as e:
print(e)
if "slow" in seq:
slow_failures += 1
if "medium" in seq:
medium_failures += 1
if "fast" in seq:
fast_failures += 1
print(f"SLOW - mean RMSE: {np.mean(slow)}")
print(f"SLOW - overall RMSE: {overall_rmse(slow, T)}")
print(f"SLOW - failure rate: {100.0 * slow_failures / 20.0} %")
print(f"MEDIUM - mean RMSE: {np.mean(medium)}")
print(f"MEDIUM - overall RMSE: {overall_rmse(medium, T)}")
print(f"MEDIUM - failure rate: {100.0 * medium_failures / 20.0} %")
print(f"FAST - mean RMSE: {np.mean(fast)}")
print(f"FAST - overall RMSE: {overall_rmse(fast, T)}")
print(f"FAST - failure rate: {100.0 * fast_failures / 20.0} %")
print("EVALUATING SINGERLO...")
resultpath = "/home/krb/ASRL/temp/steam_icp/boreas_velodyne/singerlo/"
slow = []
medium = []
fast = []
slow_failures = 0
medium_failures = 0
fast_failures = 0
for i, seq in tqdm(enumerate(sequences)):
try:
gt_seq_path = os.path.join(gtpath, seq, "applanix", "lidar_poses_tum.txt")
result_seq = f"{seq}_tum.txt"
result_seq_path = os.path.join(resultpath, result_seq)
arg = f"evo_ape tum {gt_seq_path} {result_seq_path}"
# print(arg)
with open("/tmp/evo_result.txt", "w") as f:
p = subprocess.Popen(arg.split(), stdout=f)
p.wait()
# print(p.returncode)
with open("/tmp/evo_result.txt") as f:
line = [line for line in f.readlines() if "rmse" in line]
rmse = float(line[0].split()[1])
# print(rmse)
if "slow" in seq:
slow.append(rmse)
if "medium" in seq:
medium.append(rmse)
if "fast" in seq:
fast.append(rmse)
except Exception as e:
print(e)
if "slow" in seq:
slow_failures += 1
if "medium" in seq:
medium_failures += 1
if "fast" in seq:
fast_failures += 1
print(f"SLOW - mean RMSE: {np.mean(slow)}")
print(f"SLOW - overall RMSE: {overall_rmse(slow, T)}")
print(f"SLOW - failure rate: {100.0 * slow_failures / 20.0} %")
print(f"MEDIUM - mean RMSE: {np.mean(medium)}")
print(f"MEDIUM - overall RMSE: {overall_rmse(medium, T)}")
print(f"MEDIUM - failure rate: {100.0 * medium_failures / 20.0} %")
print(f"FAST - mean RMSE: {np.mean(fast)}")
print(f"FAST - overall RMSE: {overall_rmse(fast, T)}")
print(f"FAST - failure rate: {100.0 * fast_failures / 20.0} %")