/
calc_metrics.py
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/
calc_metrics.py
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import glob
import json
import os.path
import numpy as np
import time
import argparse
from utils.func import R_from_2poses, merge_from_2hands, matrix_from_quaternion, quaternion_from_matrix
from consistency import mpjpe_per_hand
JNUM = 21
def R_from_2hands(hands1, hands2, valids1, valids2, R12=None, R21=None):
for i in range(hands1.shape[0]):
pred1, pred2 = hands1[i], hands2[i]
p1, p2 = [], []
for j in range(pred1.shape[0]):
if valids1[i, j] and valids2[i, j]:
p1.append(pred1[j])
p2.append(pred2[j])
if R12 is None:
R12 = R_from_2poses(p1, p2, is_torch=False)
if R21 is None:
R21 = R_from_2poses(p2, p1, is_torch=False)
return R12, R21
def matrix2angle(m):
"""
Calculates Rotation Matrix given euler angles.
:param theta: 1-by-3 list [rx, ry, rz] angle in degree
:return:
RPY, the object will be rotated with the order of [rx, ry, rz]
"""
x = np.arctan2(m[2, 1], m[2, 2])
y = np.arcsin(-m[2, 0])
z = np.arctan2(m[1, 0], m[0, 0])
return np.array([x, y, z])
def calc_R(log_path):
lines = open(log_path).readlines()
lines.pop(0)
mono = float(lines.pop(-1).split(':')[-1])
line_num = len(lines)
R_ls, R_gt = [], None
for i in range(min(512, line_num // 2)):
view0 = lines[2 * i].split()
view1 = lines[2 * i + 1].split()
pred0 = np.array(view0[4].split(',')).astype(np.float64).reshape(JNUM, 3)
valid0 = np.array(view0[6].split(',')).astype(np.float64).reshape(JNUM, )
pred1 = np.array(view1[4].split(',')).astype(np.float64).reshape(JNUM, 3)
valid1 = np.array(view1[6].split(',')).astype(np.float64).reshape(JNUM, )
R_pred, _ = R_from_2hands(pred0[np.newaxis], pred1[np.newaxis],
valid0[np.newaxis, :], valid1[np.newaxis, :])
R_ls.append(R_pred)
quan_ls = [quaternion_from_matrix(R) for R in R_ls]
quan_avg = np.mean(quan_ls, axis=0)
quan_avg /= np.linalg.norm(quan_avg)
R_est = matrix_from_quaternion(np.mean(quan_ls, axis=0))
return R_est
def calc_merged_metric(log_path, use_gt=False, R12=None, R21=None, dynamic_R=False):
if dynamic_R:
print('calculating new R...')
R12 = calc_R(log_path)
R21 = R12.T
print('done')
name = os.path.basename(log_path).split('.')[0]
lines = open(log_path).readlines()
lines.pop(0)
mono = float(lines.pop(-1).split(':')[-1])
line_num = len(lines)
sumf = []
mpjpe_ls, q_diff_ls = [], []
for i in range(line_num // 2):
view0 = lines[2 * i].split()
view1 = lines[2 * i + 1].split()
pred0 = np.array(view0[4].split(',')).astype(np.float64).reshape(JNUM, 3)
gt0 = np.array(view0[5].split(',')).astype(np.float64).reshape(JNUM, 3)
R0 = np.array(view0[7].split(',')).astype(np.float64).reshape(3, 3)
valid0 = np.array(view0[6].split(',')).astype(np.float64).reshape(JNUM, )
pred1 = np.array(view1[4].split(',')).astype(np.float64).reshape(JNUM, 3)
gt1 = np.array(view1[5].split(',')).astype(np.float64).reshape(JNUM, 3)
R1 = np.array(view1[7].split(',')).astype(np.float64).reshape(3, 3)
valid1 = np.array(view1[6].split(',')).astype(np.float64).reshape(JNUM, )
R = R_from_2poses(pred0, pred1)
R_gt = R0 @ R1.T
q_diff = matrix2angle(R) - matrix2angle(R_gt)
q_diff_ls.append(q_diff)
if not use_gt:
merge0, merge1 = merge_from_2hands(pred0[np.newaxis, :, :], pred1[np.newaxis, :, :],
valid0[np.newaxis, :], valid1[np.newaxis, :])
else:
if R12 is None:
R12 = R0 @ R1.T
if R21 is None:
R21 = R1 @ R0.T
merge0, merge1 = merge_from_2hands(pred0[np.newaxis, :, :], pred1[np.newaxis, :, :],
valid0[np.newaxis, :], valid1[np.newaxis, :],
R12=R12, R21=R21)
merge0, merge1 = np.squeeze(merge0), np.squeeze(merge1)
# f0 = np.array(mpjpe_per_hand(merge0, gt0, valid0)) < np.array(mpjpe_per_hand(pred0, gt0, valid0))
# f1 = np.array(mpjpe_per_hand(merge1, gt1, valid1)) < np.array(mpjpe_per_hand(pred1, gt1, valid1))
# sumf.append(sum(f0 == f1))
mpjpe_ls.append(mpjpe_per_hand(merge0, gt0, valid0))
mpjpe_ls.append(mpjpe_per_hand(merge1, gt1, valid1))
q_mean = np.mean(np.abs(q_diff_ls), axis=0)
q_mean_str = ','.join([f'{u:.2f}' for u in q_mean])
print(f'{name} num:{len(mpjpe_ls)}, Mono-M:{mono:.2f}, Dual-M:{np.nanmean(mpjpe_ls):.2f}, '
f'Rotation_quat_err:({q_mean_str})')
# print(np.mean(sumf))
return len(mpjpe_ls), mono, np.nanmean(mpjpe_ls), q_diff_ls
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch Train: DetNet')
# Dataset setting
parser.add_argument('--checkpoint', type=str, default='in_dataset_adapt/evaluation/ah',
help='save dir of the test logs')
parser.add_argument('--setup', type=int, default=0, help='id of headset')
parser.add_argument('--pair', type=str, default='1,2', help='id of dual-camera pair')
parser.add_argument('-eid', '--evaluate_id', default=None, type=int, metavar='N',
help='number of data loading workers (default: 8)')
args = parser.parse_args()
eid = args.evaluate_id if args.evaluate_id is not None else '*'
logs = glob.glob(os.path.join(args.checkpoint, f'{eid}-set{args.setup}-{args.pair}.log'))
logs.sort()
print(logs)
R_config = json.load(open('./R_config.json'))
q_diffs = []
num, mono, merge = 0, 0, 0
for log in logs:
print(log)
setup, pair = os.path.basename(log).split('.')[0].split('-')[1:]
R12 = np.array(R_config[f'{setup}-{pair}']['R_pred'])
n, mo, mer, q_diff = calc_merged_metric(log, use_gt=True, R12=R12, R21=R12.T, dynamic_R=False)
num += n
mono += mo * n
merge += mer * n
q_diffs += q_diff
print(f'total:{num}, mean Mono-M:{mono / num:.2f}, mean Dual-M:{merge / num:.2f}')