File size: 6,643 Bytes
e170a8e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import numpy as np
import argparse
from tqdm import tqdm
import torch
import pandas as pd
# from lightglue.utils import load_image
from configs.default import get_cfg_defaults
from datasets import dataset_dict
from baselines.pose import PoseRecover
from utils.metrics import relative_pose_error, rotation_angular_error, error_auc, add, adi, compute_continuous_auc
def main(args):
config = get_cfg_defaults()
config.merge_from_file(args.config)
task = config.DATASET.TASK
dataset = config.DATASET.DATA_SOURCE
# try:
# data_root = config.DATASET.TEST.DATA_ROOT
# except:
# data_root = config.DATASET.DATA_ROOT
build_fn = dataset_dict[task][dataset]
testset = build_fn('test', config)
testloader = torch.utils.data.DataLoader(testset, batch_size=1)
device = args.device
img_resize = args.resize
poseRec = PoseRecover(matcher=args.matcher, solver=args.solver, img_resize=img_resize, device=device)
preprocess_times, extract_times, match_times, recover_times = [], [], [], []
R_errs, t_errs = [], []
ts_errs = []
adds, adis = [], []
for i, data in enumerate(tqdm(testloader)):
if dataset == 'ho3d' and args.obj_name is not None and data['objName'][0] != args.obj_name:
continue
image0, image1 = data['images'][0].to(device)
# if dataset == 'megadepth':
# image0 = load_image(os.path.join(data_root, data['pair_names'][0][0])).to(device)
# image1 = load_image(os.path.join(data_root, data['pair_names'][1][0])).to(device)
# else:
# image0, image1 = data['images'][0].to(device)
bbox0, bbox1 = None, None
if task == 'object':
bbox0, bbox1 = data['bboxes'][0]
x1, y1, x2, y2 = bbox0
u1, v1, u2, v2 = bbox1
image0 = image0[:, y1:y2, x1:x2]
image1 = image1[:, v1:v2, u1:u2]
mask0, mask1 = None, None
if args.mask:
mask0, mask1 = data['masks'][0].to(device)
depth0, depth1 = None, None
if args.depth:
depth0, depth1 = data['depths'][0]
K0, K1 = data['intrinsics'][0]
T = torch.eye(4)
T[:3, :3] = data['rotation'][0]
T[:3, 3] = data['translation'][0]
T = T.numpy()
R, t, points0, points1, preprocess_time, extract_time, match_time, recover_time = poseRec.recover(image0, image1, K0, K1, bbox0, bbox1, mask0, mask1, depth0, depth1)
preprocess_times.append(preprocess_time)
extract_times.append(extract_time)
match_times.append(match_time)
recover_times.append(recover_time)
if np.isnan(R).any():
R_err = 180
R = np.identity(3)
t_err = 180
t = np.array([0., 0., 0.])
else:
t_err, R_err = relative_pose_error(T, R, t, ignore_gt_t_thr=0.0)
R_errs.append(R_err)
t_errs.append(t_err)
if args.depth:
t = np.nan_to_num(t)
ts_errs.append(torch.tensor(T[:3, 3] - t).norm(2))
if task == 'object':
if np.isnan(R).any():
adds.append(1.)
adis.append(1.)
else:
adds.append(add(R, t, T[:3, :3], T[:3, 3], data['point_cloud'][0].numpy()))
adis.append(adi(R, t, T[:3, :3], T[:3, 3], data['point_cloud'][0].numpy()))
metrics = []
values = []
preprocess_times = np.array(preprocess_time) * 1000
extract_times = np.array(extract_time) * 1000
match_times = np.array(match_times) * 1000
recover_times = np.array(recover_time) * 1000
metrics.append('Extracting Time (ms)')
values.append(f'{np.mean(extract_times):.1f}')
metrics.append('Matching Time (ms)')
values.append(f'{np.mean(match_times):.1f}')
metrics.append('Recovering Time (ms)')
values.append(f'{np.mean(recover_times):.1f}')
metrics.append('Total Time (ms)')
values.append(f'{np.mean(extract_times) + np.mean(match_times) + np.mean(recover_times):.1f}')
# pose auc
angular_thresholds = [5, 10, 20]
pose_errors = np.max(np.stack([R_errs, t_errs]), axis=0)
aucs = error_auc(pose_errors, angular_thresholds, mode='Pose estimation') # (auc@5, auc@10, auc@20)
for k in aucs:
metrics.append(k)
values.append(f'{aucs[k] * 100:.2f}')
R_errs = torch.tensor(R_errs)
t_errs = torch.tensor(t_errs)
metrics.append('Rotation Avg. Error (°)')
values.append(f'{R_errs.mean():.2f}')
metrics.append('Rotation Med. Error (°)')
values.append(f'{R_errs.median():.2f}')
metrics.append('Rotation @30° ACC')
values.append(f'{(R_errs < 30).float().mean() * 100:.1f}')
metrics.append('Rotation @15° ACC')
values.append(f'{(R_errs < 15).float().mean() * 100:.1f}')
if args.depth:
ts_errs = torch.tensor(ts_errs)
metrics.append('Translation Avg. Error (m)')
values.append(f'{ts_errs.mean():.4f}')
metrics.append('Translation Med. Error (m)')
values.append(f'{ts_errs.median():.4f}')
metrics.append('Translation @1m ACC')
values.append(f'{(ts_errs < 1.0).float().mean() * 100:.1f}')
metrics.append('Translation @10cm ACC')
values.append(f'{(ts_errs < 0.1).float().mean() * 100:.1f}')
if task == 'object':
metrics.append('Object ADD')
values.append(f'{compute_continuous_auc(adds, np.linspace(0.0, 0.1, 1000)) * 100:.1f}')
metrics.append('Object ADD-S')
values.append(f'{compute_continuous_auc(adis, np.linspace(0.0, 0.1, 1000)) * 100:.1f}')
res = pd.DataFrame({'Metrics': metrics, 'Values': values})
print(res)
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('config', type=str, help='.yaml configure file path')
parser.add_argument('matcher', type=str)
parser.add_argument('--solver', type=str, default='procrustes')
parser.add_argument('--resize', type=int, default=None)
parser.add_argument('--depth', action='store_true')
parser.add_argument('--mask', action='store_true')
parser.add_argument('--obj_name', type=str, default=None)
parser.add_argument('--device', type=str, default='cuda:0')
return parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
main(args)
|