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import numpy as np
import torch
from dkm.utils import *
from PIL import Image
from tqdm import tqdm
import torch.nn.functional as F
class Megadepth1500Benchmark:
def __init__(self, data_root="data/megadepth", scene_names = None) -> None:
if scene_names is None:
self.scene_names = [
"0015_0.1_0.3.npz",
"0015_0.3_0.5.npz",
"0022_0.1_0.3.npz",
"0022_0.3_0.5.npz",
"0022_0.5_0.7.npz",
]
else:
self.scene_names = scene_names
self.scenes = [
np.load(f"{data_root}/{scene}", allow_pickle=True)
for scene in self.scene_names
]
self.data_root = data_root
def benchmark(self, model):
with torch.no_grad():
data_root = self.data_root
tot_e_t, tot_e_R, tot_e_pose = [], [], []
for scene_ind in range(len(self.scenes)):
scene = self.scenes[scene_ind]
pairs = scene["pair_infos"]
intrinsics = scene["intrinsics"]
poses = scene["poses"]
im_paths = scene["image_paths"]
pair_inds = range(len(pairs))
for pairind in tqdm(pair_inds):
idx1, idx2 = pairs[pairind][0]
K1 = intrinsics[idx1].copy()
T1 = poses[idx1].copy()
R1, t1 = T1[:3, :3], T1[:3, 3]
K2 = intrinsics[idx2].copy()
T2 = poses[idx2].copy()
R2, t2 = T2[:3, :3], T2[:3, 3]
R, t = compute_relative_pose(R1, t1, R2, t2)
im1_path = f"{data_root}/{im_paths[idx1]}"
im2_path = f"{data_root}/{im_paths[idx2]}"
im1 = Image.open(im1_path)
w1, h1 = im1.size
im2 = Image.open(im2_path)
w2, h2 = im2.size
scale1 = 1200 / max(w1, h1)
scale2 = 1200 / max(w2, h2)
w1, h1 = scale1 * w1, scale1 * h1
w2, h2 = scale2 * w2, scale2 * h2
K1[:2] = K1[:2] * scale1
K2[:2] = K2[:2] * scale2
dense_matches, dense_certainty = model.match(im1_path, im2_path)
sparse_matches,_ = model.sample(
dense_matches, dense_certainty, 5000
)
kpts1 = sparse_matches[:, :2]
kpts1 = (
torch.stack(
(
w1 * (kpts1[:, 0] + 1) / 2,
h1 * (kpts1[:, 1] + 1) / 2,
),
axis=-1,
)
)
kpts2 = sparse_matches[:, 2:]
kpts2 = (
torch.stack(
(
w2 * (kpts2[:, 0] + 1) / 2,
h2 * (kpts2[:, 1] + 1) / 2,
),
axis=-1,
)
)
for _ in range(5):
shuffling = np.random.permutation(np.arange(len(kpts1)))
kpts1 = kpts1[shuffling]
kpts2 = kpts2[shuffling]
try:
norm_threshold = 0.5 / (
np.mean(np.abs(K1[:2, :2])) + np.mean(np.abs(K2[:2, :2])))
R_est, t_est, mask = estimate_pose(
kpts1.cpu().numpy(),
kpts2.cpu().numpy(),
K1,
K2,
norm_threshold,
conf=0.99999,
)
T1_to_2_est = np.concatenate((R_est, t_est), axis=-1) #
e_t, e_R = compute_pose_error(T1_to_2_est, R, t)
e_pose = max(e_t, e_R)
except Exception as e:
print(repr(e))
e_t, e_R = 90, 90
e_pose = max(e_t, e_R)
tot_e_t.append(e_t)
tot_e_R.append(e_R)
tot_e_pose.append(e_pose)
tot_e_pose = np.array(tot_e_pose)
thresholds = [5, 10, 20]
auc = pose_auc(tot_e_pose, thresholds)
acc_5 = (tot_e_pose < 5).mean()
acc_10 = (tot_e_pose < 10).mean()
acc_15 = (tot_e_pose < 15).mean()
acc_20 = (tot_e_pose < 20).mean()
map_5 = acc_5
map_10 = np.mean([acc_5, acc_10])
map_20 = np.mean([acc_5, acc_10, acc_15, acc_20])
return {
"auc_5": auc[0],
"auc_10": auc[1],
"auc_20": auc[2],
"map_5": map_5,
"map_10": map_10,
"map_20": map_20,
}
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