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from PIL import Image
import numpy as np
import os
from tqdm import tqdm
from dkm.utils import pose_auc
import cv2
class HpatchesHomogBenchmark:
"""Hpatches grid goes from [0,n-1] instead of [0.5,n-0.5]"""
def __init__(self, dataset_path) -> None:
seqs_dir = "hpatches-sequences-release"
self.seqs_path = os.path.join(dataset_path, seqs_dir)
self.seq_names = sorted(os.listdir(self.seqs_path))
# Ignore seqs is same as LoFTR.
self.ignore_seqs = set(
[
"i_contruction",
"i_crownnight",
"i_dc",
"i_pencils",
"i_whitebuilding",
"v_artisans",
"v_astronautis",
"v_talent",
]
)
def convert_coordinates(self, query_coords, query_to_support, wq, hq, wsup, hsup):
offset = 0.5 # Hpatches assumes that the center of the top-left pixel is at [0,0] (I think)
query_coords = (
np.stack(
(
wq * (query_coords[..., 0] + 1) / 2,
hq * (query_coords[..., 1] + 1) / 2,
),
axis=-1,
)
- offset
)
query_to_support = (
np.stack(
(
wsup * (query_to_support[..., 0] + 1) / 2,
hsup * (query_to_support[..., 1] + 1) / 2,
),
axis=-1,
)
- offset
)
return query_coords, query_to_support
def benchmark(self, model, model_name=None):
n_matches = []
homog_dists = []
for seq_idx, seq_name in tqdm(
enumerate(self.seq_names), total=len(self.seq_names)
):
if seq_name in self.ignore_seqs:
continue
im1_path = os.path.join(self.seqs_path, seq_name, "1.ppm")
im1 = Image.open(im1_path)
w1, h1 = im1.size
for im_idx in range(2, 7):
im2_path = os.path.join(self.seqs_path, seq_name, f"{im_idx}.ppm")
im2 = Image.open(im2_path)
w2, h2 = im2.size
H = np.loadtxt(
os.path.join(self.seqs_path, seq_name, "H_1_" + str(im_idx))
)
dense_matches, dense_certainty = model.match(im1_path, im2_path)
good_matches, _ = model.sample(dense_matches, dense_certainty, 5000)
pos_a, pos_b = self.convert_coordinates(
good_matches[:, :2], good_matches[:, 2:], w1, h1, w2, h2
)
try:
H_pred, inliers = cv2.findHomography(
pos_a,
pos_b,
method=cv2.RANSAC,
confidence=0.99999,
ransacReprojThreshold=3 * min(w2, h2) / 480,
)
except:
H_pred = None
if H_pred is None:
H_pred = np.zeros((3, 3))
H_pred[2, 2] = 1.0
corners = np.array(
[[0, 0, 1], [0, h1 - 1, 1], [w1 - 1, 0, 1], [w1 - 1, h1 - 1, 1]]
)
real_warped_corners = np.dot(corners, np.transpose(H))
real_warped_corners = (
real_warped_corners[:, :2] / real_warped_corners[:, 2:]
)
warped_corners = np.dot(corners, np.transpose(H_pred))
warped_corners = warped_corners[:, :2] / warped_corners[:, 2:]
mean_dist = np.mean(
np.linalg.norm(real_warped_corners - warped_corners, axis=1)
) / (min(w2, h2) / 480.0)
homog_dists.append(mean_dist)
n_matches = np.array(n_matches)
thresholds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
auc = pose_auc(np.array(homog_dists), thresholds)
return {
"hpatches_homog_auc_3": auc[2],
"hpatches_homog_auc_5": auc[4],
"hpatches_homog_auc_10": auc[9],
}
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