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import cv2
import os
from tqdm import tqdm
import torch
import numpy as np
import sys
import poselib
sys.path.append(os.path.join(os.path.dirname(__file__),'..'))
import argparse
import datetime
parser=argparse.ArgumentParser(description='HPatch dataset evaluation script')
parser.add_argument('--name',type=str,default='LiftFeat',help='experiment name')
parser.add_argument('--gpu',type=str,default='0',help='GPU ID')
args=parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
top_k = None
n_i = 52
n_v = 56
DATASET_ROOT = os.path.join(os.path.dirname(__file__),'../data/HPatch')
from evaluation.eval_utils import *
from models.liftfeat_wrapper import LiftFeat
poselib_config = {"ransac_th": 3.0, "options": {}}
class PoseLibHomographyEstimator:
def __init__(self, conf):
self.conf = conf
def estimate(self, mkpts0,mkpts1):
M, info = poselib.estimate_homography(
mkpts0,
mkpts1,
{
"max_reproj_error": self.conf["ransac_th"],
**self.conf["options"],
},
)
success = M is not None
if not success:
M = np.eye(3,dtype=np.float32)
inl = np.zeros(mkpts0.shape[0],dtype=np.bool_)
else:
inl = info["inliers"]
estimation = {
"success": success,
"M_0to1": M,
"inliers": inl,
}
return estimation
estimator=PoseLibHomographyEstimator(poselib_config)
def poselib_homography_estimate(mkpts0,mkpts1):
data=estimator.estimate(mkpts0,mkpts1)
return data
def generate_standard_image(img,target_size=(1920,1080)):
sh,sw=img.shape[0],img.shape[1]
rh,rw=float(target_size[1])/float(sh),float(target_size[0])/float(sw)
ratio=min(rh,rw)
nh,nw=int(ratio*sh),int(ratio*sw)
ph,pw=target_size[1]-nh,target_size[0]-nw
nimg=cv2.resize(img,(nw,nh))
nimg=cv2.copyMakeBorder(nimg,0,ph,0,pw,cv2.BORDER_CONSTANT,value=(0,0,0))
return nimg,ratio,ph,pw
def benchmark_features(match_fn):
lim = [1, 9]
rng = np.arange(lim[0], lim[1] + 1)
seq_names = sorted(os.listdir(DATASET_ROOT))
n_feats = []
n_matches = []
seq_type = []
i_err = {thr: 0 for thr in rng}
v_err = {thr: 0 for thr in rng}
i_err_homo = {thr: 0 for thr in rng}
v_err_homo = {thr: 0 for thr in rng}
for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):
# load reference image
ref_img = cv2.imread(os.path.join(DATASET_ROOT, seq_name, "1.ppm"))
ref_img_shape=ref_img.shape
# load query images
for im_idx in range(2, 7):
# read ground-truth homography
homography = np.loadtxt(os.path.join(DATASET_ROOT, seq_name, "H_1_" + str(im_idx)))
query_img = cv2.imread(os.path.join(DATASET_ROOT, seq_name, f"{im_idx}.ppm"))
mkpts_a,mkpts_b=match_fn(ref_img,query_img)
pos_a = mkpts_a
pos_a_h = np.concatenate([pos_a, np.ones([pos_a.shape[0], 1])], axis=1)
pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))
pos_b_proj = pos_b_proj_h[:, :2] / pos_b_proj_h[:, 2:]
pos_b = mkpts_b
dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))
n_matches.append(pos_a.shape[0])
seq_type.append(seq_name[0])
if dist.shape[0] == 0:
dist = np.array([float("inf")])
for thr in rng:
if seq_name[0] == "i":
i_err[thr] += np.mean(dist <= thr)
else:
v_err[thr] += np.mean(dist <= thr)
# estimate homography
gt_homo = homography
pred_homo, _ = cv2.findHomography(mkpts_a,mkpts_b,cv2.USAC_MAGSAC)
if pred_homo is None:
homo_dist = np.array([float("inf")])
else:
corners = np.array(
[
[0, 0],
[ref_img_shape[1] - 1, 0],
[0, ref_img_shape[0] - 1],
[ref_img_shape[1] - 1, ref_img_shape[0] - 1],
]
)
real_warped_corners = homo_trans(corners, gt_homo)
warped_corners = homo_trans(corners, pred_homo)
homo_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1))
for thr in rng:
if seq_name[0] == "i":
i_err_homo[thr] += np.mean(homo_dist <= thr)
else:
v_err_homo[thr] += np.mean(homo_dist <= thr)
seq_type = np.array(seq_type)
n_feats = np.array(n_feats)
n_matches = np.array(n_matches)
return i_err, v_err, i_err_homo, v_err_homo, [seq_type, n_feats, n_matches]
if __name__ == "__main__":
errors = {}
weights=os.path.join(os.path.dirname(__file__),'../weights/LiftFeat.pth')
liftfeat=LiftFeat(weight=weights)
errors = benchmark_features(liftfeat.match_liftfeat)
i_err, v_err, i_err_hom, v_err_hom, _ = errors
cur_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print(f'\n==={cur_time}==={args.name}===')
print(f"MHA@3 MHA@5 MHA@7")
for thr in [3, 5, 7]:
ill_err_hom = i_err_hom[thr] / (n_i * 5)
view_err_hom = v_err_hom[thr] / (n_v * 5)
print(f"{ill_err_hom * 100:.2f}%-{view_err_hom * 100:.2f}%")
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