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import torch
import numpy as np
import cv2
from hloc import matchers, extractors
from hloc.utils.base_model import dynamic_load
from hloc import match_dense, match_features, extract_features
from .plotting import draw_matches, fig2im
from .visualize_util import plot_images, plot_color_line_matches
device = "cuda" if torch.cuda.is_available() else "cpu"
def get_model(match_conf):
Model = dynamic_load(matchers, match_conf["model"]["name"])
model = Model(match_conf["model"]).eval().to(device)
return model
def get_feature_model(conf):
Model = dynamic_load(extractors, conf["model"]["name"])
model = Model(conf["model"]).eval().to(device)
return model
def display_matches(pred: dict):
img0 = pred["image0_orig"]
img1 = pred["image1_orig"]
num_inliers = 0
if "keypoints0_orig" in pred.keys() and "keypoints1_orig" in pred.keys():
mkpts0 = pred["keypoints0_orig"]
mkpts1 = pred["keypoints1_orig"]
num_inliers = len(mkpts0)
if "mconf" in pred.keys():
mconf = pred["mconf"]
else:
mconf = np.ones(len(mkpts0))
fig_mkpts = draw_matches(
mkpts0,
mkpts1,
img0,
img1,
mconf,
dpi=300,
titles=["Image 0 - matched keypoints", "Image 1 - matched keypoints"],
)
fig = fig_mkpts
if "line0_orig" in pred.keys() and "line1_orig" in pred.keys():
# lines
mtlines0 = pred["line0_orig"]
mtlines1 = pred["line1_orig"]
num_inliers = len(mtlines0)
fig_lines = plot_images(
[img0.squeeze(), img1.squeeze()],
["Image 0 - matched lines", "Image 1 - matched lines"],
dpi=300,
)
fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2)
fig_lines = fig2im(fig_lines)
# keypoints
mkpts0 = pred["line_keypoints0_orig"]
mkpts1 = pred["line_keypoints1_orig"]
if mkpts0 is not None and mkpts1 is not None:
num_inliers = len(mkpts0)
if "mconf" in pred.keys():
mconf = pred["mconf"]
else:
mconf = np.ones(len(mkpts0))
fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300)
fig_lines = cv2.resize(fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0]))
fig = np.concatenate([fig_mkpts, fig_lines], axis=0)
else:
fig = fig_lines
return fig, num_inliers
# Matchers collections
matcher_zoo = {
"gluestick": {"config": match_dense.confs["gluestick"], "dense": True},
"sold2": {"config": match_dense.confs["sold2"], "dense": True},
# 'dedode-sparse': {
# 'config': match_dense.confs['dedode_sparse'],
# 'dense': True # dense mode, we need 2 images
# },
"loftr": {"config": match_dense.confs["loftr"], "dense": True},
"topicfm": {"config": match_dense.confs["topicfm"], "dense": True},
"aspanformer": {"config": match_dense.confs["aspanformer"], "dense": True},
"dedode": {
"config": match_features.confs["Dual-Softmax"],
"config_feature": extract_features.confs["dedode"],
"dense": False,
},
"superpoint+superglue": {
"config": match_features.confs["superglue"],
"config_feature": extract_features.confs["superpoint_max"],
"dense": False,
},
"superpoint+lightglue": {
"config": match_features.confs["superpoint-lightglue"],
"config_feature": extract_features.confs["superpoint_max"],
"dense": False,
},
"disk": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["disk"],
"dense": False,
},
"disk+dualsoftmax": {
"config": match_features.confs["Dual-Softmax"],
"config_feature": extract_features.confs["disk"],
"dense": False,
},
"superpoint+dualsoftmax": {
"config": match_features.confs["Dual-Softmax"],
"config_feature": extract_features.confs["superpoint_max"],
"dense": False,
},
"disk+lightglue": {
"config": match_features.confs["disk-lightglue"],
"config_feature": extract_features.confs["disk"],
"dense": False,
},
"superpoint+mnn": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["superpoint_max"],
"dense": False,
},
"sift+sgmnet": {
"config": match_features.confs["sgmnet"],
"config_feature": extract_features.confs["sift"],
"dense": False,
},
"sosnet": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["sosnet"],
"dense": False,
},
"hardnet": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["hardnet"],
"dense": False,
},
"d2net": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["d2net-ss"],
"dense": False,
},
"d2net-ms": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["d2net-ms"],
"dense": False,
},
"alike": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["alike"],
"dense": False,
},
"lanet": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["lanet"],
"dense": False,
},
"r2d2": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["r2d2"],
"dense": False,
},
"darkfeat": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["darkfeat"],
"dense": False,
},
"sift": {
"config": match_features.confs["NN-mutual"],
"config_feature": extract_features.confs["sift"],
"dense": False,
},
"roma": {"config": match_dense.confs["roma"], "dense": True},
"DKMv3": {"config": match_dense.confs["dkm"], "dense": True},
}
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