Spaces:
Running
Running
File size: 6,216 Bytes
9223079 474fd5c 9223079 474fd5c 9223079 |
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 |
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},
}
|