Spaces:
Running
Running
File size: 9,830 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 |
# Copyright 2019-present NAVER Corp.
# CC BY-NC-SA 3.0
# Available only for non-commercial use
from PIL import Image
from tools import common
from tools.dataloader import norm_RGB
from nets.patchnet import *
from os import path
from extract import load_network, NonMaxSuppression, extract_multiscale
# Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion)
# and more generally sensor-acquired data
# it can be installed with
# pip install kapture
# for more information check out https://github.com/naver/kapture
import kapture
from kapture.io.records import get_image_fullpath
from kapture.io.csv import kapture_from_dir
from kapture.io.csv import (
get_feature_csv_fullpath,
keypoints_to_file,
descriptors_to_file,
)
from kapture.io.features import (
get_keypoints_fullpath,
keypoints_check_dir,
image_keypoints_to_file,
)
from kapture.io.features import (
get_descriptors_fullpath,
descriptors_check_dir,
image_descriptors_to_file,
)
from kapture.io.csv import get_all_tar_handlers
def extract_kapture_keypoints(args):
"""
Extract r2d2 keypoints and descritors to the kapture format directly
"""
print("extract_kapture_keypoints...")
with get_all_tar_handlers(
args.kapture_root,
mode={
kapture.Keypoints: "a",
kapture.Descriptors: "a",
kapture.GlobalFeatures: "r",
kapture.Matches: "r",
},
) as tar_handlers:
kdata = kapture_from_dir(
args.kapture_root,
None,
skip_list=[
kapture.GlobalFeatures,
kapture.Matches,
kapture.Points3d,
kapture.Observations,
],
tar_handlers=tar_handlers,
)
assert kdata.records_camera is not None
image_list = [
filename for _, _, filename in kapture.flatten(kdata.records_camera)
]
if args.keypoints_type is None:
args.keypoints_type = path.splitext(path.basename(args.model))[0]
print(f"keypoints_type set to {args.keypoints_type}")
if args.descriptors_type is None:
args.descriptors_type = path.splitext(path.basename(args.model))[0]
print(f"descriptors_type set to {args.descriptors_type}")
if (
kdata.keypoints is not None
and args.keypoints_type in kdata.keypoints
and kdata.descriptors is not None
and args.descriptors_type in kdata.descriptors
):
print(
"detected already computed features of same keypoints_type/descriptors_type, resuming extraction..."
)
image_list = [
name
for name in image_list
if name not in kdata.keypoints[args.keypoints_type]
or name not in kdata.descriptors[args.descriptors_type]
]
if len(image_list) == 0:
print("All features were already extracted")
return
else:
print(f"Extracting r2d2 features for {len(image_list)} images")
iscuda = common.torch_set_gpu(args.gpu)
# load the network...
net = load_network(args.model)
if iscuda:
net = net.cuda()
# create the non-maxima detector
detector = NonMaxSuppression(
rel_thr=args.reliability_thr, rep_thr=args.repeatability_thr
)
if kdata.keypoints is None:
kdata.keypoints = {}
if kdata.descriptors is None:
kdata.descriptors = {}
if args.keypoints_type not in kdata.keypoints:
keypoints_dtype = None
keypoints_dsize = None
else:
keypoints_dtype = kdata.keypoints[args.keypoints_type].dtype
keypoints_dsize = kdata.keypoints[args.keypoints_type].dsize
if args.descriptors_type not in kdata.descriptors:
descriptors_dtype = None
descriptors_dsize = None
else:
descriptors_dtype = kdata.descriptors[args.descriptors_type].dtype
descriptors_dsize = kdata.descriptors[args.descriptors_type].dsize
for image_name in image_list:
img_path = get_image_fullpath(args.kapture_root, image_name)
print(f"\nExtracting features for {img_path}")
img = Image.open(img_path).convert("RGB")
W, H = img.size
img = norm_RGB(img)[None]
if iscuda:
img = img.cuda()
# extract keypoints/descriptors for a single image
xys, desc, scores = extract_multiscale(
net,
img,
detector,
scale_f=args.scale_f,
min_scale=args.min_scale,
max_scale=args.max_scale,
min_size=args.min_size,
max_size=args.max_size,
verbose=True,
)
xys = xys.cpu().numpy()
desc = desc.cpu().numpy()
scores = scores.cpu().numpy()
idxs = scores.argsort()[-args.top_k or None :]
xys = xys[idxs]
desc = desc[idxs]
if keypoints_dtype is None or descriptors_dtype is None:
keypoints_dtype = xys.dtype
descriptors_dtype = desc.dtype
keypoints_dsize = xys.shape[1]
descriptors_dsize = desc.shape[1]
kdata.keypoints[args.keypoints_type] = kapture.Keypoints(
"r2d2", keypoints_dtype, keypoints_dsize
)
kdata.descriptors[args.descriptors_type] = kapture.Descriptors(
"r2d2",
descriptors_dtype,
descriptors_dsize,
args.keypoints_type,
"L2",
)
keypoints_config_absolute_path = get_feature_csv_fullpath(
kapture.Keypoints, args.keypoints_type, args.kapture_root
)
descriptors_config_absolute_path = get_feature_csv_fullpath(
kapture.Descriptors, args.descriptors_type, args.kapture_root
)
keypoints_to_file(
keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]
)
descriptors_to_file(
descriptors_config_absolute_path,
kdata.descriptors[args.descriptors_type],
)
else:
assert kdata.keypoints[args.keypoints_type].dtype == xys.dtype
assert kdata.descriptors[args.descriptors_type].dtype == desc.dtype
assert kdata.keypoints[args.keypoints_type].dsize == xys.shape[1]
assert kdata.descriptors[args.descriptors_type].dsize == desc.shape[1]
assert (
kdata.descriptors[args.descriptors_type].keypoints_type
== args.keypoints_type
)
assert kdata.descriptors[args.descriptors_type].metric_type == "L2"
keypoints_fullpath = get_keypoints_fullpath(
args.keypoints_type, args.kapture_root, image_name, tar_handlers
)
print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}")
image_keypoints_to_file(keypoints_fullpath, xys)
kdata.keypoints[args.keypoints_type].add(image_name)
descriptors_fullpath = get_descriptors_fullpath(
args.descriptors_type, args.kapture_root, image_name, tar_handlers
)
print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}")
image_descriptors_to_file(descriptors_fullpath, desc)
kdata.descriptors[args.descriptors_type].add(image_name)
if not keypoints_check_dir(
kdata.keypoints[args.keypoints_type],
args.keypoints_type,
args.kapture_root,
tar_handlers,
) or not descriptors_check_dir(
kdata.descriptors[args.descriptors_type],
args.descriptors_type,
args.kapture_root,
tar_handlers,
):
print(
"local feature extraction ended successfully but not all files were saved"
)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
"Extract r2d2 local features for all images in a dataset stored in the kapture format"
)
parser.add_argument("--model", type=str, required=True, help="model path")
parser.add_argument(
"--keypoints-type",
default=None,
help="keypoint type_name, default is filename of model",
)
parser.add_argument(
"--descriptors-type",
default=None,
help="descriptors type_name, default is filename of model",
)
parser.add_argument(
"--kapture-root", type=str, required=True, help="path to kapture root directory"
)
parser.add_argument("--top-k", type=int, default=5000, help="number of keypoints")
parser.add_argument("--scale-f", type=float, default=2**0.25)
parser.add_argument("--min-size", type=int, default=256)
parser.add_argument("--max-size", type=int, default=1024)
parser.add_argument("--min-scale", type=float, default=0)
parser.add_argument("--max-scale", type=float, default=1)
parser.add_argument("--reliability-thr", type=float, default=0.7)
parser.add_argument("--repeatability-thr", type=float, default=0.7)
parser.add_argument(
"--gpu", type=int, nargs="+", default=[0], help="use -1 for CPU"
)
args = parser.parse_args()
extract_kapture_keypoints(args)
|