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extract_kapture_modif.py
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# 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
import os
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]
# Set output path to parent directory of kapture_root
output_path = path.join(args.kapture_root, os.pardir)
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,
output_path)
descriptors_config_absolute_path = get_feature_csv_fullpath(kapture.Descriptors,
args.descriptors_type,
output_path)
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, output_path,
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, output_path,
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,
output_path, tar_handlers) or \
not descriptors_check_dir(kdata.descriptors[args.descriptors_type], args.descriptors_type,
output_path, 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("--output", type=str, required=True, help='path to output 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)