# 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)