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feature_r2d2.py
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feature_r2d2.py
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"""
* This file is part of PYSLAM.
* Adapted from https://raw.githubusercontent.com/naver/r2d2/master/extract.py, see the licence therein.
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
# adapted from from https://raw.githubusercontent.com/naver/r2d2/master/extract.py
import config
config.cfg.set_lib('r2d2')
import os, pdb
from PIL import Image
import numpy as np
import torch
import cv2
from threading import RLock
from r2d2.tools import common
from r2d2.tools.dataloader import norm_RGB
from r2d2.nets.patchnet import *
import argparse
from utils_sys import Printer
kVerbose = True
def load_network(model_fn):
checkpoint = torch.load(model_fn)
print("\n>> Creating net = " + checkpoint['net'])
net = eval(checkpoint['net'])
nb_of_weights = common.model_size(net)
print(f" ( Model size: {nb_of_weights/1000:.0f}K parameters )")
# initialization
weights = checkpoint['state_dict']
net.load_state_dict({k.replace('module.',''):v for k,v in weights.items()})
return net.eval()
class NonMaxSuppression (torch.nn.Module):
def __init__(self, rel_thr=0.7, rep_thr=0.7):
nn.Module.__init__(self)
self.max_filter = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.rel_thr = rel_thr
self.rep_thr = rep_thr
def forward(self, reliability, repeatability, **kw):
assert len(reliability) == len(repeatability) == 1
reliability, repeatability = reliability[0], repeatability[0]
# local maxima
maxima = (repeatability == self.max_filter(repeatability))
# remove low peaks
maxima *= (repeatability >= self.rep_thr)
maxima *= (reliability >= self.rel_thr)
return maxima.nonzero().t()[2:4]
def extract_multiscale( net, img, detector, scale_f=2**0.25,
min_scale=0.0, max_scale=1,
min_size=256, max_size=1024,
verbose=False):
old_bm = torch.backends.cudnn.benchmark
torch.backends.cudnn.benchmark = False # speedup
# extract keypoints at multiple scales
B, three, H, W = img.shape
assert B == 1 and three == 3, "should be a batch with a single RGB image"
assert max_scale <= 1
s = 1.0 # current scale factor
level = 0
L = []
X,Y,S,C,Q,D = [],[],[],[],[],[]
while s+0.001 >= max(min_scale, min_size / max(H,W)):
if s-0.001 <= min(max_scale, max_size / max(H,W)):
nh, nw = img.shape[2:]
if verbose: print(f"extracting at scale x{s:.02f} = {nw:4d}x{nh:3d} - level {level}")
# extract descriptors
with torch.no_grad():
res = net(imgs=[img])
# get output and reliability map
descriptors = res['descriptors'][0]
reliability = res['reliability'][0]
repeatability = res['repeatability'][0]
# normalize the reliability for nms
# extract maxima and descs
y,x = detector(**res) # nms
c = reliability[0,0,y,x]
q = repeatability[0,0,y,x]
d = descriptors[0,:,y,x].t()
n = d.shape[0]
# accumulate multiple scales
X.append(x.float() * W/nw)
Y.append(y.float() * H/nh)
S.append((32/s) * torch.ones(n, dtype=torch.float32, device=d.device))
C.append(c)
Q.append(q)
D.append(d)
L_tmp =level * np.ones(n,dtype=np.int32)
L = np.concatenate((L, L_tmp), axis=0).astype(np.int32)
level += 1
s /= scale_f
# down-scale the image for next iteration
nh, nw = round(H*s), round(W*s)
img = F.interpolate(img, (nh,nw), mode='bilinear', align_corners=False)
# restore value
torch.backends.cudnn.benchmark = old_bm
Y = torch.cat(Y)
X = torch.cat(X)
S = torch.cat(S) # scale
scores = torch.cat(C) * torch.cat(Q) # scores = reliability * repeatability
XYS = torch.stack([X,Y,S], dim=-1)
D = torch.cat(D)
return XYS, D, scores, L
# convert matrix of pts into list of keypoints
def convert_pts_to_keypoints(pts, scores, sizes, levels):
assert(len(pts)==len(scores))
kps = []
if pts is not None:
# convert matrix [Nx2] of pts into list of keypoints
kps = [ cv2.KeyPoint(p[0], p[1], _size=sizes[i], _response=scores[i], _octave=levels[i]) for i,p in enumerate(pts) ]
return kps
# TODO: fix the octave field of the output keypoints
# interface for pySLAM
class R2d2Feature2D:
def __init__(self,
num_features = 2000,
scale_f = 2**0.25,
min_size = 256,
max_size = 1300, #1024,
min_scale = 0,
max_scale = 1,
reliability_thr = 0.7,
repeatability_thr = 0.7,
do_cuda=True):
print('Using R2d2Feature2D')
self.lock = RLock()
self.model_base_path = config.cfg.root_folder + '/thirdparty/r2d2'
self.model_weights_path = self.model_base_path + '/models/r2d2_WASF_N16.pt'
#print('model_weights_path:',self.model_weights_path)
self.pts = []
self.kps = []
self.des = []
self.frame = None
self.num_features = num_features
self.scale_f = scale_f
self.min_size = min_size
self.max_size = max_size
self.min_scale = min_scale
self.max_scale = max_scale
self.reliability_thr = reliability_thr
self.repeatability_thr = repeatability_thr
self.do_cuda = do_cuda
if do_cuda:
gpus = [0]
else:
gpus = -1
self.gpus = gpus
self.do_cuda = common.torch_set_gpu(gpus)
print('==> Loading pre-trained network.')
self.net = load_network(self.model_weights_path)
if self.do_cuda: self.net = self.net.cuda()
# create the non-maxima detector
self.detector = NonMaxSuppression(rel_thr=reliability_thr, rep_thr=repeatability_thr)
print('==> Successfully loaded pre-trained network.')
def compute_kps_des(self,img):
with self.lock:
H, W = img.shape[:2]
img = norm_RGB(img)[None]
if self.do_cuda: img = img.cuda()
# extract keypoints/descriptors for a single image
xys, desc, scores, levels = extract_multiscale(self.net, img, self.detector,
scale_f = self.scale_f,
min_scale = self.min_scale,
max_scale = self.max_scale,
min_size = self.min_size,
max_size = self.max_size,
verbose = kVerbose)
xys = xys.cpu().numpy()
desc = desc.cpu().numpy()
scores = scores.cpu().numpy()
idxs = scores.argsort()[-self.num_features or None:]
selected_xys = xys[idxs]
self.pts = selected_xys[:,:2]
sizes = selected_xys[:,2]
des = desc[idxs]
scores = scores[idxs]
levels = np.array(levels)[idxs]
kps = convert_pts_to_keypoints(self.pts, scores, sizes, levels)
return kps, des
def detectAndCompute(self, frame, mask=None): #mask is a fake input
with self.lock:
self.frame = frame
self.kps, self.des = self.compute_kps_des(frame)
if kVerbose:
print('detector: R2D2 , descriptor: R2D2 , #features: ', len(self.kps), ', frame res: ', frame.shape[0:2])
return self.kps, self.des
# return keypoints if available otherwise call detectAndCompute()
def detect(self, frame, mask=None): # mask is a fake input
with self.lock:
if self.frame is not frame:
self.detectAndCompute(frame)
return self.kps
# return descriptors if available otherwise call detectAndCompute()
def compute(self, frame, kps=None, mask=None): # kps is a fake input, mask is a fake input
with self.lock:
if self.frame is not frame:
Printer.orange('WARNING: R2D2 is recomputing both kps and des on last input frame', frame.shape)
self.detectAndCompute(frame)
return self.kps, self.des