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feature_d2net.py
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feature_d2net.py
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"""
* This file is part of PYSLAM
* Adapted from https://github.com/mihaidusmanu/d2-net/blob/master/extract_features.py, see the license 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 https://github.com/mihaidusmanu/d2-net/blob/master/extract_features.py
import config
config.cfg.set_lib('d2net')
import os
import argparse
import cv2
import numpy as np
import imageio
from threading import RLock
import torch
from tqdm import tqdm
import scipy
import scipy.io
import scipy.misc
from utils_sys import Printer
from lib.model_test import D2Net
from lib.utils import preprocess_image
from lib.pyramid import process_multiscale
kVerbose = True
# convert matrix of pts into list of keypoints
def convert_pts_to_keypoints(pts, scores, size=1):
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=size, _response=scores[i]) for i,p in enumerate(pts) ]
return kps
# interface for pySLAM
# from https://github.com/mihaidusmanu/d2-net
# N.B.: The singlescale features require less than 6GB of VRAM for 1200x1600 images.
# The multiscale flag can be used to extract multiscale features - for this, we recommend at least 12GB of VRAM.
class D2NetFeature2D:
def __init__(self,
use_relu=True, # remove ReLU after the dense feature extraction module
multiscale=False, # extract multiscale features (read the note above)
max_edge=1600, # maximum image size at network input
max_sum_edges=2800, # maximum sum of image sizes at network input
preprocessing='torch', # image preprocessing (caffe or torch)
do_cuda=True):
print('Using D2NetFeature2D')
self.lock = RLock()
self.model_base_path = config.cfg.root_folder + '/thirdparty/d2net/'
self.models_path = self.model_base_path + 'models/d2_ots.pth' # best performances obtained with 'd2_ots.pth'
self.use_relu = use_relu
self.multiscale = multiscale
self.max_edge = max_edge
self.max_sum_edges = max_sum_edges
self.preprocessing = preprocessing
self.pts = []
self.kps = []
self.des = []
self.frame = None
self.keypoint_size = 20 # just a representative size for visualization and in order to convert extracted points to cv2.KeyPoint
self.do_cuda = do_cuda & torch.cuda.is_available()
print('cuda:',self.do_cuda)
self.device = torch.device("cuda:0" if self.do_cuda else "cpu")
torch.set_grad_enabled(False)
print('==> Loading pre-trained network.')
# Creating CNN model
self.model = D2Net(
model_file=self.models_path,
use_relu=use_relu,
use_cuda=do_cuda)
if self.do_cuda:
print('Extracting on GPU')
else:
print('Extracting on CPU')
print('==> Successfully loaded pre-trained network.')
def compute_kps_des(self, image):
with self.lock:
print('D2Net image shape:',image.shape)
if len(image.shape) == 2:
image = image[:, :, np.newaxis]
image = np.repeat(image, 3, -1)
# TODO: switch to PIL.Image due to deprecation of scipy.misc.imresize.
resized_image = image
if max(resized_image.shape) > self.max_edge:
resized_image = scipy.misc.imresize(
resized_image,
self.max_edge / max(resized_image.shape)
).astype('float')
if sum(resized_image.shape[: 2]) > self.max_sum_edges:
resized_image = scipy.misc.imresize(
resized_image,
self.max_sum_edges / sum(resized_image.shape[: 2])
).astype('float')
fact_i = image.shape[0] / resized_image.shape[0]
fact_j = image.shape[1] / resized_image.shape[1]
print('scale factors: {}, {}'.format(fact_i,fact_j))
input_image = preprocess_image(
resized_image,
preprocessing=self.preprocessing
)
with torch.no_grad():
if self.multiscale:
self.pts, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
device=self.device
),
self.model
)
else:
self.pts, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
device=self.device
),
self.model,
scales=[1]
)
# Input image coordinates
self.pts[:, 0] *= fact_i
self.pts[:, 1] *= fact_j
# i, j -> u, v
self.pts = self.pts[:, [1, 0, 2]]
#print('pts.shape: ', self.pts.shape)
#print('pts:', self.pts)
self.kps = convert_pts_to_keypoints(self.pts, scores, self.keypoint_size)
self.des = descriptors
return self.kps, self.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: D2NET, descriptor: D2NET, #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: D2NET is recomputing both kps and des on last input frame', frame.shape)
self.detectAndCompute(frame)
return self.kps, self.des