# Copyright (C) 2019 Titus Cieslewski, RPG, University of Zurich, Switzerland
# You can contact the author at <titus at ifi dot uzh dot ch>
# Copyright (C) 2019 Michael Bloesch,
# Dept. of Computing, Imperial College London, United Kingdom
# Copyright (C) 2019 Davide Scaramuzza, RPG, University of Zurich, Switzerland
#
# This file is part of imips_open.
#
# imips_open 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.
#
# imips_open 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 imips_open. If not, see <http:#www.gnu.org/licenses/>.
from absl import flags
import cv2
import numpy as np
FLAGS = flags.FLAGS
def withinRadiusOfBorder(point_rc, image_shape, radius):
below = np.any(point_rc < radius)
above = np.any(point_rc >= np.array(image_shape) - radius)
return below or above
class ForwardPass(object):
def __init__(self, ips_rc, ip_scores, descriptors, scales=1.,
image_scores=None):
assert descriptors.shape[0] == ips_rc.shape[1]
self.ips_rc = ips_rc
assert ips_rc.dtype == int
self.ip_scores = ip_scores
self.descriptors = descriptors
self.scales = scales
if type(self.scales) == np.ndarray:
assert len(scales) == ips_rc.shape[1]
self.image_scores = image_scores
def __getitem__(self, slc):
# Only applies for subselection; matched forward passes are ok to have
# non-ordered score.
assert np.all(self.ip_scores[:-1] >= self.ip_scores[1:])
if type(self.scales) == np.ndarray:
scales = self.scales[slc]
else:
scales = self.scales
return ForwardPass(self.ips_rc[:, slc], self.ip_scores[slc],
self.descriptors[slc, :], scales, self.image_scores)
def __len__(self):
return len(self.ip_scores)
def reducedTo(self, num_ips):
return self[:num_ips]
def hicklable(self):
return [self.ips_rc, self.ip_scores, self.descriptors, self.scales]
def render(self, n_max=0, fallback_im=None):
if self.image_scores is not None:
im = cv2.applyColorMap((self.image_scores * 255).astype(np.uint8),
cv2.COLORMAP_JET)
else:
assert fallback_im is not None
im = cv2.cvtColor(fallback_im, cv2.COLOR_GRAY2BGR)
if n_max == 0:
n_max = self.ips_rc.shape[1]
for i in range(n_max):
thickness_relevant_score = \
np.clip(self.ip_scores[i], 0.2, 0.6) - 0.2
thickness = int(thickness_relevant_score * 20)
if type(self.scales) == np.ndarray:
radius = int(self.scales[i] * 10)
else:
radius = 10
cv2.circle(im, tuple(self.ips_rc[[1, 0], i]),
radius, (0, 255, 0), thickness, cv2.LINE_AA)
return im
def match(forward_passes):
if FLAGS.baseline == 'orb':
print('Using Hamming matching for ORB...')
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
else:
matcher = cv2.BFMatcher(crossCheck=True)
matches = matcher.match(forward_passes[0].descriptors,
forward_passes[1].descriptors)
return [np.array([x.queryIdx for x in matches]),
np.array([x.trainIdx for x in matches])]
