def main(argv=None): # pylint: disable=unused-argument
"""Program entrance."""
# create sift detector.
sift_wrapper = SiftWrapper(n_sample=FLAGS.max_kpt_num)
sift_wrapper.half_sigma = FLAGS.half_sigma
sift_wrapper.pyr_off = FLAGS.pyr_off
sift_wrapper.ori_off = FLAGS.ori_off
sift_wrapper.create()
# create deep feature extractor.
graph = load_frozen_model(FLAGS.model_path, print_nodes=False)
sess = tf.Session(graph=graph)
# extract deep feature from images.
deep_feat1, cv_kpts1, img1 = extract_deep_features(sift_wrapper,
sess,
FLAGS.img1_path,
qtz=False)
deep_feat2, cv_kpts2, img2 = extract_deep_features(sift_wrapper,
sess,
FLAGS.img2_path,
qtz=False)
# match features by OpenCV brute-force matcher (CPU).
matcher_wrapper = MatcherWrapper()
# the ratio criterion is set to 0.89 for GeoDesc as described in the paper.
deep_good_matches, deep_mask = matcher_wrapper.get_matches(
deep_feat1,
deep_feat2,
cv_kpts1,
cv_kpts2,
ratio=0.89,
cross_check=True,
info='deep')
deep_display = matcher_wrapper.draw_matches(img1, cv_kpts1, img2, cv_kpts2,
deep_good_matches, deep_mask)
# compare with SIFT.
if FLAGS.cf_sift:
sift_feat1 = sift_wrapper.compute(img1, cv_kpts1)
sift_feat2 = sift_wrapper.compute(img2, cv_kpts2)
sift_good_matches, sift_mask = matcher_wrapper.get_matches(
sift_feat1,
sift_feat2,
cv_kpts1,
cv_kpts2,
ratio=0.80,
cross_check=True,
info='sift')
sift_display = matcher_wrapper.draw_matches(img1, cv_kpts1, img2,
cv_kpts2,
sift_good_matches,
sift_mask)
display = np.concatenate((sift_display, deep_display), axis=0)
else:
display = deep_display
cv2.imshow('display', display)
cv2.waitKey()
sess.close()
def get_data(self, seq_idx, dense_desc):
random.seed(0)
if self.suffix is None:
sift_wrapper = SiftWrapper(n_feature=self.sample_num, peak_thld=0.04)
sift_wrapper.ori_off = self.upright
sift_wrapper.create()
hseq_data = HSeqData()
seq_name = self.seqs[seq_idx]
for img_idx in range(1, 7):
# read image features.
img_feat = np.load(os.path.join(seq_name, '%d_img_feat.npy' % img_idx))
# read images.
img = cv2.imread(os.path.join(seq_name, '%d.ppm' % img_idx))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_size = img.shape
if self.suffix is None:
npy_kpts, cv_kpts = sift_wrapper.detect(gray)
if not dense_desc:
sift_wrapper.build_pyramid(gray)
patches = sift_wrapper.get_patches(cv_kpts)
else:
patches = None
else:
with open(os.path.join(seq_name, ('%d' + self.suffix + '.pkl') % img_idx), 'rb') as handle:
data_dict = pickle.load(handle, encoding='latin1')
npy_kpts = data_dict['npy_kpts']
if not dense_desc:
patches = data_dict['patches']
else:
patches = None
kpt_num = npy_kpts.shape[0]
# compose affine crop matrix.
crop_mat = np.zeros((kpt_num, 6))
# rely on the SIFT orientation estimation.
m_cos = np.cos(-npy_kpts[:, 3]) * self.patch_scale * npy_kpts[:, 2]
m_sin = np.sin(-npy_kpts[:, 3]) * self.patch_scale * npy_kpts[:, 2]
crop_mat[:, 0] = m_cos / float(img_size[1])
crop_mat[:, 1] = m_sin / float(img_size[1])
crop_mat[:, 2] = (npy_kpts[:, 0] - img_size[1] / 2.) / (img_size[1] / 2.)
crop_mat[:, 3] = -m_sin / float(img_size[0])
crop_mat[:, 4] = m_cos / float(img_size[0])
crop_mat[:, 5] = (npy_kpts[:, 1] - img_size[0] / 2.) / (img_size[0] / 2.)
npy_kpts = npy_kpts[:, 0:2]
# read homography matrix.
if img_idx > 1:
homo_mat = open(os.path.join(seq_name, 'H_1_%d' % img_idx)).read().splitlines()
homo_mat = np.array([float(i) for i in ' '.join(homo_mat).split()])
homo_mat = np.reshape(homo_mat, (3, 3))
else:
homo_mat = None
hseq_data.img.append(img)
hseq_data.kpt_param.append(crop_mat)
hseq_data.patch.append(patches)
hseq_data.coord.append(npy_kpts)
hseq_data.h**o.append(homo_mat)
hseq_data.img_feat.append(img_feat)
return seq_name, hseq_data
