def detect(detector, fn, splt_num=64, simu_type="default"):
full_fn = myfsys.get_template_file_full_path_(fn)
img = read_image(full_fn)
cv2.imshow('hoge', img)
with Timer('Detection with [ ' + simu_type + ' ]'):
splt_kp, splt_desc = spltA.affine_detect_into_mesh(detector, splt_num, img, simu_param=simu_type)
return img, splt_kp, splt_desc
def split_asift_detect(detector, fn, split_num):
img = emod.read_image(fn)
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
with Timer('Detection with [ ASIFT ]'):
splt_kp, splt_desc = saf.affine_detect_into_mesh(detector,
split_num,
img,
simu_param='asift')
return img, splt_kp, splt_desc
def detect_and_match(detector, matcher, set_fn, splt_num=64, simu_type="default"):
"""
SplitA実験
set_fn:
"""
fnQ, testcase, fnT = set_fn
def get_expt_names():
tmpf, tmpext = os.path.splitext(fnT)
return (os.path.basename(__file__), testcase, tmpf)
expt_names = get_expt_names()
logger = setup(expt_names)
logger.info(__doc__)
full_fnQ = myfsys.getf_template((fnQ,))
full_fnT = myfsys.getf_input(testcase, fnT)
imgQ, imgT = read_images(full_fnQ, full_fnT, logger)
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
with Timer('Detection with SPLIT-ASIFT', logger):
splt_kpQ, splt_descQ = spltA.affine_detect_into_mesh(detector, splt_num, imgQ, simu_param=simu_type)
with Timer('Detection with SFIT', logger):
kpT, descT = affine_detect(detector, imgT, pool=pool, simu_param='test')
logger.info('imgQ - {0} features, imgT - {1} features'.format(spltA.count_keypoints(splt_kpQ), len(kpT)))
with Timer('matching', logger):
mesh_pQ, mesh_pT, mesh_pairs = spltA.match_with_cross(matcher, splt_descQ, splt_kpQ, descT, kpT)
Hs = []
statuses = []
kp_pairs_long = []
Hs_stable = []
kp_pairs_long_stable = []
for pQ, pT, pairs in zip(mesh_pQ, mesh_pT, mesh_pairs):
pairs, H, status = calclate_Homography(pQ, pT, pairs)
Hs.append(H)
statuses.append(status)
if status is not None and not len(status) == 0 and np.sum(status)/len(status) >= 0.4:
Hs_stable.append(H)
else:
Hs_stable.append(None)
for p in pairs:
kp_pairs_long.append(p)
if status is not None and not len(status) == 0 and np.sum(status)/len(status) >= 0.4:
kp_pairs_long_stable.append(p)
vis = draw_matches_for_meshes(imgQ, imgT, Hs=Hs)
cv2.imwrite(myfsys.getf_output(expt_names, 'meshes.png'), vis)
visS = draw_matches_for_meshes(imgQ, imgT, Hs=Hs_stable)
cv2.imwrite(myfsys.getf_output(expt_names, 'meshes_stable.png'), visS)
viw = explore_match_for_meshes('affine find_obj', imgQ, imgT, kp_pairs_long_stable, Hs=Hs_stable)
cv2.imwrite(myfsys.getf_output(expt_names, 'meshes_and_keypoints_stable.png'), viw)
return vis, visS, viw
def get_split_keypoint_detector(_template_fn, _temp_inf, _detector, _imgQ):
try:
with splta.Timer('Lording pickle'):
splt_kpQ, splt_descQ = splta.affine_load_into_mesh(
_template_fn, _temp_inf.get_splitnum())
except ValueError as e:
print(e.args)
print('If you need to save {} to file as datavase. ¥n' +
' Execute makedb/make_split_combine_featureDB_from_templates.py')
with splta.Timer('Detection and dividing'):
splt_kpQ, splt_descQ = splta.affine_detect_into_mesh(
_detector, _temp_inf.get_splitnum(), _imgQ, simu_param='asift')
return splt_kpQ, splt_descQ
def test_affine_detect_into_mesh(self):
with Timer('Detection with split into mesh'):
splits_kp, splits_desc = splta2.affine_detect_into_mesh(
self.detector, self.splt_num, self.img1, simu_param='test2')
self.assertIsNotNone(splits_kp)
self.assertIsNotNone(splits_desc)
self.assertEqual(len(splits_kp), self.splt_num, "It is not same")
self.assertEqual(len(splits_desc), self.splt_num, "It is not same")
lenskp = 0
lensdescr = 0
for skp, sdesc in zip(splits_kp, splits_desc):
if not skp:
self.assertTrue(False, "Keypoints of mesh is Empty")
else:
self.assertEqual(len(skp), 3, "It is not same")
for mesh_kp in skp:
lenskp += len(mesh_kp)
if not sdesc:
self.assertTrue(False, "Descriptors of mesh is Empty")
self.assertNotEqual(sdesc[0].size, 0, "Descriptor is Empty")
self.assertEqual(sdesc[0].shape[1], 128, "SIFT features")
self.assertEqual(len(sdesc), 3, "It is not same")
for mesh_desc in sdesc:
lensdescr += mesh_desc.shape[0]
with Timer('Detection'):
kp, desc = affine_detect(self.detector,
self.img1,
simu_param='test2')
kps, descs = splta.affine_detect_into_mesh(self.detector,
self.splt_num,
self.img1,
simu_param='test2')
a = splta.count_keypoints(kps)
print("{0} == {1}, {2}, {3}".format(lenskp, a, len(kp), lensdescr))
self.assertEqual(lenskp, len(kp), "Some keypoints were droped out.")
self.assertEqual(lensdescr, len(desc),
"Some descriptors were droped out.")
self.assertEqual(lenskp, a, "Some keypoints were droped out.")
self.assertEqual(lensdescr, a, "Some descriptors were droped out.")
"_scols": scols,
"_srows": srows,
"_nneighbor": 4
}
temp_inf = splta.TmpInf(**template_information)
try:
with splta.Timer('Lording pickle'):
splt_kpQ, splt_descQ = splta.affine_load_into_mesh(
template_fn, temp_inf.get_splitnum())
except ValueError as e:
print(e.args)
print('If you need to save {} to file as datavase. ¥n' +
' Execute makedb/make_split_combine_featureDB_from_templates.py')
with splta.Timer('Detection and dividing'):
splt_kpQ, splt_descQ = splta.affine_detect_into_mesh(
detector, temp_inf.get_splitnum(), imgQ, simu_param='asift')
mesh_k_num = splta.np.array([len(keypoints) for keypoints in splt_kpQ
]).reshape(temp_inf.get_mesh_shape())
median = splta.np.nanmedian(mesh_k_num)
fn, ext = os.path.splitext(os.path.basename(fn2_full))
testset_name = os.path.basename(os.path.dirname(fn2_full))
imgT = splta.cv2.imread(fn2_full, 0)
if imgT is None:
print('Failed to load fn2:', fn2_full)
sys.exit(1)
print("INPUT: {}".format(fn2_full))
pool = splta.ThreadPool(processes=splta.cv2.getNumberOfCPUs())
with splta.Timer('Detection'):
sys.exit(1)
print('Using :', feature_name)
template_fn, ext = os.path.splitext(os.path.basename(fn1))
template_information = {"_fn": "tmp.png", "template_img": template_fn,
"_cols": 800, "_rows": 600, "_scols": 8, "_srows": 8, "_nneighbor": 4}
temp_inf = TmpInf(**template_information)
try:
with Timer('Lording pickle'):
splt_kpQ, splt_descQ = affine_load_into_mesh(template_fn, temp_inf.get_splitnum())
except ValueError as e:
print(e)
print('If you need to save {} to file as datavase. ¥n'
+ ' Execute /Users/tiwasaki/PycharmProjects/makedb/make_split_combine_featureDB_from_templates.py')
with Timer('Detection and dividing'):
splt_kpQ, splt_descQ = affine_detect_into_mesh(detector, temp_inf.get_splitnum(),
imgQ, simu_param='default')
sk_num = count_keypoints(splt_kpQ)
m_skQ, m_sdQ, m_k_num, merged_map = combine_mesh_compact(splt_kpQ, splt_descQ, temp_inf)
if not sk_num == count_keypoints(m_skQ) and not count_keypoints(m_skQ) == np.sum(m_k_num):
print('{0}, {1}, {2}'.format(sk_num, count_keypoints(m_skQ), np.sum(m_k_num)))
sys.exit(1)
median = np.nanmedian(m_k_num)
list_merged_mesh_id = list(set(np.ravel(merged_map)))
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
with Timer('Detection'):
kpT, descT = affine_detect(detector, imgT, pool=pool, simu_param='test')
with Timer('matching'):
mesh_pQ, mesh_pT, mesh_pairs = match_with_cross(matcher, m_sdQ, m_skQ, descT, kpT)
def test_result(self):
s_kp, s_desc = splta.affine_detect_into_mesh(self.detector,
self.splt_num, self.img1)
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
kp2, desc2 = affine_detect(self.detector, self.img2, pool=pool)
len_s_kp = 0
for kps in s_kp:
len_s_kp += len(kps)
print('imgQ - %d features, imgT - %d features' % (len_s_kp, len(kp2)))
def calc_H(kp1, kp2, desc1, desc2):
with Timer('matching'):
raw_matches = self.matcher.knnMatch(desc2, desc1, 2) #2
p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
print('%d / %d inliers/matched' %
(np.sum(status), len(status)))
# do not draw outliers (there will be a lot of them)
kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
else:
H, status = None, None
print(
'%d matches found, not enough for homography estimation' %
len(p1))
return kp_pairs, H, status
def match_and_draw(win):
list_kp_pairs = []
Hs = []
statuses = []
i = 0
for kps, desc in zip(s_kp, s_desc):
assert type(desc) == type(desc2), "EORROR TYPE"
with Timer('matching'):
raw_matches = self.matcher.knnMatch(desc2,
trainDescriptors=desc,
k=2) #2
p2, p1, kp_pairs = filter_matches(kp2, kps, raw_matches)
if len(p1) >= 4:
H, status = cv2.findHomography(p2, p1, cv2.RANSAC, 5.0)
print('%d / %d inliers/matched' %
(np.sum(status), len(status)))
# do not draw outliers (there will be a lot of them)
list_kp_pairs.extend(
[kpp for kpp, flag in zip(kp_pairs, status) if flag])
else:
H, status = None, None
print(
'%d matches found, not enough for homography estimation'
% len(p1))
Hs.append(H)
statuses.extend(status)
i += 1
vis = show(win, self.img2, self.img1, list_kp_pairs, statuses, Hs)
match_and_draw('affine find_obj')
cv2.waitKey()
cv2.destroyAllWindows()
self.assertEqual(1, 1)