def extract_local_features(gray_list, model_path):
cv_kpts_list = []
loc_info_list = []
loc_feat_list = []
sift_feat_list = []
#model = get_model('loc_model')(model_path, **{'sift_desc': True,
model = LocModel(
model_path, **{
'sift_desc': True,
'n_feature': 2000,
'n_sample': FLAGS.n_sample,
'peak_thld': 0.04,
'dense_desc': FLAGS.dense_desc,
'upright': False
})
for _, val in enumerate(gray_list):
loc_feat, kpt_mb, normalized_xy, cv_kpts, sift_desc = model.run_test_data(
val)
raw_kpts = [
np.array((i.pt[0], i.pt[1], i.size, i.angle, i.response))
for i in cv_kpts
]
raw_kpts = np.stack(raw_kpts, axis=0)
loc_info = np.concatenate((raw_kpts, normalized_xy, loc_feat, kpt_mb),
axis=-1)
cv_kpts_list.append(cv_kpts)
loc_info_list.append(loc_info)
sift_feat_list.append(sift_desc)
loc_feat_list.append(loc_feat /
np.linalg.norm(loc_feat, axis=-1, keepdims=True))
model.close()
return cv_kpts_list, loc_info_list, loc_feat_list, sift_feat_list
def __init__(self,
num_features=2000,
n_sample=2048, # Maximum number of sampled keypoints per octave
dense_desc=False, # Whether to use dense descriptor model
model_type='pb',
do_tf_logging=False):
print('Using ContextDescFeature2D')
self.lock = RLock()
self.model_base_path= config.cfg.root_folder + '/thirdparty/contextdesc/'
set_tf_logging(do_tf_logging)
self.num_features = num_features
self.n_sample = n_sample
self.model_type = model_type
self.dense_desc = dense_desc
self.quantize = ContextDescFeature2D.quantize
self.loc_model_path = self.model_base_path + 'pretrained/contextdesc++'
self.reg_model_path = self.model_base_path + 'pretrained/retrieval_model'
if self.model_type == 'pb':
reg_model_path = os.path.join(self.reg_model_path, 'reg.pb')
loc_model_path = os.path.join(self.loc_model_path, 'loc.pb')
aug_model_path = os.path.join(self.loc_model_path, 'aug.pb')
elif self.model_type == 'ckpt':
reg_model_path = os.path.join(self.reg_model_path, 'model.ckpt-550000')
loc_model_path = os.path.join(self.loc_model_path, 'model.ckpt-400000')
aug_model_path = os.path.join(self.loc_model_path, 'model.ckpt-400000')
else:
raise NotImplementedError
self.keypoint_size = 10 # just a representative size for visualization and in order to convert extracted points to cv2.KeyPoint
self.pts = []
self.kps = []
self.des = []
self.scales = []
self.scores = []
self.frame = None
print('==> Loading pre-trained network.')
self.ref_model = RegModel(reg_model_path) #get_model('reg_model')(reg_model_path) #RegModel(reg_model_path)
self.loc_model = LocModel(loc_model_path, **{'sift_desc': False, # compute or not SIFT descriptor (we do not need them here!)
'n_feature': self.num_features,
'n_sample': self.n_sample,
'peak_thld': 0.04,
'dense_desc': self.dense_desc,
'upright': False})
self.aug_model = AugModel(aug_model_path, **{'quantz': self.quantize})
print('==> Successfully loaded pre-trained network.')
class ContextDescFeature2D:
quantize=False # Wheter to quantize or not the output descriptor
def __init__(self,
num_features=2000,
n_sample=2048, # Maximum number of sampled keypoints per octave
dense_desc=False, # Whether to use dense descriptor model
model_type='pb',
do_tf_logging=False):
print('Using ContextDescFeature2D')
self.lock = RLock()
self.model_base_path= config.cfg.root_folder + '/thirdparty/contextdesc/'
set_tf_logging(do_tf_logging)
self.num_features = num_features
self.n_sample = n_sample
self.model_type = model_type
self.dense_desc = dense_desc
self.quantize = ContextDescFeature2D.quantize
self.loc_model_path = self.model_base_path + 'pretrained/contextdesc++'
self.reg_model_path = self.model_base_path + 'pretrained/retrieval_model'
if self.model_type == 'pb':
reg_model_path = os.path.join(self.reg_model_path, 'reg.pb')
loc_model_path = os.path.join(self.loc_model_path, 'loc.pb')
aug_model_path = os.path.join(self.loc_model_path, 'aug.pb')
elif self.model_type == 'ckpt':
reg_model_path = os.path.join(self.reg_model_path, 'model.ckpt-550000')
loc_model_path = os.path.join(self.loc_model_path, 'model.ckpt-400000')
aug_model_path = os.path.join(self.loc_model_path, 'model.ckpt-400000')
else:
raise NotImplementedError
self.keypoint_size = 10 # just a representative size for visualization and in order to convert extracted points to cv2.KeyPoint
self.pts = []
self.kps = []
self.des = []
self.scales = []
self.scores = []
self.frame = None
print('==> Loading pre-trained network.')
self.ref_model = RegModel(reg_model_path) #get_model('reg_model')(reg_model_path) #RegModel(reg_model_path)
self.loc_model = LocModel(loc_model_path, **{'sift_desc': False, # compute or not SIFT descriptor (we do not need them here!)
'n_feature': self.num_features,
'n_sample': self.n_sample,
'peak_thld': 0.04,
'dense_desc': self.dense_desc,
'upright': False})
self.aug_model = AugModel(aug_model_path, **{'quantz': self.quantize})
print('==> Successfully loaded pre-trained network.')
def __del__(self):
with self.lock:
self.ref_model.close()
self.loc_model.close()
self.aug_model.close()
def prep_img(self,img):
rgb_list = []
gray_list = []
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[..., np.newaxis]
img = img[..., ::-1]
rgb_list.append(img)
gray_list.append(gray)
return rgb_list, gray_list
# extract regional features
def extract_regional_features(self,rgb_list):
reg_feat_list = []
#model = get_model('reg_model')(model_path)
for _, val in enumerate(rgb_list):
reg_feat = self.ref_model.run_test_data(val)
reg_feat_list.append(reg_feat)
#model.close()
return reg_feat_list
# extract local features and keypoint matchability
def extract_local_features(self,gray_list):
cv_kpts_list = []
loc_info_list = []
loc_feat_list = []
sift_feat_list = []
# model = get_model('loc_model')(model_path, **{'sift_desc': True,
# 'n_sample': FLAGS.n_sample,
# 'peak_thld': 0.04,
# 'dense_desc': FLAGS.dense_desc,
# 'upright': False})
for _, val in enumerate(gray_list):
loc_feat, kpt_mb, normalized_xy, cv_kpts, sift_desc = self.loc_model.run_test_data(val)
raw_kpts = [np.array((i.pt[0], i.pt[1], i.size, i.angle, i.response)) for i in cv_kpts]
raw_kpts = np.stack(raw_kpts, axis=0)
loc_info = np.concatenate((raw_kpts, normalized_xy, loc_feat, kpt_mb), axis=-1)
cv_kpts_list.append(cv_kpts)
loc_info_list.append(loc_info)
sift_feat_list.append(sift_desc)
loc_feat_list.append(loc_feat / np.linalg.norm(loc_feat, axis=-1, keepdims=True))
#model.close()
return cv_kpts_list, loc_info_list, loc_feat_list, sift_feat_list
# extract augmented features
def extract_augmented_features(self,reg_feat_list, loc_info_list):
aug_feat_list = []
#model = get_model('aug_model')(model_path, **{'quantz': False})
assert len(reg_feat_list) == len(loc_info_list)
for idx, _ in enumerate(reg_feat_list):
aug_feat, _ = self.aug_model.run_test_data([reg_feat_list[idx], loc_info_list[idx]])
aug_feat_list.append(aug_feat)
#model.close()
return aug_feat_list
def compute_kps_des(self, frame):
with self.lock:
rgb_list, gray_list = self.prep_img(frame)
# extract regional features.
reg_feat_list = self.extract_regional_features(rgb_list)
# extract local features and keypoint matchability.
cv_kpts_list, loc_info_list, loc_feat_list, sift_feat_list = self.extract_local_features(gray_list)
# extract augmented features.
aug_feat_list = self.extract_augmented_features(reg_feat_list,loc_info_list)
self.kps = cv_kpts_list[0]
self.des = aug_feat_list[0]
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: CONTEXTDESC, descriptor: CONTEXTDESC, #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: CONTEXTDESC is recomputing both kps and des on last input frame', frame.shape)
self.detectAndCompute(frame)
return self.kps, self.des