def push_back(self,I):
"""
Compute features for current image, push to stack
"""
h,w = I.shape[:2]
self.h = h
self.w = w
if self.params['features_clahe'] > 0:
Ibw = clahe.convert_bw(I)
else:
if I.ndim > 2:
Ibw = cv2.cvtColor(I,7)
else:
Ibw = I
for i,s in enumerate(self.scale_ranges):
hn = int(np.round(h/s))
wn = int(np.round(w/s))
# WORKAROUNDS for some quirks in libviso2.
# We need to make sure that the image has an odd number of rows, and an
# even number of columns.
hn = hn - (hn % 2 == 0)
wn = wn - (wn % 2 == 1)
I_ = cv2.resize(Ibw,(wn,hn))
self.matchers[i].pushBack(I_)
def push_back(self, I):
"""
Compute features for current image, push to stack
"""
h, w = I.shape[:2]
self.h = h
self.w = w
if self.params['features_clahe'] > 0:
Ibw = clahe.convert_bw(I)
else:
if I.ndim > 2:
Ibw = cv2.cvtColor(I, 7)
else:
Ibw = I
for i, s in enumerate(self.scale_ranges):
hn = int(np.round(h / s))
wn = int(np.round(w / s))
# WORKAROUNDS for some quirks in libviso2.
# We need to make sure that the image has an odd number of rows, and an
# even number of columns.
hn = hn - (hn % 2 == 0)
wn = wn - (wn % 2 == 1)
I_ = cv2.resize(Ibw, (wn, hn))
self.matchers[i].pushBack(I_)
def push_back(self, I):
"""
Compute features for current image, push to stack
"""
h, w = I.shape[:2]
self.h = h
self.w = w
if self.params['features_clahe'] > 0:
if I.ndim > 2:
I_ = clahe.convert_color(I)
else:
I_ = clahe.convert_bw(I)
else:
I_ = I.copy()
print(I_.dtype)
kp_in_opencv, desc = self.extractor.detectAndCompute(I_, None)
kp_in = np.array([P.pt for P in kp_in_opencv])
if self.params['features_prune_border'] > 0:
prune = self.params['features_prune_border']
border_x = w * prune
border_y = h * prune
ind_valid = np.logical_and(
np.logical_and(kp_in[:, 0] >= border_x,
kp_in[:, 0] <= w - border_x),
np.logical_and(kp_in[:, 1] >= border_y,
kp_in[:, 1] <= h - border_y))
kp_in = kp_in[ind_valid, :]
# Clunky filtering of python list
# (Doing this via list(array(...)[inds]) is much slower).
kp_in_opencv = [
kp_in_opencv[i] for i in xrange(len(kp_in_opencv))
if ind_valid[i]
]
desc = desc[ind_valid, :]
#kp,desc = self.brisk_descriptor_extractor.compute(I_,kp_in_opencv)
#if I_.ndim > 2:
# kp,desc = self.opponentbrief_descriptor_extractor.compute(I_,kp_in)
#else:
# kp,desc = self.brief_descriptor_extractor.compute(I_,kp_in)
self.keypoints.append(kp_in_opencv)
self.descriptors.append(desc)
def push_back(self,I):
"""
Compute features for current image, push to stack
"""
h,w = I.shape[:2]
self.h = h
self.w = w
if self.params['features_clahe'] > 0:
if I.ndim > 2:
I_ = clahe.convert_color(I)
else:
I_ = clahe.convert_bw(I)
else:
I_ = I.copy()
print(I_.dtype)
kp_in_opencv,desc = self.extractor.detectAndCompute(I_,None)
kp_in = np.array([P.pt for P in kp_in_opencv])
if self.params['features_prune_border'] > 0:
prune = self.params['features_prune_border']
border_x = w * prune
border_y = h * prune
ind_valid = np.logical_and(np.logical_and(kp_in[:,0] >= border_x,
kp_in[:,0] <= w-border_x),
np.logical_and(kp_in[:,1] >= border_y,
kp_in[:,1] <= h-border_y))
kp_in = kp_in[ind_valid,:]
# Clunky filtering of python list
# (Doing this via list(array(...)[inds]) is much slower).
kp_in_opencv = [kp_in_opencv[i] for i in xrange(len(kp_in_opencv)) if ind_valid[i]]
desc = desc[ind_valid,:]
#kp,desc = self.brisk_descriptor_extractor.compute(I_,kp_in_opencv)
#if I_.ndim > 2:
# kp,desc = self.opponentbrief_descriptor_extractor.compute(I_,kp_in)
#else:
# kp,desc = self.brief_descriptor_extractor.compute(I_,kp_in)
self.keypoints.append(kp_in_opencv)
self.descriptors.append(desc)
