def compute(self, img, kps, mask=None): #mask is a fake input
num_kps = len(kps)
des = []
if num_kps>0:
if not self.process_all:
# compute descriptor for each patch
patches = extract_patches_tensor(img, kps, patch_size=32, mag_factor=self.mag_factor)
# patches /= 255.
# patches -= 0.443728476019
# patches /= 0.20197947209
patches = (patches/255. - 0.443728476019)/0.20197947209
des = self.compute_des_batches(patches).astype(np.float32)
else:
# compute descriptor by feeeding the full patch tensor to the network
t = time.time()
if False:
# use python code
patches = extract_patches_array(img, kps, patch_size=32, mag_factor=self.mag_factor)
else:
# use faster cpp code
patches = extract_patches_array_cpp(img, kps, patch_size=32, mag_factor=self.mag_factor)
patches = np.asarray(patches)
if kVerbose:
print('patches.shape:',patches.shape)
# patches /= 255.
# patches -= 0.443728476019
# patches /= 0.20197947209
patches = (patches/255. - 0.443728476019)/0.20197947209
if kVerbose:
print('patch elapsed: ', time.time()-t)
des = self.compute_des(patches)
# if kVerbose:
# print('descriptor: HARDNET, #features: ', len(kps), ', frame res: ', img.shape[0:2])
return kps, des
def compute(self, frame, kps, mask=None): #mask is a fake input
#print('kps: ', kps)
if len(kps) > 0:
#des = tfeat_utils.describe_opencv(self.model, frame, kps, 32, self.mag_factor)
# extract the keypoint patches
#t = time.time()
if False:
# use python code
patches = extract_patches_array(frame,
kps,
patch_size=32,
mag_factor=self.mag_factor)
else:
# use faster cpp code
patches = extract_patches_array_cpp(frame,
kps,
patch_size=32,
mag_factor=self.mag_factor)
patches = np.asarray(patches)
#if kVerbose:
# print('patches.shape:',patches.shape)
#if kVerbose:
# print('patch elapsed: ', time.time()-t)
# compute descriptor by feeeding the full patch tensor to the network
des = self.compute_des(patches)
else:
des = []
# if kVerbose:
# print('descriptor: TFEAT, #features: ', len(kps), ', frame res: ', frame.shape[0:2])
return kps, des
def compute(self, frame, kps, mask=None): #mask is a fake input
#print('kps: ', kps)
if len(kps)>0:
if False:
# use python code
patches = extract_patches_array(frame, kps, patch_size=32, mag_factor=self.mag_factor)
else:
# use faster cpp code
patches = extract_patches_array_cpp(frame, kps, patch_size=32, mag_factor=self.mag_factor)
patches = np.asarray(patches)
des = self.process_patches(patches)
else:
des = []
if kVerbose:
print('descriptor: GEODESC, #features: ', len(kps), ', frame res: ', frame.shape[0:2])
return kps, des
def compute(self, frame, kps, mask=None): #mask is a fake input
#print('kps: ', kps)
if len(kps)>0:
if False:
# use python code
patches = extract_patches_array(frame, kps, patch_size=32, mag_factor=self.mag_factor)
else:
# use faster cpp code
patches = extract_patches_array_cpp(frame, kps, patch_size=32, mag_factor=self.mag_factor)
patches = np.asarray(patches)
patches = np.expand_dims(patches, -1)
self.des = self.l2net.calc_descriptors(patches)
else:
self.des = []
if kVerbose:
print('descriptor: L2NET, #features: ', len(kps), ', frame res: ', frame.shape[0:2])
return kps, self.des