def AffNetHardNet_describeFromKeys(img_np, KPlist):
img = torch.autograd.Variable(torch.from_numpy(img_np.astype(np.float32)),
volatile=True)
img = img.view(1, 1, img.size(0), img.size(1))
HessianAffine = ScaleSpaceAffinePatchExtractor(mrSize=5.192,
num_features=0,
border=0,
num_Baum_iters=0)
if USE_CUDA:
HessianAffine = HessianAffine.cuda()
img = img.cuda()
with torch.no_grad():
HessianAffine.createScaleSpace(
img) # to generate scale pyramids and stuff
descriptors = []
Alist = []
n = 0
# for patch_np in patches:
for kp in KPlist:
x, y = np.float32(kp.pt)
LAFs = normalizeLAFs(
torch.tensor([[AffNetPix.PS / 2, 0, x], [0, AffNetPix.PS / 2,
y]]).reshape(1, 2, 3),
img.size(3), img.size(2))
with torch.no_grad():
patch = HessianAffine.extract_patches_from_pyr(denormalizeLAFs(
LAFs, img.size(3), img.size(2)),
PS=AffNetPix.PS)
if WRITE_IMGS_DEBUG:
SaveImageWithKeys(patch.detach().cpu().numpy().reshape([32, 32]),
[], 'p2/' + str(n) + '.png')
if USE_CUDA:
# or ---> A = AffNetPix(subpatches.cuda()).cpu()
with torch.no_grad():
A = batched_forward(AffNetPix, patch.cuda(), 256).cpu()
else:
with torch.no_grad():
A = AffNetPix(patch)
new_LAFs = torch.cat([torch.bmm(A, LAFs[:, :, 0:2]), LAFs[:, :, 2:]],
dim=2)
dLAFs = denormalizeLAFs(new_LAFs, img.size(3), img.size(2))
with torch.no_grad():
patchaff = HessianAffine.extract_patches_from_pyr(dLAFs, PS=32)
if WRITE_IMGS_DEBUG:
SaveImageWithKeys(
patchaff.detach().cpu().numpy().reshape([32, 32]), [],
'p1/' + str(n) + '.png')
SaveImageWithKeys(img_np, [kp], 'im1/' + str(n) + '.png')
descriptors.append(
HardNetDescriptor(patchaff).cpu().numpy().astype(np.float32))
Alist.append(
convertLAFs_to_A23format(LAFs.detach().cpu().numpy().astype(
np.float32)))
n = n + 1
return descriptors, Alist
def HessAff_Detect(img, PatchSize=60, Nfeatures=500):
var_image = torch.autograd.Variable(torch.from_numpy(img.astype(
np.float32)),
volatile=True)
var_image_reshape = var_image.view(1, 1, var_image.size(0),
var_image.size(1))
HessianAffine = ScaleSpaceAffinePatchExtractor(mrSize=5.192,
num_features=Nfeatures,
border=PatchSize / 2,
num_Baum_iters=1)
# if USE_CUDA:
# HessianAffine = HessianAffine.cuda()
# var_image_reshape = var_image_reshape.cuda()
with torch.no_grad():
LAFs, responses = HessianAffine(var_image_reshape, do_ori=True)
patches = HessianAffine.extract_patches_from_pyr(LAFs,
PS=PatchSize).cpu()
# these are my affine maps to work with
Alist = convertLAFs_to_A23format(LAFs).cpu().numpy().astype(np.float32)
KPlist = [
cv2.KeyPoint(x=A[0, 2],
y=A[1, 2],
_size=10,
_angle=0.0,
_response=1,
_octave=packSIFTOctave(0, 0),
_class_id=1) for A in Alist
]
return KPlist, np.array(patches), Alist, responses.cpu()