def getAffmaps_from_Affnet(patches_np): sp1, sp2 = np.shape(patches_np[0]) subpatches = torch.autograd.Variable( torch.zeros([len(patches_np), 1, 32, 32], dtype=torch.float32), volatile=True).view(len(patches_np), 1, 32, 32) for k in range(0, len(patches_np)): subpatch = patches_np[k][int(sp1 / 2) - 16:int(sp2 / 2) + 16, int(sp1 / 2) - 16:int(sp2 / 2) + 16].reshape( 1, 1, 32, 32) subpatches[k, :, :, :] = torch.from_numpy(subpatch.astype( np.float32)) #=subpatch x, y = subpatches.shape[3] / 2.0 + 2, subpatches.shape[2] / 2.0 + 2 LAFs = normalizeLAFs( torch.tensor([[AffNetPix.PS / 2, 0, x], [0, AffNetPix.PS / 2, y]]).reshape(1, 2, 3), subpatches.shape[3], subpatches.shape[2]) baseLAFs = torch.zeros([subpatches.shape[0], 2, 3], dtype=torch.float32) for m in range(subpatches.shape[0]): baseLAFs[m, :, :] = LAFs if USE_CUDA: # or ---> A = AffNetPix(subpatches.cuda()).cpu() with torch.no_grad(): A = batched_forward(AffNetPix, subpatches.cuda(), 256).cpu() else: with torch.no_grad(): A = AffNetPix(subpatches) LAFs = torch.cat([torch.bmm(A, baseLAFs[:, :, 0:2]), baseLAFs[:, :, 2:]], dim=2) dLAFs = denormalizeLAFs(LAFs, subpatches.shape[3], subpatches.shape[2]) Alist = convertLAFs_to_A23format(dLAFs.detach().cpu().numpy().astype( np.float32)) return 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()
def visualize_LAFs_on_fig(ax, LAFs, color='r'): work_LAFs = convertLAFs_to_A23format(LAFs) lines = {} for i in range(len(work_LAFs)): ell = LAF2pts(work_LAFs[i, :, :]) lines[str(i)], = ax.plot(ell[:, 0], ell[:, 1], color) return lines, ax
def visualize_LAFs_on_fig_mod(laf_dict, line_dict, dname, i): work_LAFs = convertLAFs_to_A23format(laf_dict[dname][i]) ells = [] for jj in range(len(work_LAFs)): ell = LAF2pts(work_LAFs[jj, :, :]) line_dict[dname][str(i)].set_data(ell[:, 0], ell[:, 1]) return line_dict[dname], ax
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 AffNetHardNet_describe(patches): descriptors = np.zeros(shape=[patches.shape[0], 128], dtype=np.float32) HessianAffine = [] subpatches = torch.autograd.Variable(torch.zeros([len(patches), 1, 32, 32], dtype=torch.float32), volatile=True).view( len(patches), 1, 32, 32) baseLAFs = torch.zeros([len(patches), 2, 3], dtype=torch.float32) for m in range(patches.shape[0]): patch_np = patches[m, :, :, 0].reshape(np.shape(patches)[1:3]) HessianAffine.append( ScaleSpaceAffinePatchExtractor(mrSize=5.192, num_features=0, border=0, num_Baum_iters=0)) with torch.no_grad(): var_image = torch.autograd.Variable(torch.from_numpy( patch_np.astype(np.float32)), volatile=True) patch = var_image.view(1, 1, var_image.size(0), var_image.size(1)) with torch.no_grad(): HessianAffine[m].createScaleSpace( patch) # to generate scale pyramids and stuff x, y = patch.size(3) / 2.0 + 2, patch.size(2) / 2.0 + 2 LAFs = normalizeLAFs( torch.tensor([[AffNetPix.PS / 2, 0, x], [0, AffNetPix.PS / 2, y]]).reshape(1, 2, 3), patch.size(3), patch.size(2)) baseLAFs[m, :, :] = LAFs with torch.no_grad(): subpatch = HessianAffine[m].extract_patches_from_pyr( denormalizeLAFs(LAFs, patch.size(3), patch.size(2)), PS=AffNetPix.PS) if WRITE_IMGS_DEBUG: SaveImageWithKeys( subpatch.detach().cpu().numpy().reshape([32, 32]), [], 'p1/' + str(n) + '.png') # This subpatch has been blured by extract_patches _from_pyr... # let't us crop it manually to obtain fair results agains other methods subpatch = patch_np[16:48, 16:48].reshape(1, 1, 32, 32) #var_image = torch.autograd.Variable(torch.from_numpy(subpatch.astype(np.float32)), volatile = True) #subpatch = var_image.view(1, 1, 32,32) subpatches[m, :, :, :] = torch.from_numpy( subpatch.astype(np.float32)) #=subpatch if WRITE_IMGS_DEBUG: SaveImageWithKeys( subpatch.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, subpatches.cuda(), 256).cpu() else: with torch.no_grad(): A = AffNetPix(subpatches) LAFs = torch.cat([torch.bmm(A, baseLAFs[:, :, 0:2]), baseLAFs[:, :, 2:]], dim=2) dLAFs = denormalizeLAFs(LAFs, patch.size(3), patch.size(2)) Alist = convertLAFs_to_A23format(dLAFs.detach().cpu().numpy().astype( np.float32)) for m in range(patches.shape[0]): with torch.no_grad(): patchaff = HessianAffine[m].extract_patches_from_pyr( dLAFs[m, :, :].reshape(1, 2, 3), PS=32) if WRITE_IMGS_DEBUG: SaveImageWithKeys( patchaff.detach().cpu().numpy().reshape([32, 32]), [], 'im1/' + str(n) + '.png') SaveImageWithKeys(patch_np, [], 'im2/' + str(n) + '.png') subpatches[m, :, :, :] = patchaff if USE_CUDA: with torch.no_grad(): # descriptors = HardNetDescriptor(subpatches.cuda()).detach().cpu().numpy().astype(np.float32) descriptors = batched_forward(HardNetDescriptor, subpatches.cuda(), 256).cpu().numpy().astype(np.float32) else: with torch.no_grad(): descriptors = HardNetDescriptor( subpatches).detach().cpu().numpy().astype(np.float32) return descriptors, Alist