def extract_query(net,
dataset,
q_idx,
scale=800,
crop=True,
flip=None,
rotate=None):
# Load query image
img = Image.open(dataset.get_query_filename(q_idx))
# Crop the query ROI
if crop:
img = img.crop(tuple(dataset.get_query_roi(q_idx)))
# Apply transformations
img = trf.resize_image(img, scale)
# Flip
if flip: img = trf.flip_image(img, flip)
# Rotation
if rotate: img = trf.rotate(img, rotate)
# Convert to Pytorch's tensor and normalize
I = trf.to_tensor(img)
I = trf.normalize(
I, dict(rgb_means=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
I = I.unsqueeze(0) #.to(device)
# Forward pass to extract the features
with torch.no_grad():
print('Extracting features using input={} pixels...'.format(scale))
q_feat = net(I).cpu().numpy()
return q_feat, img
def get_training_pair_images(file, centre_fraction, acceleration):
"""
:param file: The training image
:param centre_fraction: randomly generated centre fraction
:param acceleration: randomly generated
:return: true gold standard and the fft of the masked k-space image
"""
hf = h5py.File(file)
volume_kspace = hf['kspace'][()]
volume_image = hf['reconstruction_esc'][()]
mask_func = MaskFunc(center_fractions=[centre_fraction],
accelerations=[acceleration
]) # Create the mask function object
volume_kspace_tensor = T.to_tensor(volume_kspace)
masked_kspace, mask = T.apply_mask(volume_kspace_tensor, mask_func)
##masked_kspace_np=masked_kspace.numpy().reshape(masked_kspace.shape)
recon_image = T.ifft2(masked_kspace) # complex image
recon_image_abs = T.complex_abs(
masked_kspace) # compute absolute value to get a real image
#recon_image_rss= T.root_sum_of_square(recon_image_abs,dim=0) # compute absolute rss
return np.expand_dims(volume_image, 3), recon_image_abs.numpy()
def demo(args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load the Oxford5k database
dataset = create('Oxford')
print(dataset)
# initialize architecture and load weights
print('Loading the model...')
model = resnet50_rank_DA().to(device)
model.eval()
print('Done\n')
# load the precomputed dataset features
d_feats_file = 'data/features/resnet50-rnk-lm-da_ox.npy'
try:
d_feats = np.load(d_feats_file)
except OSError as e:
print(
'ERROR: File {} not found. Please follow the instructions to download the pre-computed features.'
.format(d_feats_file))
sys.exit()
# Load the query image
img = Image.open(dataset.get_query_filename(args.qidx))
# Crop the query ROI
img = img.crop(tuple(dataset.get_query_roi(args.qidx)))
# Apply transformations
img = trf.resize_image(img, 800)
I = trf.to_tensor(img)
I = trf.normalize(
I, dict(rgb_means=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
I = I.unsqueeze(0).to(device)
# Forward pass to extract the features
with torch.no_grad():
print('Extracting the representation of the query...')
q_feat = model(I).cpu().numpy()
print('Done\n')
# Rank the database and visualize the top-k most similar images in the database
dataset.vis_top(d_feats,
args.qidx,
q_feat=q_feat,
topk=args.topk,
out_image_file='results/out.png')
def get_training_pair(file, centre_fraction, acceleration):
"""
:param file: The training image
:param centre_fraction: randomly generated centre fraction
:param acceleration: randomly generated
:return:
"""
hf = h5py.File(file)
volume_kspace = hf['kspace'][()]
volume_image = hf['reconstruction_esc'][()]
mask_func = MaskFunc(center_fractions=[centre_fraction],
accelerations=[acceleration
]) # Create the mask function object
volume_kspace_tensor = T.to_tensor(volume_kspace)
masked_kspace, mask = T.apply_mask(volume_kspace_tensor, mask_func)
masked_kspace_np = masked_kspace.numpy().reshape(masked_kspace.shape)
return np.expand_dims(volume_image, 3), masked_kspace_np
d_feats_file = 'data/features/resnet50-rnk-lm-da_ox.npy'
try:
d_feats = np.load(d_feats_file)
except OSError as e:
print(
'ERROR: File {} not found. Please follow the instructions to download the pre-computed features.'
.format(d_feats_file))
sys.exit()
# Load the query image
img = Image.open(dataset.get_query_filename(args.qidx))
# Crop the query ROI
img = img.crop(tuple(dataset.get_query_roi(args.qidx)))
# Apply transformations
img = trf.resize_image(img, 800)
I = trf.to_tensor(img)
I = trf.normalize(
I, dict(rgb_means=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
I = I.unsqueeze(0).to(device)
# Forward pass to extract the features
with torch.no_grad():
print('Extracting the representation of the query...')
q_feat = model(I).numpy()
print('Done\n')
# Rank the database and visualize the top-k most similar images in the database
dataset.vis_top(d_feats,
args.qidx,
q_feat=q_feat,
topk=args.topk,
out_image_file='out.png')