def inpainting_lista(missing_pixels, dicsize, n_nonzero_coefs, blksize,
overlap, maxiter, grayimg):
img_ori, masked_img, patchvec, maskvec = generate_inpainting_data(
missing_pixels=missing_pixels,
blksize=blksize,
overlap=overlap,
grayimg=grayimg)
# settings
h, w = img_ori.shape[0], img_ori.shape[1]
m = patchvec.shape[0]
n = dicsize
s = n_nonzero_coefs / n
print('[INFO] Dictionary size ({}, {}).'.format(m, n))
print('[INFO] Sparsity {}.'.format(n_nonzero_coefs))
print('[INFO] OMP max iter {}.'.format(maxiter))
D = get_init_dict(dicsize=dicsize, blksize=blksize)
Y = patchvec
# training
model = train_lista(D, Y, maskvec, s, img_ori, blksize, overlap)
# testing
with torch.no_grad():
Y_pt = torch.from_numpy(Y.T / 255.0).cuda()
coef_pt = model(Y_pt)
coef = coef_pt.cpu().detach().numpy().T
# testing
recovered_img = patch2im_for_inpainting(patch_vecs=255.0 *
np.matmul(D, coef),
mask_vecs=maskvec,
imgsize=img_ori.shape,
blksize=blksize,
overlap=overlap)
# save results
if not os.path.exists('./save_lista'):
os.mkdir('./save_lista')
np.save('./save_lista/img_ori', img_ori)
np.save('./save_lista/masked_img', masked_img)
np.save('./save_lista/recovered_img', recovered_img)
return img_ori, masked_img, recovered_img
def inpainting_ksvd(missing_pixels, dicsize, n_nonzero_coefs, blksize, overlap, maxiter, grayimg=False):
img_ori, masked_img, patchvec, maskvec = generate_inpainting_data(missing_pixels=missing_pixels,
blksize=blksize, overlap=overlap, grayimg=grayimg)
# img_ori, masked_img, patchvec, maskvec = load_inpainting_image(blksize=blksize, overlap=overlap)
# settings
h, w = img_ori.shape[0], img_ori.shape[1]
m = patchvec.shape[0]
n = dicsize
print('[INFO] Dictionary size ({}, {}).'.format(m, n))
print('[INFO] Sparsity {}.'.format(n_nonzero_coefs))
print('[INFO] OMP max iter {}.'.format(maxiter))
load_results = True
if load_results:
print('[INFO] Load OMP results.')
if grayimg:
dict = np.load('./D.npy')
coef = np.load('./coef.npy')
recovered_img = patch2im_for_inpainting(patch_vecs=np.matmul(dict, coef), mask_vecs=maskvec,
imgsize=(h, w), blksize=blksize, overlap=overlap)
else:
dictR = np.load('./DR.npy')
dictG = np.load('./DG.npy')
dictB = np.load('./DB.npy')
coefR = np.load('./coefR.npy')
coefG = np.load('./coefG.npy')
coefB = np.load('./coefB.npy')
recovered_imgR = patch2im_for_inpainting(patch_vecs=np.matmul(dictR, coefR), mask_vecs=maskvec,
imgsize=(h, w), blksize=blksize, overlap=overlap)
recovered_imgG = patch2im_for_inpainting(patch_vecs=np.matmul(dictG, coefG), mask_vecs=maskvec,
imgsize=(h, w), blksize=blksize, overlap=overlap)
recovered_imgB = patch2im_for_inpainting(patch_vecs=np.matmul(dictB, coefB), mask_vecs=maskvec,
imgsize=(h, w), blksize=blksize, overlap=overlap)
recovered_img = np.stack((recovered_imgR, recovered_imgG, recovered_imgB), axis=2)
return img_ori, masked_img, recovered_img
else:
# OMP for inpainting
print('[INFO] Run OMP for image inpainting')
if grayimg:
D = get_init_dict(dicsize=dicsize, blksize=blksize)
Y = patchvec
newD, coef = inpainting_ksvd_once(Y, D, maskvec, n_nonzero_coefs=50, maxiter=maxiter)
print('[INFO] Saving dictionary ...')
np.save('./D', newD)
print('[INFO] Saving coefficients ...')
np.save('./coef', coef)
# recover data
print('[INFO] Reconstruction ...')
recovered_img = patch2im_for_inpainting(patch_vecs=np.matmul(newD, coef), mask_vecs=maskvec, imgsize=img_ori.shape,
blksize=blksize, overlap=overlap)
else:
D = get_init_dict(dicsize=dicsize, blksize=blksize)
RGBname = ['R', 'G', 'B']
recovered_img = np.zeros_like(img_ori)
# do R-G-B
for channel in range(3):
Y = patchvec[:, :, channel]
newD, coef = inpainting_ksvd_once(Y, D, maskvec, n_nonzero_coefs=50, maxiter=maxiter)
print('[INFO] Saving dictionary ...')
np.save('./D{}'.format(RGBname[channel]), newD)
print('[INFO] Saving coefficients ...')
np.save('./coef{}'.format(RGBname[channel]), coef)
# recover data
print('[INFO] Reconstruction ...')
recovered_img_c = patch2im_for_inpainting(patch_vecs=np.matmul(newD, coef), mask_vecs=maskvec, imgsize=img_ori.shape,
blksize=blksize, overlap=overlap)
recovered_img[:, :, channel] = recovered_img_c
return img_ori, masked_img, recovered_img
def train_lista(D, Y, maskvec, s, img_ori, blksize, overlap):
"""
Trianing LISTA network.
:param D: size [m, n], e.g [64, 256]
:param Y: size [64, K], e.g [64, 10000]
:param s:
:param epochs:
:return:
"""
# training settings
bs = 128
learning_rate = 1e-2
epochs = 50
K = Y.shape[1]
iter_epoch = K // bs
eig, eig_vector = np.linalg.eig(D.T.dot(D))
L = np.max(eig) # Lipschitz constant
theta = s / L
m, n = D.shape
# normalize image
Y = (Y / 255.0) * maskvec
# convert the data into tensors
Y = Y.T # batch should be the 0-dim in pytorch
Y = torch.from_numpy(Y).float().cuda()
D = torch.from_numpy(D).float().cuda()
maskvec = torch.from_numpy(maskvec.T).cuda()
# network initialization
ListaNet = LISTA(m, n, D, L, theta, max_iter=30)
ListaNet.weights_init()
ListaNet = ListaNet.cuda()
# build the optimizer and criterion
criterion1 = nn.MSELoss()
criterion2 = nn.L1Loss()
all_zeros = torch.zeros(bs, n).cuda()
optimizer = torch.optim.SGD(ListaNet.parameters(),
lr=learning_rate,
momentum=0.9)
psnr_list = np.zeros(epochs)
for i_epoch in range(epochs):
indexs = np.arange(K)
np.random.shuffle(indexs)
epoch_loss = 0
epoch_loss1 = 0
epoch_loss2 = 0
for i_iter in range(iter_epoch):
Y_batch = Y[indexs[i_iter * bs:(i_iter + 1) * bs]]
mask = maskvec[indexs[i_iter * bs:(i_iter + 1) * bs]]
# get the outputs
X = ListaNet(Y_batch)
Y_recons = torch.mm(X, D.T)
# compute the losss
loss1 = criterion1(Y_batch * mask, Y_recons * mask)
loss2 = (1 - s) * criterion2(X, all_zeros)
loss = loss1 + loss2
loss.backward()
optimizer.step()
optimizer.zero_grad()
epoch_loss1 += loss1.item()
epoch_loss2 += loss2.item()
epoch_loss += loss.item()
# validation
with torch.no_grad():
coef_pt = ListaNet(Y)
coef = coef_pt.cpu().detach().numpy().T
# testing
recovered_img = patch2im_for_inpainting(
patch_vecs=255.0 * np.matmul(D.cpu().detach().numpy(), coef),
mask_vecs=maskvec,
imgsize=img_ori.shape,
blksize=blksize,
overlap=overlap)
psnr = PSNR(img_ori, recovered_img)
psnr_list[i_epoch] = psnr
print('[INFO] Epoch: {}/{} loss: {:.3f} ({:f}, {:f}) PSNR: {:.2f}'.
format(i_epoch, epochs, epoch_loss / iter_epoch,
epoch_loss1 / iter_epoch, epoch_loss2 / iter_epoch, psnr))
if psnr > 30:
print('[INFO] => ListaNet best PSNR:{:.2f}'.format(
psnr_list.max()))
return ListaNet
print('[INFO] => ListaNet best PSNR:{:.2f}'.format(psnr_list.max()))
return ListaNet