def load_mrc(path, standardize=False):
with open(path, 'rb') as f:
content = f.read()
image, header, extended_header = mrc.parse(content)
if standardize:
image = image - header.amean
image /= header.rms
return Image.fromarray(image)
def get(self, *args, **kwargs):
ext = self.pathspec.format(*args, **kwargs) + '.' + self.format
path = os.path.join(self.rootdir, ext)
if self.format == 'mrc':
with open(path, 'rb') as f:
content = f.read()
image, header, extended_header = mrc.parse(content)
if self.standardize:
image = image - header.amean
image /= header.rms
else:
image = Image.open(path)
fp = image.fp
image.load()
fp.close()
image = np.array(image, copy=False)
if self.standardize:
image = (image - image.mean())/image.std()
return Image.fromarray(image)
def main(args):
# set the number of threads
num_threads = args.num_threads
from topaz.torch import set_num_threads
set_num_threads(num_threads)
## set the device
use_cuda = topaz.cuda.set_device(args.device)
print('# using device={} with cuda={}'.format(args.device, use_cuda),
file=sys.stderr)
cutoff = args.pixel_cutoff # pixel truncation limit
do_train = (args.dir_a is not None
and args.dir_b is not None) or (args.hdf is not None)
if do_train:
method = args.method
paired = (method == 'noise2noise')
preload = args.preload
holdout = args.holdout # fraction of image pairs to holdout for validation
if args.hdf is None: #use dirA/dirB
crop = args.crop
dir_as = args.dir_a
dir_bs = args.dir_b
dset_train = []
dset_val = []
for dir_a, dir_b in zip(dir_as, dir_bs):
random = np.random.RandomState(44444)
if paired:
dataset_train, dataset_val = make_paired_images_datasets(
dir_a,
dir_b,
crop,
random=random,
holdout=holdout,
preload=preload,
cutoff=cutoff)
else:
dataset_train, dataset_val = make_images_datasets(
dir_a,
dir_b,
crop,
cutoff=cutoff,
random=random,
holdout=holdout)
dset_train.append(dataset_train)
dset_val.append(dataset_val)
dataset_train = dset_train[0]
for i in range(1, len(dset_train)):
dataset_train.x += dset_train[i].x
if paired:
dataset_train.y += dset_train[i].y
dataset_val = dset_val[0]
for i in range(1, len(dset_val)):
dataset_val.x += dset_val[i].x
if paired:
dataset_val.y += dset_val[i].y
shuffle = True
else: # make HDF datasets
dataset_train, dataset_val = make_hdf5_datasets(args.hdf,
paired=paired,
cutoff=cutoff,
holdout=holdout,
preload=preload)
shuffle = preload
# initialize the model
arch = args.arch
if arch == 'unet':
model = dn.UDenoiseNet()
elif arch == 'unet-small':
model = dn.UDenoiseNetSmall()
elif arch == 'unet2':
model = dn.UDenoiseNet2()
elif arch == 'unet3':
model = dn.UDenoiseNet3()
elif arch == 'fcnet':
model = dn.DenoiseNet(32)
elif arch == 'fcnet2':
model = dn.DenoiseNet2(64)
elif arch == 'affine':
model = dn.AffineDenoise()
else:
raise Exception('Unknown architecture: ' + arch)
if use_cuda:
model = model.cuda()
# train
optim = args.optim
lr = args.lr
batch_size = args.batch_size
num_epochs = args.num_epochs
digits = int(np.ceil(np.log10(num_epochs)))
num_workers = args.num_workers
print('epoch', 'loss_train', 'loss_val')
#criteria = nn.L1Loss()
criteria = args.criteria
if method == 'noise2noise':
iterator = dn.train_noise2noise(model,
dataset_train,
lr=lr,
optim=optim,
batch_size=batch_size,
criteria=criteria,
num_epochs=num_epochs,
dataset_val=dataset_val,
use_cuda=use_cuda,
num_workers=num_workers,
shuffle=shuffle)
elif method == 'masked':
iterator = dn.train_mask_denoise(model,
dataset_train,
lr=lr,
optim=optim,
batch_size=batch_size,
criteria=criteria,
num_epochs=num_epochs,
dataset_val=dataset_val,
use_cuda=use_cuda,
num_workers=num_workers,
shuffle=shuffle)
for epoch, loss_train, loss_val in iterator:
print(epoch, loss_train, loss_val)
sys.stdout.flush()
# save the model
if args.save_prefix is not None:
path = args.save_prefix + ('_epoch{:0' + str(digits) +
'}.sav').format(epoch)
#path = args.save_prefix + '_epoch{}.sav'.format(epoch)
model.cpu()
model.eval()
torch.save(model, path)
if use_cuda:
model.cuda()
models = [model]
else: # load the saved model(s)
models = []
for arg in args.model:
if arg == 'none':
print('# Warning: no denoising model will be used',
file=sys.stderr)
else:
print('# Loading model:', arg, file=sys.stderr)
model = dn.load_model(arg)
model.eval()
if use_cuda:
model.cuda()
models.append(model)
# using trained model
# denoise the images
normalize = args.normalize
if args.format_ == 'png' or args.format_ == 'jpg':
# always normalize png and jpg format
normalize = True
format_ = args.format_
suffix = args.suffix
lowpass = args.lowpass
gaus = args.gaussian
if gaus > 0:
gaus = dn.GaussianDenoise(gaus)
if use_cuda:
gaus.cuda()
else:
gaus = None
inv_gaus = args.inv_gaussian
if inv_gaus > 0:
inv_gaus = dn.InvGaussianFilter(inv_gaus)
if use_cuda:
inv_gaus.cuda()
else:
inv_gaus = None
deconvolve = args.deconvolve
deconv_patch = args.deconv_patch
ps = args.patch_size
padding = args.patch_padding
count = 0
# we are denoising a single MRC stack
if args.stack:
with open(args.micrographs[0], 'rb') as f:
content = f.read()
stack, _, _ = mrc.parse(content)
print('# denoising stack with shape:', stack.shape, file=sys.stderr)
total = len(stack)
denoised = np.zeros_like(stack)
for i in range(len(stack)):
mic = stack[i]
# process and denoise the micrograph
mic = denoise_image(mic,
models,
lowpass=lowpass,
cutoff=cutoff,
gaus=gaus,
inv_gaus=inv_gaus,
deconvolve=deconvolve,
deconv_patch=deconv_patch,
patch_size=ps,
padding=padding,
normalize=normalize,
use_cuda=use_cuda)
denoised[i] = mic
count += 1
print('# {} of {} completed.'.format(count, total),
file=sys.stderr,
end='\r')
print('', file=sys.stderr)
# write the denoised stack
path = args.output
print('# writing', path, file=sys.stderr)
with open(path, 'wb') as f:
mrc.write(f, denoised)
else:
# stream the micrographs and denoise them
total = len(args.micrographs)
# make the output directory if it doesn't exist
if not os.path.exists(args.output):
os.makedirs(args.output)
for path in args.micrographs:
name, _ = os.path.splitext(os.path.basename(path))
mic = np.array(load_image(path), copy=False).astype(np.float32)
# process and denoise the micrograph
mic = denoise_image(mic,
models,
lowpass=lowpass,
cutoff=cutoff,
gaus=gaus,
inv_gaus=inv_gaus,
deconvolve=deconvolve,
deconv_patch=deconv_patch,
patch_size=ps,
padding=padding,
normalize=normalize,
use_cuda=use_cuda)
# write the micrograph
if not args.output:
if suffix == '' or suffix is None:
suffix = '.denoised'
# write the file to the same location as input
no_ext, ext = os.path.splitext(path)
outpath = no_ext + suffix + '.' + format_
else:
outpath = args.output + os.sep + name + suffix + '.' + format_
save_image(mic, outpath) #, mi=None, ma=None)
count += 1
print('# {} of {} completed.'.format(count, total),
file=sys.stderr,
end='\r')
print('', file=sys.stderr)
def denoise(model, path, outdir, patch_size=128, padding=128, batch_size=1):
with open(path, 'rb') as f:
content = f.read()
tomo,header,_ = mrc.parse(content)
name = os.path.basename(path)
mu = tomo.mean()
std = tomo.std()
# denoise in patches
d = next(iter(model.parameters())).device
denoised = np.zeros_like(tomo)
with torch.no_grad():
if patch_size < 1:
x = (tomo - mu)/std
x = torch.from_numpy(x).to(d)
x = model(x.unsqueeze(0).unsqueeze(0)).squeeze().cpu().numpy()
x = std*x + mu
denoised[:] = x
else:
patch_data = PatchDataset(tomo, patch_size, padding)
total = len(patch_data)
count = 0
batch_iterator = torch.utils.data.DataLoader(patch_data, batch_size=batch_size)
for index,x in batch_iterator:
x = x.to(d)
x = (x - mu)/std
x = x.unsqueeze(1) # batch x channel
# denoise
x = model(x).squeeze(1).cpu().numpy()
# stitch into denoised volume
for b in range(len(x)):
i,j,k = index[b]
xb = x[b]
patch = denoised[i:i+patch_size,j:j+patch_size,k:k+patch_size]
pz,py,px = patch.shape
xb = xb[padding:padding+pz,padding:padding+py,padding:padding+px]
denoised[i:i+patch_size,j:j+patch_size,k:k+patch_size] = xb
count += 1
print('# [{}/{}] {:.2%}'.format(count, total, count/total), name, file=sys.stderr, end='\r')
print(' '*100, file=sys.stderr, end='\r')
## save the denoised tomogram
outpath = outdir + os.sep + name
# use the read header except for a few fields
header = header._replace(mode=2) # 32-bit real
header = header._replace(amin=denoised.min())
header = header._replace(amax=denoised.max())
header = header._replace(amean=denoised.mean())
with open(outpath, 'wb') as f:
mrc.write(f, denoised, header=header)
def load_mrc(self, path):
with open(path, 'rb') as f:
content = f.read()
tomo,_,_ = mrc.parse(content)
return tomo
def denoise(model,
path,
outdir,
suffix,
patch_size=128,
padding=128,
batch_size=1,
volume_num=1,
total_volumes=1):
with open(path, 'rb') as f:
content = f.read()
tomo, header, extended_header = mrc.parse(content)
tomo = tomo.astype(np.float32)
name = os.path.basename(path)
mu = tomo.mean()
std = tomo.std()
# denoise in patches
d = next(iter(model.parameters())).device
denoised = np.zeros_like(tomo)
with torch.no_grad():
if patch_size < 1:
x = (tomo - mu) / std
x = torch.from_numpy(x).to(d)
x = model(x.unsqueeze(0).unsqueeze(0)).squeeze().cpu().numpy()
x = std * x + mu
denoised[:] = x
else:
patch_data = PatchDataset(tomo, patch_size, padding)
total = len(patch_data)
count = 0
batch_iterator = torch.utils.data.DataLoader(patch_data,
batch_size=batch_size)
for index, x in batch_iterator:
x = x.to(d)
x = (x - mu) / std
x = x.unsqueeze(1) # batch x channel
# denoise
x = model(x)
x = x.squeeze(1).cpu().numpy()
# restore original statistics
x = std * x + mu
# stitch into denoised volume
for b in range(len(x)):
i, j, k = index[b]
xb = x[b]
patch = denoised[i:i + patch_size, j:j + patch_size,
k:k + patch_size]
pz, py, px = patch.shape
xb = xb[padding:padding + pz, padding:padding + py,
padding:padding + px]
denoised[i:i + patch_size, j:j + patch_size,
k:k + patch_size] = xb
count += 1
print('# [{}/{}] {:.2%}'.format(volume_num, total_volumes,
count / total),
name,
file=sys.stderr,
end='\r')
print(' ' * 100, file=sys.stderr, end='\r')
## save the denoised tomogram
if outdir is None:
# write denoised tomogram to same location as input, but add the suffix
if suffix is None: # use default
suffix = '.denoised'
no_ext, ext = os.path.splitext(path)
outpath = no_ext + suffix + ext
else:
if suffix is None:
suffix = ''
no_ext, ext = os.path.splitext(name)
outpath = outdir + os.sep + no_ext + suffix + ext
# use the read header except for a few fields
header = header._replace(mode=2) # 32-bit real
header = header._replace(amin=denoised.min())
header = header._replace(amax=denoised.max())
header = header._replace(amean=denoised.mean())
with open(outpath, 'wb') as f:
mrc.write(f, denoised, header=header, extended_header=extended_header)
def load_mrc(self, path):
with open(path, 'rb') as f:
content = f.read()
tomo, _, _ = mrc.parse(content)
tomo = tomo.astype(np.float32)
return tomo
def main(args):
## set the device
use_cuda = False
if args.device >= 0:
use_cuda = torch.cuda.is_available()
if use_cuda:
torch.cuda.set_device(args.device)
print('# using device={} with cuda={}'.format(args.device, use_cuda),
file=sys.stderr)
do_train = (args.dir_a is not None
and args.dir_b is not None) or (args.hdf is not None)
if do_train:
if args.hdf is None: #use dirA/dirB
crop = args.crop
dir_a = args.dir_a
dir_b = args.dir_b
random = np.random.RandomState(44444)
dataset_train, dataset_val = make_paired_images_datasets(
dir_a, dir_b, crop, random=random)
shuffle = True
else: # make HDF datasets
dataset_train, dataset_val = make_hdf5_datasets(args.hdf)
shuffle = False
# initialize the model
#model = dn.DenoiseNet(32)
model = dn.UDenoiseNet()
if use_cuda:
model = model.cuda()
# train
lr = args.lr
batch_size = args.batch_size
num_epochs = args.num_epochs
num_workers = args.num_workers
print('epoch', 'loss_train', 'loss_val')
#criteria = nn.L1Loss()
criteria = args.criteria
for epoch, loss_train, loss_val in dn.train_noise2noise(
model,
dataset_train,
lr=lr,
batch_size=batch_size,
criteria=criteria,
num_epochs=num_epochs,
dataset_val=dataset_val,
use_cuda=use_cuda,
num_workers=num_workers,
shuffle=shuffle):
print(epoch, loss_train, loss_val)
sys.stdout.flush()
# save the model
if args.save_prefix is not None:
path = args.save_prefix + '_epoch{}.sav'.format(epoch)
model.cpu()
model.eval()
torch.save(model, path)
if use_cuda:
model.cuda()
else: # load the saved model
if args.model in ['L0', 'L1', 'L2']:
if args.model in ['L0', 'L1']:
print(
'ERROR: L0 and L1 models are not implemented in the current version',
file=sys.stderr)
sys.exit(1)
model = dn.load_model(args.model)
else:
model = torch.load(args.model)
print('# using model:', args.model, file=sys.stderr)
model.eval()
if use_cuda:
model.cuda()
if args.stack:
# we are denoising a single MRC stack
with open(args.micrographs[0], 'rb') as f:
content = f.read()
stack, _, _ = mrc.parse(content)
print('# denoising stack with shape:', stack.shape, file=sys.stderr)
denoised = dn.denoise_stack(model, stack, use_cuda=use_cuda)
# write the denoised stack
path = args.output
print('# writing', path, file=sys.stderr)
with open(path, 'wb') as f:
mrc.write(f, denoised)
else:
# using trained model
# stream the micrographs and denoise as we go
normalize = args.normalize
if args.format_ == 'png' or args.format_ == 'jpg':
# always normalize png and jpg format
normalize = True
format_ = args.format_
count = 0
total = len(args.micrographs)
bin_ = args.bin
ps = args.patch_size
padding = args.patch_padding
# now, stream the micrographs and denoise them
for path in args.micrographs:
name, _ = os.path.splitext(os.path.basename(path))
mic = np.array(load_image(path), copy=False)
if bin_ > 1:
mic = downsample(mic, bin_)
mu = mic.mean()
std = mic.std()
# denoise
mic = (mic - mu) / std
mic = dn.denoise(model,
mic,
patch_size=ps,
padding=padding,
use_cuda=use_cuda)
if normalize:
mic = (mic - mic.mean()) / mic.std()
else:
# add back std. dev. and mean
mic = std * mic + mu
# write the micrograph
outpath = args.output + os.sep + name + '.' + format_
save_image(mic, outpath)
count += 1
print('# {} of {} completed.'.format(count, total),
file=sys.stderr,
end='\r')
print('', file=sys.stderr)
def main(args):
particles = pd.read_csv(args.file, sep='\t')
print('#', 'Loaded', len(particles), 'particles', file=sys.stderr)
# threshold the particles
if 'score' in particles:
particles = particles.loc[particles['score'] >= args.threshold]
print('#', 'Thresholding at', args.threshold, file=sys.stderr)
print('#', 'Extracting', len(particles), 'particles', file=sys.stderr)
N = len(particles)
size = args.size
resize = args.resize
if resize < 0:
resize = size
#
wrote_header = False
read_metadata = False
metadata = []
# write the particles iteratively
i = 0
with open(args.output, 'wb') as f:
for image_name, coords in particles.groupby('image_name'):
print('#', image_name, len(coords), 'particles', file=sys.stderr)
# load the micrograph
image_name = image_name + args.image_ext
path = os.path.join(args.image_root, image_name)
with open(path, 'rb') as fm:
content = fm.read()
micrograph, header, extended_header = mrc.parse(content)
if len(micrograph.shape) < 3:
micrograph = micrograph[
np.newaxis] # add z dim if micrograph is image
if not wrote_header: # load a/px and angles from micrograph header and write the stack header
mz = micrograph.shape[0]
dtype = micrograph.dtype
cella = (header.xlen, header.ylen, header.zlen)
cellb = (header.alpha, header.beta, header.gamma)
shape = (N * mz, resize, resize)
header = mrc.make_header(shape,
cella,
cellb,
mz=mz,
dtype=dtype)
buf = mrc.header_struct.pack(*list(header))
f.write(buf)
wrote_header = True
_, n, m = micrograph.shape
x_coord = coords['x_coord'].values
y_coord = coords['y_coord'].values
scores = None
if 'score' in coords:
scores = coords['score'].values
# crop out the particles
for j in range(len(coords)):
x = x_coord[j]
y = y_coord[j]
if scores is not None:
metadata.append((image_name, x, y, scores[j]))
else:
metadata.append((image_name, x, y))
left = x - size // 2
upper = y - size // 2
right = left + size
lower = upper + size
c = micrograph[:,
max(0, upper):min(n, lower),
max(0, left):min(m, right)]
c = (c - c.mean()) / c.std()
stack = np.zeros((mz, size, size), dtype=dtype)
#stack = np.zeros((mz, size, size), dtype=dtype) + c.mean().astype(dtype)
stack[:,
max(0, -upper):min(size + n - lower, size),
max(0, -left):min(size + m - right, size)] = c
# write particle to mrc file
if resize != size:
restack = downsample(stack, 0, shape=(resize, resize))
#print(restack.shape, restack.mean(), restack.std())
restack = (restack - restack.mean()) / restack.std()
f.write(restack.tobytes())
else:
f.write(stack.tobytes())
i += 1
#print('# wrote', i, 'out of', N, 'particles', end='\r', flush=True)
## write the particle stack mrcs
#with open(args.output, 'wb') as f:
# mrc.write(f, stack, ax=ax, ay=ay, az=az, alpha=alpha, beta=beta, gamma=gamma)
image_name = os.path.basename(args.output)
star_path = os.path.splitext(args.output)[0] + '.star'
## create the star file
columns = ['MicrographName', star.X_COLUMN_NAME, star.Y_COLUMN_NAME]
if 'score' in particles:
columns.append(star.SCORE_COLUMN_NAME)
metadata = pd.DataFrame(metadata, columns=columns)
metadata['ImageName'] = [
str(i + 1) + '@' + image_name for i in range(len(metadata))
]
if mz > 1:
metadata['NrOfFrames'] = mz
micrograph_metadata = None
if args.metadata is not None:
with open(args.metadata, 'r') as f:
micrograph_metadata = star.parse_star(f)
metadata = pd.merge(metadata,
micrograph_metadata,
on='MicrographName',
how='left')
if resize != size:
# rescale the detector pixel size
pix = metadata['DetectorPixelSize'].values.astype(float)
metadata['DetectorPixelSize'] = pix * (size / resize)
## write the star file
with open(star_path, 'w') as f:
star.write(metadata, f)
