From 399b3e4f353004bb270ea146f21009bc94e05c5f Mon Sep 17 00:00:00 2001 From: Kris Thielemans Date: Mon, 27 Jul 2020 21:32:51 +0100 Subject: [PATCH 01/11] fix handling of arguments in get_mmr_fromfile It ignored values for petNoise etc Fixes #3 --- brainweb/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 8033e8e..1911411 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -256,8 +256,8 @@ def get_mmr_fromfile(brainweb_file, '.bin.gz', '.npz' if PetClass == FDG else '.{}.npz'.format(PetClass.__name__)), dat, - petNoise=1.0, t1Noise=0.75, t2Noise=0.75, - petSigma=1.0, t1Sigma=1.0, t2Sigma=1.0, + petNoise=petNoise, t1Noise=t1Noise, t2Noise=t2Noise, + petSigma=petSigma, t1Sigma=t1Sigma, t2Sigma=t2Sigma, PetClass=PetClass) From cf1044e2161d74bc7e2ffe778642625f3950c3db Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 12:19:31 +0100 Subject: [PATCH 02/11] fix `toPetMmr` ignoring `modes` - fixes #7 --- brainweb/utils.py | 40 ++++++++++------------------------------ 1 file changed, 10 insertions(+), 30 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 1911411..2d05ec8 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -406,38 +406,17 @@ def toPetMmr(im, pad=True, dtype=np.float32, outres="mMR", modes=None, out_res = getattr(Res, outres) out_shape = getattr(Shape, outres) - if modes is None: - modes = [PetClass, Mu, T1, T2] - - # PET - # res = np.zeros(im.shape, dtype=dtype) - res = np.zeros_like(im) - for attr in PetClass.attrs: - log.debug("PET:%s:%d" % (attr, getattr(PetClass, attr))) - res[Act.indices(im, attr)] = getattr(PetClass, attr) - - # uMap - uMap = np.zeros(im.shape, dtype=dtype) - for attr in Mu.attrs: - log.debug("uMap:%s:%d" % (attr, getattr(Mu, attr))) - uMap[Act.indices(im, attr)] = getattr(Mu, attr) - - # MR - # t1 = np.zeros(im.shape, dtype=dtype) - t1 = np.zeros_like(im) - for attr in T1.attrs: - log.debug("T1:%s:%d" % (attr, getattr(T1, attr))) - t1[Act.indices(im, attr)] = getattr(T1, attr) - # t2 = np.zeros(im.shape, dtype=dtype) - t2 = np.zeros_like(im) - for attr in T2.attrs: - log.debug("T2:%s:%d" % (attr, getattr(T2, attr))) - t2[Act.indices(im, attr)] = getattr(T2, attr) - - # resize new_shape = np.rint(np.asarray(im.shape) * Res.brainweb / out_res) padLR, padR = divmod((np.array(out_shape) - new_shape), 2) + def getModeIms(modes): + for MClass in modes: + res = np.zeros_like(im, dtype=dtype) # dtype only needed for uMap? + for attr in MClass.attrs: + log.debug("%s:%s:%d" % (MClass.__name__, attr, getattr(MClass, attr))) + res[Act.indices(im, attr)] = getattr(MClass, attr) + yield res + def resizeToMmr(arr): # oldMax = arr.max() # arr = arr.astype(np.float32) @@ -451,7 +430,8 @@ def resizeToMmr(arr): mode="constant") return arr - return [resizeToMmr(i) for i in [res, uMap, t1, t2]] + return list( + map(resizeToMmr, map(getModeIms, modes or [PetClass, Mu, T1, T2]))) def ellipsoid(out_shape, radii, position, dtype=np.float32): From 6a4beddd2fc101f07af655d5c6123ce6bfd12485 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 12:19:43 +0100 Subject: [PATCH 03/11] minor linting --- brainweb/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 2d05ec8..d4e3b8c 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -32,8 +32,8 @@ LINKS = "04 05 06 18 20 38 41 42 43 44 45 46 47 48 49 50 51 52 53 54" LINKS = [ - 'http://brainweb.bic.mni.mcgill.ca/cgi/brainweb1' + - '?do_download_alias=subject' + i + '_crisp&format_value=raw_short' + + 'http://brainweb.bic.mni.mcgill.ca/cgi/brainweb1' + '?do_download_alias=subject' + i + '_crisp&format_value=raw_short' '&zip_value=gnuzip&download_for_real=%5BStart+download%21%5D' for i in LINKS.split()] RE_SUBJ = re.compile('.*(subject)([0-9]+).*') @@ -300,7 +300,7 @@ def volshow(vol, else: for v in vol: if v.ndim not in [3, 4]: - raise IndexError("Input should be (one or a list of)" + + raise IndexError("Input should be (one or a list of)" " 3D and/or 4D array(s)") if cmaps is None: From 112b3621a85e57cb22888de614f30c5d8d9d41d1 Mon Sep 17 00:00:00 2001 From: Kris Thielemans Date: Tue, 28 Jul 2020 00:24:08 +0100 Subject: [PATCH 04/11] add capability of using different resolution/shape in get_mmr_* pass the `outres` parameter to ToPetMmr. This meant I had to encode the resolution in the cached filename. --- brainweb/utils.py | 35 +++++++++++++++++++++++++---------- 1 file changed, 25 insertions(+), 10 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index d4e3b8c..5116309 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -218,17 +218,21 @@ def get_files(cache_dir=None, progress=True): def get_mmr(cache_file, raw_data, petNoise=1.0, t1Noise=0.75, t2Noise=0.75, petSigma=1.0, t1Sigma=1.0, t2Sigma=1.0, - PetClass=FDG): + outres="mMR", PetClass=FDG): """ Return contents of specified `*.npz` file, creating it from BrainWeb `raw_data` 3darray if it doesn't exist. + + @param outres: attribute to use from Res/Class [default: "mMR"] """ if not path.exists(cache_file): - pet, uMap, t1, t2 = toPetMmr(raw_data, PetClass=PetClass) + pet, uMap, t1, t2 = toPetMmr(raw_data, outres=outres, PetClass=PetClass) pet = noise(pet, petNoise, sigma=petSigma)[:, ::-1] t1 = noise(t1, t1Noise, sigma=t1Sigma)[:, ::-1] t2 = noise(t2, t2Noise, sigma=t2Sigma)[:, ::-1] uMap = uMap[:, ::-1] + out_res = getattr(Res, outres) + np.savez_compressed( cache_file, PET=pet, uMap=uMap, T1=t1, T2=t2, @@ -237,28 +241,38 @@ def get_mmr(cache_file, raw_data, t2Noise=np.float32(t2Noise), petSigma=np.float32(petSigma), t1Sigma=np.float32(t1Sigma), - t2Sigma=np.float32(t2Sigma)) + t2Sigma=np.float32(t2Sigma), + res=np.float32(out_res)) - return np.load(cache_file, allow_pickle=True) + cached= np.load(cache_file, allow_pickle=True) + # handle case of cached file that didn't stored the res key yet + if not 'res' in cached: + # need to add it, but cannot do that directly to a npzfile + # so create a dict (trick from https://stackoverflow.com/a/46550796) + cached = dict(cached) + cached['res'] = getattr(Res, 'mMR') + return cached def get_mmr_fromfile(brainweb_file, petNoise=1.0, t1Noise=0.75, t2Noise=0.75, petSigma=1.0, t1Sigma=1.0, t2Sigma=1.0, - PetClass=FDG): + outres="mMR", PetClass=FDG): """ - mMR resolution ground truths from a cached `np.load`able file generated + ground truths from a cached `np.load`able file generated from `brainweb_file`. + + @param outres: attribute to use from Res/Class [default: "mMR"] """ dat = load_file(brainweb_file) # read raw data return get_mmr( brainweb_file.replace( - '.bin.gz', '.npz' if PetClass == FDG else - '.{}.npz'.format(PetClass.__name__)), + '.bin.gz', '.npz' if PetClass == FDG and outres=="mMR" else + '.{}.{}.npz'.format(PetClass.__name__, outres)), dat, petNoise=petNoise, t1Noise=t1Noise, t2Noise=t2Noise, petSigma=petSigma, t1Sigma=t1Sigma, t2Sigma=t2Sigma, - PetClass=PetClass) + outres=outres, PetClass=PetClass) def volshow(vol, @@ -398,8 +412,9 @@ def noise(im, n, warn_zero=True, sigma=1): def toPetMmr(im, pad=True, dtype=np.float32, outres="mMR", modes=None, PetClass=FDG): """ + @param outres: attribute to use from Res/Class [default: "mMR"] @param modes : [default: [PetClass, Mu, T1, T2]] - @return out : list of `modes`, each shape [127, 344, 344] + @return out : list of `modes`, each res/shape specified from the outres attribute of Res/Shape """ log = logging.getLogger(__name__) From 68760b93e8349c514484dcd3ab46777b453825cc Mon Sep 17 00:00:00 2001 From: Kris Thielemans Date: Tue, 28 Jul 2020 18:38:22 +0100 Subject: [PATCH 05/11] add get_label_probabilities allow returning a 4D array with the masks (useful for PVC for instance) --- README.rst | 11 ++++++++++- brainweb/utils.py | 33 +++++++++++++++++++++++++++++++-- 2 files changed, 41 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index cf2b4c0..c866ffb 100644 --- a/README.rst +++ b/README.rst @@ -99,6 +99,7 @@ Convert raw image data: - Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344) - PET/T1/T2/uMap intensities +- PET given with FDG intensity ratios - randomised structure for PET/T1/T2 - t (1 + g [2 G_sigma(r) - 1]), where @@ -111,12 +112,14 @@ Convert raw image data: brainweb.seed(1337) + PetClass = brainweb.FDG + for f in tqdm(files, desc="mMR ground truths", unit="subject"): vol = brainweb.get_mmr_fromfile( f, petNoise=1, t1Noise=0.75, t2Noise=0.75, petSigma=1, t1Sigma=1, t2Sigma=1, - PetClass=brainweb.FDG) + PetClass=PetClass) .. code:: python @@ -137,6 +140,12 @@ Convert raw image data: .. code:: python + # get a 4D array with weights for each region for the last subject + label_probs = brainweb.get_label_probabilities(files[-1]) + # print their names + print(PetClass.all_labels) + + # add some lesions brainweb.seed(1337) im3d = brainweb.add_lesions(vol['PET']) diff --git a/brainweb/utils.py b/brainweb/utils.py index 5116309..6afeafc 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -20,11 +20,11 @@ # necessary "volshow", "get_files", "get_mmr_fromfile", # useful utils - "get_file", "load_file", "gunzip_array", "ellipsoid", "add_lesions", + "get_file", "load_file", "gunzip_array", "ellipsoid", "add_lesions", "get_label_probabilities", # nothing to do with BrainWeb but still useful "register", # intensities - "FDG", "Amyloid", "T1", "T2", + "FDG", "Amyloid", "T1", "T2", "Mu", # scanner params "Res", "Shape", # probably not needed @@ -47,6 +47,9 @@ class Act(object): = [i << 4 for i in range(12)] bone = skull | marrow | dura + all_labels = ['background', 'csf', 'greyMatter', 'whiteMatter', 'fat', 'muscle', 'skin', 'skull', 'vessels',\ + 'aroundFat', 'dura', 'marrow'] + @classmethod def indices(cls, im, attr): matter = None @@ -275,6 +278,32 @@ def get_mmr_fromfile(brainweb_file, outres=outres, PetClass=PetClass) +def get_label_probabilities(brainweb_file, outres="mMR", progress=True): + """ + @return a 4D array with the masks resampled to the `outres` shape and resolution. This is useful for PVC. + """ + log = logging.getLogger(__name__) + + out_res = getattr(Res, outres) + out_shape = getattr(Shape, outres) + + raw_data = load_file(brainweb_file) # read raw data + + num_classes = len(Act.all_labels) + res = np.zeros((num_classes,) + tuple(out_shape), dtype='float32') + for i in tqdm(range(num_classes), unit="label", desc="BrainWeb labels", + disable=not progress): + attr = Act.all_labels[i] + # create a class derived from Act + one_class = type("class_" + attr, (Act,), {}) + # add just one attribute for the current label + one_class.attrs = [attr] + setattr(one_class, attr, 1) + # compute image + res[i,:,:,:] = toPetMmr(raw_data, outres=outres, PetClass=one_class)[0] + + return res + def volshow(vol, cmaps=None, colorbars=None, xlabels=None, ylabels=None, titles=None, From dcaf64d7d467d970b6baa993401e4067b6551c3b Mon Sep 17 00:00:00 2001 From: Kris Thielemans Date: Fri, 31 Jul 2020 16:16:22 +0000 Subject: [PATCH 06/11] Expose Act to be able to see brainweb labels --- brainweb/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 6afeafc..0d1118a 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -24,7 +24,7 @@ # nothing to do with BrainWeb but still useful "register", # intensities - "FDG", "Amyloid", "T1", "T2", "Mu", + "Act", "FDG", "Amyloid", "T1", "T2", "Mu", # scanner params "Res", "Shape", # probably not needed From 976aa6ce5bbcffe1091345b63162b5abcda56adf Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 14:27:20 +0100 Subject: [PATCH 07/11] add trim_zeros_ROI, update documentation --- README.ipynb | 24 +++++++++++++++++++----- README.rst | 30 +++++++++++++++++++++--------- brainweb/utils.py | 29 +++++++++++++++++++++++++++-- pmasks.png | Bin 0 -> 69114 bytes 4 files changed, 67 insertions(+), 16 deletions(-) create mode 100644 pmasks.png diff --git a/README.ipynb b/README.ipynb index b005efa..9319959 100644 --- a/README.ipynb +++ b/README.ipynb @@ -97,6 +97,7 @@ "\n", "- Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)\n", "- PET/T1/T2/uMap intensities\n", + " + PET defaults to FDG intensity ratios; could use e.g. Amyloid instead\n", "- randomised structure for PET/T1/T2\n", " + $\\bm{\\theta} \\circ (\\bm{1} + \\gamma[2G_\\sigma(\\bm{\\rho}) - \\bm{1}])$\n", " * $\\bm{\\rho} = rand(127, 344, 344) \\in [0, 1)$\n", @@ -105,6 +106,18 @@ " * $\\bm{\\theta}$ is the PET or MR piecewise constant phantom" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# show region probability masks\n", + "PetClass = brainweb.FDG\n", + "label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)\n", + "volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);" + ] + }, { "cell_type": "code", "execution_count": null, @@ -118,7 +131,7 @@ " f,\n", " petNoise=1, t1Noise=0.75, t2Noise=0.75,\n", " petSigma=1, t1Sigma=1, t2Sigma=1,\n", - " PetClass=brainweb.FDG)" + " PetClass=PetClass)" ] }, { @@ -128,13 +141,14 @@ "outputs": [], "source": [ "# show last subject\n", - "print(f)\n", + "print(files[-1])\n", "volshow([vol['PET' ][:, 100:-100, 100:-100],\n", " vol['uMap'][:, 100:-100, 100:-100],\n", " vol['T1' ][:, 100:-100, 100:-100],\n", " vol['T2' ][:, 100:-100, 100:-100]],\n", " cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],\n", - " titles=[\"PET\", \"uMap\", \"T1\", \"T2\"]);" + " titles=[\"PET\", \"uMap\", \"T1\", \"T2\"],\n", + " frameon=False);" ] }, { @@ -172,9 +186,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/README.rst b/README.rst index c866ffb..e6d61f5 100644 --- a/README.rst +++ b/README.rst @@ -99,20 +99,30 @@ Convert raw image data: - Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344) - PET/T1/T2/uMap intensities -- PET given with FDG intensity ratios + + - PET defaults to FDG intensity ratios; could use e.g. Amyloid instead + - randomised structure for PET/T1/T2 -- t (1 + g [2 G_sigma(r) - 1]), where - - r = rand(127, 344, 344) in [0, 1), - - Gaussian smoothing sigma = 1, - - g = 1 for PET; 0.75 for MR, and - - t = the PET or MR piecewise constant phantom + - t (1 + g [2 G_sigma(r) - 1]), where -.. code:: python + - r = rand(127, 344, 344) in [0, 1), + - Gaussian smoothing sigma = 1, + - g = 1 for PET; 0.75 for MR, and + - t = the PET or MR piecewise constant phantom - brainweb.seed(1337) +.. code:: python + # show region probability masks PetClass = brainweb.FDG + label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels) + volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False); + +.. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/pmasks.png + +.. code:: python + + brainweb.seed(1337) for f in tqdm(files, desc="mMR ground truths", unit="subject"): vol = brainweb.get_mmr_fromfile( @@ -130,7 +140,8 @@ Convert raw image data: vol['T1' ][:, 100:-100, 100:-100], vol['T2' ][:, 100:-100, 100:-100]], cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'], - titles=["PET", "uMap", "T1", "T2"]); + titles=["PET", "uMap", "T1", "T2"], + frameon=False); :: @@ -145,6 +156,7 @@ Convert raw image data: # print their names print(PetClass.all_labels) +.. code:: python # add some lesions brainweb.seed(1337) diff --git a/brainweb/utils.py b/brainweb/utils.py index 0d1118a..00c689b 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -4,7 +4,7 @@ from numpy.random import seed from skimage.transform import resize from scipy.ndimage.filters import gaussian_filter -from tqdm.auto import tqdm +from tqdm.auto import tqdm, trange import functools import requests import re @@ -22,7 +22,7 @@ # useful utils "get_file", "load_file", "gunzip_array", "ellipsoid", "add_lesions", "get_label_probabilities", # nothing to do with BrainWeb but still useful - "register", + "trim_zeros_ROI", "register", # intensities "Act", "FDG", "Amyloid", "T1", "T2", "Mu", # scanner params @@ -558,6 +558,31 @@ def matify(mat, dtype=np.float32, transpose=None): return mat.transpose(transpose).astype(dtype) +def trim_zeros_ROI(arr, trim='fb', progress=True): + """Returns a ROI corresponding to `numpy.trim_zeros`. Works on ndarrays.""" + trim = trim.upper() + res = [] + for i in trange(arr.ndim, desc="Trimming ROI", + disable=not progress, leave=False): + filt = np.swapaxes(arr, i, 0) if i else arr + first = 0 + if 'F' in trim: + for i in filt: + if i.any(): + break + else: + first += 1 + last = len(filt) + if 'B' in trim: + for i in filt[::-1]: + if i.any(): + break + else: + last -= 1 + res.append(slice(first, last)) + return tuple(res) + + def register(src, target=None, ROI=None, target_shape=Shape.mMR, src_resolution=Res.MR, target_resolution=Res.mMR, method="CoM", diff --git a/pmasks.png b/pmasks.png new file mode 100644 index 0000000000000000000000000000000000000000..e4e5f2011c6dc917866a2f6033b8158d2bfe65d1 GIT binary patch literal 69114 zcmaI82RN5+|3CaOvu7a`sZ=tH2xX>3g(PGv$(E7qG9s&z?5wnqkxh0q5S3MA6_uUT z$9`T{-{XG9ar}?xcl0~%`!0O0>pb7*`~6y{aGjG!nHhK)NF)-o<}r0W5{WX9M4}j? zqs8CIboPHGkpxMa>MHtPDU&a*80!Bho2ht8cjN0evyJ7HBi|oWWtna9`1+mVD2u%O 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zaX+&0i}A+rh8dFLv)OR|Lir~!{62`IcIhst*jUKi-Lb9jk6s4#~3Tu{|Ek>BLx5e literal 0 HcmV?d00001 From 2eec32e39ebc241f7254fff9f8291113aac7fad0 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 14:28:01 +0100 Subject: [PATCH 08/11] tidy & fix code --- brainweb/utils.py | 79 +++++++++++++++++++++++------------------------ 1 file changed, 38 insertions(+), 41 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 00c689b..3a341f7 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -5,6 +5,7 @@ from skimage.transform import resize from scipy.ndimage.filters import gaussian_filter from tqdm.auto import tqdm, trange +from tqdm.contrib import tenumerate import functools import requests import re @@ -38,6 +39,7 @@ for i in LINKS.split()] RE_SUBJ = re.compile('.*(subject)([0-9]+).*') LINKS = dict((RE_SUBJ.sub(r'\1_\2.bin.gz', i), i) for i in LINKS) +log = logging.getLogger(__name__) class Act(object): @@ -155,8 +157,6 @@ def get_file(fname, origin, cache_dir=None): defaults to `~/.brainweb`. @return : Path to the downloaded file """ - log = logging.getLogger(__name__) - if cache_dir is None: cache_dir = path.join('~', '.brainweb') cache_dir = path.expanduser(cache_dir) @@ -247,8 +247,8 @@ def get_mmr(cache_file, raw_data, t2Sigma=np.float32(t2Sigma), res=np.float32(out_res)) - cached= np.load(cache_file, allow_pickle=True) - # handle case of cached file that didn't stored the res key yet + cached = np.load(cache_file, allow_pickle=True) + # handle case of cached file that didn't stored the res key yet if not 'res' in cached: # need to add it, but cannot do that directly to a npzfile # so create a dict (trick from https://stackoverflow.com/a/46550796) @@ -278,32 +278,32 @@ def get_mmr_fromfile(brainweb_file, outres=outres, PetClass=PetClass) -def get_label_probabilities(brainweb_file, outres="mMR", progress=True): +def get_label_probabilities(brainweb_file, labels=None, outres="mMR", progress=True, dtype=np.float32): """ - @return a 4D array with the masks resampled to the `outres` shape and resolution. This is useful for PVC. + @param labels : list of strings, [default: Act.all_labels] + @return out : 4D array of masks resampled as per `outres` (useful for PVC) """ - log = logging.getLogger(__name__) - - out_res = getattr(Res, outres) out_shape = getattr(Shape, outres) - - raw_data = load_file(brainweb_file) # read raw data - - num_classes = len(Act.all_labels) - res = np.zeros((num_classes,) + tuple(out_shape), dtype='float32') - for i in tqdm(range(num_classes), unit="label", desc="BrainWeb labels", - disable=not progress): - attr = Act.all_labels[i] - # create a class derived from Act - one_class = type("class_" + attr, (Act,), {}) - # add just one attribute for the current label - one_class.attrs = [attr] - setattr(one_class, attr, 1) - # compute image - res[i,:,:,:] = toPetMmr(raw_data, outres=outres, PetClass=one_class)[0] + raw_data = load_file(brainweb_file) + if labels is None: + labels = Act.all_labels + if set(labels).difference(Act.all_labels): + raise KeyError( + "labels (%s) must be in Act.all_labels (%s)" % ( + ", ".join(labels), ", ".join(Act.all_labels))) + + num_classes = len(labels) + res = np.zeros((num_classes,) + tuple(out_shape), dtype=dtype) + for i, attr in tenumerate(labels, unit="label", desc="BrainWeb labels", + disable=not progress): + class MAct(Act): + attrs = [attr] + setattr(MAct, attr, 1) + res[i] = toPetMmr(raw_data, outres=outres, modes=[MAct])[0][:, ::-1] return res + def volshow(vol, cmaps=None, colorbars=None, xlabels=None, ylabels=None, titles=None, @@ -329,7 +329,6 @@ def volshow(vol, """ import matplotlib.pyplot as plt import ipywidgets as ipyw - log = logging.getLogger(__name__) if hasattr(vol, "keys") and hasattr(vol, "values"): if titles is not None: @@ -427,7 +426,6 @@ def noise(im, n, warn_zero=True, sigma=1): @param sigma : float, smoothing of noise component @return[out] im : array like im with +-n *100%im noise added """ - log = logging.getLogger(__name__) if n < 0: raise ValueError("Noise must be positive") elif n == 0: @@ -441,26 +439,19 @@ def noise(im, n, warn_zero=True, sigma=1): def toPetMmr(im, pad=True, dtype=np.float32, outres="mMR", modes=None, PetClass=FDG): """ - @param outres: attribute to use from Res/Class [default: "mMR"] + @param outres: attribute to use from `Res` & `Shape` classes [default: "mMR"] @param modes : [default: [PetClass, Mu, T1, T2]] - @return out : list of `modes`, each res/shape specified from the outres attribute of Res/Shape + @param PetClass : [default: FDG]. Ignored if `modes` is explicitly specified + @return out : 4D array with same length as `modes` """ - log = logging.getLogger(__name__) - + if modes is None: + modes = [PetClass, Mu, T1, T2] out_res = getattr(Res, outres) out_shape = getattr(Shape, outres) new_shape = np.rint(np.asarray(im.shape) * Res.brainweb / out_res) padLR, padR = divmod((np.array(out_shape) - new_shape), 2) - def getModeIms(modes): - for MClass in modes: - res = np.zeros_like(im, dtype=dtype) # dtype only needed for uMap? - for attr in MClass.attrs: - log.debug("%s:%s:%d" % (MClass.__name__, attr, getattr(MClass, attr))) - res[Act.indices(im, attr)] = getattr(MClass, attr) - yield res - def resizeToMmr(arr): # oldMax = arr.max() # arr = arr.astype(np.float32) @@ -474,8 +465,15 @@ def resizeToMmr(arr): mode="constant") return arr - return list( - map(resizeToMmr, map(getModeIms, modes or [PetClass, Mu, T1, T2]))) + res = np.zeros((len(modes),) + tuple(out_shape)) + for i, MClass in enumerate(modes): + arr = np.zeros_like(im, dtype=dtype) # dtype only needed for uMap? + for attr in MClass.attrs: + log.debug("%s:%s:%d" % (MClass.__name__, attr, getattr(MClass, attr))) + arr[Act.indices(im, attr)] = getattr(MClass, attr) + res[i] = resizeToMmr(arr) + + return res def ellipsoid(out_shape, radii, position, dtype=np.float32): @@ -608,7 +606,6 @@ def register(src, target=None, ROI=None, target_shape=Shape.mMR, [default: "CoM"] : centre of mass. """ from dipy.align.imaffine import AffineMap, transform_centers_of_mass - log = logging.getLogger(__name__) assert src.ndim == 3 if target is not None: From 0cbc9384bd953cbaa2a6221c0d26f91f4e150a20 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 14:56:18 +0100 Subject: [PATCH 09/11] misc linting, tidy and safety --- brainweb/utils.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/brainweb/utils.py b/brainweb/utils.py index 3a341f7..8444c1f 100644 --- a/brainweb/utils.py +++ b/brainweb/utils.py @@ -21,7 +21,8 @@ # necessary "volshow", "get_files", "get_mmr_fromfile", # useful utils - "get_file", "load_file", "gunzip_array", "ellipsoid", "add_lesions", "get_label_probabilities", + "get_file", "load_file", "gunzip_array", "ellipsoid", "add_lesions", + "get_label_probabilities", # nothing to do with BrainWeb but still useful "trim_zeros_ROI", "register", # intensities @@ -47,11 +48,11 @@ class Act(object): background, csf, greyMatter, whiteMatter, fat, muscle, skin, skull, vessels,\ aroundFat, dura, marrow\ = [i << 4 for i in range(12)] + all_labels = [ + 'background', 'csf', 'greyMatter', 'whiteMatter', 'fat', 'muscle', 'skin', + 'skull', 'vessels', 'aroundFat', 'dura', 'marrow'] bone = skull | marrow | dura - all_labels = ['background', 'csf', 'greyMatter', 'whiteMatter', 'fat', 'muscle', 'skin', 'skull', 'vessels',\ - 'aroundFat', 'dura', 'marrow'] - @classmethod def indices(cls, im, attr): matter = None @@ -248,15 +249,16 @@ def get_mmr(cache_file, raw_data, res=np.float32(out_res)) cached = np.load(cache_file, allow_pickle=True) - # handle case of cached file that didn't stored the res key yet - if not 'res' in cached: - # need to add it, but cannot do that directly to a npzfile - # so create a dict (trick from https://stackoverflow.com/a/46550796) - cached = dict(cached) - cached['res'] = getattr(Res, 'mMR') + if 'res' not in cached: + log.info("%s:converting old data format", cache_file) + data = dict(cached) + data['res'] = getattr(Res, 'mMR') + np.savez_compressed(cache_file, **data) + cached = np.load(cache_file, allow_pickle=True) return cached + def get_mmr_fromfile(brainweb_file, petNoise=1.0, t1Noise=0.75, t2Noise=0.75, petSigma=1.0, t1Sigma=1.0, t2Sigma=1.0, @@ -270,15 +272,16 @@ def get_mmr_fromfile(brainweb_file, dat = load_file(brainweb_file) # read raw data return get_mmr( brainweb_file.replace( - '.bin.gz', '.npz' if PetClass == FDG and outres=="mMR" else - '.{}.{}.npz'.format(PetClass.__name__, outres)), + '.bin.gz', ('.%s.%s.npz' % (PetClass.__name__, outres)).replace( + '.FDG.mMR', '')), dat, petNoise=petNoise, t1Noise=t1Noise, t2Noise=t2Noise, petSigma=petSigma, t1Sigma=t1Sigma, t2Sigma=t2Sigma, outres=outres, PetClass=PetClass) -def get_label_probabilities(brainweb_file, labels=None, outres="mMR", progress=True, dtype=np.float32): +def get_label_probabilities(brainweb_file, labels=None, outres="mMR", + progress=True, dtype=np.float32): """ @param labels : list of strings, [default: Act.all_labels] @return out : 4D array of masks resampled as per `outres` (useful for PVC) From 6932821a1c966336cc045343a099ba46c529a17a Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 15:00:32 +0100 Subject: [PATCH 10/11] update rquirements --- requirements.txt | 2 +- setup.cfg | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index da776e6..58980ad 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -tqdm +tqdm>=4.42.0 requests numpy scikit-image diff --git a/setup.cfg b/setup.cfg index 830fc47..fa39f17 100644 --- a/setup.cfg +++ b/setup.cfg @@ -30,7 +30,7 @@ keywords = pet-mr, volume-rendering, neuroimaging, fdg, mri [options] packages = brainweb install_requires = - tqdm + tqdm>=4.42.0 requests numpy scikit-image From 26da82e1afa641a13906fbfde9a7a626f950791b Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 16 Sep 2020 15:07:18 +0100 Subject: [PATCH 11/11] missing doc tidy --- README.rst | 7 ------- mMR.png | Bin 64722 -> 113841 bytes 2 files changed, 7 deletions(-) diff --git a/README.rst b/README.rst index e6d61f5..e6b4690 100644 --- a/README.rst +++ b/README.rst @@ -149,13 +149,6 @@ Convert raw image data: .. image:: https://raw.githubusercontent.com/casperdcl/brainweb/master/mMR.png -.. code:: python - - # get a 4D array with weights for each region for the last subject - label_probs = brainweb.get_label_probabilities(files[-1]) - # print their names - print(PetClass.all_labels) - .. code:: python # add some lesions diff --git a/mMR.png b/mMR.png index ead7389ed6daa377064a40ac848f25a92afb9ee7..4da71c214b33464d58e2444fb1e5f5318cfe9ae0 100644 GIT binary patch literal 113841 zcmeFZWmuHm7e4yTkkTM2ozf|tD$<~mN+TuG4N?P$NJvZ9D+q#gHzOz=3QBhk-7+-i zd3@jBIsXsm>-ly*aB(p+&dlC>?X~XvUiUKMiIy@U-W@yu00>o86tw{WjsO7I0xmZA 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