- implement sliced views, including min, max, sum and mean operations
This commit is contained in:
Wim Pomp
File diff suppressed because it is too large
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@@ -21,7 +21,7 @@ except ImportError:
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DataFrame, Series, concat = None, None, None
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if hasattr(yaml, 'full_load'):
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if hasattr(yaml, "full_load"):
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yamlload = yaml.full_load
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else:
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yamlload = yaml.load
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@@ -34,7 +34,7 @@ class Transforms(dict):
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@classmethod
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def from_file(cls, file, C=True, T=True):
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with open(Path(file).with_suffix('.yml')) as f:
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with open(Path(file).with_suffix(".yml")) as f:
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return cls.from_dict(yamlload(f), C, T)
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@classmethod
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@@ -42,7 +42,9 @@ class Transforms(dict):
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new = cls()
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for key, value in d.items():
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if isinstance(key, str) and C:
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new[key.replace(r'\:', ':').replace('\\\\', '\\')] = Transform.from_dict(value)
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new[key.replace(r"\:", ":").replace("\\\\", "\\")] = (
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Transform.from_dict(value)
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)
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elif T:
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new[key] = Transform.from_dict(value)
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return new
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@@ -69,11 +71,19 @@ class Transforms(dict):
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return new
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def asdict(self):
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return {key.replace('\\', '\\\\').replace(':', r'\:') if isinstance(key, str) else key: value.asdict()
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for key, value in self.items()}
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return {
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key.replace("\\", "\\\\").replace(":", r"\:")
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if isinstance(key, str)
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else key: value.asdict()
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for key, value in self.items()
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}
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def __getitem__(self, item):
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return np.prod([self[i] for i in item[::-1]]) if isinstance(item, tuple) else super().__getitem__(item)
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return (
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np.prod([self[i] for i in item[::-1]])
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if isinstance(item, tuple)
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else super().__getitem__(item)
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)
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def __missing__(self, key):
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return self.default
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@@ -88,7 +98,7 @@ class Transforms(dict):
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return hash(frozenset((*self.__dict__.items(), *self.items())))
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def save(self, file):
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with open(Path(file).with_suffix('.yml'), 'w') as f:
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with open(Path(file).with_suffix(".yml"), "w") as f:
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yaml.safe_dump(self.asdict(), f, default_flow_style=None)
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def copy(self):
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@@ -109,8 +119,10 @@ class Transforms(dict):
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transform_channels = {key for key in self.keys() if isinstance(key, str)}
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if set(channel_names) - transform_channels:
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mapping = key_map(channel_names, transform_channels)
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warnings.warn(f'The image file and the transform do not have the same channels,'
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f' creating a mapping: {mapping}')
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warnings.warn(
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f"The image file and the transform do not have the same channels,"
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f" creating a mapping: {mapping}"
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)
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for key_im, key_t in mapping.items():
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self[key_im] = self[key_t]
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@@ -124,37 +136,54 @@ class Transforms(dict):
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def coords_pandas(self, array, channel_names, columns=None):
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if isinstance(array, DataFrame):
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return concat([self.coords_pandas(row, channel_names, columns) for _, row in array.iterrows()], axis=1).T
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return concat(
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[
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self.coords_pandas(row, channel_names, columns)
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for _, row in array.iterrows()
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],
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axis=1,
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).T
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elif isinstance(array, Series):
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key = []
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if 'C' in array:
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key.append(channel_names[int(array['C'])])
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if 'T' in array:
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key.append(int(array['T']))
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if "C" in array:
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key.append(channel_names[int(array["C"])])
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if "T" in array:
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key.append(int(array["T"]))
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return self[tuple(key)].coords(array, columns)
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else:
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raise TypeError('Not a pandas DataFrame or Series.')
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raise TypeError("Not a pandas DataFrame or Series.")
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def with_beads(self, cyllens, bead_files):
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assert len(bead_files) > 0, 'At least one file is needed to calculate the registration.'
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transforms = [self.calculate_channel_transforms(file, cyllens) for file in bead_files]
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assert len(bead_files) > 0, (
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"At least one file is needed to calculate the registration."
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)
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transforms = [
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self.calculate_channel_transforms(file, cyllens) for file in bead_files
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]
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for key in {key for transform in transforms for key in transform.keys()}:
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new_transforms = [transform[key] for transform in transforms if key in transform]
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new_transforms = [
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transform[key] for transform in transforms if key in transform
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]
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if len(new_transforms) == 1:
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self[key] = new_transforms[0]
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else:
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self[key] = Transform()
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self[key].parameters = np.mean([t.parameters for t in new_transforms], 0)
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self[key].dparameters = (np.std([t.parameters for t in new_transforms], 0) /
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np.sqrt(len(new_transforms))).tolist()
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self[key].parameters = np.mean(
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[t.parameters for t in new_transforms], 0
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)
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self[key].dparameters = (
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np.std([t.parameters for t in new_transforms], 0)
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/ np.sqrt(len(new_transforms))
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).tolist()
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return self
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@staticmethod
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def get_bead_files(path):
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from . import Imread
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files = []
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for file in path.iterdir():
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if file.name.lower().startswith('beads'):
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if file.name.lower().startswith("beads"):
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try:
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with Imread(file):
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files.append(file)
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@@ -162,32 +191,36 @@ class Transforms(dict):
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pass
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files = sorted(files)
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if not files:
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raise Exception('No bead file found!')
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raise Exception("No bead file found!")
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checked_files = []
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for file in files:
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try:
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if file.is_dir():
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file /= 'Pos0'
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file /= "Pos0"
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with Imread(file): # check for errors opening the file
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checked_files.append(file)
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except (Exception,):
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continue
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if not checked_files:
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raise Exception('No bead file found!')
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raise Exception("No bead file found!")
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return checked_files
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@staticmethod
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def calculate_channel_transforms(bead_file, cyllens):
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""" When no channel is not transformed by a cylindrical lens, assume that the image is scaled by a factor 1.162
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in the horizontal direction """
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"""When no channel is not transformed by a cylindrical lens, assume that the image is scaled by a factor 1.162
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in the horizontal direction"""
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from . import Imread
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with Imread(bead_file, axes='zcyx') as im: # noqa
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max_ims = im.max('z')
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with Imread(bead_file, axes="zcyx") as im: # noqa
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max_ims = im.max("z")
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goodch = [c for c, max_im in enumerate(max_ims) if not im.is_noise(max_im)]
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if not goodch:
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goodch = list(range(len(max_ims)))
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untransformed = [c for c in range(im.shape['c']) if cyllens[im.detector[c]].lower() == 'none']
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untransformed = [
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c
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for c in range(im.shape["c"])
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if cyllens[im.detector[c]].lower() == "none"
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]
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good_and_untrans = sorted(set(goodch) & set(untransformed))
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if good_and_untrans:
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@@ -200,54 +233,81 @@ class Transforms(dict):
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matrix[0, 0] = 0.86
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transform.matrix = matrix
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transforms = Transforms()
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for c in tqdm(goodch, desc='Calculating channel transforms'): # noqa
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for c in tqdm(goodch, desc="Calculating channel transforms"): # noqa
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if c == masterch:
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transforms[im.channel_names[c]] = transform
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else:
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transforms[im.channel_names[c]] = Transform.register(max_ims[masterch], max_ims[c]) * transform
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transforms[im.channel_names[c]] = (
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Transform.register(max_ims[masterch], max_ims[c]) * transform
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)
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return transforms
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@staticmethod
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def save_channel_transform_tiff(bead_files, tiffile):
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from . import Imread
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n_channels = 0
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for file in bead_files:
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with Imread(file) as im:
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n_channels = max(n_channels, im.shape['c'])
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n_channels = max(n_channels, im.shape["c"])
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with IJTiffFile(tiffile) as tif:
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for t, file in enumerate(bead_files):
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with Imread(file) as im:
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with Imread(file).with_transform() as jm:
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for c in range(im.shape['c']):
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tif.save(np.hstack((im(c=c, t=0).max('z'), jm(c=c, t=0).max('z'))), c, 0, t)
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for c in range(im.shape["c"]):
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tif.save(
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np.hstack(
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(im(c=c, t=0).max("z"), jm(c=c, t=0).max("z"))
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),
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c,
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0,
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t,
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)
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def with_drift(self, im):
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""" Calculate shifts relative to the first frame
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divide the sequence into groups,
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compare each frame to the frame in the middle of the group and compare these middle frames to each other
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"""Calculate shifts relative to the first frame
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divide the sequence into groups,
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compare each frame to the frame in the middle of the group and compare these middle frames to each other
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"""
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im = im.transpose('tzycx')
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t_groups = [list(chunk) for chunk in Chunks(range(im.shape['t']), size=round(np.sqrt(im.shape['t'])))]
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im = im.transpose("tzycx")
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t_groups = [
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list(chunk)
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for chunk in Chunks(
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range(im.shape["t"]), size=round(np.sqrt(im.shape["t"]))
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)
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]
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t_keys = [int(np.round(np.mean(t_group))) for t_group in t_groups]
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t_pairs = [(int(np.round(np.mean(t_group))), frame) for t_group in t_groups for frame in t_group]
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t_pairs = [
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(int(np.round(np.mean(t_group))), frame)
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for t_group in t_groups
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for frame in t_group
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]
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t_pairs.extend(zip(t_keys, t_keys[1:]))
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fmaxz_keys = {t_key: filters.gaussian(im[t_key].max('z'), 5) for t_key in t_keys}
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fmaxz_keys = {
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t_key: filters.gaussian(im[t_key].max("z"), 5) for t_key in t_keys
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}
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def fun(t_key_t, im, fmaxz_keys):
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t_key, t = t_key_t
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if t_key == t:
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return 0, 0
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else:
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fmaxz = filters.gaussian(im[t].max('z'), 5)
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return Transform.register(fmaxz_keys[t_key], fmaxz, 'translation').parameters[4:]
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fmaxz = filters.gaussian(im[t].max("z"), 5)
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return Transform.register(
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fmaxz_keys[t_key], fmaxz, "translation"
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).parameters[4:]
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shifts = np.array(pmap(fun, t_pairs, (im, fmaxz_keys), desc='Calculating image shifts.'))
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shifts = np.array(
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pmap(fun, t_pairs, (im, fmaxz_keys), desc="Calculating image shifts.")
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)
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shift_keys_cum = np.zeros(2)
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for shift_keys, t_group in zip(np.vstack((-shifts[0], shifts[im.shape['t']:])), t_groups):
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for shift_keys, t_group in zip(
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np.vstack((-shifts[0], shifts[im.shape["t"] :])), t_groups
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):
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shift_keys_cum += shift_keys
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shifts[t_group] += shift_keys_cum
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for i, shift in enumerate(shifts[:im.shape['t']]):
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for i, shift in enumerate(shifts[: im.shape["t"]]):
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self[i] = Transform.from_shift(shift)
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return self
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@@ -257,9 +317,11 @@ class Transform:
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if sitk is None:
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self.transform = None
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else:
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self.transform = sitk.ReadTransform(str(Path(__file__).parent / 'transform.txt'))
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self.dparameters = [0., 0., 0., 0., 0., 0.]
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self.shape = [512., 512.]
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self.transform = sitk.ReadTransform(
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str(Path(__file__).parent / "transform.txt")
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)
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self.dparameters = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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self.shape = [512.0, 512.0]
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self.origin = [255.5, 255.5]
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self._last, self._inverse = None, None
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@@ -274,12 +336,14 @@ class Transform:
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@classmethod
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def register(cls, fix, mov, kind=None):
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""" kind: 'affine', 'translation', 'rigid' """
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"""kind: 'affine', 'translation', 'rigid'"""
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if sitk is None:
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raise ImportError('SimpleElastix is not installed: '
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'https://simpleelastix.readthedocs.io/GettingStarted.html')
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raise ImportError(
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"SimpleElastix is not installed: "
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"https://simpleelastix.readthedocs.io/GettingStarted.html"
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)
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new = cls()
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kind = kind or 'affine'
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kind = kind or "affine"
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new.shape = fix.shape
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fix, mov = new.cast_image(fix), new.cast_image(mov)
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# TODO: implement RigidTransform
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@@ -290,16 +354,18 @@ class Transform:
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tfilter.SetParameterMap(sitk.GetDefaultParameterMap(kind))
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tfilter.Execute()
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transform = tfilter.GetTransformParameterMap()[0]
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if kind == 'affine':
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new.parameters = [float(t) for t in transform['TransformParameters']]
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new.shape = [float(t) for t in transform['Size']]
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new.origin = [float(t) for t in transform['CenterOfRotationPoint']]
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elif kind == 'translation':
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new.parameters = [1.0, 0.0, 0.0, 1.0] + [float(t) for t in transform['TransformParameters']]
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new.shape = [float(t) for t in transform['Size']]
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if kind == "affine":
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new.parameters = [float(t) for t in transform["TransformParameters"]]
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new.shape = [float(t) for t in transform["Size"]]
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new.origin = [float(t) for t in transform["CenterOfRotationPoint"]]
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elif kind == "translation":
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new.parameters = [1.0, 0.0, 0.0, 1.0] + [
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float(t) for t in transform["TransformParameters"]
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]
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new.shape = [float(t) for t in transform["Size"]]
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new.origin = [(t - 1) / 2 for t in new.shape]
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else:
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raise NotImplementedError(f'{kind} tranforms not implemented (yet)')
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raise NotImplementedError(f"{kind} tranforms not implemented (yet)")
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new.dparameters = 6 * [np.nan]
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return new
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@@ -315,18 +381,35 @@ class Transform:
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@classmethod
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def from_file(cls, file):
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with open(Path(file).with_suffix('.yml')) as f:
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with open(Path(file).with_suffix(".yml")) as f:
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return cls.from_dict(yamlload(f))
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@classmethod
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def from_dict(cls, d):
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new = cls()
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new.origin = None if d['CenterOfRotationPoint'] is None else [float(i) for i in d['CenterOfRotationPoint']]
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new.parameters = ((1., 0., 0., 1., 0., 0.) if d['TransformParameters'] is None else
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[float(i) for i in d['TransformParameters']])
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new.dparameters = ([(0., 0., 0., 0., 0., 0.) if i is None else float(i) for i in d['dTransformParameters']]
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if 'dTransformParameters' in d else 6 * [np.nan] and d['dTransformParameters'] is not None)
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new.shape = None if d['Size'] is None else [None if i is None else float(i) for i in d['Size']]
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new.origin = (
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None
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if d["CenterOfRotationPoint"] is None
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else [float(i) for i in d["CenterOfRotationPoint"]]
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)
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new.parameters = (
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(1.0, 0.0, 0.0, 1.0, 0.0, 0.0)
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if d["TransformParameters"] is None
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else [float(i) for i in d["TransformParameters"]]
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)
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new.dparameters = (
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[
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(0.0, 0.0, 0.0, 0.0, 0.0, 0.0) if i is None else float(i)
|
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for i in d["dTransformParameters"]
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]
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if "dTransformParameters" in d
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else 6 * [np.nan] and d["dTransformParameters"] is not None
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)
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new.shape = (
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None
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if d["Size"] is None
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else [None if i is None else float(i) for i in d["Size"]]
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)
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return new
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def __mul__(self, other): # TODO: take care of dmatrix
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@@ -359,9 +442,13 @@ class Transform:
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@property
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def matrix(self):
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return np.array(((*self.parameters[:2], self.parameters[4]),
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(*self.parameters[2:4], self.parameters[5]),
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(0, 0, 1)))
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return np.array(
|
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(
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(*self.parameters[:2], self.parameters[4]),
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(*self.parameters[2:4], self.parameters[5]),
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(0, 0, 1),
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)
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)
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@matrix.setter
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def matrix(self, value):
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@@ -370,9 +457,13 @@ class Transform:
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@property
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def dmatrix(self):
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return np.array(((*self.dparameters[:2], self.dparameters[4]),
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(*self.dparameters[2:4], self.dparameters[5]),
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(0, 0, 0)))
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return np.array(
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(
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(*self.dparameters[:2], self.dparameters[4]),
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(*self.dparameters[2:4], self.dparameters[5]),
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(0, 0, 0),
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)
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)
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@dmatrix.setter
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def dmatrix(self, value):
|
||||
@@ -384,7 +475,7 @@ class Transform:
|
||||
if self.transform is not None:
|
||||
return list(self.transform.GetParameters())
|
||||
else:
|
||||
return [1., 0., 0., 1., 0., 0.]
|
||||
return [1.0, 0.0, 0.0, 1.0, 0.0, 0.0]
|
||||
|
||||
@parameters.setter
|
||||
def parameters(self, value):
|
||||
@@ -420,43 +511,62 @@ class Transform:
|
||||
self.shape = shape[:2]
|
||||
|
||||
def asdict(self):
|
||||
return {'CenterOfRotationPoint': self.origin, 'Size': self.shape, 'TransformParameters': self.parameters,
|
||||
'dTransformParameters': np.nan_to_num(self.dparameters, nan=1e99).tolist()}
|
||||
return {
|
||||
"CenterOfRotationPoint": self.origin,
|
||||
"Size": self.shape,
|
||||
"TransformParameters": self.parameters,
|
||||
"dTransformParameters": np.nan_to_num(self.dparameters, nan=1e99).tolist(),
|
||||
}
|
||||
|
||||
def frame(self, im, default=0):
|
||||
if self.is_unity():
|
||||
return im
|
||||
else:
|
||||
if sitk is None:
|
||||
raise ImportError('SimpleElastix is not installed: '
|
||||
'https://simpleelastix.readthedocs.io/GettingStarted.html')
|
||||
raise ImportError(
|
||||
"SimpleElastix is not installed: "
|
||||
"https://simpleelastix.readthedocs.io/GettingStarted.html"
|
||||
)
|
||||
dtype = im.dtype
|
||||
im = im.astype('float')
|
||||
intp = sitk.sitkBSpline if np.issubdtype(dtype, np.floating) else sitk.sitkNearestNeighbor
|
||||
return self.cast_array(sitk.Resample(self.cast_image(im), self.transform, intp, default)).astype(dtype)
|
||||
im = im.astype("float")
|
||||
intp = (
|
||||
sitk.sitkBSpline
|
||||
if np.issubdtype(dtype, np.floating)
|
||||
else sitk.sitkNearestNeighbor
|
||||
)
|
||||
return self.cast_array(
|
||||
sitk.Resample(self.cast_image(im), self.transform, intp, default)
|
||||
).astype(dtype)
|
||||
|
||||
def coords(self, array, columns=None):
|
||||
""" Transform coordinates in 2 column numpy array,
|
||||
or in pandas DataFrame or Series objects in columns ['x', 'y']
|
||||
"""Transform coordinates in 2 column numpy array,
|
||||
or in pandas DataFrame or Series objects in columns ['x', 'y']
|
||||
"""
|
||||
if self.is_unity():
|
||||
return array.copy()
|
||||
elif DataFrame is not None and isinstance(array, (DataFrame, Series)):
|
||||
columns = columns or ['x', 'y']
|
||||
columns = columns or ["x", "y"]
|
||||
array = array.copy()
|
||||
if isinstance(array, DataFrame):
|
||||
array[columns] = self.coords(np.atleast_2d(array[columns].to_numpy()))
|
||||
elif isinstance(array, Series):
|
||||
array[columns] = self.coords(np.atleast_2d(array[columns].to_numpy()))[0]
|
||||
array[columns] = self.coords(np.atleast_2d(array[columns].to_numpy()))[
|
||||
0
|
||||
]
|
||||
return array
|
||||
else: # somehow we need to use the inverse here to get the same effect as when using self.frame
|
||||
return np.array([self.inverse.transform.TransformPoint(i.tolist()) for i in np.asarray(array)])
|
||||
return np.array(
|
||||
[
|
||||
self.inverse.transform.TransformPoint(i.tolist())
|
||||
for i in np.asarray(array)
|
||||
]
|
||||
)
|
||||
|
||||
def save(self, file):
|
||||
""" save the parameters of the transform calculated
|
||||
with affine_registration to a yaml file
|
||||
"""save the parameters of the transform calculated
|
||||
with affine_registration to a yaml file
|
||||
"""
|
||||
if not file[-3:] == 'yml':
|
||||
file += '.yml'
|
||||
with open(file, 'w') as f:
|
||||
if not file[-3:] == "yml":
|
||||
file += ".yml"
|
||||
with open(file, "w") as f:
|
||||
yaml.safe_dump(self.asdict(), f, default_flow_style=None)
|
||||
|
||||
Reference in New Issue
Block a user