Ma Nan
ce397b35aa
fix: contrast formulation cause leak of high value fix: empty series_id cause fail to read chore: restructure folder chore!: change layer vars BREAKING CHANGE: not support 4.1 any more
132 lines
3.5 KiB
Python
132 lines
3.5 KiB
Python
import copy
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import numpy as np
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from . import scipy
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from . import skimage as ski
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# 3rd-party
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import transforms3d
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# TODO: turn to dataclasses
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class Layer():
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def __init__(self,
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data: np.ndarray,
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name: str,
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kind="scalar",
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affine=np.identity(4)) -> None:
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if data.ndim != 5:
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raise Exception("Data shape order should be TXYZC")
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affine = np.array(affine)
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if affine.shape != (4, 4):
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raise Exception("affine shape should be (4,4)")
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self.data = data
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self.name = name
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self.kind = kind
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self.affine = affine
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@property
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def bioxel_size(self):
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t, r, z, s = transforms3d.affines.decompose44(self.affine)
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return z.tolist()
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@property
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def shape(self):
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return self.data.shape[1:4]
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@property
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def dtype(self):
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return self.data.dtype
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@property
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def origin(self):
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t, r, z, s = transforms3d.affines.decompose44(self.affine)
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return t.tolist()
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@property
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def euler(self):
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t, r, z, s = transforms3d.affines.decompose44(self.affine)
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return list(transforms3d.euler.mat2euler(r))
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@property
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def min(self):
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return float(np.min(self.data))
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@property
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def frame_count(self):
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return self.data.shape[0]
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@property
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def channel_count(self):
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return self.data.shape[-1]
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@property
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def max(self):
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return float(np.max(self.data))
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def copy(self):
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return copy.deepcopy(self)
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def fill(self, value: float, mask: np.ndarray):
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mask_frames = ()
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if mask.ndim == 4:
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if mask.shape[0] != self.frame_count:
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raise Exception("Mask frame count is not same as ")
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for f in range(self.frame_count):
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mask_frame = mask[f, :, :, :]
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mask_frame = scipy.median_filter(
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mask_frame.astype(np.float32), size=2)
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mask_frames += (mask_frame,)
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elif mask.ndim == 3:
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for f in range(self.frame_count):
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mask_frame = mask[:, :, :]
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mask_frame = scipy.median_filter(
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mask_frame.astype(np.float32), size=2)
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mask_frames += (mask_frame,)
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else:
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raise Exception("Mask shape order should be TXYZ or XYZ")
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_mask = np.stack(mask_frames)
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_mask = np.expand_dims(_mask, axis=-1)
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self.data = _mask * value + (1-_mask) * self.data
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def resize(self, shape:tuple, progress_callback=None):
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if len(shape) != 3:
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raise Exception("Shape must be 3 dim")
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data = self.data
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# TXYZC > TXYZ
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if self.kind in ['label', 'scalar']:
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data = np.amax(data, -1)
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if self.kind in ['scalar']:
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dtype = data.dtype
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data = data.astype(np.float32)
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data_frames = ()
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for f in range(self.frame_count):
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if progress_callback:
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progress_callback(f, self.frame_count)
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frame = ski.resize(data[f, :, :, :],
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shape,
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preserve_range=True,
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anti_aliasing=data.dtype.kind != "b")
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data_frames += (frame,)
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data = np.stack(data_frames)
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if self.kind in ['scalar']:
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data = data.astype(dtype)
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# TXYZ > TXYZC
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if self.kind in ['label', 'scalar']:
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data = np.expand_dims(data, axis=-1) # expend channel
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self.data = data
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