Affine Transformations¶
API¶
Python wrapper for CUDA-accelerated 3D deconvolution.
- pycudadecon.affineGPU(im, tmat, dzyx=None)[source]¶
Perform 3D affine transformation of image given a 4x4 transformation matrix.
optional dzyx parameter specifies the voxel size of the image (dz, dy, dx). If it is provided, it will be used to transform the image from intrinsic coordinates to world coordinates prior to transformation, e.g.:
\(x = 0.5 + (x - 0.5) * dx\)
and then back to intrinsic coords afterwards… e.g.:
\(tu = 0.5 + (tu - 0.5) / dx\)
- Parameters:
im (np.ndarray) – 3D input volume
tmat (np.ndarray) – Affine transformation matrix
dzyx (3-tuple of int) – Voxel size of input volume (dz, dy, dx). If provided, the transformation matrix is assumed to be in units of sample space. otherwise the transformation is performed in image coordinates. by default None
- Returns:
volume – Transformed volume
- Return type:
np.ndarray
- Raises:
ValueError – If the dimensions of the transformation matrix do not the input volume. For instance, a 3D input volume requires a 4 x 4 tranforamtion matrix.
Examples
Perform simple translation
>>> nx, ny, nz = (10, 20, 3) >>> T = np.array([[1, 0, 0, nx], [0, 1, 0, ny], [0, 0, 1, nz], [0, 0, 0, 1]]) >>> rotated = affineGPU(im, T)
Perform a rotation about the Y axis… (this is the underlying code for
rotateGPU())>>> theta = angle * np.pi / 180 >>> nz, ny, nx = im.shape >>> xzRatio = dxdata / (np.deg2rad(angle) * dzdata) >>> # first translate the middle of the image to the origin >>> T1 = np.array([[1, 0, 0, nx / 2], >>> [0, 1, 0, ny / 2], >>> [0, 0, 1, nz / 2], >>> [0, 0, 0, 1]]) >>> # then scale (resample) the Z axis the dz/dx ratio >>> S = np.array([[1, 0, 0, 0], >>> [0, 1, 0, 0], >>> [0, 0, xzRatio, 0], >>> [0, 0, 0, 1]]) >>> # then rotate theta degrees about the Y axis >>> R = np.array([[np.cos(theta), 0, -np.sin(theta), 0], >>> [0, 1, 0, 0], >>> [np.sin(theta), 0, np.cos(theta), 0], >>> [0, 0, 0, 1]]) >>> # then translate back to the original origin >>> T2 = np.array([[1, 0, 0, -nx / 2], >>> [0, 1, 0, -ny / 2], >>> [0, 0, 1, -nz / 2], >>> [0, 0, 0, 1]]) >>> T = np.eye(4) >>> T = np.dot(np.dot(np.dot(np.dot(T, T1), S), R), T2) >>> rotated = affineGPU(im, T)
- pycudadecon.deskewGPU(im, dxdata=0.1, dzdata=0.5, angle=31.5, width=0, shift=0, pad_val=None)[source]¶
Deskew data acquired in stage-scanning mode on GPU.
Simple affine transform variant to perform a shear operation to correct for volume shearing.
- Parameters:
im (np.ndarray) – Image volume to deskew
dxdata (float, optional) – XY Pixel size of image volume, by default 0.1
dzdata (float, optional) – Z-step size in image volume. In a typical light sheet stage-scanning acquisition, this corresponds to the step size that the stage takes between planes, NOT the final Z-step size between planeds after deskewing along the optical axis of the detection objective, by default 0.5
angle (float, optional) – Deskew angle (usually, angle between sheet and axis of stage motion), by default 31.5
width (int, optional) – If not 0, crop output image to specified width, by default 0
shift (int, optional) – If not 0, shift image center by this value, by default 0
pad_val (int, optional) – Value to pad image with when deskewing. If None the median value of the last Z plane will be used, by default None
- Returns:
Deskewed volume
- Return type:
np.ndarray
- pycudadecon.rotateGPU(im, dzdata, dxdata=0.1, angle=31.5, reverse=False)[source]¶
Rotate image around Y axis by some angle.
This is a convenience function that will apply the appropriate affine transformation for rotating a volume around the Y axis by some angle. This is typically done with images acquired on inverted light sheet microscopes where the image plane is not parallel to the coverslip (such as lattice light sheet, or diSPIM microscopes), in order to change the coordinate system of the image volume such that the Z axis is orthogonal to the coverslip
- Parameters:
- Returns:
Rotated 3D volume
- Return type:
np.ndarray