Update patch extraction API

examples/on_the_fly_tomogram_amplitude_spectrum.py

@@ -0,0 +1,47 @@
+import mrcfile
+import numpy as np
+import torch
+import time
+
+from libtilt.rotational_averaging import rotational_average_dft_3d
+from libtilt.patch_extraction import extract_cubes
+
+# https://zenodo.org/records/6504891
+TOMOGRAM_FILE = '/Users/burta2/Downloads/01_10.00Apx.mrc'
+N_CUBES = 20
+SIDELENGTH = 128
+
+tomogram = torch.tensor(mrcfile.read(TOMOGRAM_FILE), dtype=torch.float32)
+
+# sample some points in the volume (could be smarter and sample from masked regions)
+d, h, w = tomogram.shape
+lower_bound = SIDELENGTH // 2
+z = np.random.uniform(low=lower_bound, high=d - lower_bound, size=N_CUBES)
+y = np.random.uniform(low=lower_bound, high=h - lower_bound, size=N_CUBES)
+x = np.random.uniform(low=lower_bound, high=w - lower_bound, size=N_CUBES)
+
+zyx = torch.tensor(np.stack([z, y, x], axis=-1), dtype=torch.float32)
+
+# start timing here
+t0 = time.time()
+
+# extract cubes at those points
+cubes = extract_cubes(image=tomogram, positions=zyx, sidelength=SIDELENGTH)
+
+# calculate amplitude spectra and rotational average
+cubes_amplitudes = torch.fft.rfftn(cubes, dim=(-3, -2, -1)).abs().pow(2)
+raps, bins = rotational_average_dft_3d(cubes_amplitudes, rfft=True, fftshifted=False,
+                                       image_shape=(SIDELENGTH, SIDELENGTH, SIDELENGTH))
+raps = torch.mean(raps, dim=0)  # average over each of 10 cubes
+
+# end timing here
+t1 = time.time()
+
+print(f"Elapsed time: {t1 - t0:.2f} seconds")
+
+# plot
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.plot(np.log(raps.numpy()))
+plt.show()

examples/tsa_real_data_multiregion.py

@@ -7,7 +7,7 @@
 from libtilt.backprojection import backproject_fourier
 from libtilt.coordinate_utils import homogenise_coordinates
 from libtilt.fft_utils import dft_center
-from libtilt.patch_extraction import extract_patches
+from libtilt.patch_extraction import extract_squares
 from libtilt.projection import project_fourier
 from libtilt.rescaling.rescale_fourier import rescale_2d
 from libtilt.shapes import circle
@@ -34,8 +34,8 @@
 n_tilts, h, w = tilt_series.shape
 center = dft_center((h, w), rfft=False, fftshifted=True)
 center = einops.repeat(center, 'yx -> b yx', b=len(tilt_series))
-tilt_series = extract_patches(
-    images=tilt_series,
+tilt_series = extract_squares(
+    image=tilt_series,
     positions=center,
     sidelength=min(h, w),
 )
@@ -86,8 +86,8 @@
         projected_yx = Mproj @ positions_homogenous.view((-1, 1, 4, 1))
         projected_yx = projected_yx.view((8, -1, 2))
 
-        local_ts = extract_patches(
-            images=tilt_series,
+        local_ts = extract_squares(
+            image=tilt_series,
             positions=projected_yx,
             sidelength=s
         )

examples/tsa_real_data_prior_shifts.py

@@ -5,7 +5,7 @@
 
 from libtilt.backprojection import backproject_fourier
 from libtilt.fft_utils import dft_center
-from libtilt.patch_extraction import extract_patches
+from libtilt.patch_extraction import extract_squares
 from libtilt.projection import project_fourier
 from libtilt.rescaling.rescale_fourier import rescale_2d
 from libtilt.shapes import circle
@@ -21,8 +21,8 @@
 
 tilt_series = torch.as_tensor(mrcfile.read(IMAGE_FILE))
 df = pd.read_csv(FID_CENTERS_FILE)
-fiducial_tilt_series = extract_patches(
-    images=tilt_series,
+fiducial_tilt_series = extract_squares(
+    image=tilt_series,
     positions=torch.tensor(df[['axis-1', 'axis-2']].to_numpy()).float(),
     sidelength=256
 )

examples/virtual_tomogram.py

@@ -11,7 +11,7 @@
 
 from libtilt.transformations import Rx, Ry, Rz, T, S
 from libtilt.coordinate_utils import homogenise_coordinates
-from libtilt.patch_extraction.patch_extraction_spp import extract_patches
+from libtilt.patch_extraction.subpixel_square_patch_extraction import extract_squares
 from libtilt.rescaling import rescale_2d
 from libtilt.backprojection import backproject_fourier
 from libtilt.fft_utils import dft_center
@@ -111,8 +111,8 @@ def extract_local_tilt_series(
             self, target_pixel_size=self.target_pixel_size
         )
         projected_positions = self.calculate_projected_positions(position_in_tomogram)
-        particle_tilt_series = extract_patches(
-            images=rescaled_tilt_series,
+        particle_tilt_series = extract_squares(
+            image=rescaled_tilt_series,
             positions=projected_positions,
             sidelength=sidelength,
         )

src/libtilt/grids/__init__.py

@@ -1,12 +1,12 @@
 from .coordinate_grid import coordinate_grid
 from .fftfreq_grid import fftfreq_grid
 from .central_slice_grid import central_slice_grid, rotated_central_slice_grid
-from .patch_grid import patch_grid
+from libtilt.patch_extraction.patch_extraction_on_grid import extract_patches_on_grid
 
 __all__ = [
     'coordinate_grid',
     'fftfreq_grid',
     'central_slice_grid',
     'rotated_central_slice_grid',
-    'patch_grid',
+    'extract_patches_on_grid',
 ]

src/libtilt/patch_extraction/__init__.py

@@ -1 +1,3 @@
-from .patch_extraction_spp import extract_patches
+from .subpixel_square_patch_extraction import extract_squares
+from .subpixel_cubic_patch_extraction import extract_cubes
+from .patch_extraction_on_grid import extract_patches_on_grid

src/libtilt/grids/_patch_grid_utils/_patch_grid_indices.py → src/libtilt/patch_extraction/_patch_grid_utils/_patch_grid_indices.py

@@ -3,7 +3,7 @@
 import einops
 import torch
 
-from libtilt.grids._patch_grid_utils._patch_grid_centers import _patch_centers_1d
+from libtilt.patch_extraction._patch_grid_utils._patch_grid_centers import _patch_centers_1d
 
 
 def patch_grid_indices(

src/libtilt/grids/patch_grid.py → src/libtilt/patch_extraction/patch_extraction_on_grid.py

@@ -1,9 +1,9 @@
 import torch
 
-from libtilt.grids._patch_grid_utils import patch_grid_centers, patch_grid_indices
+from ._patch_grid_utils import patch_grid_centers, patch_grid_indices
 
 
-def patch_grid(
+def extract_patches_on_grid(
     images: torch.Tensor,
     patch_shape: tuple[int, int] | tuple[int, int, int],
     patch_step: tuple[int, int] | tuple[int, int, int],
@@ -13,14 +13,14 @@ def patch_grid(
         raise ValueError('patch shape and step must have the same number of dimensions.')
     ndim = len(patch_shape)
     if ndim == 2:
-        patches, patch_centers = _patch_grid_2d(
+        patches, patch_centers = _extract_2d_patches_on_2d_grid(
             images=images,
             patch_shape=patch_shape,
             patch_step=patch_step,
             distribute_patches=distribute_patches,
         )
     elif ndim == 3:
-        patches, patch_centers = _patch_grid_3d(
+        patches, patch_centers = _extract_3d_patches_on_3d_grid(
             images=images,
             patch_shape=patch_shape,
             patch_step=patch_step,
@@ -31,7 +31,7 @@ def patch_grid(
     return patches, patch_centers
 
 
-def _patch_grid_2d(
+def _extract_2d_patches_on_2d_grid(
     images: torch.Tensor,
     patch_shape: tuple[int, int],
     patch_step: tuple[int, int],
@@ -76,7 +76,7 @@ def _patch_grid_2d(
     return patches, patch_centers
 
 
-def _patch_grid_3d(
+def _extract_3d_patches_on_3d_grid(
     images: torch.Tensor,
     patch_shape: tuple[int, int, int],
     patch_step: tuple[int, int, int],

src/libtilt/patch_extraction/subpixel_cubic_patch_extraction.py

@@ -0,0 +1,88 @@
+import einops
+import torch
+from torch.nn import functional as F
+
+from libtilt.grids import coordinate_grid
+from libtilt.shift.shift_image import shift_3d
+from libtilt.coordinate_utils import array_to_grid_sample
+from libtilt.fft_utils import dft_center
+
+
+def extract_cubes(
+    image: torch.Tensor, positions: torch.Tensor, sidelength: int,
+):
+    """Extract cubic patches from a 3D image at positions with subpixel precision.
+
+    Patches are extracted at the nearest integer coordinates then phase shifted
+    such that the requested position is at the center of the patch.
+
+    Parameters
+    ----------
+    image: torch.Tensor
+        `(d, h, w)` array containing a 3D image.
+    positions: torch.Tensor
+        `(..., 3)`
+    sidelength: int
+        Sidelength of cubic patches extracted from `image`.
+
+
+    Returns
+    -------
+    patches: torch.Tensor
+        `(..., sidelength, sidelength, sidelength)` array of cubic patches from `image`
+        with their centers at `positions`.
+    """
+    # pack arbitrary dimensions up into one new batch dim 'b'
+    positions, ps = einops.pack([positions], pattern='* zyx')
+
+    # extract cubic patches from 3D image
+    patches = _extract_cubic_patches_from_single_3d_image(
+        image=image, positions=positions, sidelength=sidelength
+    )
+
+    # reassemble patches into arbitrary dimensional stacks
+    [patches] = einops.unpack(patches, pattern='* d h w', packed_shapes=ps)
+    return patches
+
+
+def _extract_cubic_patches_from_single_3d_image(
+    image: torch.Tensor,  # (h, w)
+    positions: torch.Tensor,  # (b, 3) zyx
+    sidelength: int,
+) -> torch.Tensor:
+    d, h, w = image.shape
+    b, _ = positions.shape
+
+    # find integer positions and shifts to be applied
+    integer_positions = torch.round(positions)
+    shifts = integer_positions - positions
+
+    # generate coordinate grids for sampling around each integer position
+    # add 1px border to leave space for subpixel phase shifting
+    pd, ph, pw = (sidelength + 2, sidelength + 2, sidelength + 2)
+    coordinates = coordinate_grid(
+        image_shape=(pd, ph, pw),
+        center=dft_center(
+            image_shape=(pd, ph, pw),
+            rfft=False, fftshifted=True,
+            device=image.device
+        ),
+        device=image.device
+    )  # (d, h, w, 3)
+    broadcastable_positions = einops.rearrange(integer_positions, 'b zyx -> b 1 1 1 zyx')
+    grid = coordinates + broadcastable_positions  # (b, d, h, w, 3)
+
+    # extract patches, grid sample handles boundaries
+    patches = F.grid_sample(
+        input=einops.repeat(image, 'd h w -> b 1 d h w', b=b),
+        grid=array_to_grid_sample(grid, array_shape=(d, h, w)),
+        mode='nearest',
+        padding_mode='zeros',
+        align_corners=True
+    )
+    patches = einops.rearrange(patches, 'b 1 d h w -> b d h w')
+
+    # phase shift to center images then remove border
+    patches = shift_3d(images=patches, shifts=shifts)
+    patches = F.pad(patches, pad=(-1, -1, -1, -1, -1, -1))
+    return patches

src/libtilt/patch_extraction/patch_extraction_spp.py → src/libtilt/patch_extraction/subpixel_square_patch_extraction.py

@@ -8,52 +8,61 @@
 from libtilt.fft_utils import dft_center
 
 
-def extract_patches(
-    images: torch.Tensor, positions: torch.Tensor, sidelength: int,
+def extract_squares(
+    image: torch.Tensor, positions: torch.Tensor, sidelength: int,
 ):
-    """Extract patches from 2D images at positions with subpixel precision.
+    """Extract square patches from 2D images at positions with subpixel precision.
 
     Patches are extracted at the nearest integer coordinates then phase shifted
     such that the requested position is at the center of the patch.
 
     Parameters
     ----------
-    images: torch.Tensor
-        `(t, h, w)` or `(h, w)` array of 2D images.
+    image: torch.Tensor
+        `(h, w)` or `(b, h, w)` array containing a 2D image or 2D images.
     positions: torch.Tensor
-        `(..., t, 2)` or `(..., 2)` array of coordinates for patch centers.
+        `(..., 2)` or `(..., b, 2)` array of coordinates for patch centers.
     sidelength: int
-        Sidelength of square 2D patches extracted from `images`.
+        Sidelength of square patches extracted from `images`.
 
 
     Returns
     -------
     patches: torch.Tensor
-        `(..., t, sidelength, sidelength)` array of patches from `images` with their
-        centers at `positions`.
+        `(..., sidelength, sidelength)` or `(..., b, sidelength, sidelength)`
+        array of patches from `images` with their centers at `positions`.
     """
-    if images.ndim == 2:
-        images = einops.rearrange(images, 'h w -> 1 h w')
+    images_had_batch_dim = True
+    if image.ndim == 2:  # add empty batch dim
+        images_had_batch_dim = False
+        image = einops.rearrange(image, 'h w -> 1 h w')
         positions = einops.rearrange(positions, '... yx -> ... 1 yx')
-    positions, ps = einops.pack([positions], pattern='* t yx')
-    positions = einops.rearrange(positions, 'b t yx -> t b yx')
-    patches = einops.rearrange(
-        [
-            _extract_patches_from_single_image(
-                image=_image,
-                positions=_positions,
-                output_image_sidelength=sidelength
-            )
-            for _image, _positions
-            in zip(images, positions)
-        ],
-        pattern='t b h w -> b t h w'
-    )
-    [patches] = einops.unpack(patches, pattern='* t h w', packed_shapes=ps)
+
+    # pack arbitrary dimensions up into one new batch dim 'b1'
+    positions, ps = einops.pack([positions], pattern='* b2 yx')
+    positions = einops.rearrange(positions, 'b1 b2 yx -> b2 b1 yx')
+
+    # extract patches from each 2D image
+    patches = [
+        _extract_square_patches_from_single_2d_image(
+            image=_image,
+            positions=_positions,
+            output_image_sidelength=sidelength
+        )
+        for _image, _positions
+        in zip(image, positions)
+    ]
+
+    # reassemble patches into arbitrary dimensional stacks
+    patches = einops.rearrange(patches, pattern='b2 b1 h w -> b1 b2 h w')
+    [patches] = einops.unpack(patches, pattern='* b2 h w', packed_shapes=ps)
+
+    if images_had_batch_dim is False:
+        patches = einops.rearrange(patches, pattern='... 1 h w -> ... h w')
     return patches
 
 
-def _extract_patches_from_single_image(
+def _extract_square_patches_from_single_2d_image(
     image: torch.Tensor,  # (h, w)
     positions: torch.Tensor,  # (b, 2) yx
     output_image_sidelength: int,