Add support for post_targeting_field in flow estimation.

flow_field.py

@@ -486,6 +486,8 @@ def flow_field(
       post_patch_size: int | Sequence[int] | None = None,
       pre_targeting_field: np.ndarray | None = None,
       pre_targeting_step: int | Sequence[int] | None = None,
+      post_targeting_field: np.ndarray | None = None,
+      post_targeting_step: int | Sequence[int] | None = None,
       progress_fn: Callable[[list[T]], Iterator[T]] = _silent_fn,
   ):
     """Computes the flow field from post to pre.
@@ -516,6 +518,10 @@ def flow_field(
         'flow_field'
       pre_targeting_step: step size at which 'pre_targeting_field' values were
         sampled (same units as 'step', yx order)
+      post_targeting_field: like 'pre_targeting_field', but for shifting the
+        'post_image' patches
+      post_targeting_step: step size at which 'post_targeting_field' values were
+        sampled (same units as 'step', yx order)
       progress_fn: function taking a list of batches of 'post' z[yx] start
         positions to process; can be used with tqdm to track progress
 
@@ -642,7 +648,36 @@ def flow_field(
 
         pre_starts = pre_starts + tg_offsets
 
+      post_offsets = None
+      if post_targeting_field is not None and post_targeting_step is not None:
+        post_center = (np.array(post_patch_size) // 2).reshape((1, -1))
+        tg_step = np.array(post_targeting_step).reshape((1, -1))
+        query = np.round((post_starts + post_center) / tg_step)
+        query = query.astype(int)  # [b, [z]yx]
+        q = []
+        for i in range(query.shape[-1]):
+          q.append(
+              np.clip(query[:, i], 0, post_targeting_field.shape[i + 1] - 1)
+          )
+
+        field_indexer = (slice(None),) + tuple(q)
+        post_offsets = np.nan_to_num((post_targeting_field[field_indexer].T))
+        post_offsets = post_offsets.astype(int)[
+            :, ::-1
+        ]  # [b, xy[z]] -> [b, [z]yx]
+        new_starts = post_starts + post_offsets
+
+        # Clip offsets that would cause the 'post' patch to go out of bounds.
+        post_offsets = post_offsets - np.minimum(new_starts, 0)
+        img_shape = np.array(post_image.shape)[None, ...]
+        new_ends = new_starts + np.array(post_patch_size)[None, ...]
+        overshoot = np.maximum(new_ends, img_shape) - img_shape
+        post_offsets = post_offsets - overshoot
+
+        post_starts = post_starts + post_offsets
+
       pre_starts = np.clip(pre_starts, 0, np.inf).astype(int)
+      post_starts = np.clip(post_starts, 0, np.inf).astype(int)
 
       logging.info('.. estimating %d patches.', len(pos_zyx))
       peaks = np.array(
@@ -666,7 +701,11 @@ def flow_field(
       for i, coord in enumerate(pos_zyx):
         v = peaks[i]
         if tg_offsets is not None:
-          v[:d] = v[:d] + tg_offsets[i, ::-1]
+          v[:d] = v[:d] + tg_offsets[i, ::-1]  # xy[z]
+
+        if post_offsets is not None:
+          v[:d] = v[:d] - post_offsets[i, ::-1]  # xy[z]
+
         output[np.index_exp[:] + tuple(coord)] = v
 
     logging.info('Flow field estimation complete.')

mesh.py

@@ -522,8 +522,8 @@ def fire_step(t, state):
 
 
 def relax_mesh(
-    x: jnp.ndarray,
-    prev: jnp.ndarray | None,
+    x: jax.Array,
+    prev: jax.Array | None,
     config: IntegrationConfig,
     mesh_force=inplane_force,
     prev_fn=None,

tests/flow_field_test.py

@@ -93,6 +93,37 @@ def test_jax_peak(self):
     self.assertEqual(peaks[0, 2], peak_max / peak_support)  # sharpness
     self.assertEqual(peaks[0, 3], 0)  # peak ratio
 
+  def test_post_targeting(self):
+    pre_image = np.zeros((120, 120), dtype=np.uint8)
+    post_image = np.zeros((120, 120), dtype=np.uint8)
+
+    pre_image[50, 55] = 255
+    post_image[100, 100] = 255
+
+    calculator = flow_field.JAXMaskedXCorrWithStatsCalculator()
+
+    # Without targeting, the features are too far apart to be picked up.
+    field = calculator.flow_field(
+        pre_image, post_image, patch_size=80, step=40, batch_size=4)
+    np.testing.assert_array_equal(np.isnan(field[:, 0, 0]), True)
+
+    post_targeting_field = np.full((2, 2, 2), 40.0, dtype=np.float32)
+
+    # With targeting, a flow field of magnitude larger than the
+    # normally possible max of patch_size // 2 can be estimated.
+    field = calculator.flow_field(
+        pre_image,
+        post_image,
+        patch_size=80,
+        step=40,
+        batch_size=4,
+        post_targeting_field=post_targeting_field,
+        post_targeting_step=40)
+
+    np.testing.assert_array_equal([4, 2, 2], field.shape)
+    np.testing.assert_array_equal(-45 * np.ones((2, 2)), field[0, ...])
+    np.testing.assert_array_equal(-50 * np.ones((2, 2)), field[1, ...])
+
 
 if __name__ == '__main__':
   absltest.main()