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Fix offset #8

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merged 11 commits into from
May 10, 2024
Merged

Fix offset #8

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8ca4afc
Changed PatchSampler to take as base the patche size instead of the i…
fercer Mar 29, 2024
4720c76
Reverted change in the computation when masks elements are relative s…
fercer Mar 29, 2024
9e1e985
Fixed spatial chunk size computation when patch sizes are grater than…
fercer Apr 5, 2024
d7fb5f5
Fixed missing patches from chunks smaller than the input image chunk …
fercer Apr 30, 2024
1826378
Padding and stride added to PatchSampler and ImageBase classes to all…
fercer May 1, 2024
b894985
Added tests for stride and pad parameters of PatchSampler class
fercer May 2, 2024
aaa51f6
Fixed patch slices generation in PatchSampler to always retrieve patc…
fercer May 2, 2024
7885749
Standardized patch sampling method to handle smaller and bigger mask …
fercer May 7, 2024
a5a4097
Added example notebook to documentation
fercer May 7, 2024
6ca445d
Fixed incorrect sampling of patches on masked regions
fercer May 10, 2024
5bb0390
Fixed incorrect offset of mask coordinates
fercer May 10, 2024
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2 changes: 1 addition & 1 deletion docs/source/examples/advanced_example_pytorch_inference.md
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ pad = dict(Y=16, X=16)
patch_sampler = zds.PatchSampler(patch_size=patch_size, pad=pad, allow_incomplete_patches=True)
```

Create a dataset from the list of filenames. All those files should be stored within their respective group "0".
Create a dataset from the list of filenames. All those files should be stored within their respective group "4", which in this case it correspond to a downsampled version of the full resolution image by a factor of 16.

Also, specify that the axes order in the image is Time-Channel-Depth-Height-Width (TCZYX), so the data can be handled correctly

Expand Down
58 changes: 38 additions & 20 deletions zarrdataset/_samplers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,26 +142,32 @@ def _compute_corners(self, coordinates: np.ndarray, scale: np.ndarray

return corners

def _compute_reference_indices(self, reference_coordinates: np.ndarray
def _compute_reference_indices(self, reference_coordinates: np.ndarray,
reference_axes_sizes: np.ndarray
) -> Tuple[List[np.ndarray],
List[Tuple[int]]]:
reference_per_axis = list(map(
lambda coords: np.append(np.full((1, ), fill_value=-float("inf")),
np.unique(coords)),
reference_coordinates.T
lambda coords, axis_size: np.concatenate((
np.full((1, ), fill_value=-float("inf")),
np.unique(coords),
np.full((1, ), fill_value=np.max(coords) + axis_size))),
reference_coordinates.T,
reference_axes_sizes
))

reference_idx = map(
lambda coord_axis, ref_axis:
np.argmax(ref_axis[None, ...]
* (coord_axis[..., None] >= ref_axis[None, ...]),
axis=-1),
np.max(np.arange(ref_axis.size)
* (coord_axis.reshape(-1, 1) >= ref_axis[None, ...]),
axis=1),
reference_coordinates.T,
reference_per_axis
)
reference_idx = np.stack(tuple(reference_idx), axis=-1)
reference_idx = reference_idx.reshape(reference_coordinates.T.shape)

reference_idx = [
tuple(tls_coord - 1)
tuple(tls_coord)
for tls_coord in reference_idx.reshape(-1, len(reference_per_axis))
]

Expand All @@ -172,13 +178,14 @@ def _compute_overlap(self, corners_coordinates: np.ndarray,
np.ndarray]:
tls_idx = map(
lambda coord_axis, ref_axis:
np.argmax(ref_axis[None, None, ...]
* (coord_axis[..., None] >= ref_axis[None, None, ...]),
axis=-1),
np.max(np.arange(ref_axis.size)
* (coord_axis.reshape(-1, 1) >= ref_axis[None, ...]),
axis=1),
np.moveaxis(corners_coordinates, -1, 0),
reference_per_axis
)
tls_idx = np.stack(tuple(tls_idx), axis=-1)
tls_idx = tls_idx.reshape(corners_coordinates.shape)

tls_coordinates = map(
lambda tls_coord, ref_axis: ref_axis[tls_coord],
Expand All @@ -192,11 +199,12 @@ def _compute_overlap(self, corners_coordinates: np.ndarray,
dist2cut = np.fabs(corners_coordinates - corners_cut[None])
coverage = np.prod(dist2cut, axis=-1)

return coverage, tls_idx - 1
return coverage, tls_idx

def _compute_grid(self, chunk_tlbr: dict, mask: ImageBase,
patch_size: dict,
image_size: dict,
min_area: float,
allow_incomplete_patches: bool = False):
mask_scale = np.array([mask.scale.get(ax, 1)
for ax in self.spatial_axes],
Expand All @@ -223,7 +231,7 @@ def _compute_grid(self, chunk_tlbr: dict, mask: ImageBase,
]

if min(image_blocks) == 0:
return []
return []

image_scale = np.array([patch_size.get(ax, 1)
for ax in self.spatial_axes],
Expand Down Expand Up @@ -254,6 +262,7 @@ def _compute_grid(self, chunk_tlbr: dict, mask: ImageBase,
for ax in self.spatial_axes],
dtype=np.float32
)

chunk_br_coordinates = np.array(
[chunk_tlbr[ax].stop
if chunk_tlbr[ax].stop is not None
Expand All @@ -271,32 +280,39 @@ def _compute_grid(self, chunk_tlbr: dict, mask: ImageBase,
)
mask_coordinates = mask_coordinates[in_chunk]

# Translate the mask coordinates to the origin for comparison with
# image coordinates.
mask_coordinates -= chunk_tl_coordinates

if all(map(operator.ge, image_scale, mask_scale)):
mask_corners = self._compute_corners(mask_coordinates, mask_scale)

(reference_per_axis,
reference_idx) =\
self._compute_reference_indices(image_coordinates)
self._compute_reference_indices(image_coordinates, image_scale)

(coverage,
corners_idx) = self._compute_overlap(mask_corners,
reference_per_axis)

covered_indices = [
reference_idx.index(tuple(idx))
if tuple(idx) in reference_idx else len(reference_idx)
for idx in corners_idx.reshape(-1, len(self.spatial_axes))
]

patches_coverage = np.bincount(covered_indices,
weights=coverage.flatten(),
minlength=np.prod(image_blocks))
minlength=len(reference_idx) + 1)
patches_coverage = patches_coverage[:-1]

else:
image_corners = self._compute_corners(image_coordinates,
image_scale)

(reference_per_axis,
reference_idx) = self._compute_reference_indices(mask_coordinates)
reference_idx) = self._compute_reference_indices(mask_coordinates,
mask_scale)

(coverage,
corners_idx) = self._compute_overlap(image_corners,
Expand All @@ -309,10 +325,6 @@ def _compute_grid(self, chunk_tlbr: dict, mask: ImageBase,

patches_coverage = np.sum(covered_indices * coverage, axis=0)

min_area = self._min_area
if min_area < 1:
min_area *= np.prod(list(patch_size.values()))

minimum_covered_tls = image_coordinates[patches_coverage > min_area]
minimum_covered_tls = minimum_covered_tls.astype(np.int64)

Expand Down Expand Up @@ -396,6 +408,7 @@ def compute_chunks(self,
mask,
self._max_chunk_size,
image_size,
min_area=1,
allow_incomplete_patches=True
)

Expand Down Expand Up @@ -428,11 +441,16 @@ def compute_patches(self, image_collection: ImageCollection,
for ax in self.spatial_axes
}

min_area = self._min_area
if min_area < 1:
min_area *= np.prod(list(patch_size.values()))

valid_mask_toplefts = self._compute_valid_toplefts(
chunk_tlbr,
mask,
stride,
image_size=image_size,
min_area=min_area,
allow_incomplete_patches=self._allow_incomplete_patches
)

Expand Down
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