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Implement better normalization for embedding computation #1058

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74 changes: 52 additions & 22 deletions micro_sam/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -593,25 +593,39 @@ def get_model_names() -> Iterable:
#


def _to_image(input_):
# we require the input to be uint8
if input_.dtype != np.dtype("uint8"):
# first normalize the input to [0, 1]
input_ = input_.astype("float32") - input_.min()
input_ = input_ / input_.max()
# then bring to [0, 255] and cast to uint8
input_ = (input_ * 255).astype("uint8")

if input_.ndim == 2:
image = np.concatenate([input_[..., None]] * 3, axis=-1)
elif input_.ndim == 3 and input_.shape[-1] == 3:
image = input_
else:
raise ValueError(f"Invalid input image of shape {input_.shape}. Expect either 2D grayscale or 3D RGB image.")
def _normalize_channel(input_, min_val=None, max_val=None):
# First normalize the input to [0, 1].
input_ = input_.astype("float32")
min_val = np.percentile(input_, 1) if min_val is None else min_val
input_ = input_ - min_val
max_val = np.percentile(input_, 99) if max_val is None else max_val
input_ = input_ / (max_val + 1e-7)
# Then bring it to [0, 255] and cast to uint8.
input_ = (np.clip(input_, 0, 1) * 255).astype("uint8")
return input_


def _to_image(input_, min_=None, max_=None):
# Explicitly return a numpy array for compatibility with torchvision,
# because the input_ array could be something like dask array.
image = np.array(input_)

if image.ndim == 2:
image = image[..., None]
elif image.ndim != 3 or (image.shape[-1] not in (1, 3)):
raise ValueError(f"Invalid input image of shape {input_.shape}. Expect either grayscale or RGB image.")

image_normalized = np.zeros(image.shape, dtype="uint8")
for c in range(image.shape[2]):
min_val = None if min_ is None else min_[c]
max_val = None if max_ is None else max_[c]
image_normalized[..., c] = _normalize_channel(image[..., c], min_val=min_val, max_val=max_val)

# explicitly return a numpy array for compatibility with torchvision
# because the input_ array could be something like dask array
return np.array(image)
if image_normalized.shape[-1] == 1:
image_normalized = np.concatenate([image_normalized] * 3, axis=-1)
assert image_normalized.shape[2] == 3, f"{image_normalized.shape}"

return image_normalized


@torch.no_grad
Expand Down Expand Up @@ -690,6 +704,7 @@ def _compute_tiled_features_2d(predictor, input_, tile_shape, halo, f, pbar_init
pbar_init(n_tiles, "Compute Image Embeddings 2D tiled")

n_batches = int(np.ceil(n_tiles / batch_size))
input_ = _to_image(input_)
for batch_id in range(n_batches):
tile_start = batch_id * batch_size
tile_stop = min(tile_start + batch_size, n_tiles)
Expand All @@ -698,7 +713,7 @@ def _compute_tiled_features_2d(predictor, input_, tile_shape, halo, f, pbar_init
for tile_id in range(tile_start, tile_stop):
tile = tiling.getBlockWithHalo(tile_id, list(halo))
outer_tile = tuple(slice(beg, end) for beg, end in zip(tile.outerBlock.begin, tile.outerBlock.end))
tile_input = _to_image(input_[outer_tile])
tile_input = input_[outer_tile]
batched_images.append(tile_input)

batched_embeddings, original_sizes, input_sizes = _compute_embeddings_batched(predictor, batched_images)
Expand All @@ -715,6 +730,18 @@ def _compute_tiled_features_2d(predictor, input_, tile_shape, halo, f, pbar_init
return features


def _precompute_stats(input_):
stats = {}
input_ = input_[..., None] if input_.ndim == 3 else input_
assert input_.ndim == 4
for z in range(input_.shape[0]):
min_ = {c: np.percentile(input_[z, ..., c], 1) for c in range(input_.shape[3])}
# TODO double check
max_ = {c: np.percentile(input_[z, ..., c], 99) - min_[c] for c in range(input_.shape[3])}
stats[z] = {"min": min_, "max": max_}
return stats


def _compute_tiled_features_3d(predictor, input_, tile_shape, halo, f, pbar_init, pbar_update, batch_size):
assert input_.ndim == 3

Expand All @@ -733,6 +760,9 @@ def _compute_tiled_features_3d(predictor, input_, tile_shape, halo, f, pbar_init
# We batch across the z axis.
n_batches = int(np.ceil(n_slices / batch_size))

# Precompute min and max for each slice.
stats = _precompute_stats(input_)

for tile_id in range(n_tiles):
tile = tiling.getBlockWithHalo(tile_id, list(halo))
outer_tile = tuple(slice(beg, end) for beg, end in zip(tile.outerBlock.begin, tile.outerBlock.end))
Expand All @@ -744,7 +774,7 @@ def _compute_tiled_features_3d(predictor, input_, tile_shape, halo, f, pbar_init

batched_images = []
for z in range(z_start, z_stop):
tile_input = _to_image(input_[z][outer_tile])
tile_input = _to_image(input_[z][outer_tile], min_=stats[z]["min"], max_=stats[z]["max"])
batched_images.append(tile_input)

batched_embeddings, original_sizes, input_sizes = _compute_embeddings_batched(predictor, batched_images)
Expand Down Expand Up @@ -858,8 +888,8 @@ def _compute_3d(input_, predictor, f, save_path, lazy_loading, pbar_init, pbar_u
# Skip feature computation in case of partial features in non-zero slice.
if partial_features and np.count_nonzero(features[z]) != 0:
continue
tile_input = _to_image(input_[z])
batched_images.append(tile_input)
batch_input = _to_image(input_[z])
batched_images.append(batch_input)
batched_z.append(z)

batched_embeddings, original_sizes, input_sizes = _compute_embeddings_batched(predictor, batched_images)
Expand Down
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