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⚡️ Speed up function create_tiles by 7% #785

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⚡️ Speed up function create_tiles by 7% #785

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20 changes: 10 additions & 10 deletions inference/core/utils/drawing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@

import numpy as np

from inference.core.models.utils.batching import create_batches
from inference.core.utils.preprocess import letterbox_image

MAX_COLUMNS_FOR_SINGLE_ROW_GRID = 3
Expand Down Expand Up @@ -101,16 +100,17 @@ def _generate_tiles(
tile_margin_color: Tuple[int, int, int],
) -> np.ndarray:
rows, columns = grid_size
tiles_elements = list(create_batches(sequence=images, batch_size=columns))
while len(tiles_elements[-1]) < columns:
tiles_elements[-1].append(
_generate_color_image(shape=single_tile_size, color=tile_padding_color)
)
while len(tiles_elements) < rows:
tiles_elements.append(
[_generate_color_image(shape=single_tile_size, color=tile_padding_color)]
* columns
# Compute number of image slots required
total_slots = rows * columns
n_images = len(images)
# Pad missing images (if any) in a single step, reducing repetitive list operations
if n_images < total_slots:
pad_img = _generate_color_image(
shape=single_tile_size, color=tile_padding_color
)
images = images + [pad_img] * (total_slots - n_images)
# Slice images into batches efficiently (no batching generator overhead)
tiles_elements = [images[i * columns : (i + 1) * columns] for i in range(rows)]
return _merge_tiles_elements(
tiles_elements=tiles_elements,
grid_size=grid_size,
Expand Down
11 changes: 4 additions & 7 deletions inference/core/utils/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,17 +5,14 @@
import numpy as np
from skimage.exposure import rescale_intensity

from inference.core.env import (
DISABLE_PREPROC_CONTRAST,
DISABLE_PREPROC_GRAYSCALE,
DISABLE_PREPROC_STATIC_CROP,
USE_PYTORCH_FOR_PREPROCESSING,
)
from inference.core.env import (DISABLE_PREPROC_CONTRAST,
DISABLE_PREPROC_GRAYSCALE,
DISABLE_PREPROC_STATIC_CROP,
USE_PYTORCH_FOR_PREPROCESSING)

if USE_PYTORCH_FOR_PREPROCESSING:
import torch


from inference.core.exceptions import PreProcessingError
from inference.core.utils.onnx import ImageMetaType

Expand Down
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