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SAM: image_to_scaled_tensor gray images

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Pierre Colle 2024-04-15 15:38:18 +00:00 committed by Colle
parent f48712ee29
commit bf7852b88e
3 changed files with 14 additions and 12 deletions
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2
src/refiners/fluxion/utils.py
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@ -141,7 +141,7 @@ def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtyp
If the image is in mode `RGB` the tensor will have shape `[3, H, W]`, If the image is in mode `RGB` the tensor will have shape `[3, H, W]`,
otherwise `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`. otherwise `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`.
Values are clamped to the range `[0, 1]`. Values are normalized to the range `[0, 1]`.
""" """
image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype) image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype)

15
src/refiners/foundationals/segment_anything/utils.py
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@ -1,8 +1,7 @@
import numpy as np
from PIL import Image from PIL import Image
from torch import Size, Tensor, device as Device, dtype as DType, tensor from torch import Size, Tensor, device as Device, dtype as DType
from refiners.fluxion.utils import interpolate, normalize, pad from refiners.fluxion.utils import image_to_tensor, interpolate, normalize, pad
def compute_scaled_size(size: tuple[int, int], image_encoder_resolution: int) -> tuple[int, int]: def compute_scaled_size(size: tuple[int, int], image_encoder_resolution: int) -> tuple[int, int]:
@ -40,11 +39,8 @@ def image_to_scaled_tensor(
""" """
h, w = scaled_size h, w = scaled_size
resized = image.resize((w, h), resample=Image.Resampling.BILINEAR) # type: ignore resized = image.resize((w, h), resample=Image.Resampling.BILINEAR) # type: ignore
return tensor(
np.array(resized).astype(np.float32).transpose(2, 0, 1), return image_to_tensor(resized, device=device, dtype=dtype) * 255.0
device=device,
dtype=dtype,
).unsqueeze(0)
def preprocess_image( def preprocess_image(
@ -61,9 +57,10 @@ def preprocess_image(
Returns: Returns:
The preprocessed image. The preprocessed image.
""" """
scaled_size = compute_scaled_size((image.height, image.width), image_encoder_resolution) scaled_size = compute_scaled_size((image.height, image.width), image_encoder_resolution)
image_tensor = image_to_scaled_tensor(image, scaled_size, device, dtype) image_tensor = image_to_scaled_tensor(image, scaled_size, device=device, dtype=dtype)
return pad_image_tensor( return pad_image_tensor(
normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]),

9
tests/foundationals/segment_anything/test_utils.py
View file

@ -22,13 +22,18 @@ def test_compute_scaled_size(image_encoder_resolution: int) -> None:
assert scaled_size == (512, 1024) assert scaled_size == (512, 1024)
def test_image_to_scaled_tensor() -> None: def test_rgb_image_to_scaled_tensor() -> None:
image = Image.new("RGB", (1536, 768)) image = Image.new("RGB", (1536, 768))
tensor = image_to_scaled_tensor(image, (512, 1024)) tensor = image_to_scaled_tensor(image, (512, 1024))
assert tensor.shape == (1, 3, 512, 1024) assert tensor.shape == (1, 3, 512, 1024)
def test_grayscale_image_to_scaled_tensor() -> None:
image = Image.new("L", (1536, 768))
tensor = image_to_scaled_tensor(image, (512, 1024))
assert tensor.shape == (1, 1, 512, 1024)
def test_preprocess_image(image_encoder_resolution: int) -> None: def test_preprocess_image(image_encoder_resolution: int) -> None:
image = Image.new("RGB", (1536, 768)) image = Image.new("RGB", (1536, 768))
preprocessed = preprocess_image(image, image_encoder_resolution) preprocessed = preprocess_image(image, image_encoder_resolution)