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Remove TensorFlow dependency from image.py #433

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Remove TensorFlow dependency from image.py #433

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9daf5e0
Remove TensorFlow dependency from multimodal/image.py
AshNicolus Oct 17, 2025
912340e
Merge branch 'google-deepmind:main' into main
AshNicolus Oct 17, 2025
a9b44e5
Merge branch 'google-deepmind:main' into main
AshNicolus Oct 24, 2025
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38 changes: 26 additions & 12 deletions gemma/multimodal/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,10 @@
from kauldron import typing
import numpy as np
from PIL import Image
import tensorflow as tf
import warnings
# Removed TensorFlow dependency: use PIL + NumPy + JAX for decoding/resizing.
# Note: inputs are expected to be image arrays (H,W,C) in uint8 or floats.
# Keep a small fallback warning if the input array isn't a standard type.

_IMAGE_MEAN = (127.5,) * 3
_IMAGE_STD = (127.5,) * 3
Expand Down Expand Up @@ -69,17 +72,28 @@ def pre_process_image(
Returns:
The pre-processed image.
"""
# all inputs are expected to have been jpeg compressed.
# TODO(eyvinec): we should remove tf dependency.
image = jnp.asarray(
tf.image.decode_jpeg(tf.io.encode_jpeg(image), channels=3)
)
image = jax.image.resize(
image,
shape=(image_height, image_width, 3),
method="bilinear",
antialias=True,
)
# Accept numpy / jax arrays or PIL images. Convert to uint8 ndarray for PIL.
arr = np.asarray(image)

# If floats in [0, 1], convert to 0-255 uint8
if np.issubdtype(arr.dtype, np.floating):
if arr.max() <= 1.0:
arr = (arr * 255.0).round().astype(np.uint8)
else:
arr = np.clip(arr, 0, 255).round().astype(np.uint8)
else:
arr = arr.astype(np.uint8)

# PIL expects shape (W, H) ordering for resize tuple; Image.fromarray handles H,W,C.
pil = Image.fromarray(arr)
# Use bilinear resizing; PIL's LANCZOS is a high-quality downsample filter but
# bilinear better matches previous `jax.image.resize(..., method='bilinear')`.
pil = pil.resize((image_width, image_height), resample=Image.BILINEAR)

# Back to numpy -> jax
resized = np.asarray(pil).astype(np.float32)
image = jnp.asarray(resized)

image = normalize_images(image)
image = jnp.clip(image, -1, 1)
return image
Expand Down