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"""Image preprocessing helpers for ollama vision models."""
from __future__ import annotations
import io
from pathlib import Path
from PIL import Image
from ...config import settings
Image.MAX_IMAGE_PIXELS = settings.max_image_pixels
_IMAGE_FORMATS: dict[str, str] = {
"png": "PNG",
"jpg": "JPEG",
"jpeg": "JPEG",
"gif": "GIF",
"bmp": "BMP",
"webp": "WEBP",
"tif": "TIFF",
"tiff": "TIFF",
}
def _format_from_name(name: str) -> str:
ext = Path(name).suffix.lstrip(".").lower()
return _IMAGE_FORMATS.get(ext, "JPEG")
def resize_image(
data: bytes,
name: str = "image.jpg",
max_dimension: int = 1024,
quality: int = 85,
) -> bytes:
"""Resize and re-encode an image to reduce payload size for ollama."""
if not data:
return data
try:
with Image.open(io.BytesIO(data)) as img:
if img.width * img.height > settings.max_image_pixels:
return data
processed = img.convert("RGB")
width, height = processed.size
if width > max_dimension or height > max_dimension:
ratio = min(max_dimension / width, max_dimension / height)
new_size = (int(width * ratio), int(height * ratio))
processed = processed.resize(new_size, Image.Resampling.LANCZOS)
fmt = _format_from_name(name)
output = io.BytesIO()
save_kwargs: dict[str, object] = {}
if fmt == "JPEG":
save_kwargs["quality"] = quality
save_kwargs["optimize"] = True
elif fmt == "WEBP":
save_kwargs["quality"] = quality
processed.save(output, format=fmt, **save_kwargs)
return output.getvalue()
except Exception: # noqa: BLE001
return data
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