import importlib
import multiprocessing
from typing import Optional, Sequence, List, Tuple
import numpy as np
from chromadb.api.types import URI, DataLoader, Image, URIs
from concurrent.futures import ThreadPoolExecutor
class ImageLoader(DataLoader[List[Optional[Image]]]):
def __init__(self, max_workers: int = multiprocessing.cpu_count()) -> None:
try:
self._PILImage = importlib.import_module("PIL.Image")
self._max_workers = max_workers
except ImportError:
raise ValueError(
"The PIL python package is not installed. Please install it with `pip install pillow`"
)
def _load_image(self, uri: Optional[URI]) -> Optional[Image]:
return np.array(self._PILImage.open(uri)) if uri is not None else None
def __call__(self, uris: Sequence[Optional[URI]]) -> List[Optional[Image]]:
with ThreadPoolExecutor(max_workers=self._max_workers) as executor:
return list(executor.map(self._load_image, uris))
class ChromaLangchainPassthroughDataLoader(DataLoader[List[Optional[Image]]]):
# This is a simple pass through data loader that just returns the input data with "images"
# flag which lets the langchain embedding function know that the data is image uris
def __call__(self, uris: URIs) -> Tuple[str, URIs]: # type: ignore
return ("images", uris)