99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
from pathlib import Path
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from typing import Dict, List, Optional
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from llama_index.readers.base import BaseReader
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from llama_index.schema import Document, ImageDocument
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from llama_index.utils import infer_torch_device
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class ImageCaptionReader(BaseReader):
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"""Image parser.
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Caption image using Blip.
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"""
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def __init__(
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self,
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parser_config: Optional[Dict] = None,
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keep_image: bool = False,
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prompt: Optional[str] = None,
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):
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"""Init params."""
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if parser_config is None:
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"""Init parser."""
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try:
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import sentencepiece # noqa
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import torch
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from PIL import Image # noqa
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from transformers import BlipForConditionalGeneration, BlipProcessor
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except ImportError:
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raise ImportError(
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"Please install extra dependencies that are required for "
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"the ImageCaptionReader: "
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"`pip install torch transformers sentencepiece Pillow`"
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)
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device = infer_torch_device()
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dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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processor = BlipProcessor.from_pretrained(
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"Salesforce/blip-image-captioning-large"
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)
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model = BlipForConditionalGeneration.from_pretrained(
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"Salesforce/blip-image-captioning-large", torch_dtype=dtype
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)
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parser_config = {
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"processor": processor,
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"model": model,
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"device": device,
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"dtype": dtype,
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}
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self._parser_config = parser_config
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self._keep_image = keep_image
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self._prompt = prompt
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def load_data(
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self, file: Path, extra_info: Optional[Dict] = None
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) -> List[Document]:
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"""Parse file."""
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from PIL import Image
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from llama_index.img_utils import img_2_b64
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# load document image
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image = Image.open(file)
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if image.mode != "RGB":
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image = image.convert("RGB")
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# Encode image into base64 string and keep in document
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image_str: Optional[str] = None
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if self._keep_image:
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image_str = img_2_b64(image)
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# Parse image into text
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model = self._parser_config["model"]
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processor = self._parser_config["processor"]
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device = self._parser_config["device"]
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dtype = self._parser_config["dtype"]
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model.to(device)
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# unconditional image captioning
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inputs = processor(image, self._prompt, return_tensors="pt").to(device, dtype)
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out = model.generate(**inputs)
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text_str = processor.decode(out[0], skip_special_tokens=True)
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return [
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ImageDocument(
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text=text_str,
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image=image_str,
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image_path=str(file),
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metadata=extra_info or {},
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)
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]
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