Note

This is a community plugin, an external project maintained by its respective author. Community plugins are not part of FiftyOne core and may change independently. Please review each plugin’s documentation and license before use.

PDF Loader Plugin#

A FiftyOne plugin for loading PDFs as images.

Installation#

If you haven’t already, install FiftyOne:

pip install fiftyone

Then install the plugin and its dependencies:

fiftyone plugins download https://github.com/brimoor/pdf-loader

brew install poppler
pip install pdf2image

Usage#

Using the App UI#

  1. Launch the App:

import fiftyone as fo

dataset = fo.Dataset()
session = fo.launch_app(dataset)

  1. Press ` or click the Browse operations icon above the grid

  2. Run the pdf_loader operator

Using the SDK#

You can use the plugin programmatically from Python:

import fiftyone as fo
import fiftyone.operators as foo

import requests
import os

# Download a PDF from a URL (optional - you can use any local PDF)
url = "https://arxiv.org/pdf/2309.11419"
filename = url.split('/')[-1] + ".pdf"  # Add .pdf extension

response = requests.get(url)

if response.status_code == 200:
    with open(filename, 'wb') as f:
        f.write(response.content)
    print(f"Downloaded {filename}")
else:
    print(f"Failed to download {filename}. Status code: {response.status_code}")

# Load the PDF loader operator
pdf_loader = foo.get_operator("@brimoor/pdf-loader/pdf_loader")

# Create a dataset for the PDF pages
pdf_dataset = fo.Dataset("pdf_dataset")

# Convert PDF to images and add to dataset
pdf_loader(
    pdf_dataset,
    input_path="./2309.11419.pdf",  # Path to your PDF file
    output_dir="./pdf_images",     # Directory to save the images
    dpi=200,                        # Image quality in DPI
    fmt="png",                      # Image format (png or jpg)
    tags=None,                      # Optional tags for samples
    delegate=False                  # Set to True for async execution
)

What next?#

Install the PyTesseract OCR and Semantic Document Search plugins to make your documents searchable!

  1. Install the plugins and their dependencies:

fiftyone plugins download https://github.com/jacobmarks/pytesseract-ocr-plugin
pip install pytesseract

<video controls width="100%" style="max-width: 600px; height: auto;"><source src="https://github.com/jacobmarks/semantic-document-search-plugin
pip install qdrant_client
pip install sentence_transformers

  1. Launch a Qdrant server:

docker run -p "6333:6333" -p "6334:6334" -d qdrant/qdrant" type="video/mp4"></video>

  1. Run the run_ocr_engine operator to detect text blocks

  2. Run the create_semantic_document_index operator to generate a semantic index for the text blocks

  3. Run the semantically_search_documents operator to perform arbitrary searches against the index!

Implementation#

This plugin is a basically a wrapper around the following code:

import os
from pdf2image import convert_from_path

INPUT_PATH = "/path/to/your.pdf"
OUTPUT_DIR = "/path/for/page/images"

os.makedirs(OUTPUT_DIR, exist_ok=True)
convert_from_path(INPUT_PATH, output_folder=OUTPUT_DIR, fmt="jpg")

dataset.add_images_dir(OUTPUT_DIR)