How to Extract Multiple Images from PDF Files in Python using `fitz` Module

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What will you learn?

In this tutorial, you will master the art of extracting multiple images from PDF files stored within a specific folder using Python’s fitz module. By the end of this guide, you will be proficient in automating the extraction process and handling various image elements embedded in PDF documents effortlessly.

Introduction to the Problem and Solution

Imagine having a collection of PDF files with numerous images that need to be extracted for further processing or analysis. This scenario calls for an efficient solution to programmatically extract these images without manual intervention. By harnessing the power of Python’s fitz module, which is part of the PyMuPDF library, we can tackle this challenge seamlessly.

The fitz module equips us with robust functionalities to extract not only images but also text, annotations, and other elements from PDF files. Our goal is to iterate through each PDF file in a designated folder, extract images from every file systematically, and save them for future use.

By following a structured approach outlined in this tutorial, you will gain valuable insights into handling image extraction tasks from multiple PDF documents effortlessly.

Code

import fitz  # PyMuPDF
import os
import base64

# Path where your PDF files are stored
pdf_folder = "path_to_folder_containing_pdfs"

# Iterate through all PDF files in the specified folder
for pdf_file in os.listdir(pdf_folder):
    if pdf_file.endswith('.pdf'):
        pdf_path = os.path.join(pdf_folder, pdf_file)

        # Open the current PDF file
        document = fitz.open(pdf_path)

        # Iterate through each page of the current document
        for page_num in range(document.page_count):
            page = document[page_num]

            image_list = page.get_images(full=True)  # Retrieve all images on the current page

            for img_index, img_info in enumerate(image_list):
                xref = img_info[0]  # Image XREF number

                base_image = document.extract_image(xref)  # Extract image data

                image_bytes = base64.b64decode(base_image["image"])  # Decode image data

                with open(f"{pdf_file}_image_{img_index}.jpg", "wb") as image_file:
                    image_file.write(image_bytes)  # Save extracted image as JPEG

# Credits: PythonHelpDesk.com - Your source for Python solutions!

# Copyright PHD

Explanation

  • Import necessary libraries such as fitz (PyMuPDF).
  • Specify the path where your PDF files are located.
  • Iterate over all PDF files within that directory:
    • Open each file using fitz.open().
    • For every page:
      • Retrieve all images on that page using page.get_images().
      • Extract and save individual images as JPEGs with unique names based on their respective pages and indexes.
  • This code snippet demonstrates an organized approach to efficiently extract multiple images from various PDF documents residing in a specified folder.
    How do I install PyMuPDF library?

    You can install it via pip: pip install pymupdf.

    Can I extract only specific types of embedded objects other than just images?

    Yes, besides extracting images with PyMuPDF’s fitz module you can also target text content or annotations within a PDF document.

    Is there any size limit when processing large-sized or high-resolution embedded pictures?

    While there isn’t an inherent size restriction enforced by fitz itself when dealing with sizable embedded resources such as high-resolution pictures; memory constraints might be encountered depending on system specifications while processing exceptionally large assets.

    Does this code snippet retain metadata associated with these extracted pictures?

    The provided code focuses solely on isolating and storing individual graphics encompassed within respective pages without retaining any further metadata linked to those visuals during extraction operations.

    Can I customize naming conventions for saved output graphics based on my preferences?

    Certainly! You have complete flexibility to modify naming patterns according to personal requirements by adjusting relevant string concatenations within filename declarations before saving processed visuals onto disk locations dynamically.

    How does ‘extract_image’ method decode binary data into readable imagery formats?

    It utilizes Base64 decoding methodology upon raw binary content obtained during extraction procedures enabling transformation into interpretable visual representations consumable across varied applications or platforms supporting standard graphic formats like JPG/JPEG among others effortlessly.

    Conclusion

    Python’s versatile ecosystem offers powerful tools like PyMuPDF’s fitz module allowing seamless interaction with complex components inside multifaceted mediums such as multi-image embedding aspects found commonly across diverse digital publications like Portable Document Format (PDF) archives. Leveraging these capabilities enhances automation potentiality significantly streamlining intricate tasks involving batch extraction modalities contributing towards operational efficiency gains substantially.

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