Case Study: Solving Document Size Mismatch for Printing ID Cards Using Python

Imagine you're tasked with printing a set of ID cards that are included as images inside a PDF document. However, the images are not formatted to fit the standard size of an ID card, such as 90 mm x 58 mm. Printing the document as is would result in improperly scaled or misaligned images, causing issues during the printing process.

To solve this issue, you can use Python and the PyMuPDF library to create a custom application that resizes and arranges the images from the document to match the required dimensions. This solution ensures that the images fit perfectly on the page before printing, providing professional results without needing to manually adjust each ID card.

Problem Overview:

The PDF document containing ID cards may have different image sizes, and they need to be resized and positioned to fit within the 90 mm x 58 mm format, which is a standard ID card size. Additionally, multiple cards may need to be printed on the same page to optimize printing.

Requirements:

To tackle this issue, you will need:

• Python installed on your system.
• PyMuPDF (also known as fitz) installed. Install it using:

  bash
  pip install PyMuPDF
  

Solution: Python Script to Resize and Combine ID Cards

The following script will open a PDF document containing the ID card images, resize each image to the 90 mm x 58 mm dimensions, and arrange up to four cards per page in a 2x2 grid for efficient printing.

Solution: Python Script to Resize and Combine ID Cards

The following script will open a PDF document containing the ID card images, resize each image to the 90 mm x 58 mm dimensions, and arrange up to four cards per page in a 2x2 grid for efficient printing.

Task:

You are tasked with combining multiple pages of a PDF document into a 2x2 grid format, where four original pages are combined into one larger page. The resulting PDF should have properly scaled and aligned pages, maintaining the correct physical dimensions for each original page (90 mm x 56 mm), but doubling the size in the combined output (180 mm x 112 mm).

Approach:

The program uses Python and the PyMuPDF library (also known as fitz) to:

1. Open the input PDF file.
2. Create a new output PDF file.
3. Loop through the pages of the input PDF in chunks of 4, placing 4 pages onto one output page, arranged in a 2x2 grid.
4. Each output page has a width of 180 mm and a height of 112 mm, converted to pixels based on a DPI setting of 72.
5. Save the resulting combined PDF document to the specified output path.

Key Features:

• The code supports any PDF, regardless of the number of pages.
• It handles edge cases where the total number of pages is not divisible by 4.
• Proper placement of individual pages is handled by calculating offsets for a 2x2 layout.

Python Script

python
Download source code

How It Works:

1. Opening the Input PDF: The script starts by opening the input PDF containing the ID card images.
2. Defining Target Dimensions: The desired size for each ID card is set to 90 mm x 58 mm, which is converted to points for accurate positioning.
3. Combining Pages: The script processes the PDF in batches of four pages, placing up to four ID card images on one output page. Each card is resized to fit precisely within its allocated space on the page.
4. Saving the Output: The final combined PDF is saved, with all ID card images formatted and positioned correctly for printing.

Practical Use Case:

This script is highly useful in cases where an institution, like a university or corporate office, needs to print multiple ID cards in bulk. Often, the original ID card images may be scanned or exported from another system at varying sizes. Instead of manually resizing and arranging them, this Python-based solution automatically resizes and positions the cards, optimizing the document for print.

Conclusion:

By leveraging Python and PyMuPDF, we can automate the process of resizing and combining ID card images into a well-formatted PDF for printing. This method not only ensures precision in size but also saves a significant amount of time by automating what would otherwise be a manual and repetitive task. This case study provides a scalable solution for anyone handling document processing tasks where resizing and formatting consistency is crucial.