Extract Image from PDF

-

 Extract Images from PDFs Using PyMuPDF in Python

I/p:-

PDF-


O/p - Images -





Why PyMuPDF?

PyMuPDF (fitz) is a powerful library for PDF processing. It allows easy access to various elements within a PDF, including text, images, and metadata. It’s particularly well-suited for tasks that involve reading and manipulating PDFs.

Prerequisites

pip install pymupdf

Code Steps : 

1. Importing Required Libraries

import fitz  # PyMuPDF
import os

2. Function to Extract Images from a PDF

def extract_images_from_pdf(pdf_path, output_folder):
    # Open the PDF file
    pdf_document = fitz.open(pdf_path)
    filename = os.path.basename(pdf_path).split('.')[0]
    
    # Create the output directory if it doesn't exist
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)

    # Loop through each page
    for page_number in range(len(pdf_document)):
        page = pdf_document.load_page(page_number)
        images = page.get_images(full=True)

        for image_index, image in enumerate(images):
            # Extract the image reference
            xref = image[0]
            
            # Extract the image bytes
            base_image = pdf_document.extract_image(xref)
            image_bytes = base_image["image"]
            image_ext = base_image["ext"]  # image extension, e.g., png, jpeg
            
            # Save the image
            image_filename = f"{filename}_page_{page_number + 1}_image_{image_index + 1}.{image_ext}"
            image_filepath = os.path.join(output_folder, image_filename)
            
            with open(image_filepath, "wb") as image_file:
                image_file.write(image_bytes)
                
            print(f"Saved image {image_filename}")

    print(f"Extraction complete. Images saved to {output_folder}")

3. Example Usage

pdf_folder = "/data/imageExtraction/PDFs"  # Path to your folder containing PDFs
output_folder = "/data/imageExtraction/pdfImages"  # Folder where images will be saved

# Loop through each PDF in the folder and extract images
for filename in os.listdir(pdf_folder):
    pdf_path = os.path.join(pdf_folder, filename)
    extract_images_from_pdf(pdf_path, output_folder)

Conclusion

Extracting images from PDFs using PyMuPDF is not only easy but also highly customizable. This script provides a basic framework that you can adapt for various needs, such as filtering images by size, resolution, or type.



Comments

Post a Comment

Popular posts from this blog

Extracting Tables and Text from Images Using Python

-
Image
In this blog, we'll explore a complete Python solution that detects and extracts tables and text from images using libraries like Transformers, OpenCV, PaddleOCR, and easyOCR. This step-by-step breakdown includes code to detect tables, extract content from individual table cells, and retrieve any remaining text in the image. Overview When working with scanned documents, such as invoices or forms, it is essential to accurately extract both structured information (like tables) and unstructured text. The approach we’ll explore uses Microsoft's pretrained object detection model to locate tables and OCR techniques to extract the text from both table cells and the rest of the image. Steps: 1. This code first detects table using microsoft's model. and save that image which contains detected table only  2. After that, from the detected table , we make a seperate image for each cell.  3. Then we read text from the image of each cell  4. Now, to read the extra texts except for the ...
Read more

Getting Started with ML

-
Image
 Types of ML :- 1) Supervised ML :- Before going for supervised learning, you need to be aware of two terminologies: Suppose, you have the data of student's age and weight. Now, for new student, just by knowing age, you want to predict his weight. So, in this case i. Dependent Variable : Age ii. Independent Variable : Weight Regression : It is applied on Numeric Data such as temperature, weight etc. Classification : It is applied on Categorial Data. There are 2 types of classification: 1.Binary Classification - Yes/No, Spam/Not Spam, etc. 2. Multiclass Classification - Small/Medium/Large, Bad/Average/Good/Excellent etc. 2) Unsupervised ML :- i. Clustering : If we have some data where there is no dependent/independent variable, then we can use clustering. suppose, we have data for salary and age. There is no relation between salary and age. So, clustering groups the data and keep the data with same characteristics together. ii. Dimensionality Reduction : Suppose, we have 100...
Read more

Linear Regression

-
Image
 Linear Regression shows the Linear Relationship between dependent features(Y-axis) and independent features(X-axis).  There are 2 types of Linear Regression: 1. Simple Linear Regression -  It has only one independent variable. Hypothesis Function for Simple Linear Regression : where: Y is the dependent variable X is the independent variable  β0 is the intercept ( the value where the regression line crosses the y-axis) β1 is the slope/weight ex : calculate weight based on age. In above figure Red dots represent Actual Data Points and black dots represent predicted values. error is calculated by (Actual Value - Predicted Value) 2. Multiple Linear Regression -  It has more than one independent features.  Hypothesis Function for Simple Linear Regression : y= a0 + a1x1 + a2x2 + a3x3 + ..  Let's understand this by example, we all know that if the size of the house is bigger, then the price also increases. But along with this, there are many other factors ...
Read more