A Comparative Exploration of Image Preprocessing in Python and C++

6 min readJan 7, 2024

In the world of understanding and working with images, getting the images ready for analysis is crucial. It’s like preparing ingredients before cooking you want them in the right form for better results. Now, two popular tools for this image preparation job are Python and C++. They’re like different chef’s knives, each with its strengths. This article is all about comparing how Python and C++ handle this image preparation process. We’ll explore what each is good at, so by the end, you’ll know which one suits your needs better. Let’s dive into the world of getting images ready using Python and C++.

Python for Image Preprocessing

In Python, we have some handy tools for handling images, and three big players are OpenCV, PIL (Pillow), and scikit-image. Think of them like toolkits for image magic. let’s break down the basics of image preprocessing with Python for beginners. I’ll provide step-by-step descriptions along with code snippets for each stage.

Step 1: Importing Necessary Libraries

To start, we need to import the libraries that will help us handle images. In this example, we’ll use OpenCV, a powerful and popular library for image processing in Python.

import cv2
import numpy as np
from matplotlib import pyplot as plt
from google. colab.patches import cv2_imshow

Step 2: Loading an Image

Now, let’s load an image from a file. Replace ‘your_image.jpg’ with the filename of the image you want to work with.

image = cv2.imread('your_image.jpg')

Step 3: Resizing the Image

Adjusting the size of your picture, like making it smaller or bigger.

width, height = 800, 700
resized_image = cv2.resize(image, (width, height))

Step 4: Mean Subtraction and Standardization

We’re making sure the colors are balanced and standardized for better analysis.

# Convert the image to float32
float_image = image.astype(np.float32)

# Mean subtraction
mean_subtracted_image = float_image - np.mean(float_image)

# Standardization
standardized_image = mean_subtracted_image / np.std(float_image)

Step 5: Grayscale Conversion

Transforming the image to black and white can simplify analysis.

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Step 6: Histogram Equalization

Improving visibility by balancing dark and bright parts.

equalized_image = cv2.equalizeHist(gray_image)

Step 7: Noise Reduction (Gaussian Blur)

Making the image smoother by reducing noise.

blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

Step 8: Edge Detection

Finding edges in the image.

edges = cv2.Canny(gray_image, 50, 150)

Python is widely used in both academic research and industry for image processing applications. This diverse usage leads to a rich ecosystem of tools and frameworks, ensuring that developers have access to the latest advancements and best practices.

Python’s strength lies in its ability to facilitate quick prototyping and experimentation through clear syntax, rich libraries, and interactive development environments. The extensive support from the Python community further enhances the accessibility and effectiveness of image processing projects. Whether you are a beginner or an experienced developer, Python provides a robust platform for tackling image processing challenges.

C++ for Image Preprocessing

In the realm of image preprocessing with C++, OpenCV stands out as a powerhouse library, offering a robust set of tools for image manipulation and analysis. OpenCV, short for Open Source Computer Vision Library, is a go-to choice for C++ developers delving into the world of image processing. Its popularity stems not only from its versatility but also from the raw performance it brings to the table. C++ excels in computationally intensive tasks, making it an ideal language for handling complex image operations with efficiency. In this journey through C++ image preprocessing, we’ll explore the capabilities of OpenCV and harness the performance benefits it provides, empowering you to elevate your image processing skills with the speed and precision that C++ is renowned for. I’ll provide step-by-step descriptions along with code snippets for each stage.

Step 1: Resizing the Image

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("puppy.jpg");


    // Resize the image to a specific width and height
    cv::Size newSize(800, 700);
    cv::Mat resize_image;
    cv::resize(image, resize_image, newSize);

    // Display the original image (optional)
    cv::namedWindow("Original Image");
    cv::imshow("Original Image", image);
    //cv::waitKey(0);
    //cv::destroyAllWindows();

    // Display the resized image (optional)
    cv::namedWindow("Resized Image");
    cv::imshow("Resized Image", resize_image);
    cv::waitKey(0);
   
    return 0;
}

Step 2: Mean Subtraction and Standardization

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("your_image.jpg");

    // Convert the image to float32 for better precision
    cv::Mat floatImage;
    image.convertTo(floatImage, CV_32F);

    // Mean subtraction
    cv::subtract(floatImage, cv::mean(floatImage), floatImage);

    // Standardization
    cv::divide(floatImage, cv::stddev(floatImage), floatImage);

    // Display the mean-subtracted and standardized image (optional)
    cv::imshow("Processed Image", floatImage);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

Step 3: Grayscale Conversion

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("your_image.jpg");

    // Convert the image to grayscale
    cv::Mat grayscaleImage;
    cv::cvtColor(image, grayscaleImage, cv::COLOR_BGR2GRAY);

    // Display the grayscale image (optional)
    cv::imshow("Grayscale Image", grayscaleImage);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

Step 4: Histogram Equalization

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("your_image.jpg");

    // Convert the image to grayscale
    cv::Mat grayscaleImage;
    cv::cvtColor(image, grayscaleImage, cv::COLOR_BGR2GRAY);

    // Apply histogram equalization
    cv::equalizeHist(grayscaleImage, grayscaleImage);

    // Display the histogram-equalized image (optional)
    cv::imshow("Histogram Equalized Image", grayscaleImage);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

Step 5: Noise Reduction (Gaussian Blur)

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("your_image.jpg");

    // Apply Gaussian blur for noise reduction
    cv::GaussianBlur(image, image, cv::Size(5, 5), 0);

    // Display the image after noise reduction (optional)
    cv::imshow("Image with Gaussian Blur", image);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

Step 6: Edge Detection

#include <opencv2/opencv.hpp>

int main() {
    // Load the image
    cv::Mat image = cv::imread("your_image.jpg");

    // Convert the image to grayscale
    cv::Mat grayscaleImage;
    cv::cvtColor(image, grayscaleImage, cv::COLOR_BGR2GRAY);

    // Apply Canny edge detection
    cv::Mat edges;
    cv::Canny(grayscaleImage, edges, 50, 150);

    // Display the edges detected image (optional)
    cv::imshow("Edges Detected Image", edges);
    cv::waitKey(0);
    cv::destroyAllWindows();

    return 0;
}

C++ supports a multi-paradigm programming approach, allowing developers to seamlessly blend procedural and object-oriented programming styles. This flexibility enables the creation of modular and maintainable image processing code, making it easier to manage and extend as project requirements evolve.

C++ shines in image processing due to its exceptional performance capabilities, efficient use of resources, seamless integration with existing codebases, and support for a multi-paradigm programming approach. These characteristics make C++ an ideal choice for tackling complex image processing challenges in research and industry applications.

In the realm of image preprocessing, the choice between Python and C++ emerges as a pivotal decision, each presenting its unique strengths. Python, with its clear syntax, extensive libraries, and vibrant community, provides an accessible platform for beginners to swiftly grasp and experiment with image processing concepts. The ease of rapid prototyping, coupled with the wealth of resources, positions Python as an excellent choice for those entering the field or focusing on projects that demand flexibility and readability.

On the other hand, C++ shines in performance-critical scenarios, offering unparalleled speed and efficiency for computationally intensive image processing tasks. Its low-level memory manipulation capabilities, seamless integration with existing codebases, and optimized libraries like OpenCV make it the language of choice for projects demanding raw computational power.

As we navigate through the comparative analysis, it becomes evident that the decision between Python and C++ hinges on project requirements, team expertise, and performance considerations. Python facilitates a dynamic and intuitive journey for beginners, while C++ empowers seasoned developers with the tools necessary to tackle large-scale, performance-critical image processing tasks.

Ultimately, the choice is not binary, and many projects find a harmonious blend, leveraging the strengths of both languages where needed. Whether you opt for Python’s expressive simplicity or harness C++’s performance prowess, the landscape of image preprocessing is rich with possibilities, inviting exploration and innovation. Each language, in its own right, contributes to the vibrant tapestry of image processing, ensuring that the journey from pixels to insights is as diverse and dynamic as the field itself.