The Use Of OpenCV In Image Processing – 5 Examples

Computer vision is one of the most active areas of AI in 2026 — powering everything from defect detection on factory lines to medical imaging diagnostics, autonomous driving and document understanding. Behind a remarkable share of these systems sits OpenCV — an open-source library that has been the foundation of practical image processing for over two decades and remains, in its current 4.x release, one of the most widely used computer vision libraries in production today.

What OpenCV does best is what it has always done: handle the image processing layer — the preparation, cleanup, transformation, and feature extraction that classical computer vision and modern deep learning models both depend on. This article walks through five of the most common ways OpenCV is used in image processing, with practical Python examples you can adapt directly. By the end, you’ll know when to reach for OpenCV, when to combine it with deep learning, and when a different tool is the better fit.

Key Takeaways

• OpenCV (Open Source Computer Vision Library) is the most widely used open-source library for image processing — currently version 4.x, with over 2,500 optimized algorithms covering classical computer vision and integration with deep learning models.
• The five most common image processing tasks OpenCV is used for: image enhancement, feature extraction, OCR pipelines (combined with Tesseract or PaddleOCR), background removal, and bulk geometric operations (resize, rotate, crop).
• OpenCV’s DNN module lets you run pre-trained deep learning models from TensorFlow, PyTorch, ONNX, and Caffe inside an OpenCV pipeline — making it a bridge between classical CV and modern AI.
• OpenCV isn’t an OCR engine — for text extraction it typically does preprocessing (deskewing, denoising, thresholding) before passing images to Tesseract, EasyOCR, or PaddleOCR.
• In 2026, OpenCV is most often used as the preprocessing and post-processing layer around deep learning vision models (PyTorch, TensorFlow, Vision Transformers), not as a standalone replacement for them.

Computer Vision and Image Processing – What do you have to know?

Generally speaking, image processing is all about transforming (processing) the input image file. Typically, it’s done with the usage of specific software, to name just Adobe Photoshop or GIMP. Some of these transformations are done manually (by the graphic, for instance, adding new layers) or automatically (by the built-in algorithm, for example, sharpening).

Where can you find applications of image processing? One of the most outstanding examples is medicine. We have written a number of articles about AI in healthcare. Just browse through our blog section to find them. Currently, image processing in medicine is used in order to enhance the medical image’s quality and perceptibility. As a result of this image enhancement process, a physician can make a quicker and more accurate diagnosis, simply put, because they see a more clear picture.

On the other hand, computer vision works entirely differently. Here, nothing happens to the file itself. This is due to the goal, which is to interpret the image and its contents. One of the most significant usages of computer vision is in the motor industry. Computer vision is used here as an assistant for the driver that scans the vehicle’s surroundings and analyzes them for potential threats, obstacles, and other relevant situations.

Before you begin

The thing is, you can’t just start working on your computer vision/image processing program. You have to prepare data (i.e. files) first because when data is in its raw format, it’s rarely useful. Consider an example. Let’s say that you own a parking area, and you want to build a machine learning license plate recognition system.

Your very first step would be to gather hundreds of license plate pictures for the ML algorithm to learn from. Naturally, you can download them from the web, but the vast majority of the downloaded images would not be of the same size and quality. Some of them would contain only a license plate, and some of them the entire car, and maybe some elements of the background even. So before you can upload them to your app for training, you have to prepare them–in other words, unify them.

Then, you need a library, which serves as a base for your future work. Generally speaking, a CV library provides the necessary tools for processing and analyzing images. That’s including recognizing objects in pictures (such as aforementioned license plates), tracking objects, converting images, and identifying common elements in various image files[1].

What is the OpenCV library?

To begin with, the OpenCV library is an open-source (hence its full name: Open Source Computer Vision[2]) computer vision and machine learning software library. As we can read on its website, the OpenCV library was built primarily to provide an infrastructure for computer vision applications[3].

According to the aforementioned source, the library has over 2,500 optimized algorithms, which include either the computer vision and machine learning algorithms. These algorithms can be used by companies and single programmers to:

• Detect and recognize faces (so-called face recognition)
• Identify objects
• Classify human actions in videos
• Track camera movements
• Track moving objects
• Extract 3D models of objects
• Find similar images from an image database
• Follow eye movements
• Recognize scenery
• Establish markers to overlay it with augmented reality

OpenCV is currently in its 4.x release (with version 5 in active development), and its scale today is significantly larger than even a few years ago:

• Over 2,500 optimized algorithms across classical computer vision and machine learning
• More than 75,000 stars on GitHub, making it one of the most-starred computer vision projects in open source
• Tens of millions of downloads per month via PyPI alone
• Production deployments at Google, Microsoft, Intel, IBM, Sony, Honda, Toyota, NVIDIA, and across virtually every major manufacturing, automotive, and robotics company
• Strong ecosystem in Python, C++, Java, and JavaScript (OpenCV.js for the browser)

What’s changed most since the early days is OpenCV’s role in modern AI pipelines. The library now includes a deep neural network module (cv2.dnn) that can load and run pre-trained models from TensorFlow, PyTorch, ONNX, and Caffe — making OpenCV one of the easiest ways to deploy a deep learning vision model in production without depending on the full ML framework.

Installing OpenCV in Python

The fastest way to get OpenCV running in Python is via pip:

pip install opencv-python

If you also need the “extra” algorithms (SIFT, SURF, contrib modules), install:

pip install opencv-contrib-python

For headless servers (no GUI display needed, useful in Docker containers or production environments):

pip install opencv-python-headless

Verify the install with:

import cv2
print(cv2.__version__)

A quick note: don’t install both opencv-python and opencv-python-headless in the same environment — they conflict. Pick the one that matches your deployment target.

Examples of using OpenCV in image processing

We are going to examine the five most significant use cases, where the OpenCV library plays a key role. As you already know, image processing is all about modifying or improving a given image. Sometimes in order to speed up work, sometimes in order to harness it into the computer vision system. As it turns out, image processing techniques are used on many occasions in computer vision. That’s why both these disciplines are closely interlinked.

Image enhancement

Depending on the use case, there are various methods available within OpenCV, which could be applied to enhance your images. For instance, the OpenCV algorithms can help you in[4]:

• Histogram equalization[5]. It’s a technique used to improve contrast in images. This method usually increases the contrast level in images, particularly when a picture has many areas of lower local contrast, and you want them to gain a higher contrast in order to make them more readable.
• Image noise reduction. Generally speaking, image noise is a random variation of brightness or color information in images. In most cases, not only is it useless, but it also deteriorates the image. It originates from the camera itself and usually happens when pictures are taken in dark places, and not enough light reaches the object-glass. The OpenCV library has appropriate algorithms to help you with the image denoising.

A typical preprocessing pipeline that combines histogram equalization and noise reduction:

Feature extraction

One of the most common reasons to use OpenCV is to extract meaningful features from an image — edges, corners, keypoints, contours, color histograms — that can then be used for further analysis or as input to a machine learning model. OpenCV provides battle-tested implementations of the algorithms that dominate classical computer vision:

• Edge detection with the Canny algorithm — finding the outlines of objects in an image
• Corner and keypoint detection with SIFT, ORB, or AKAZE — used for image stitching, object matching, and tracking
• Contour detection — finding the closed boundaries of shapes, essential for object isolation and measurement
• Color histograms — analyzing the distribution of colors, useful for classification, retrieval, and anomaly detection

A typical pipeline reads in an image, converts it to the appropriate color space (often grayscale), and applies one of these algorithms in just a few lines of code:

python
import cv2

img = cv2.imread(“input.jpg”)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, threshold1=100, threshold2=200)
cv2.imwrite(“edges.jpg”, edges)

These features are still the backbone of many production systems in 2026 — particularly in industrial inspection, robotics, and metrology, where you need explicit, explainable, and fast geometric measurements rather than a deep learning “black box.”

Text extraction (OCR pipelines)

Strictly speaking, OpenCV is not an OCR engine — it doesn’t recognize text characters on its own. What it does, and does very well, is the image preprocessing that makes OCR work reliably: deskewing tilted photos, removing noise, increasing contrast, thresholding the image to pure black-and-white, and isolating the region containing text (for example, a license plate within a full vehicle photo).
A real-world license plate recognition pipeline typically chains OpenCV with a dedicated OCR engine:

pythonimport cv2
import pytesseract # Tesseract OCR Python wrapper

img = cv2.imread(“car.jpg”)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.threshold(blur, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

plate_text = pytesseract.image_to_string(thresh)
print(plate_text.strip())

The OCR engine choices have expanded significantly. The most widely used in 2026 are:

• Tesseract (open-source, mature, multilingual — the default for many years)
• EasyOCR (PyTorch-based, strong on natural-scene text and non-Latin scripts)
• PaddleOCR (very accurate, broad language support, popular in Asia and increasingly globally)
• Cloud OCR APIs (Google Cloud Vision, AWS Textract, Azure AI Vision) — for production document-processing workloads where accuracy and SLAs matter more than self-hosting
• Vision-language models (GPT-5, Gemini 2.5 Pro, Claude Opus 4) — for unstructured documents where the LLM can simultaneously transcribe and interpret the content

OpenCV is the preprocessing layer behind almost all of these in production — handling everything before the actual text recognition step.

Background removal

This finds applications, particularly in the eCommerce sector. Naturally, every online store wants to present their products as favorably as possible. That’s why every picture before it can be published online, has to go through a background removal stage. Within this stage, the background is removed and replaced with transparency or solid color (particularly white). As a result, you have a picture where only the product in question is visible, and nothing distracts the viewer. It’s another vital and commonly-used image processing method often done with the assistance of the OpenCV algorithms.

Background removal has changed dramatically over the past few years. Classical OpenCV methods (thresholding, GrabCut, watershed segmentation) still work well for controlled scenes — a product on a uniform backdrop, for example. But for complex backgrounds, modern pipelines combine OpenCV with deep learning models that have been specifically trained for foreground segmentation:

• U²-Net and MODNet — open-source models that handle complex backgrounds, hair, and transparency
• Segment Anything Model (SAM) from Meta AI — can isolate any object given a click or bounding box
• rembg — a popular Python library wrapping U²-Net and other models for one-line background removal

A typical 2026 background-removal pipeline runs the deep learning segmentation first to produce a mask, then uses OpenCV to refine edges, composite the result onto a new background, and handle batch processing:

Other simple OpenCV in image processing techniques

Last but not least, we have other simple OpenCV image processing techniques. They comprise:

• Rotating
• Cropping
• Resizing and several others

Looping over thousands of images with OpenCV typically takes minutes — manually editing them in Photoshop would take days.

Why are they helpful? One might argue that these processes are so straightforward, they can be easily done even in MS Paint, not to mention more advanced graphic software. And it’s true, these techniques raise no difficulties.

But what happens when you want to resize or rotate 10,000 pictures? It’s a different story, isn’t it? That’s why you want an ML algorithm to do it for you. OpenCV has the ready-made algorithms that will help you speed up this process. Image processing algorithms are not necessarily only about executing complicated and sophisticated processes. In many instances, they do simple corrections or modifications, which are time-consuming when done by hand.

OpenCV and deep learning in 2026

1. The most important shift since this article was first written is that OpenCV is no longer used in isolation. Modern computer vision systems combine OpenCV — for preprocessing, post-processing, and classical algorithms — with deep learning models for the actual perception tasks. There are three ways this typically looks in production:
   OpenCV preprocesses, a deep learning framework predicts.
   The most common pattern. OpenCV reads, resizes, denoises, and normalizes images, then hands them to a PyTorch, TensorFlow, or ONNX model for classification, detection, or segmentation.
2. OpenCV runs the model directly with cv2.dnn.
   The DNN module loads pre-trained models from many frameworks (TensorFlow, PyTorch via ONNX, Caffe, Darknet) and runs them entirely inside OpenCV. This means you can deploy a deep learning vision model without installing the full ML framework — useful in edge devices, robotics, and embedded systems.
3. OpenCV post-processes deep learning outputs.
   Drawing bounding boxes, non-maximum suppression, contour cleanup, applying overlays, generating heatmaps — all the visual output that makes a model’s predictions useful to a human end-user.
   The takeaway: OpenCV’s role hasn’t shrunk in the deep learning era — it has shifted. In 2026, OpenCV is most often the layer that surrounds your model, handling everything before and after the inference step.

When to reach for something other than OpenCV?

OpenCV is broad, but it’s not always the best fit. Most modern Python computer vision projects use it alongside specialized libraries:

• Pillow (PIL) — simpler API, ideal for basic file format conversion, resizing, and JPEG/PNG handling. Doesn’t include computer vision algorithms.
• scikit-image — pure Python, scientific focus. Better for research and notebooks; OpenCV is faster for production.
• PyTorch torchvision / TensorFlow Keras Vision — the standard for deep learning models. Use OpenCV for preprocessing, these for the actual model.
• albumentations — fast, modern data augmentation library. Built on OpenCV internally, but with a cleaner API for ML training pipelines.
• MediaPipe (Google) — pre-built pipelines for face detection, hand tracking, pose estimation, and segmentation. Great when you need a working solution fast.
• Detectron2 / MMDetection — frameworks built on PyTorch for state-of-the-art object detection and segmentation.
• Vision-language models (GPT-5, Gemini 2.5 Pro, Claude Opus 4 with vision) — for unstructured visual understanding where you want a model to describe what’s in an image, not just detect specific objects.

The most common 2026 pattern: OpenCV for the geometric and pixel-level work, PyTorch or a vision-language model for the semantic understanding, albumentations for training data, and a cloud API for OCR or speech-to-text where the SLA matters more than self-hosting.

OpenCV in image processing – Conclusion

OpenCV remains the foundation of practical image processing in 2026 — not because it’s the newest tool, but because it’s reliable, fast, well-documented, and works seamlessly with the deep learning ecosystem that has grown up around it. For most production computer vision systems, OpenCV is the preprocessing and post-processing layer that makes everything else possible.

The five use cases in this article — image enhancement, feature extraction, OCR pipelines, background removal, and bulk geometric operations — are the patterns you’ll see most often in real-world systems, whether the underlying task is industrial defect detection, medical imaging, document understanding, or e-commerce product photography. What’s changed is how OpenCV combines with deep learning models and vision-language models to produce results that weren’t possible a few years ago.

If you’d like help applying computer vision to a specific business problem — defect detection, document processing, visual inspection, or anything else — book a 30-minute call with our team. We’ve built computer vision systems for manufacturing, aviation, retail, and healthcare clients. You can also explore our computer vision solutions for a deeper look at how we approach these projects.

References

References
[1] OpenCV. Official documentation and library home. URL: https://opencv.org/
[2] OpenCV. Python tutorials and image processing guide. URL: https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html
[3] Wikipedia. OpenCV. URL: https://en.wikipedia.org/wiki/OpenCV
[4] OpenCV. Deep Neural Networks (dnn module) documentation. URL: https://docs.opencv.org/4.13.0/d2/d58/tutorial_table_of_content_dnn.html
[5] PyPI. opencv-python package. URL: https://pypi.org/project/opencv-python/