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OpenCV VS pytest

Compare OpenCV VS pytest and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
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  3. » Reviews
  4. » Alternatives

OpenCV logo OpenCV

OpenCV is the world's biggest computer vision library

pytest logo pytest

Javascript Testing Framework
  • OpenCV Landing page
    Landing page //
    2023-07-29
  • pytest Landing page
    Landing page //
    2023-10-15

OpenCV features and specs

  • Comprehensive Library
    OpenCV offers a wide range of tools for various aspects of computer vision, including image processing, machine learning, and video analysis.
  • Cross-Platform Compatibility
    OpenCV is designed to run on multiple platforms, including Windows, Linux, macOS, Android, and iOS, which makes it versatile for development across different environments.
  • Open Source
    Being open-source, OpenCV is freely available for use and allows developers to inspect, modify, and enhance the code according to their needs.
  • Large Community Support
    A large community of developers and researchers actively contributes to OpenCV, providing extensive support, tutorials, forums, and continuously updated documentation.
  • Real-Time Performance
    OpenCV is highly optimized for real-time applications, making it suitable for performance-critical tasks in various industries such as robotics and interactive installations.
  • Extensive Integration
    OpenCV can easily be integrated with other libraries and frameworks such as TensorFlow, PyTorch, and OpenCL, enhancing its capabilities in deep learning and GPU acceleration.
  • Rich Collection of examples
    OpenCV provides a large number of example codes and sample applications, which can significantly reduce the learning curve for beginners.

Possible disadvantages of OpenCV

  • Steep Learning Curve
    Due to the vast array of functionalities and the complexity of some of its advanced features, beginners may find it challenging to learn and use effectively.
  • Documentation Gaps
    While the documentation is extensive, it can sometimes be incomplete or outdated, requiring users to rely on community forums or external sources for solutions.
  • Resource Intensive
    Some functions and algorithms in OpenCV can be quite resource-intensive, requiring significant processing power and memory, which can be a limitation for low-end devices.
  • Limited High-Level Abstractions
    OpenCV provides a wealth of low-level functions, but it may lack higher-level abstractions and frameworks, necessitating more hands-on coding and algorithm development.
  • Dependency Management
    Setting up and managing dependencies can be cumbersome, especially when integrating OpenCV with other libraries or on certain operating systems.
  • Backward Compatibility Issues
    With frequent updates and new versions, backward compatibility can sometimes be problematic, potentially breaking existing code when updating.

pytest features and specs

  • Easy to Use
    Pytest is designed to be simple and easy to use, with minimal boilerplate code required to write tests. Its straightforward syntax allows users to quickly write and understand tests.
  • Extensive Plugin System
    Pytest has a flexible and powerful plugin architecture, with a wide range of community-maintained plugins available, allowing for easy customization and extension of its functionality.
  • Detailed Information on Failures
    Pytest provides detailed and informative feedback on failures, enhancing the debugging process by highlighting where and why a test failed.
  • Fixture Support
    Pytest's fixture system allows for easy setup and teardown of test environments, encouraging the reuse of setup code and reducing code duplication.
  • Compatibility
    Pytest is compatible with standard Python testing frameworks such as unittest, allowing for easy migration and integration of existing tests.

Possible disadvantages of pytest

  • Steeper Learning Curve for Advanced Features
    While basic usage is straightforward, mastering advanced pytest features, such as writing custom plugins and fixtures, can have a steeper learning curve.
  • Performance Overhead
    For very large projects, the additional features and flexibility of pytest can introduce some performance overhead when running tests, compared to simpler testing frameworks.
  • Complexity in Parameterized Testing
    While pytest supports parameterized testing, setting up and managing complex parameterizations can become cumbersome and might require additional abstraction layers.
  • Plugin Conflicts
    With a vast ecosystem of plugins, there is a potential for conflicts or compatibility issues between different plugins, especially when they modify similar pytest behaviors.

OpenCV videos

+ Add

AI Courses by OpenCV.org

More videos:

  • Review - Practical Python and OpenCV

pytest videos

+ Add

getting started with pytest (beginner - intermediate) anthony explains #518

More videos:

  • Review - Python Code Review: Adding Pytest Tests to an Existing Python Web Scraper
  • Review - pytest: everything you need to know about fixtures (intermediate) anthony explains #487

Category Popularity

0-100% (relative to OpenCV and pytest)

OpenCV

pytest

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Automated Testing
0 0%
Automated Testing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Testing
0 0%
Testing
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare OpenCV and pytest

OpenCV Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
OpenCV is the go-to library for computer vision tasks. It boasts a vast collection of algorithms and functions that facilitate tasks such as image and video processing, feature extraction, object detection, and more. Its simple interface, extensive documentation, and compatibility with various platforms make it a preferred choice for both beginners and experts in the field.
Source: clouddevs.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
OpenCV is an open-source computer vision and machine learning software library that was first released in 2000. It was initially developed by Intel, and now it is maintained by the OpenCV Foundation. OpenCV provides a set of tools and software development kits (SDKs) that help developers create computer vision applications. It is written in C++, but it supports several...
Source: www.uubyte.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
These are some of the most basic operations that can be performed with the OpenCV on an image. Apart from this, OpenCV can perform operations such as Image Segmentation, Face Detection, Object Detection, 3-D reconstruction, feature extraction as well.
Source: neptune.ai
5 Ultimate Python Libraries for Image Processing
Pillow is an image processing library for Python derived from the PIL or the Python Imaging Library. Although it is not as powerful and fast as openCV it can be used for simple image manipulation works like cropping, resizing, rotating and greyscaling the image. Another benefit is that it can be used without NumPy and Matplotlib.
Source: towardsdatascience.com

pytest Reviews

25 Python Frameworks to Master
Pytest is a widely adopted testing framework that is designed to be easy to use and extend. It helps you to write elegant tests in both small and complex Python codebases.
Source: kinsta.com

Social recommendations and mentions

Based on our record, OpenCV seems to be a lot more popular than pytest. While we know about 59 links to OpenCV, we've tracked only 5 mentions of pytest. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OpenCV mentions (59)

  • Top Programming Languages for AI Development in 2025
    Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / 8 days ago
  • Why 2024 Was the Best Year for Visual AI (So Far)
    Almost everyone has heard of libraries like OpenCV, Pytorch, and Torchvision. But there have been incredible leaps and bounds in other libraries to help support new tasks that have helped push research even further. It would be impossible to thank each and every project and the thousands of contributors who have helped make the entire community better. MedSAM2 has been helping bring the awesomeness of SAM2 to the... - Source: dev.to / 4 months ago
  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision. - Source: dev.to / 6 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library is used for image and video processing, offering functions for tasks like object detection, filtering, and transformations in computer vision. - Source: dev.to / 7 months ago
  • Built in Days, Acquired for $20K: The NuloApp Story
    First of all, OpenCV, an open-source computer vision library, was used as the main editing tool. This is how NuloApp is able to get the correct aspect ratio for smartphone content, and do other cool things like centering the video on the speaker so that they aren't out of frame when the aspect ratio is changed. - Source: dev.to / 8 months ago
View more

pytest mentions (5)

  • An Introduction to Testing with Django for Python
    Pytest is an excellent alternative to unittest. Even though it doesn't come built-in to Python itself, it is considered more pythonic than unittest. It doesn't require a TestClass, has less boilerplate code, and has a plain assert statement. Pytest has a rich plugin ecosystem, including a specific Django plugin, pytest-django. - Source: dev.to / about 1 year ago
  • How I Added Continuous Integration (CI) to a C++ Project
    For this lab exercise I had the opportunity to add unit tests to a classmate's project and experience their CI workflow. For this exercise I worked on go-go-web by kliu57. Go-Go Web is written in Python and uses the pytest testing framework. This was my first time writing tests for pytest, but I found the pytest docs helpful. However, more helpful was the information provided in the associated issue and the tests... - Source: dev.to / over 1 year ago
  • CI/CD Part 1: Unit/Integration Testing
    This week, in a setup for a CI/CD pipeline, I added unit and integration testing using Pytest to my Python CLI and utilized pytest-cov for generating a coverage report. As always, the merged commit for changes to the repo can be found here. - Source: dev.to / over 1 year ago
  • Testing in Python
    After looking through the various unit testing tools available for Python like pytest, unittest (built-in), and nose, I went with pytest for its simlpicity and ease of use. - Source: dev.to / over 1 year ago
  • Testing and Refactoring With pytest and pytest-cov
    Up until now we've been using python's unittest module. This was chosen as a first step since it comes with python out of the box. Now that we've gone over dev dependencies I think it's a good time to look at pytest as a unit test alternative. I highly recommend getting accustomed to pytest as it's used quite often in the python ecosystem to handle testing for projects. It's also a bit more user friendly in how it... - Source: dev.to / over 1 year ago

What are some alternatives?

When comparing OpenCV and pytest, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

JUnit - JUnit is a simple framework to write repeatable tests.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Cucumber - Cucumber is a BDD tool for specification of application features and user scenarios in plain text.

NumPy - NumPy is the fundamental package for scientific computing with Python

unittest - Testing Frameworks

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