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Keywords Everywhere VS OpenCV

Compare Keywords Everywhere VS OpenCV and see what are their differences

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Keywords Everywhere logo Keywords Everywhere

Free browser add-on for keyword volume, CPC & competition

OpenCV logo OpenCV

OpenCV is the world's biggest computer vision library
  • Keywords Everywhere Landing page
    Landing page //
    2023-09-19
  • OpenCV Landing page
    Landing page //
    2023-07-29

Keywords Everywhere features and specs

  • Comprehensive Metrics
    Keywords Everywhere provides detailed metrics such as search volume, CPC, and competition data, helping users make informed decisions for SEO and PPC strategies.
  • Ease of Use
    The browser extension integrates seamlessly with essential tools like Google Search, YouTube, and Google Analytics, making it convenient to access keyword data directly from these platforms.
  • Affordability
    Offers a pay-as-you-go pricing model, which can be more cost-effective for small businesses and individual users compared to subscription-based services.
  • Data Across Platforms
    Provides keyword data for multiple platforms including Google, YouTube, Amazon, and more, which is valuable for diverse digital marketing strategies.
  • Time Saver
    By displaying keyword metrics directly in search engine results and other tools, it significantly reduces the time needed to gather and analyze keyword data.

Possible disadvantages of Keywords Everywhere

  • Limited Free Version
    The free version offers very limited features, driving users to purchase credits for more comprehensive data.
  • Dependency on Browser Extension
    Requires a browser extension to function, which may not be suitable for all users or devices and could raise privacy/security concerns.
  • Accuracy Variability
    As with many keyword tools, the accuracy of the data can occasionally be inconsistent, which may affect strategic decisions.
  • Limited Advanced Features
    While great for basic keyword research, it lacks some of the advanced features offered by more robust SEO tools, such as detailed competitive analysis or site audits.
  • Potential for Data Overload
    The abundance of data displayed can sometimes be overwhelming, particularly for beginners who may struggle to interpret and utilize it effectively.

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.

Analysis of OpenCV

Overall verdict

  • Yes, OpenCV is considered a good and reliable choice for computer vision tasks, particularly due to its extensive functionality, active community, and flexibility.

Why this product is good

  • OpenCV (Open Source Computer Vision Library) is widely regarded as a robust and versatile library for computer vision applications. It offers a comprehensive collection of functions and algorithms for image processing, video capture, machine learning, and more. Its open-source nature encourages community involvement, making it highly adaptable and continuously improving. OpenCV's cross-platform support and ease of integration with other libraries and languages further enhance its appeal.

Recommended for

  • Developers and researchers working on computer vision projects
  • People looking to implement real-time video analysis
  • Individuals exploring machine learning applications related to image and video processing
  • Anyone interested in experimenting with or learning computer vision concepts

Keywords Everywhere videos

+ Add

How to use Keywords Everywhere - SEO keyword research tool

More videos:

  • Review - KEYWORDS EVERYWHERE is now a PAID TOOL - Here's What To Do - Keywords Everywhere Alternative
  • Tutorial - Keywords Everywhere | A Tutorial + Advice on Keywords for YouTube
  • Review - Keywords Everywhere Review: Better Alternative to Google Keyword Planner
  • Review - Keywords Everywhere Review | Best Keyword Search Volume Chrome Extension! 🚀
  • Review - Keywords Everywhere Review 2021 | Keyword research Tool

OpenCV videos

+ Add

AI Courses by OpenCV.org

More videos:

  • Review - Practical Python and OpenCV

Category Popularity

0-100% (relative to Keywords Everywhere and OpenCV)

Keywords Everywhere

OpenCV

SEO Tools
100 100%
SEO Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
SEO
100 100%
SEO
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Keywords Everywhere and OpenCV

Keywords Everywhere Reviews

112 Best Chrome Extensions You Should Try (2021 List)
Keywords Everywhere is an alternative to Ubersuggest, a freemium keyword research tool. It shows the search query data on more than 15 websites. For free users, it shows a trend chart, long-tail keywords, and keywords from ‘people also search for’. But, paid users can see monthly search volume, CPC, competition, and trend data. Although solely for keyword research, you do...
Source: www.einsstark.tech
9 Free Keyword Research Tools (That CRUSH Google Keyword Planner)
Keywords Everywhere is a free add-on for Chrome (or Firefox). It adds search volume, CPC & competition data to all your favourite websites.
Source: ahrefs.com
73 Best SEO tools 2021 – The Most Epic List You Shouldn’t Miss
While most use this tool strictly for Paid ads, Keywords Everywhere is very useful to help you discover long-tail keywords related to the ones you are searching for on Google.
Source: blog.linkody.com

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

Social recommendations and mentions

Based on our record, OpenCV should be more popular than Keywords Everywhere. It has been mentiond 60 times since March 2021. 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.

Keywords Everywhere mentions (16)

  • SEO 101 for Software Developers
    To find keywords I use the tool Keywords Everywhere. It gives you information on how many people search for a particular keyword a month, how difficult it will be to rank for, as well ideas for additional keywords. - Source: dev.to / over 1 year ago
  • How to Manage Your Time as a Software Developer ⌛️
    For example, I do a lot of keyword research for my blog posts and YouTube videos. This generally consists of searching for keywords on Google and then copying the numbers that I get from Keywords Everywhere into a spreadsheet. - Source: dev.to / about 2 years ago
  • My Guide To Shopify Store Keyword Research
    You may be thinking to yourself well that's it right? I know what works and what doesn't, well not exactly because you don't just want to copy everything your competition does or you'll be competing with them all the time and that's a losing battle for most small stores. So step 2 is I cross reference it with another tool called keywords everywhere. As I mentioned this tool can be similar to Ahrefs as you can scan... Source: about 2 years ago
  • GMB Stats?
    Keywords everywhere again, not sure if it's match for you. Source: about 2 years ago
  • Keyword research
    Step 2: keywordseverywhere.com ($10 for 100K SV check - it's a chrome extension), run your list through this and get all SV. Source: about 2 years ago
View more

OpenCV mentions (60)

  • Grasping Computer Vision Fundamentals Using Python
    To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / about 1 month ago
  • Top Programming Languages for AI Development in 2025
    Ideal For: Computer vision, NLP, deep learning, and machine learning. - Source: dev.to / about 2 months 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 / 6 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 / 7 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 / 9 months ago
View more

What are some alternatives?

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

KeywordTool.io - KeywordTool.io is the best FREE alternative to Google Keyword Planner and Ubersuggest. It uses Google's autocomplete feature to get over 750+ long-tail keywords for any given query.

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

Moz - Backed by industry-leading data and the largest community of SEOs on the planet, Moz builds tools that make inbound marketing easy.

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

Ahrefs - Ahrefs is a toolset for SEO and marketing. We have tools for backlink research, organic traffic research, keyword research, content marketing & more. Give Ahrefs a try!

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

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