Compare OpenCV and scikit-image 
- Users report that OpenCV excels in "Object Detection" capabilities, providing robust algorithms that are widely used in real-time applications, while scikit-image is noted for its "Image Processing" features that are more suited for academic and research purposes.
- Reviewers mention that scikit-image offers a more user-friendly experience with a higher "Ease of Use" score (8.8) compared to OpenCV (7.8), making it a better choice for beginners in image processing.
- G2 users highlight that OpenCV has a strong community and extensive documentation, which contributes to its "Quality of Support" rating of 8.4, although scikit-image slightly edges out with a score of 8.5, indicating a solid support system as well.
- Users on G2 report that OpenCV's "Model Training" capabilities are more advanced, allowing for custom model development, while scikit-image is praised for its "Labeling" features that simplify the process of preparing datasets for machine learning.
- Reviewers say that OpenCV's "Facial Recognition" and "Motion Analysis" features are highly effective for real-time applications, whereas scikit-image is often preferred for its "Scene Reconstruction" capabilities, which are beneficial in research settings.
- Users report that OpenCV's integration with various programming languages and platforms is a significant advantage, while scikit-image is recognized for its seamless integration with scientific libraries like NumPy and SciPy, making it ideal for data analysis tasks.
It is open source.That is the biggest plus point in contrast to Matlab which also has great image processing functions. I use it all the time especially when I need to to rapid prototyping with python as openCV supports python
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
I like the seamless implementation of this library and methods, and it's pretty easy to embed them in your code. It can be coupled up with visualization using the matplotlib library in Python which is pretty cool.
While it is very extensive, the library does have its limits with some data sets where the data will not be processed. Sometimes there is error when running it in MATLAB so that should be improved.
It is open source.That is the biggest plus point in contrast to Matlab which also has great image processing functions. I use it all the time especially when I need to to rapid prototyping with python as openCV supports python
I like the seamless implementation of this library and methods, and it's pretty easy to embed them in your code. It can be coupled up with visualization using the matplotlib library in Python which is pretty cool.
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
While it is very extensive, the library does have its limits with some data sets where the data will not be processed. Sometimes there is error when running it in MATLAB so that should be improved.
