Home/ Image Recognition Software/ OpenCV/ Reviews
90% SW Score The SW Score ranks the products within a particular category on a variety of parameters, to provide a definite ranking system. Read more
An open-source machine learning software
75%
19.2%
3.8%
0%
1.9%
PROS: I really love the community that has sprung around this tool, which has been around for decades now. Because of that community, it's been really easy for me to find help for anything, whether that's solutions to tiny errors I encounter or to algorithms that I need. It also comes in multiple languages, which I'm sure users will really appreciate since they may have a preferred coding language that they use. I think all of those things make this tool really powerful.
CONS: This tool has really helped me out with any problems I've encountered involving Computer Visions. There isn't anything bad I can say about it.
PROS: I like a number of things about this tool. For one, it is free and provides you with online support that is dependable. I didn't find it hard to learn how to use it and didn't have to contend with a steep learning curve. I also really like how there are a number of options available to me when it comes to tweaking the properties of videos and images. I've been able to use this tool to blur images, sharpen images, detect edges, and detect faces and objects. I also like the function that allows me to take colored images and turn them gray.
CONS: Frankly, I can think of anything about this tool to complain about.
PROS: I really like how easy this is to use. All I needed was to look at a few examples provided and I was able to come up with code that I needed. With this tool, I've been able to successfully perform a number of computer vision applications. Some of those include eyeball tracking, color tracking, face recognition, and gesture recognition. Implementing these was also easy to do with Python. This tool has really been very useful to me.
CONS: Honestly, I can't think of anything that I don't like about this tool. So far, it is the best of its kind.
PROS: I think that there's isn't a better user of API than this tool when it comes to image processing and computer vision. I like that it allows me to take the frame from a live feed or an image from the web and then perform processing on it. I also like how easy it is to manipulate an image with this tool because I only need to edit the numpy array. This is because the image itself has been used as the numpy array object. I truly think there's nothing that tops this API when it comes to performing image processing.
CONS: Frankly, I don't have anything to complain about when it comes to this tool. I think it is top-notch.
PROS: There are multiple things I like about this too, with the first one being that it is open source. I can use it In C++ as well as Python. I don't need to look at other tools since all the algorithms that I'm going to need for Computer Vision tasks are already available. I also really appreciate how easily it works with other well-known libraries like Matplotlib and Numpy.
CONS: There is a huge community that you can turn to for support, so I haven't really had any trouble using it and I can't think of one particular thing that I can complain about.
As a new start-up, open-source software is important because of its cost.
improving the accuracy of image recognition
Recognition of fruit buds on fruit Trees
PROS: When it comes to CV libraries that use Python for CV algorithm implementation, you can't do better than this tool. First off, you can get lots of resources and support for this tool on the internet. Another thing I really liked and I think other users will like is the numerous algorithms it provides, ranging from converting an image to grayscale, color tracking, and color masking. It's also very easy to have hand recognition implemented with this tool.
CONS: I do wish that they would have this available for Python 3. Currently, it runs on Python 2.7 and setting that up can be a little tedious. I hope future versions are compatible to Python 3.
PROS: There are a number of things that I appreciate about this tool. First off, even if it is running on a mobile device, it still perfroms exceptionally. It performs pretty well when it comes to object detection accuracy. Another exceptional thing it can do is being able to take live videos, capture frames from it, and then process it with very little latency time. It's definitely the tool to use when you want to implement object detection and image recognition in mobile applications.
CONS: i would like to see it be more compatible with other programming languages in the future. Currently, it works best with the Python programming language.
PROS: The thing that I like the most about this tool is that despite the simplicity of its functions, I can still do things like transforming, resizing, and other prcoessing techniques on images. It's so easy to do on this tool that I've even chosen to use this instead of more conventional image editors.
CONS: honestly, there isn't anything about this tool that I can complain about. It ticks all the boxes that I am looking for.
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1. As a Data Scientist, I have to work on computer vision projects involving complex image processing tasks. The OpenCV library makes it easy to manipulate the image data as per our requirements.
2. It is fast and with a lot of inbuilt functionalities along with classical and State-of-the-art algorithms, which makes our job easier.
3. It has very good memory management which makes it efficient.
I faced some issues while displaying images using the OpenCV library. It used to freeze the display window on my Macbook. Later I had to replace it with matplotlib. Other than this it has been a boon to us for all kinds of computer vision tasks.
OpenCV is a very powerful library that is being used for various tasks like Image Processing, Object detection, Image recognition, OCR and many more.
OpenCV has helped me :
1. In solving complex tasks in Kaggle competitions(Computer Vision). For example, Detecting digits/numbers in an image when the background is a checkerboard(black and white patches). Here, we had to modify the background into a desired one so that we could fetch the numbers using the contours function in OpenCV and then apply a model trained on the MNIST dataset to identify the number. It surely was a very interesting competition.
2. Also helped me in working with some real-world Face Recognition and Object detection tasks as well.