Google Cloud Vision API vs OpenCV: What are the differences?
Google Cloud Vision API: Understand the content of an image by encapsulating powerful machine learning models. Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API; OpenCV: Open Source Computer Vision Library. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Google Cloud Vision API belongs to "Image Analysis API" category of the tech stack, while OpenCV can be primarily classified under "Image Processing and Management".
Some of the features offered by Google Cloud Vision API are:
- Powerful Image Analysis
- Insight From Your Images
- Detect Inappropriate Content
On the other hand, OpenCV provides the following key features:
- C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android
- More than 47 thousand people of user community and estimated number of downloads exceeding 7 million
- Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics
OpenCV is an open source tool with 36.3K GitHub stars and 26.6K GitHub forks. Here's a link to OpenCV's open source repository on GitHub.
According to the StackShare community, OpenCV has a broader approval, being mentioned in 39 company stacks & 39 developers stacks; compared to Google Cloud Vision API, which is listed in 25 company stacks and 8 developer stacks.
What is Google Cloud Vision API?
What is OpenCV?
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What tools integrate with Google Cloud Vision API?
I used both scikit-image and OpenCV for image processing and cell identification on the backend. Trained to identify malaria cells based on image datasets online. When it comes to quick training for image processing, OpenCV and scikit-image are the two best choices in my opinion. The approach I took to cell detection was template-matching and edge detection based. Both are highly tested and very powerful features of the Scikit Image and OpenCV libraries, and also have great Python interfaces.
I use openCV to serve as "motion capture" logic for my home security cameras. Which means that instead of capturing in a dumb way based on motion, it captures video when it recognizes human faces or bodies. This saves a lot of disk, but at the expense of CPU.
CV glue. Modified libraries for pattern-detection. Some pattern training tasks. HoG matching. Transform