OpenCV vs TensorFlow: What are the differences?
Developers describe OpenCV as "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. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
OpenCV belongs to "Image Processing and Management" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".
"Computer Vision" is the top reason why over 19 developers like OpenCV, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.
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, TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to OpenCV, which is listed in 39 company stacks and 39 developer stacks.
What is OpenCV?
What is TensorFlow?
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What are the cons of using OpenCV?
What tools integrate with OpenCV?
What tools integrate with TensorFlow?
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
Machine Learning in EECS 445