Top 11 Tools to build a Computer Vision Application

Computer vision adoption has been steadily gaining traction in the last ten years. It can be easily found in everyday products, from computer vision cameras automatically setting focus on people to game consoles recognizing the gestures. Below you can read about the widely used computer vision libraries and tools developed over the years. Let’s have a look what tool is a good fit for your business.

Computer vision tools and libraries you should know about

1. OpenCV. Most popular library, multi-platform, and easy to use. It covers all the necessary techniques and algorithms to perform several image and video processing tasks, works well with C++ and Python.
2. Matlab. Applied when creating image processing apps and basically built for prototyping and research purposes. Its code is quite succinct, easy to read and debug. It also tackles a problem of the errors: proposes some ways to speed up code before being executed.
3. TensorFlow. Google’s open source framework for deep learning, has some great tools to perform image processing/classification — it is something similar API graph tensor. Moreover, Python API can be used to perform face and expression detection. Tensorflow also allows performing computer vision of tremendous magnitudes.
4. CUDA. NVIDIA’s platform for parallel computing that is easy to program and quite efficient and fast. Leveraging the power of GPUs it delivers great performance. Its toolkit includes the NVIDIA Performance Primitives library comprised with a set of image, signal, and video processing functions.
5. AForge.NET. Extremely simple to use open source C# framework applied in the computer vision, artificial intelligence industries. Image Processing Lab makes much sense for filtering options (edge detection, thresholds, and so forth) and easing viewing functionalities.
6. SimpleCV. Python framework to deliver a more human-readable programming interface when building. It provides an access to a wide range of computer vision tools (i.e OpenCV, Pygame, etc.), can be also used when quick prototyping.
7. GPUImage. Framework built on OpenGL ES 2.0 that allows applying GPU-accelerated effects and filters to live motion video, images, and movies. Running custom filters on a GPU requests a lot of code to set up and maintain.
8. Torch. Framework with a wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient in terms of fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
9. SciPy. An open source Python library used for scientific computing and technical computing. It includes for optimization, linear algebra, integration, interpolation, special functions, FFT, signal, and image processing modules and other tasks.
10. BoofCV. Open source Java library for real-time robotics and computer vision applications released under an Apache 2.0 license for both academic and commercial use. Its functionality covers a wide range of subjects including, optimized low-level image processing routines, camera calibration, feature detection/tracking, structure-from-motion, and recognition.
11. DLib. C++ toolkit that covers machine learning algorithms and tools for creating complex software. It has high-quality documentation, easy to use, no install necessary, many state-of-the-art machine learning algorithms, a high-quality face detector.

These are the top 11 tools used in the computer vision industry. Check out the list of tools above and let us know if you need any help. Provision Lab company can deliver a great digital solution for your business based on your needs and requirements.