The Open Source Framework for Machine Vision

Make computers see with SimpleCV, the Open Source Framework for Computer Vision. SimpleCV is a framework for Open Source Machine Vision, using OpenCV and the Python programming language. It provides a concise, readable interface for cameras, image manipulation, feature extraction, and format conversion. Our mission is to give casual users a comprehensive interface for basic machine vision functions and an elegant programming interface for advanced users. Even beginning programmers can write simple machine vision tests Cameras, video files, images, and video streams are all interoperable Information on image features can be extracted, sorted and filtered easily Manipulations are fast, with easy to remember names Linear algebra is strictly optional 

The friendly PIL fork (Python Imaging Library)

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool. 

Caffe: a fast open framework for deep learning.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. Check out the project site for all the details like. DIY Deep Learning for Vision with Caffe Tutorial Documentation BAIR reference models and the community model zoo Installation instructions 

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter. Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned. Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, and Waymo. 

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based slicing, fancy indexing, and subsetting of large data sets Intuitive merging and joining data sets Flexible reshaping and pivoting of data sets Hierarchical labeling of axes (possible to have multiple labels per tick) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging 

Tensors and Dynamic neural networks in Python with strong GPU acceleration

At a granular level, PyTorch is a library that consists of the following components:. A replacement for NumPy to use the power of GPUs. A deep learning research platform that provides maximum flexibility and speed. 

Classroom SD Compliance Checker

Image Detection

SimpleCVby sightmachine

SimpleCVby sightmachine

scikit-imageby scikit-image

scikit-imageby scikit-image

opencvby opencv

opencvby opencv

Pillowby python-pillow

Pillowby python-pillow

Machine Learning

caffeby BVLC

caffeby BVLC

kerasby keras-team

kerasby keras-team

pandasby pandas-dev

pandasby pandas-dev

pytorchby pytorch

pytorchby pytorch

scikit-learnby scikit-learn

scikit-learnby scikit-learn