ML Vision Solutions#
Computer vision is a field within ML that deals with how computers can be made to gain high-level understanding of digital images and videos. Machine learning is a good match for image and video classification. Even realtime. BUT from a privacy point of view usage of these solutions is questionable at best. So never use ML solutions for real time image classification on humans in the wild.
DeepFaceLab#
DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.
More than 95% of deepfake videos are created with DeepFaceLab.
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GNU General Public License (GPL) 3.0 |
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Python |
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Computer vision, Deepfakes, ML, Python |
DeepPrivacy#
DeepPrivacy is a fully automatic anonymization technique for images.
The DeepPrivacy GAN never sees any privacy sensitive information, ensuring a fully anonymized image. It utilizes bounding box annotation to identify the privacy-sensitive area, and sparse pose information to guide the network in difficult scenarios.
DeepPrivacy detects faces with state-of-the-art detection methods. Mask R-CNN is used to generate a sparse pose information of the face, and DSFD is used to detect faces in the image.
The Github repository contains the source code for the paper “DeepPrivacy: A Generative Adversarial Network for Face Anonymization”, published at ISVC 2019.
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MIT License |
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Python |
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Computer vision, ML, Privacy, Python |
DeOldify#
A Deep Learning based project for colorizing and restoring old images (and video!)
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MIT License |
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Python |
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Computer vision, ML |
Face_recognition#
The world’s simplest facial recognition api for Python and the command line.
Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library.
Built using dlib‘s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.
This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!
Full API documentation can be found here: https://face-recognition.readthedocs.io/en/latest/
Git quick-scan report:
- Date of git statics quick-scan report: 2019/12/19
- Number of files in the git repository: 96
- Total Lines of Code (of all files): 70415 total
- Most recent commit in this repository: Tue Dec 3 16:53:45 2019 +0530
- Number of authors:33
First commit info:
- Author: Adam Geitgey
- Date: Fri Mar 3 16:29:23 2017 -0800
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MIT License |
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Computer vision, face detection, ML, ML Tool, Python |
FaceSwap#
FaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.
When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. Before “deepfakes” these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.
Powered by Tensorflow, Keras and Python; Faceswap will run on Windows, macOS and Linux. And GPL licensed!
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SBB License |
GNU General Public License (GPL) 3.0 |
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Python |
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Computer vision, Deepfakes, ML, Python |
JeelizFaceFilter#
Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D…).
Enables developers to solve computer-vision problems directly from the browser.
Features:
- face detection,
- face tracking,
- face rotation detection,
- mouth opening detection,
- multiple faces detection and tracking,
- very robust for all lighting conditions,
- video acquisition with HD video ability,
- interfaced with 3D engines like THREE.JS, BABYLON.JS, A-FRAME,
- interfaced with more accessible APIs like CANVAS, CSS3D.
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SBB License |
Apache License 2.0 |
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Javascript |
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Computer vision, face detection, Javascript, ML |
libfacedetection#
This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C source files. The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler.
SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM.
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SBB License |
GNU General Public License (GPL) 2.0 |
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CPP |
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Computer vision |
Luminoth#
Luminoth is an open source toolkit for computer vision. Currently, we support object detection and image classification, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet.
Note: No longer maintained.
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BSD License 2.0 (3-clause, New or Revised) License |
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Python |
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Computer vision, ML |
makesense.ai#
makesense.ai is a free to use online tool for labelling photos. Thanks to the use of a browser it does not require any complicated installation – just visit the website and you are ready to go. It also doesn’t matter which operating system you’re running on – we do our best to be truly cross-platform. It is perfect for small computer vision deeplearning projects, making the process of preparing a dataset much easier and faster.
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SBB License |
GNU General Public License (GPL) 3.0 |
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Typescript |
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Computer vision, ML, ML Tool, Photos |
OpenCV: Open Source Computer Vision Library#
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc.
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SBB License |
BSD License 2.0 (3-clause, New or Revised) License |
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C |
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Computer vision, ML |
Raster Vision#
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery).
It allows users (who don’t need to be experts in deep learning!) to quickly and repeatably configure experiments that execute a machine learning workflow including: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment.
Some features:
- There is built-in support for chip classification, object detection, and semantic segmentation with backends using PyTorch and Tensorflow.
- Experiments can be executed on CPUs and GPUs with built-in support for running in the cloud using AWS Batch. The framework is extensible to new data sources, tasks (eg. object detection), backends (eg. TF Object Detection API), and cloud providers.
Documentation on: https://docs.rastervision.io/
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SBB License |
Apache License 2.0 |
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Python |
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Computer vision, ML |
SOD#
SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.
SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.
Designed for computational efficiency and with a strong focus on real-time applications. SOD includes a comprehensive set of both classic and state-of-the-art deep-neural networks with their pre-trained models.
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GNU General Public License (GPL) 3.0 |
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C |
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Computer vision, ML |
YOLOv3#
A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.
You only look once (YOLO) is a state-of-the-art, real-time object detection system. In depth paper on YOLOv3 is on: https://pjreddie.com/media/files/papers/YOLOv3.pdf
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SBB License |
GNU General Public License (GPL) 2.0 |
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Python |
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Computer vision, ML |
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