Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding of digital images and videos.
Learn OpenCV : C++ and Python Examples
Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.
👨 Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"
Collection of resources spanning key areas of Computer Science
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
📚 The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
[Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration
AI for GNU Image Manipulation Program
This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries.
A set of tools to visualize and interact with sequences of 3D data.
Darknet/YOLO object detection framework
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. N...
A one-stop repository for low-code easily-installable object detection pipelines.
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.
This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"
CosmiQ Works Geospatial Machine Learning Analysis Toolkit
This repository contains all the deep learning projects done as tutorial
PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!
Plant Disease Detector Web Application
AdaLAM is a fully handcrafted realtime outlier filter integrating several best practices into a single efficient and effective framework. It detects inliers by searching for significant local affine patterns in image correspondences.