Displaying 1 to 20 from 27 results

tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference

  •    PureBasic

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

AlphaPose - Multi-Person Pose Estimation System

  •    Jupyter

Alpha Pose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset. Note: Please read PoseFlow/README.md for details.


  •    Python

Example output of e2e_mask_rcnn-R-101-FPN_2x using Detectron pretrained weight. Corresponding example output from Detectron.

SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

  •    Python

SuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of SuperPoint keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. Full paper PDF: SuperGlue: Learning Feature Matching with Graph Neural Networks.

Hierarchical-Localization - Visual localization made easy with hloc

  •    Python

This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenge at CVPR 2020, in combination with SuperGlue, our graph neural network for feature matching. This codebase includes external local features as git submodules – don't forget to pull submodules with git submodule update --init --recursive. Your local features are based on TensorFlow? No problem! See below for the steps.

DeepLabCut - Markerless tracking of user-defined features with deep learning

  •    Python

Welcome to the DeepLabCut repository, a toolbox for markerless tracking of body parts of animals in lab settings performing various tasks, like trail tracking, reaching in mice and various Drosophila behaviors during egg-laying (see Mathis et al. for details). There is, however, nothing specific that makes the toolbox only applicable to these tasks and/or species. The toolbox has also already been successfully applied to rats, humans, various fish species, bacteria, leeches, various robots, and race horses. Please check out www.mousemotorlab.org/deeplabcut for video demonstrations of automated tracking. This work utilizes the feature detectors (ResNet + readout layers) of one of the state-of-the-art algorithms for human pose estimation by Insafutdinov et al., called DeeperCut, which inspired the name for our toolbox (see references below).

cpm - Convolutional Pose Machines in TensorFlow

  •    Jupyter

Convolutional Pose Machines in TensorFlow

Dataset_Synthesizer - NVIDIA Deep learning Dataset Synthesizer (NDDS)

  •    C++

NDDS is a UE4 plugin from NVIDIA to empower computer vision researchers to export high-quality synthetic images with metadata. NDDS supports images, segmentation, depth, object pose, bounding box, keypoints, and custom stencils. In addition to the exporter, the plugin includes different components for generating highly randomized images. This randomization includes lighting, objects, camera position, poses, textures, and distractors, as well as camera path following, and so forth. Together, these components allow researchers to easily create randomized scenes for training deep neural networks. Example of an image generated using NDDS, along with ground truth segmentation, depth, and object poses. For utilities to help visualize annotation data associated with synthesized images, see the NVIDIA dataset utilities (NVDU) https://github.com/NVIDIA/Dataset_Utilities.

ros-openpose - CMU's OpenPose for ROS

  •    C++

I re-implemented cmu's openpose in tensorflow with some modifications. Especially, using Mobilenet's 'Depthwise Separable Convolution', I improved it to run in realtime even in an low-computation embedded deivce or only-cpu environment.

RMPE - RMPE: Regional Multi-person Pose Estimation, forked from Caffe. Research purpose only.

  •    C++

By Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, Cewu Lu. RMPE is a two steps framework for the task of multi-person pose estimation. You can use the code to train/evaluate a model for pose estimation task. For more details, please refer to our arxiv paper.

Dataset_Utilities - NVIDIA Dataset Utilities (NVDU)

  •    Python

This project is a collection of Python scripts to help work with datasets for deep learning. For example, visualizing annotation data associated with captured sensor images generated by NVIDIA Deep learning Dataset Synthesizer (NDDS) https://github.com/NVIDIA/Dataset_Synthesizer. This module depends on OpenCV-python which currently doesn't work with Python 3.7.

MobilePose-pytorch - Single Person Pose Estimation for Mobile Device

  •    Jupyter

MobilePose is a Tiny PyTorch implementation of single person 2D pose estimation framework. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options. And final trained model can satisfy basic requirements(speed+size+accuracy) for mobile device. Some codes for mobilenetV2 and display are brought from pytorch-mobilenet-v2 and tf-pose-estimation. Thanks to the original authors.

PoseFlow - PoseFlow: Efficient Online Pose Tracking (BMVC'18)

  •    C++

Official implementation of Pose Flow: Efficient Online Pose Tracking . Firstly, using AlphaPose to generate multi-person pose estimation results on videos, please see alpha-pose-results-sample.json to know json format.

adversarial-pose-pytorch - A PyTorch implementation of adversarial pose estimation for multi-person

  •    Python

This repository implements pose estimation methods in PyTorch. The file lsp_mpii.h5 contains the annotations of MPII, LSP training data and LSP test data. Place LSP, MPII images in data/LSP/images and data/mpii/images. Place coco annotations in data/coco/annotations and images in data/coco/images, as suggested by cocoapi. The file valid_id contains the image_ids used for validation.

Docker4DeepLabCut - Dockercontainer to run DeepLabCut http://www.mousemotorlab.org/deeplabcut/

  •    Shell

This package will allow you to run DeepLabCut with everything pre-installed inside a Docker container. This Docker file is based off the Bethge lab container. Specifically, the one we provide comes with Ubuntu 14.04 + Cuda 8.0 + CuDNN v5 and Tensorflow 1.2 (ideal for the current version of DeepLabCut), and the required python packages.

iOS-OpenPose - OpenPose Example App

  •    Swift

Licensed under the terms of the MIT license.

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