PAthological QUpath Obsession - QuPath and Python conversations

Overview

PAQUO: PAthological QUpath Obsession

PyPI Version Read the Docs GitHub Workflow Status Codecov PyPI - Python Version GitHub issues

Welcome to paquo 👋 , a library for interacting with QuPath from Python.

paquo's goal is to provide a pythonic interface to important features of QuPath, and to make creating and working with QuPath projects intuitive for Python programmers.

We strive to make your lives as easy as possible: If paquo is not pythonic, unintuitive, slow or if its documentation is confusing, it's a bug in paquo. Feel free to report any issues or feature requests in the issue tracker!

Development happens on github :octocat:

Documentation

You can find paquo's documentation at paquo.readthedocs.io ❤️

Development Installation

  1. Install conda and git
  2. Clone paquo git clone https://github.com/bayer-science-for-a-better-life/paquo.git
  3. Run conda env create -f environment.yaml
  4. Activate the environment conda activate paquo

Note that in this environment paquo is already installed in development mode, so go ahead and hack.

Contributing Guidelines

  • Please follow pep-8 conventions but:
    • We allow 120 character long lines (try anyway to keep them short)
  • Please use numpy docstrings.
  • When contributing code, please try to use Pull Requests.
  • tests go hand in hand with modules on tests packages at the same level. We use pytest.

You can setup your IDE to help you adhering to these guidelines.
(Santi is happy to help you setting up pycharm in 5 minutes)

Acknowledgements

Build with love by Andreas Poehlmann and Santi Villalba from the Machine Learning Research group at Bayer. In collaboration with the Pathology Lab 2 and the Mechanistic and Toxicologic Pathology group.

paquo: copyright 2020 Bayer AG, licensed under GPL-3.0

Owner
Bayer AG
Science for a better life
Bayer AG
Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019.

VCN: Volumetric correspondence networks for optical flow [project website] Requirements python 3.6 pytorch 1.1.0-1.3.0 pytorch correlation module (opt

Gengshan Yang 144 Dec 06, 2022
Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation The code repository for "Audio-Visual Generalized Few-Shot Learning with

Kaiaicy 3 Jun 27, 2022
🛰️ List of earth observation companies and job sites

Earth Observation Companies & Jobs source Portals & Jobs Geospatial Geospatial jobs newsletter: ~biweekly newsletter with geospatial jobs by Ali Ahmad

Dahn 64 Dec 27, 2022
sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

445 Jan 02, 2023
Implementation for Curriculum DeepSDF

Curriculum-DeepSDF This repository is an implementation for Curriculum DeepSDF. Full paper is available here. Preparation Please follow original setti

Haidong Zhu 69 Dec 29, 2022
Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

TransGanFormer (wip) Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. I

Phil Wang 146 Dec 06, 2022
Jaxtorch (a jax nn library)

Jaxtorch (a jax nn library) This is my jax based nn library. I created this because I was annoyed by the complexity and 'magic'-ness of the popular ja

nshepperd 17 Dec 08, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Efficient 3D human pose estimation in video using 2D keypoint trajectories

3D human pose estimation in video with temporal convolutions and semi-supervised training This is the implementation of the approach described in the

Meta Research 3.1k Dec 29, 2022
Bio-OFC gym implementation and Gym-Fly environment

Bio-OFC gym implementation and Gym-Fly environment This repository includes the gym compatible implementation of the Bio-OFC algorithm from the paper

Siavash Golkar 1 Nov 16, 2021
A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

Feature Forge This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, e

Machinalis 380 Nov 05, 2022
This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their

Liron Bdolah 8 May 22, 2022
Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

Implicit Constraint Q-Learning This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SM

42 Dec 23, 2022
Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)

EMI-FGSM This repository contains code to reproduce results from the paper: Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021) Xiaosen Wa

John Hopcroft Lab at HUST 10 Sep 26, 2022
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following

DeciForce: Crossroads of Machine Perception and Autonomy 276 Jan 04, 2023
A python module for configuration of block devices

Blivet is a python module for system storage configuration. CI status Licence See COPYING Installation From Fedora repositories Blivet is available in

78 Dec 14, 2022
Contrastive Language-Image Pretraining

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

OpenAI 11.5k Jan 08, 2023
improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

CLIP-ViL In our paper "How Much Can CLIP Benefit Vision-and-Language Tasks?", we show the improvement of CLIP features over the traditional resnet fea

310 Dec 28, 2022
An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

Xingdong Zuo 12 Dec 02, 2022
For storing the complete exploration of Visual Question Answering for our B.Tech Project

Multi-Image vqa @authors: Akhilesh, Janhavi, Harsh Paper summary, Ideas tried and their corresponding results: on wiki Other discussions: on discussio

Harsh Raj 3 Jun 16, 2022