sklearn-friendly Python package to estimate the parameters of a t-Student
mixture distribution from data using Expectation-Maximization.
Python package to plot the latent space of a set of images with different
dimensionality reduction methods.
- vitcifar10: Python
package that provides a Vision Transformer (ViT) baseline code for training and
testing on CIFAR-10.
Python package for domain adaptation based on optimal transport.
Python package for Fourier-based domain adaptation.
- videosum: given a video
file, this Python package produces a single-image storyboard that summarises
C++ library with Python bindings that implements GrabCut
with CUDA-based Gaussian Mixture Models for real-time segmentation with
if you have a C++ computer vision pipeline that uses OpenCV and you want to
expose it to Python, this header-only library will make your code work
seamlessly in C++ and Python.
- endoseg: Python package
for contant area segmentation in endoscopic images.
Python Dash application for annotating keypoints in images.
Given an endoscopic image and a tool-background semantic segmentation,
this Python module detects the tooltips of the instruments.
- easyipc: fast and easy-to-use
Python library for inter-process communications.
- dockerx: package to
launch Docker containers with X11 support in remote systems accessible via SSH.
collection of Docker templates (e.g. PyCharm and Visual Studio Code with
PyTorch) ready for the development of Computer Vision and Machine Learning
web server that displays an RTSP video stream.
- List of updated surgical datasets
- synapi: Python package that
allows you to interact with projects and datasets stored in
Synapse as you would do with a local directory.
This repository contains the original data described in the paper
Image Compositing for Segmentation of Surgical Tools without Manual Annotations.
The data repository contains training foregrounds and backgrounds that are the
source datasets employed to generate the semi-synthetic training data, which
can be done using the code in the corresponding
The foregrounds and backgrounds in the semi-synthetic validation folder are
used to generate a small semi-synthetic dataset that is used to detect when
to stop the semi-synthetic training (mIoU convergence). As opposed to training
foregrounds, validation foregrounds have been recorded on a red chroma key.
- ipcl: this repository contains the
original data described in the paper
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology.
The dataset comprises anonymised data of a total of 114 patients (45 normal,
69 abnormal). Every patient has a ME-NBI video (30fps) recorded following
protocol in the paper. In this dataset, only magnification endoscopy
subsequences are considered. All frames are extracted and assigned to the
class normal or abnormal depending on the histopathology of the patient.
Frames that are highly degraded due to lighting artifacts (e.g. blur, flares
and reflections) up to the point where it is not possible to make a visual
judgement of whether they are normal or abnormal are not included.