Estimation Libraries

16 Components & Libraries

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Estimation Libraries

A procedural Blender pipeline for photorealistic training image generation

A procedural Blender pipeline for photorealistic rendering. The simplest way to install blenderproc is via pip: Alternatively, if you need to make changes to blenderproc or you want to make use of th…

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python

This code is BSD-licensed (3 clause). or using pip+git for the latest version of the code: or in editable mode to be able to modify the sources: If you make a modification, run the test suite before …

Python implementation of two low-light image enhancement techniques via illumination map estimation

Python implementation of two low-light image enhancement techniques via illumination map estimation, based on the following papers: Both methods are based on retinex modelling, and aim at estimating …

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

Currently the following machine learning models are supported: The following machine learning models are currently under development: At the moment the following MPC and optimal control problems can …

Python scripts for performing 3D human pose estimation using the Mobile Human Pose model in ONNX.

Python scripts for performing 3D human pose estimation using the Mobile Human Pose model.

Tensorflow based Fast Pose estimation. OpenVINO, Tensorflow Lite, NCS, NCS2 + Python.

I will make his implementation even faster with CPU only.

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

MACEst is a confidence estimator that can be used alongside any model (regression or classification) which uses previously seen data (i.e. any supervised learning model) to produce a point prediction…

python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"

python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization" 1.numpy==1.19.0 2.opencv-python 3.scipy This code is an implementation of the pap…

Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.

[Last updated on 19th Aug 2022] This is a series of lecture notes that I prepared in my statistics tutorial (used prepared for one-night new-hire statistical training in hedge fund), which are suppos…

yolov5, yolov8, segmenations, face, pose, keypoints on deepstream

Clone the repository recursively: follow the instructions as shown in the repos..

Refocus an image just by clicking on it with no additional data

Graded blurring of an image based on how far a point is from focus using just a single image with no extra data.To achieve this we generate a depth map for the image using machine learning.Create blu…

Face recognition and analytics library based on deep neural networks and ONNX runtime

C# interface

YOLOv8-Object-Detection-Tracking-Image-Segmentation-Pose-Estimation

YOLOv8 object detection, tracking, image segmentation and pose estimation app using Ultralytics API (for detection, segmentation and pose estimation), as well as DeepSORT (for tracking) in Python. This app uses an UI made with streamlit and it can be deployed with Docker.

The app can be deployed using Docker Compose using the following command: If you want to deploy the app using the official container from Docker Hub, use the following commands: