Incompatible Timesharing System

This repository contains a pure C ONNX implementation of multiple offline AI models, such as StableDiffusion (1.5 and XL), ControlNet, Midas, HED and OpenPose.

This GIT repo (C language) holds applications and utilities for the Behringer X32 and M32 mixing consoles. Additional details, documentation, implementation examples and apps can be found in my website:

Collection of scripts for generating depth maps for videos using machine learning.

Converts a depth map image to a normal map image using Python

Unity program that creates 3D scenes for VR using MiDaS model

Depth Estimate GUI - Windows, Mac, Linux

Monocular Depth Estimation with Unity Sentis

Using PyTorch's MiDaS model and Open3D's point cloud to map a scene in 3D 🏞️🔭

画像から線画を作成してUbuntuの壁紙に貼り付けるためのスクリプト

The public brain 3D vessel datasets, include TubeTK and MIDAS.

Remote control Behringer X-Air | Midas MR Mixers through OSC

The repository provides codes under the Microstructural Identification using Diffraction Analysis Software

info loker jinx

Routing tool for behringer x32 based on hardware availabilities instead of software capabilities. Aim to simplify routing and create automatic documentation.

MiDaS function for VapourSynth

A Colab notebook for generating inpainted photo-set using MiDaS and 3d-photo-inpainting.

Vision Transformers: relative and absolute depth estimation

MIDAS@IIITD NLP Task

This Python code uses the MiDaS model for real-time depth estimation on webcam video. MiDaS predicts the relative depth of objects in a scene, and the output is displayed using matplotlib. Bicubic interpolation is used to upsample the low-resolution depth map produced by the model. Output is then displayed in an interactive Streamlit web app.