vassvik

Realtime Fluid Simulation: Projection

The core of most real-time fluid simulators, like the one in EmberGen, are based on the "Stable Fluids" algorithm by Jos Stam, which to my knowledge was first presented at SIGGRAPH '99. This is a post about one part of this algorithm that's often underestimated: Projection

MG4_F32.mp4

Stable Fluids

The Stable Fluids algorithm solves a subset of the famous "Navier Stokes equations", which describe how fluids interact and move. In particular, it typically solves what's called the "incompressible Euler equations", where viscous forces are often ignored.

mmozeiko

//

struct TerminalCell
{
    // cell index into GlyphTexture, should be two 16-bit (x,y) values packed: "x | (y << 16)"
    uint GlyphIndex;

    // 0xAABBGGRR encoded colors, nonzero alpha for Foreground indicates to render colored-glyph
    // which means use RGB values from GlyphTexture directly as output, not as  ClearType blending weights
    uint Foreground;

JarkkoPFC

struct vec2f {float x, y;};
struct vec3f {float x, y, z;};

//============================================================================
// cone_uniform_vector
//============================================================================
// Returns uniformly distributed unit vector on a [0, 0, 1] oriented cone of
// given apex angle and uniform random vector xi ([x, y] in range [0, 1]).
// e.g. cos_half_apex_angle = 0 returns samples on a hemisphere (cos(pi/2)=0),
// while cos_half_apex_angle = -1 returns samples on a sphere (cos(pi)=-1)

kajott

#if 0  // self-compiling code: chmod +x this file and run it like a script
BINARY=vaapi_egl_interop_example
gcc -std=c99 -Wall -Wextra -pedantic -Werror -g -fsanitize=address -o $BINARY $0 \
    `pkg-config libavcodec libavformat libavutil libva gl egl libdrm --cflags --libs` \
    -lX11 -lva-x11 -lva-drm || exit 1
test "$1" = "--compile-only" && exit 0
exec env ASAN_OPTIONS=fast_unwind_on_malloc=0 ./$BINARY $*
#endif  /*

Minimal example application for hardware video decoding on Linux and display

baiwfg2

# References:
# https://cmake.org/cmake/help/latest/command/add_custom_target.html
# https://samthursfield.wordpress.com/2015/11/21/cmake-dependencies-between-targets-and-files-and-custom-commands/
# https://gist.github.com/socantre/7ee63133a0a3a08f3990
# https://stackoverflow.com/questions/24163778/how-to-add-custom-target-that-depends-on-make-install
# https://stackoverflow.com/questions/30719275/add-custom-command-is-not-generating-a-target
# https://stackoverflow.com/questions/26024235/how-to-call-a-cmake-function-from-add-custom-target-command
# https://blog.csdn.net/gubenpeiyuan/article/details/51096777

cmake_minimum_required(VERSION 3.10)

Erkaman

/*
The MIT License (MIT)

Copyright (c) 2018 Eric Arnebäck

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

vurtun

Gilbert–Johnson–Keerthi (GJK) 3D distance algorithm

The Gilbert–Johnson–Keerthi (GJK) distance algorithm is a method of determining the minimum distance between two convex sets. The algorithm's stability, speed which operates in near-constant time, and small storage footprint make it popular for realtime collision detection.

Unlike many other distance algorithms, it has no requirments on geometry data to be stored in any specific format, but instead relies solely on a support function to iteratively generate closer simplices to the correct answer using the Minkowski sum (CSO) of two convex shapes.

mbinna

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

krvajal

#!python
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
    r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
    The Savitzky-Golay filter removes high frequency noise from data.
    It has the advantage of preserving the original shape and
    features of the signal better than other types of filtering
    approaches, such as moving averages techniques.
    Parameters
    ----------
    y : array_like, shape (N,)

Brainiarc7

Using VAAPI's hardware accelerated video encoding on Linux with Intel's hardware on FFmpeg and libav

Hello, brethren :-)

As it turns out, the current version of FFmpeg (version 3.1 released earlier today) and libav (master branch) supports full H.264 and HEVC encode in VAAPI on supported hardware that works reliably well to be termed "production-ready".