NHZlX

#coding=utf-8
 
import os.path as osp
import sys
import copy
import os
from sys import path
import numpy as np
import google.protobuf as pb
 

grigorisg9gr

Brainiarc7

Streaming your Linux desktop to Youtube and Twitch via Nvidia's NVENC and VAAPI:

Considerations to take when live streaming:

The following best practice observations apply when using a hardware-based encoder for live streaming to any platform:

1. Set the buffer size (-bufsize:v) equal to the target bitrate (-b:v). You want to ensure that you're encoding in CBR mode.

2. Set up the encoders as shown:

UnaNancyOwen

cmake_minimum_required( VERSION 3.6 )

# Create Project
project( test )
add_executable( freetype main.cpp )

# Set StartUp Project
set_property( DIRECTORY PROPERTY VS_STARTUP_PROJECT "freetype" )

# Set CharacterSet

adejones

#!/usr/bin/env python
#-------------------------------------------------------------------------------
# Name: example-TimedRotatingFileHandler.py
# Purpose: Write log using TimedRotatingFileHandler
# Author: Adrian Jones
# Created: 2012-05-25
#
# TimedRotatingFileHandler: from handler.py source:
# Current 'when' events supported:
#   S - Seconds

xyzz

how2homebrew

Toolchain setup

• It's recommended to use vdpm to set up the toolchain: https://github.com/vitasdk/vdpm. In addition to setting up the toolchain, vdpm allows you to install dependencies such as libpng/libjpeg/vita2dlib/etc without any hassle. -- note that at the moment toolchain downloaded via vdpm will be horribly outdated, so this is no longer recommended
• Prebuilt toolchain binaries (Windows, Linux, OSX): https://github.com/vitasdk/autobuilds/releases
• Another option is to build the toolchain from source, check out https://github.com/vitasdk/buildscripts

Build process

GilLevi

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

jcoreyes

##Information

name: LSTM image captioning model based on CVPR 2015 paper "Show and tell: A neural image caption generator" and code from Karpathy's NeuralTalk.

model_file: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.py

model_weights: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.p

neon_version: v1.0.rc1

indiejoseph

ffmpeg -f mjpeg -r 5 -i "http://openwrt:openwrt@localhost:8080/?action=stream" -r 5 ./video.avi

graphific

VGG_ILSVRC_19_layers_train_val.prototxt
name: "VGG_ILSVRC_19_layers"
layers {
  name: "data"
  type: DATA
  include {
    phase: TRAIN
  }
 transform_param {
    crop_size: 224