Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
# source : http://code.google.com/p/natvpn/source/browse/trunk/stun_server_list | |
# A list of available STUN server. | |
stun.l.google.com:19302 | |
stun1.l.google.com:19302 | |
stun2.l.google.com:19302 | |
stun3.l.google.com:19302 | |
stun4.l.google.com:19302 | |
stun01.sipphone.com | |
stun.ekiga.net |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
using UnityEngine; | |
using System.Collections; | |
using Holoville.HOTween; | |
namespace FS.Handlers | |
{ | |
public class FSScrollController : MonoBehaviour { | |
public float contentOverflowY = 0f; | |
public float contentHeight = 0f; |
cmake_minimum_required( VERSION 3.0 ) | |
project( so-opencv-calibration ) | |
find_package( OpenCV 3.0.0 EXACT REQUIRED ) | |
set( INPUT_FILENAME "${CMAKE_CURRENT_LIST_DIR}/input_00.png" ) # Input file, e.g. http://i.stack.imgur.com/WjER0.png | |
add_executable( mwe mwe.cpp ) | |
target_compile_definitions( mwe PRIVATE -DINPUT_FILENAME="${INPUT_FILENAME}" ) | |
target_include_directories( mwe PRIVATE ${OpenCV_INCLUDE_DIRS} ) | |
target_link_libraries( mwe ${OpenCV_LIBS} ) |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman