lianyi

$(function () {
    "use strict";

    // for better performance - to avoid searching in DOM
    var content = $('#content');
    var input = $('#input');
    var status = $('#status');

    // my color assigned by the server
    var myColor = false;

lianyi

#!/usr/bin/env python
"""File format conversion

category: vtk, file conversion, tomb"""
import os, sys
import vtk

def vtk2vtp(invtkfile, outvtpfile, binary=False):
    """What it says on the label"""
    reader = vtk.vtkPolyDataReader()

lianyi

import os
import time
import busio
import digitalio
import board
import adafruit_mcp3xxx.mcp3008 as MCP
from adafruit_mcp3xxx.analog_in import AnalogIn

# create the spi bus
spi = busio.SPI(clock=board.SCK, MISO=board.MISO, MOSI=board.MOSI)

lianyi

Installing OpenCV 3.1 on Raspbian Jessie

Prerequisites

• Keep your system up to date:
  • $ sudo apt-get update
  • $ sudo apt-get upgrade
  • $ sudo reboot
• Make sure you've got an internet connection.
• Make sure you've got
• wget

lianyi

layout	title	permalink
default	Raspberry Pi Setup	/tutorials/Raspberry-Pi-setup/

Raspberry Pi Setup

by Chris Lovett and Ofer Dekel

lianyi

var url = require('url'),
    mongodb = require('mongodb');

var sourceUrl = 'mongodb://user:pass@host:port/db',
    targetUrl = 'mongodb://user:pass@host:port/db',
    collectionName = 'my_awesome_collection';

function openDbFromUrl(mongoUrl, cb) {
    var dbUrl = url.parse(mongoUrl),
        dbName = dbUrl.pathname.slice(1), // no slash

lianyi

#create a test index with shingle mapping
curl -XPUT localhost:9200/test -d '{
   "settings":{
      "index":{
         "analysis":{
            "analyzer":{
               "analyzer_shingle":{
                  "tokenizer":"standard",
                  "filter":["standard", "lowercase", "filter_stop", "filter_shingle"]
               }

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# --------------------------
# Source and reference
#   https://github.com/udacity/CarND-Semantic-Segmentation
#   https://gist.github.com/lianyi/a5ba8d84f5b68401c2313b05e020b062
#   https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef
# --------------------------


# --------------------------
# DATA PREPARATION

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##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

lianyi

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats