BULLETPROOF A film review by Chad Polenz Copyright 1997 Chad Polenz
** (out of 4 = fair) 1996, R, 85 minutes [1 hour, 25 minutes] [action/comedy] starring: Damon Wayans (Rock Keats/Jack Carter), Adam Sandler (Archie Moses), Kristen Wilson (Traci), James Caan (Frank Colton), written by Joe Gayton, Lewis Colick, produced by Robert Simonds, directed by Ernest Dickerson.
I'm trying to decide which has more influence over me: a really good laugh or a really bad story. I can't decide if I'd recommend "Bulletproof" for its funny comedy or not recommend it for its mindless, action-driven story. Ah, the old "cop-buddy-action-flick-with-a-humorous-undertone," we've seen these before, and only a few of them actually work. What I don't understand is how nobody can figure out that those were the exceptions, not the rules.
The story revolves around a pair of car thieves who have become the best of friends over the course of a year (so we are told).
Right away we are inundated with cop movie cliches: lots of jargon and rhetoric about a major dope deal going down (of course in a warehouse on the docks with punks, machine guns, etc.). Of course, when the criminals become suspicious of the Keats they find out he is actually a cop and of course Archie feels betrayed and of course the cops bust in and a bloodbath ensues.
Now our two main characters are at war with each other when five minutes before they were best of friends.  To make a long story short, Keats (whose real name is Jack Carter) recovers, meets a cute woman, and is assigned to protect Archie because he can turn states evidence against Colton, the most powerful drug lord who also wants to kill him.
Sandler and Wayans make a pretty funny duo, I'll have to admit it, but why put the two in a genre that doesn't fit them? Both of these actors have starred in comedies up until this point, what are they doing shooting guns and jumping from airplanes? The action scenes and the stunts in this movie are just so cliche and recycled, even James Caan can't make for a remotely convincing villain, and thus all we get is a big cartoon (although cartoons can be funny).
There are more double crosses in this movie than I've ever seen. Naming any of them would give away the entire story. By the end we get a lot of bloodshed over nothing. There's not much here to make us really care about what's going on.
For what it's worth, the funny parts of "Bulletproof" are really funny, but the bad story and mindless violence is a real turn-off. The entire film has a choppy surface to it, there's no depth to it at all. Mixing comedy with action is a difficult process, why does Hollywood continually try it?
visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz/index.html
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A computational representation of the Hilbert space basis. Refer to Section 3 of the manuscript in /docs. More...
#include <Basis.h>
Public Member Functions | |
| BasisNC (const EnvironmentNC &env) | |
| Creates an instance of class Basis. More... | |
| ~BasisNC () | |
| Destructor. More... | |
| BasisNC (const BasisNC &rhs) | |
| Copy constructor. | |
| BasisNC & | operator= (const BasisNC &rhs) |
| Overloading of the assignment operator. | |
| void | construct_int_basis () |
| Computes and distributes (using MPI) the representation of the basis elements. More... | |
| void | print_basis (const EnvironmentNC &env, bool bits=false) |
| Outputs the integer representation of the basis to stdout. More... | |
| void | construct_bit_basis (boost::dynamic_bitset<> *bit_basis) |
| Computes and distributes (using MPI) the representation of the basis elements in binary form, this is normally used for visualisation purposes. | |
Public Attributes | |
| LLInt | basis_size |
| Dimension of the Hilbert space. | |
| PetscInt | basis_local |
| Local value (MPI) of the dimension of the Hilbert space. | |
| PetscInt | basis_start |
| Global index per processor. | |
| PetscInt | nlocal |
| Local amount of rows owned by processor (PETSc). | |
| PetscInt | start |
| Global index (PETSc). | |
| PetscInt | end |
| Global index (PETSc). | |
| LLInt * | int_basis |
Private Member Functions | |
| LLInt | factorial_ (LLInt n) |
| Computes the factorial of an integer. More... | |
| LLInt | first_int_ () |
| Computes the first integer representation of the basis vector locally owned. More... | |
Private Attributes | |
| unsigned int | l_ |
| Number of sites. | |
| unsigned int | n_ |
| Subspace descriptor. | |
A computational representation of the Hilbert space basis. Refer to Section 3 of the manuscript in /docs.
For the particular case of the Node communication approach, the basis elements are fully distributed among all available processing elements, with the exception of the first process of each computational node; which allocates, computes and holds all the elements of the basis.
| BasisNC::BasisNC | ( | const EnvironmentNC & | env | ) |
Creates an instance of class Basis.
| env | An instance of the class Environment. |
This is the only available constructor of this class. A basis is required to provide a matrix representation of a Hamiltonian operator and carry out other operations in quantum mechanics. We use an integer representation of the states in the basis of the Hilbert space of the system, please refer to Section 2 (Background) and Section 3 (Basis representation) of the manuscript in /docs.
| BasisNC::~BasisNC | ( | ) |
Destructor.
Deallocates memory needed for the computational representation of the basis.
| void BasisNC::construct_int_basis | ( | ) |
Computes and distributes (using MPI) the representation of the basis elements.
This should be called after creating an instance of Basis and before constructing a Hamiltonian matrix. For more details, refer to Section 3.1 (Node communicator approach) of the manuscript in /docs. This routine will account for the specific distribution given to the first process of each node.
|
private |
Computes the factorial of an integer.
| n | Integer value. |
|
private |
Computes the first integer representation of the basis vector locally owned.
| void BasisNC::print_basis | ( | const EnvironmentNC & | env, |
| bool | bits = false |
||
| ) |
Outputs the integer representation of the basis to stdout.
| env | An instance of class Environment. |
| bits | If bits = true, outputs a bit representation. |
| LLInt* BasisNC::int_basis |
Container of the elements of the basis, locally owned. This array is of size basis_size for the first process of each node


Holly Willoughby skips I'm A Celebrity launch to attend the Frozen 2 premiere
Holly Willoughby, 38, is more than just a fan of I'm A Celebrity as she was brought in to replace Ant McPartlin last year
by Molly Pike, https://www.mirror.co.uk/authors/molly-pike/There's just hours to go until the start of I'm a Celebrity...Get Me Out of Here!, but it doesn't look like Holly Willoughby will be tuning in.
She skipped a night in front of the TV with the rest of the nation to attend the premiere of Frozen 2 in London on Sunday night.
Holly, 38, is more than just a fan of the ITV reality show as she was brought in to replace Ant McPartlin when he took a year out of I'm A Celebrity.
A self-confessed Disney fan, Holly made sure she didn't miss out on being one of the first to see the highly anticipated sequel.
She wrapped up warm in a monochrome outfit, with a chic gingham coat thrown over her shoulders.
Underneath she donned a black midi-dress with a high neck and matched it with a pair of leather gloves.
Holly kept her legs covered in a pair of knee high boots and black tights.
Her trademark blonde locks with worn straight and loose around her shoulders.
The mum-of-three sported a slick of bright red lipstick and a coat heavy black eyeliner.
Holly posed for pictures with actors dressed up as Kristoff, Anna, Elsa and Olaf, the characters from Frozen.
I'm A Celebrity kicks off tonight with a host of famous faces heading to the jungle for three weeks of gruelling challenges.
On the line-up is Olympian and reality star and transgender icon Caitlyn Jenner, former Girls Aloud star Nadine Coyle, former EastEnders star Jacqueline Jossa, Good Morning Britain presenter Kate Garraway, Radio presenter Roman Kemp, former Big Brother star Adele Roberts, comedian Andrew Maxwell, Rak-Su singer Myles Stephenson, former rugby player James Haskell, and former footballer Ian Wright.
There are also a few rumoured late additions.


| ����� | ˵�� |
|---|---|
| org.jnetpcap.nio |
Native memory and IO management classes.
|
| org.jnetpcap.packet |
Packet decoding framework.
|
| org.jnetpcap.util |
Various support utility methods.
|
| org.jnetpcap.winpcap |
WinPcap extensions to libpcap avialable on a limited set of
platforms.
|
| ���������� | ������˵�� |
|---|---|
protected int |
JMemory.peer(java.nio.ByteBuffer peer)
Peers the src structure with this instance.
|
int |
JBuffer.peer(java.nio.ByteBuffer peer)
Peers this buffer with a new buffer.
|
| ���������� | ������˵�� |
|---|---|
protected JBuffer
|
JPacket.getMemoryBuffer(java.nio.ByteBuffer buffer)
Gets the memory buffer with the supplied ByteBuffer data copied into
it.
|
int |
JPacket.State.peer(java.nio.ByteBuffer peer)
Peer.
|
int |
PcapPacket.peerHeaderAndData(PcapHeader header,
java.nio.ByteBuffer buffer)
Peer header and data.
|
int |
PcapPacket.peerStateAndData(java.nio.ByteBuffer buffer)
Peers the contents of the buffer directly with this packet.
|
int |
JMemoryPacket.peerStateAndData(java.nio.ByteBuffer buffer)
Peers the contents of the buffer directly with this packet.
|
| ��������˵�� |
|---|
JMemoryPacket(java.nio.ByteBuffer buffer)
Initializes the packet's state and data by doing a deep copy of the
contents of the buffer.
|
JMemoryPacket(int id,
java.nio.ByteBuffer buffer)
Initializes the packet's state and data by doing a deep copy of the
contents of the buffer.
|
| ���������� | ������˵�� |
|---|---|
int |
SlidingBuffer.peer(java.nio.ByteBuffer peer)
Peer.
|
| ��������˵�� |
|---|
WinPcapSendQueue(java.nio.ByteBuffer buffer)
The queue uses the supplied byte buffer which holds the buffers contents.
|


ZO-01: Text is readable
Confirm that when zoomed in to 15x the text can still be read.
When zooming in, poor fonts or images of text can become unreadable as they become pixelated.
How to check: ZO-01: Text is readable
- Use "OPTION + CMD + =" to zoom in to 15x (as per the configuration).
- Navigate around the entire page while zoomed in.
What to check: ZO-01: Text is readable
Confirm that:
- all of the text is still readable
Observations: ZO-01: Text is readable
[ Enter observations ]
ZO-02: Images are clear enough
Confirm that images which are important for context are clear at 15x.
Important images are those which are required for interactions such as icons, or those which are important for setting context, such as maps.
How to check: ZO-02: Images are clear enough
- Use "OPTION + CMD + =" to zoom in to 15x (as per the configuration).
- Navigate over each image on the page.

Observations: ZO-02: Images are clear enough
[ Enter observations ]
ZO-03: Text on hover text
Confirm that all text on the page works using the Hover Text feature.
Hover Text allows the user to view the text under the mouse pointer larger without having to zoom in.
How to check: ZO-03: Text on hover text
- Hold down "CMD" and hover over each piece of text on the page. For long paragraphs you can scroll inside the hover text using the trackpad.
What to check: ZO-03: Text on hover text
Confirm that:
- all text on the page works with the Hover Text feature
- all text in the Hover Text feature matches the visible text on the page
Observations: ZO-03: Text on hover text
[ Enter observations ]
ZO-04: Images on hover text
Confirm that all important images on the page works using the Hover Text feature.
Hover Text allows the user to view the alt text on images which may be too small for the user to distinguish.
How to check: ZO-04: Images on hover text
- Hold down "CMD" and hover over each image on the page. For long alt text you can scroll inside the hover text using the trackpad.
What to check: ZO-04: Images on hover text
Confirm that:
- all important images either show a text alternative with the Hover Text feature or that a text alternative is available in the visible page content.
- any alt text which is presented using the Hover Feature is accurate and meaningful
Observations: ZO-04: Images on hover text
[ Enter observations ]


[name]
Triangle face.
Example
var normal = new THREE.Vector3( 0, 1, 0 );
var color = new THREE.Color( 0xffaa00 );
var face = new THREE.Face3( 0, 1, 2, normal, color, 0 );
Constructor
[name]( [page:Integer a], [page:Integer b], [page:Integer c], [page:Vector3 normal], [page:Color color], [page:Integer materialIndex] )
a — Vertex A index.
b — Vertex B index.
c — Vertex C index.
normal — Face normal or array of vertex normals.
color — Face color or array of vertex colors.
materialIndex — Material index.
Properties
.[page:Integer a]
Vertex A index.
.[page:Integer b]
Vertex B index.
.[page:Integer c]
Vertex C index.
.[page:Vector3 normal]
Face normal.
.[page:Color color]
Face color.
.[page:Array vertexNormals]
Array of 3 vertex normals.
.[page:Array vertexColors]
Array of 3 vertex colors.
.[page:Array vertexTangets]
Array of 3 vertex tangets.
.[page:Integer materialIndex]
Material index (points to [page:Geometry Geometry.materials]).
.[page:Vector3 centroid]
Face centroid.
Methods
.clone()
Creates a new clone of the Face3 object.
Source
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


Today I am learning all kinds of fun stuff. It is making my neurons work, which is good.
Today is a good day
Check out my awesome myth page
Morning


Not invented here (NIH) is a stance adopted by social, corporate, or institutional cultures that avoid using or buying already existing products, research, standards, or knowledge because of their external origins and costs, such as royalties. Research illustrates a strong bi...


StarryNight
Meteor Testing and Scaffolding Tool
What is StarryNight?
StarryNight is a command line tool for use in scaffolding and testing Meteor applications. It's similar to tools like 'spacejam', 'rails generate', 'abee', and 'mrt'. It's a direct result of 2 years of working on the Meteor Cookbook.
Why StarryNight?
- application life-cycle management
- has scaffolding/boilerplates for getting people started
- lean, simple, and focused testing
- embraces the existing testing tools used by MDG
- testing without the bloat of mirrors, file monitors, html reporters, and the like
- isn't trying to be a SaaS testing platform
- assumes developers will be using 3rd party SaaS solutions
- single tool, not a collection of a dozen packages
- continous integration ready
- easy to integrate new life cycle management tools
- easy to integrate new code snippets and boilerplates
- easy to integrate new testing frameworks
Installation
npm install starrynight -g
Usage
//running the utility
$ starrynight
//arguments
-help
-audit-permissions
-scaffold [project-homepage | client-server | rest-api]
-sample [acceptance | all]
-clone url
-pattern url
-rename originalTerm newTerm directoryRoot
-refactor originalTerm newTerm directoryRoot
-survey [tiny | end-to-end | acceptance]
Examples
# create a helloworld application
$ meteor create helloworld
$ cd helloworld
# you might want to open your editor to follow along
$ atom .
$ meteor
# add acceptance tests to your application (using the nightwatch framework)
$ starrynight -sample acceptance
# make sure the acceptance testing framework is running
$ starrynight -survey acceptance
# add a scaffold/boilerplate to your project
$ starrynight -scaffold project-homepage
$ rm helloworld.*
# add acceptance tests for the project-homepage scaffold
$ starrynight -sample acceptance project-homepage
# run acceptance tests on the new scaffold
$ starrynight -survey acceptance
# add more scaffolding to your project
$ starrynight -scaffold iron-router
# see if our tests still work
$ starrynight -survey acceptance
# add the itunes connect acceptance tests (mobile first)
$ starrynight -sample acceptance itunes
# our tests should break
$ starrynight -survey acceptance
# add extra page components and routes to our app
$ starrynight -pattern https://github.com/awatson1978/clinical-static-pages
# run acceptance tests (they should break)
$ starrynight -survey acceptance
# rename files throughout your app in one go
$ starrynight -rename about marketing
$ starrynight -rename About Marketing
# find-and-replace terms throughout our app
$ starrynight -refactor "AppTitle" "Hello StarryNight"
$ starrynight -refactor "about" "marketing"
# when new files are created, we sometimes want to check file permissions
$ starrynight -audit-permissions
# run acceptance tests again (they should pass)
$ starrynight -survey acceptance
Get the Code
http://github.com/awatson1978/starrynight
About The Utility
Written by Abigail Watson


Tests that a cue is recovered when a line with a "-->" is encountered (missing blank line separator).


| components.common_modules.tree.TarsqiTree | |
| tarsqidoc | an instance of docmodel.document.TarsqiDocument |
| docelement | an instance of docmodel.document.Tag, the element that the document tree is created for td> |
| dtrs | a list of daughters, typically instances of Sentence |
| events | a dictionary (TODO: indexed on...) containing events for the TarsqiDocElement that were found by Evita, it is created by Slinket by collecting events and their attributes from the document tree in dtrs |
| alink_list | a list of AlinkTags |
| slink_list | a list of SlinkTags |
| tlink_list | a list of TlinkTags |


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We have be been notified of the problem, will be fixed shortly...


Rapper DinDin shares his longing for Goo Hara on Instagram
by honeyciderOn the evening of November 24, singer Goo Hara was found deceased in her private residence.
Since then, many artists have adjusted their schedules in respect and mourning for the young singer's passing.
DinDin, a rapper and close friend with Hara took to his Instagram to share his longing and mourning for her passing. He posted a photo of her bright smile and said,
"Hara, when I started getting noticed, you worried about me and told me to get in touch with you if I struggled. You are so beautiful and brilliant, but I'm sorry I didn't do anything and help you. I am so sorry. I'm so sorry that I should have helped you a bit.  Sorry. Thanks."
* If you are having a difficult time and wish to talk to someone, please reach out. You can find most international helplines here.


Beta This is a test version of the layout of this page. Take the survey to help us improve it
Guidance
Data protection guidance for schools considering using cloud software services ('the cloud') to hold sensitive information.
This is for:
It applies to:
It outlines how schools need to consider data security when moving services and information onto the cloud.


SixPenny System Products
Pillows & Stuffed Animals
Abstract Circle Cushion Pillow
Color: Black & White
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 11" x 11" x 4"
Striped Circle Cushion Pillow
Color: Pink, White, & Blue
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 11" x 11" x 8"
LA (Lakers) Circular Tube Pillow
Color: Purple & Yellow
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 6" x 6" x 11"
Koala Circle Cushion Pillow
Color: Grey, Black, & White
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 15" x 15" x 6"
Bear Circle Cushion Pillow
Color: Brown, Tan, & Black
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 15" x 15" x 6"
Bear Circle Cushion Pillow
Color: Grey, Black, & White (Koala) | Brown, Tan, & Black (Bear)
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 11" x 11" x 8"
Chelli Stuffed Doll
Color: Red, White, & Black
Yarn: Acrylic
Fill: Polyester Eyes: Plastic (Black)
Eyes: Plastic (Black)
Dimensions: approx. 14" tall
Rus Stuffed Doll
Color: Purple, White, & Black
Yarn: Acrylic
Fill: Polyester
Eyes: Plastic (Black)
Dimensions: approx. 16" tall
Rus & Chelli Stuffed Dolls
Color: Red, White, & Black (Chelli) | Purple, White, & Black (Rus)
Yarn: Acrylic
Fill: Polyester
Eyes: Plastic (Black)
Dimensions: approx. 16" tall (Chelli) | approx. 16" tall (Rus)
Totoro & Friends Stuffed Dolls
Color: Grey, White, & Black (Big Totoro) | Blue, White, & Black (Medium Totoro) | White & Black (Little Totoro)
Yarn: Acrylic
Fill: Polyester
Eyes: Plastic (Black)
Dimensions: Big Totoro ~ 2'6" tall | Medium Totoro ~ 1'10" tall | Little Totoro ~ 1' tall
Owl Stuffed Animal
Color: Fuschia, Pink, White, Yellow, & Black
Yarn: Acrylic
Fill: Polyester
Dimensions: approx. 11" x 8" x 6"
Clothes & Accessories
Infant Cable Button Down Cardigan
Color: Navy & Periwinkle
Yarn: Acrylic
Buttons: Tan/Brown Plastic
Size: 9-12 months
Infant Striped Button Down Cardigan
Color: Pink & White
Yarn: Acrylic
Buttons: Tan/Brown Plastic
Size: 12-18 months
Spaghetti-Strap Button Down Cropped Vest
Color: Multi-Blue
Yarn: Acrylic
Buttons: White Plastic
Size: Small Adult
Knit Cabled Scarf
Color: Black
Yarn: Acrylic
Dimensions: approx. 7'x 8"
Knit Cabled Beanie
Color: Orange
Yarn: Acrylic
Size: Small Unisex
Knit Ribbed Beanie
Color: Grey
Yarn: Acrylic
Size: Small Unisex
Knit Two-Toned Pom-Pom Beanies Set 4
Color: Assorted
Yarn: Acrylic
Size: Medium Unisex
Pack of 7 Assorted Beanies
Color: Assorted
Yarn: Acrylic
Size: Small Unisex


{{ if isset .Params "hide_footer_bio" }}{{ else }}
I’m also an Overkill Cult survivor. In 2013 I was working 70–90 hours a week — it got so bad that patches of my beard started falling out. I needed to make some changes to my lifestyle before it killed me.
In 2014 I sold everything I owned and started traveling the world. I thought it would be hard, but it’s been surprisingly easy to work remotely from places like Barcelona, London, and Chiang Mai.
What I’m doing is exciting, but I’m not special. Anyone can do this. If you’ve ever dreamed of working remotely and exploring the world, I want to help you make your dream a reality.


Who am I? My name is Matthias and I had my first computer before I had my first bike. I’ve spent most of my career working in IT project management and systems engineering, having run my own company for over a decade. I was doing well financially, but really wasn’t satisfying my own curiosity. Who wants to spend their time chasing bugs in systems they didn’t have any part in creating. I don’t.
After some wandering, I discovered what a delight it can be to write these amazing little things called ‘programs’ that come to existence almost completely through the workings of the mind. A couple of years ago, I finally decided that it was time for a major change. I knew I really wanted to be spend my time and energy in the creative process of developing applications that elegantly solve problems, so I made the move, and I haven’t looked back since.
Why am I blogging? I am fascinated by the idea of reasoning over live data streams. There are quite a few things that I still need to learn in order to turn my ideas into working code. I find that I learn particularly well when explaining things to others, so writing this blog seems like an awesome idea.
Recently I started to work on a reactive web project for my portfolio, written in Scala using the Play Framework. It presented an ideal opportunity to make this project open source and use it as the first thing to write about. This has been tremendous fun and really exciting so far. I put that project called BirdWatch on GitHub, installed a demo on a web server and showed it to a handful of people, not thinking that it was anywhere near being published in a broader sense.
Then it got mentioned on Twitter, retweeted 16 times and before I knew it, I had visitors from 272 cities in 48 countries on that web server. I know because out of curiosity I wrote a little logging routine that does GeoIP-lookup for every IP-address visiting that server on the fly, but that is the topic for a future post, potentially with a live visualization for the curious website owner.
What will I be blogging about? I will be blogging about functional programming, reactive data processing and data visualization. I am particularly interested in observable data from the real world moving through information systems, ideally for visualizations that are both useful and pleasant to look at.
For the time being, you can expect me to use Scala, Play and Akka on the server side, a beautiful technology stack that allows solving concurrent problems in surprisingly short amounts of time. For the client side I am just getting started with D3.js, a JavaScript library for data-driven DOM manipulation. It allows for absolutely stunning visualizations, even though I am nowhere near that as of yet.
You can also expect me to continue getting live prototypes up and running for the things I write about, just like the one for BirdWatch. I find code that is not executed to be a wasted effort, and there is no better proof for any code than running it. I already learned a lot regarding the architecture of BirdWatch and there are a few things that I would do differently in the future, but that is also a topic for a future post.
How can you leave feedback? I am starting this blog because I am hoping to learn a lot in the process, particularly from interaction with you, the reader. So please comment on the blog posts, correct me where I am wrong, and let me know where you think a more elegant solution could be found.
Most importantly, ask if there is anything that you find difficult to understand. My goal in all of this is to explain things as clearly as possible, so please let me know when I’ve failed to do that. Ask questions in the comments section if you think that others might benefit from the answers as well. Of course, you can also always contact me by email, though that may take longer for a response.
You can also follow me on Twitter @_MNehlsen. I just launched my feed, so you’ll get to be among the first to follow me, and it will make keeping track of when I published a new post much easier for you, as well as other things I find interesting in general.
-Matthias


The Works of Edgar Allan Poe
by Edgar Allan Poe
A Pæan
Additional Information
- Year Published: 1903
- Language: English
- Country of Origin: United States of America
- Source: Poe, E.A. (1903). The Works of Edgar Allan Poe, The Raven Edition, Volume 5. New York: P. F. Collier and Son.
-
Readability:
- Flesch–Kincaid Level: 1.0
- Word Count: 661
- Genre: Poetry
- Keywords: death, loss
- ✎ Cite This
Downloads
A PÆAN.
I. How shall the burial rite be read?
The solemn song be sung?
The requiem for the loveliest dead,
That ever died so young?
II. Her friends are gazing on her,
And on her gaudy bier,
And weep!—oh! to dishonor
Dead beauty with a tear!
III. They loved her for her wealth—
And they hated her for her pride—
But she grew in feeble health,
And they love her—that she died.
IV. They tell me (while they speak
Of her "costly broider'd pall")
That my voice is growing weak—
That I should not sing at all—
V. Or that my tone should be
Tun'd to such solemn song
So mournfully—so mournfully,
That the dead may feel no wrong.
VI. But she is gone above,
With young Hope at her side,
And I am drunk with love
Of the dead, who is my bride.—
VII.
And the life upon her hair.
VIII. Thus on the coffin loud and long
I strike—the murmur sent
Through the gray chambers to my song,
Shall be the accompaniment.
IX.  From more than friends on earth,
Thy life and love are riven,
To join the untainted mirth
Of more than thrones in heaven.—
XI. Therefore, to thee this night
I will no requiem raise,
But waft thee on thy flight,
With a Pæan of old days.


In this project, the implementation of the following Firebase services is available
Getting started with Firebase on Android
| Providers | Description |
|---|---|
|
Email/password
In this method we can also authenticate without a password, but for that we need a dynamic link. To our happiness, Firebase also offers this service for free. In the side menu we have Dynamic Link. Create a link: Then on the Authentication / Sign-in method tab, authorize the domain you just created: In the Firebase console, open the Auth section. And of course don't forget to activate the Sign-in method in Firebase: |
|
|
Smartphone
When we want to authenticate a user using a phone number, we need to define SHA1 in the Firebase panel: In this case we will use the application test sha1 in Android Studio: In the Firebase console, open the Auth section. And of course don't forget to activate the Sign-in method in Firebase: we need google-services.json at our app's app level: |
|
|
Google
When we want to authenticate a user using Google, we need to create consent screen in: https://console.cloud.google.com/apis/credentials/consent?project=project_Id And of course we need to define SHA1 in the Firebase panel as we have done above When we want to authenticate a user using Google, we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: We need our OAuth 2.0 client IDs: https://console.cloud.google.com/apis/credentials/oauthclient we need google-services.json at our app's app level: In the Firebase console, open the Auth section. And of course don't forget to activate the Sign-in method in Firebase: If you haven't yet specified your app's SHA-1 fingerprint, do so now we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: |
|
|
Play Games
Before starting, in order to use the Play Games authentication method, we must have a google play developer account. Apps/PublishWe don't need to publish a game to test this method, just create one on the google play store. Create an app in the store and define it as a game. Now in Google Play game-related service settings. Yes, my game already uses Google APIs.
Select the same Google Cloud Project. And click USE, ready, now you can continue the configuration. When we want to authenticate a user using Play Games, we need to create consent screen in: https://console.cloud.google.com/apis/credentials/consent?project=project_Id And of course we need to define SHA1 in the Firebase panel as we have done above When we want to authenticate a user using Play Games, we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: We need our OAuth 2.0 client IDs: https://console.cloud.google.com/apis/credentials/oauthclient we need google-services.json at our app's app level: In the Firebase console, open the Auth section. And of course don't forget to activate the Sign-in method in Firebase: |
|
|
Facebook
Create an app on Developer Facebook, write down the app ID and complete the setup steps Developer FacebookAfter choosing the type of application that should be Android, go to part 3 and define the package name and the deep link and save, in our case it looks like this: click Use this package name. And continue 4. Add your development and launch key hashes You will need the following: Certificate and key management tool (keytool) from the Java Development Kit openssl-for-windowsopenssl library for Windows from Google Google Code ArchiveTo generate a development key hash, run the following command at a command prompt in the SDK for Java folder: After extract folder in C:/, go inside your jdk/bin folder, open CMD type cd C:\Program Files\Java\jdk1.8.0_144\bin keytool -exportcert -alias androiddebugkey -keystore "C:\Users\computerName\.android\debug.keystore" | "C:\openssl\bin\openssl" sha1 -binary | "C:\openssl\bin\openssl" base64 To find the debug.keystore.Go to drive C:\Users\computerName\.android Keystore name: "debug.keystore" Keystore password: "android" Key alias: "androiddebugkey" Key password: "android" Hashes of keys set Hash Save and continue In your Facebook dev panel settings/basic/ get the application ID and the Application Secret Key In the Firebase console, open the Auth section. In Facebook Login providers, we will need the application ID and the Application Secret Key and we’ll put the OAuth redirect URI to your Facebook app configuration. know moreand In your Facebook dev panel fb-login/settings/ Client OAuth settings set your OAuth redirect URI that you got on firebase You can test using test users or only with your administrator account If you haven't yet specified your app's SHA-1 fingerprint, do so now we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: |
|
| Twitter
In the Firebase console, open the Auth section. and activate the Twitter login method, which will ask for two keys that we will get next from the Twitter developer panel, note the url provided by Firebase that we will use on Twitter Log in to your account and create a project, to add your callback URL to your Twitter application configuration and get both keys requested in Firebase Developer Twitterafter creating your application you will see a screen like this: find Authentication settings click on Keys and tokens select your keys and use in Firebase in the requested fields If you haven't yet specified your app's SHA-1 fingerprint, do so now we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: |
|
| Github
In the Firebase console, open the Auth section. and activate the Github login method, which will ask for two keys that we will get next from the Github developer panel, note the url provided by Firebase that we will use on Github Log in to your Github account go to settings, to create an OAuth application after creating the application scroll down the page until you get to Identifying and authorizing users and set User authorization callback URL | Webhook URL that you got in Firebase and define the other mandatory parameters as you wish now go to Generate a new client secret, and get both keys that we will put in Firebase If you haven't yet specified your app's SHA-1 fingerprint, do so now we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: |
|
| Yahoo
In the Firebase console, open the Auth section. On the Sign in method tab, enable the Yahoo provider. Add the Client ID and Client Secret from that provider's developer console to the provider configuration: note the url provided by Firebase that we will use on Yahoo In the Yahoo console, create app. Be sure to select the two OpenID Connect API permissions: profile and email When registering apps with these providers, be sure to register the url provided by Firebase domain for your project as the redirect domain for your app After creating the application, take the keys and place them in Firebase If you haven't yet specified your app's SHA-1 fingerprint, do so now we need to define SHA1 in the Firebase panel as we have done above: In this case we will use the application test sha1 in Android Studio: |
|
| Microsoft
In the Firebase console, open the Auth section. On the Sign in method tab, enable the Microsoft provider. Add the Client ID and Client Secret from that provider's developer console to the provider configuration: note the url provided by Firebase that we will use on Microsoft In the Microsoft console, follow those steps to create an app to create app select Registers of application Fill in the requested fields and use your callback URL that you got in Firebase now write down the application ID (client) and let's create our Application Secret. Fill in the requested fields. write down the application secret now, you will not be able to see it again. now just put the application ID and the secret on the Sign Microsoft provider in Firebase |
|
| Anonymous |
In the Firebase console, open the Cloud Firestore section.
and create a database
Enabling Cloud Firestore will prevent you from using Cloud Datastore in this project, especially in the application associated with App Engine
In the Firebase console, open the Realtime Database section.
and create a database
In the Firebase console, open the Storage section.
and copy path to use in app
In test mode enable the rules to read and write to Storage
The rules should only be like this in test mode, in production mode you must enable some type of authentication to protect data
We can use hosting for several purposes. In this case, we will use hosting and functions to create an end point API.
Download the hosting-android folder and follow the steps
In this step we will use nodeJS to create our API end point
Download and install NodeJS
Follow the steps in this documentation and watch the video if you still have questions
Install Firebase CLI if you haven't already
Consultation material on the Firebase CLI
Open the CMD command prompt and enter the functions folder.
Enter the command firebase init functions choose the project and DO NOT overwrite index.js and install NODE dependencies
Now install the express module
Enter the command npm i express
Okay, take the test to see if everything is correct
Enter the commandcd.. to access the hosting folder
Enter the commandfirebase serve --only functions,hosting
if everything went as expected we have a local server running on port 5000
You can now finish the server and deploy the role
Enter the command firebase deploy
In this model we will call a function of Cloud functions from the application
Call functions from your application
We will perform a function in two ways
A simple call that will return the current date
An advanced call, where we will pass data to the function to execute a currency conversion API
Download the functions-android folder and follow the steps
In this step we will use nodeJS to create a function end point
Download and install NodeJS
Install Firebase CLI if you haven't already
Consultation material on the Firebase CLI
Access functions-android folder through the CMD command prompt
Enter the command firebase init functions choose the project and DO NOT overwrite index.js and install NODE dependencies
Access the functions folder
Now install the get-json module
Enter the command npm i get-json
if everything went as expected you can deploy the role
Enter the command firebase deploy
To use Firebase Cloud Messaging, we will again rely on Cloud Functions.
Download the cloud_message-android folder and follow the steps
We will need firebase-admin to send notifications using Firebase Functions, for that we will need the credentials of one of a service account linked to the chosen project.
To obtain a credential linked to a project, follow the steps.
In the Firebase console choose the project, then Project overview, project settings, service accounts, click on Google Cloud Platform service accounts.
Now in the google cloud console, access the Firebase Admin SDK Service Agent.
Click on add key, create new JSON type key, the download is done automatically.
This file is secret, put it in the functions folder of the cloud_message-android folder.


Return the array of accessor symbols for instances of this class.
# File lib/tracks_attributes.rb, line 48 def accessors self.class.accessors end
Return a hash all of the accessor symbols and their values
# File lib/tracks_attributes.rb, line 53 def accessors_all attributes.merge Hash[accessors.collect {|v| [v, send(v.to_s)] if respond_to? "#{v}".to_sym}] end
Returns an instance of TracksAttributes from a JSON string
# File lib/tracks_attributes.rb, line 69 def from_json(json, include_root=false) hash = ActiveSupport::JSON.decode(json) hash = hash.values.first if include_root assign_attrs_from_hash hash end
Returns an instance of TracksAttributes from an XML string
# File lib/tracks_attributes.rb, line 90 def from_xml(xml) hash = Hash.from_xml(xml).values.first assign_attrs_from_hash hash end
Convert an TracksAttributes instance to JSON by delegating conversion to Hash.to_json
Without any options, the returned JSON string will include all of the attributes.
The :only and :except options can be used to limit the attributes included, and will accept 1 or more hash keys to include/exclude.
# File lib/tracks_attributes.rb, line 64 def to_json(options = nil) accessors_all.to_json(options) end
Convert an TracksAttributes instance to XML by delegating conversion to Hash.to_xml
Without any options, the returned XML string will include all of the attributes.
The :only and :except options can be used to limit the attributes included, and will accept 1 or more hash keys to include/exclude.
The :builder option uses key as the :root for the XML result
# File lib/tracks_attributes.rb, line 85 def to_xml(options = nil) accessors_all.to_xml(options || {}) end


Hi! I'm {{ .Site.Params.author }}.
I'm a 2020 graduate of Smith College, where I double majored in Statistical & Data Sciences (SDS) and Government. As of June 2020, I am an Applied Data Scientist at Civis Analytics, where I work with public sector clients to leverage data for public policy work. I find it important to think critically about ways data scientists can ensure our work is ethical, equitable, and making this world a better place.
In my free time, I enjoy working on women's sports analytics projects, particularly women's soccer.
Outside of data science, I enjoy rock climbing and trying new ramen restaurants in every city that I visit.
Want to know more about me? Take a look at some of my favorite projects or my current CV.
Note: This site is very much a work in progress, please stay tuned!
{{ partial "social.html" . }}


Demonstrates map tiling artifacts, and OpenLayer's facility for correcting this distortion.
When you render tiles with certain types of symbols, there are artifacts
at tile edges that make symbology not look continuous. In the center of
the above map (when it first loads), the large orange road is split
vertically by a tile. Open the layer switcher and change to the layer
with a 15 pixel gutter to see how the symbology looks nicer.


TSA finds handgun in carry-on bag at Southwest Florida International Airport
by WFTX Digital TeamLEE COUNTY, Fla. -- A Fort Myers man is accused of having a gun in his carry-on luggage while trying to fly out of Southwest Florida International Airport.
61-year-old Timothy Fenton was arrested Monday after a TSA agent saw a firearm in his bag on the x-ray screen.
Fenton says he forgot the Smith & Wesson .380 was in his bag.
The agent also found a magazine with five rounds. However, there was no bullet in the chamber.
Investigators say Fenton's concealed weapons permit had expired in July. He is now charged with carrying a concealed weapon.
Fenton was the Chief Operating Officer at McDonald's Corp. and retired in 2014.


Anthony starting in Trail Blazers debut vs. Pelicans
NEW ORLEANS (AP) - Portland coach Terry Stotts says Carmelo Anthony will start at forward in his Trail Blazers debut against the New Orleans Pelicans on Tuesday night.
The 35-year-old Anthony agreed to contract terms with Portland last week but was officially signed Tuesday after passing a recent physical.
The 16-year veteran's first game with his fifth NBA team comes in the same city - and across the street - from where he led Syracuse to an NCAA national title in 2003.
The 6-foot-8 Anthony began his NBA career in Denver, where he played seven-plus seasons before being traded in 2011 to the New York Knicks, where he spent six-plus seasons. He also has played one each with Oklahoma City and Houston. The Rockets traded him to Chicago last January, but the Bulls cut him before he played a game.
He has averaged 24 points in 1,064 career regular season games.
____
More AP NBA: https://apnews.com/NBA and https://twitter.com/AP_Sports
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


Michelle Visage 'at war with BBC over her axing from Strictly Come Dancing tour'
She wanted to be a part of it
by Entertainment DailyMichelle Visage is reportedly "gutted" over not being part of the 2020 Strictly live tour line up.
The Ru Paul Drag Race star has been a hit with viewers and the Strictly judges alike.
However, she apparently hasn't made the cut for the tour. Michelle's spokeswoman is reported to have said she had been looking forward to taking part.
The spokeswoman also said Michelle had been asked her availability and was waiting for the thumbs up.
Read more: Strictly viewers mock Tess Daly's dress strap
"She’d have loved to have done it. She was available for every date of the tour and the organisers were very aware of this," they said.
"But for whatever reason, they decided not to have Michelle on."
However, a TV source is reported to have told The Sun that scheduling conflicts were behind the decision not to book her.
The unnamed insider said she already agreed to be part of a fan convention in London on the opening weekend of the Strictly tour.
Read more: Strictly's Motsi Mabuse makes hilarious slip of tongue
"She wouldn’t be available for four shows in Birmingham. So to say she had cleared her diary is not true," they are reported to have said.
Reality star Michelle, who previously competed on Celebrity Big Brother, had previously made no secret that she would have loved to be on the Strictly tour.
She shared her disappointment to not be on the line-up last week by replying to several equally disappointed fans on Twitter.
After one fan tweeted she was 'gutted' Michelle wasn't included, she replied: "Me TOOOOOO I so wanted to do it!"
EntertainmentDaily has contacted Michelle Visage's representative for comment.
Do you think Michelle should be part of the tour? Share your thoughts on our Facebook page @EntertainmentDailyFix.


{% load static %}
My daily foods list
Calories counter
Calories Tracker
About
{% csrf_token %} {% if customer.customerfoods_set.count > 0 %} {% for food in customer.customerfoods_set.all %}
{{ food.name }}
See details
Delete
{% endfor %}
{% else %}
There are no foods added for this customer.
{% endif %}


| Experienced Technical Architect seeking a challenging
architect role using the latest technologies. |
|
| |
| Key Skills |
| |
| Java (Server-Side): |
J2EE, EJB, JPA, JMS, JDBC, JNDI, RMI, JUnit |
| Web technologies: |
JSPs, Servlets, JSF, Struts, SOAP, Apache Axis, HTML, Javascript, CSS |
| XML: |
SAX, DOM, XSL(T), XSL/FO |
| Application Servers: |
BEA Weblogic, IBM WebSphere, SunOne Application Server, JBoss, Netscape Application Server |
| Databases: |
Oracle 8i, DB/2, PostgreSQL, MS SQL Server, MS Access, SQL |
| Operating Systems: |
Linux, Unix (Solaris, HP-UX, AIX), Windows (NT/2000/XP) |
| IDEs: |
JBuilder, IBM RAD, Eclipse, IntelliJ IDEA |
| Middleware: |
Vitria BusinessWare, BEA Tuxedo |
| Methodologies: |
UML, Design Patterns, OO, RAD, Spring Framework |
| Other: |
C, C++, Python, Django (web development framework), Perl, PHP, Ant, Shell Scripting |
| |
| |
| Work Experience |
| |
| Dec. 2006 - Nov. 2011 |
Generali Assurances |
Position : Senior Consultant |
- Development and maintenance of a large, multi-tiered insurance application to manage Generali Partners
(UML, Java, EJB, JPA, Spring, JSPs, JSTL, Oracle, PL/SQL
- Development and maintenance of a large, multi-tiered insurance application to manage customer portfolios
(Java, EJB, JPS, Spring, JSF, Oracle, PL/SQL
- Design and development of a bespoke application to access historical data of a legacy (BULL) system
(Java, EJB, JPS, Spring, JSF, Oracle, PL/SQL
- Design and development of a batch job to identify or create Partners to be migrated from a legacy (BULL) system
- Migration of all Javascript IE only functions to multi-browser using jQuery
- Responsible for document management and document generation applications.
Java, Servlets, JDBC, LDAP API
- Responsible for a backup application for registering losses when main application unavailable (Perl, JavaScript).
- Wrote several bespoke shell scripts to aid development tasks
- Technologies used: Java, J2EE, Spring Framework, JPA, Hibernate, Spring Framework, JSPs, JSTL, WebSphere, RAD (6&7), Oracle, PL/SQL, Rational Rose.
|
| May 2004 - November 2006 |
Cambridge Technology Partners |
Position : Senior Consultant |
* Testing Environments Management for a Swiss Telecoms Company
- Planning, Management, Deployment of a large variety of applications on numerous platforms.
- Streamlining and improvement of existing deployment procedures.
- Technologies used: Vitria BusinessWare (versions 3 and 4), Bea Tuxedo, BEA WebLogic(6 & 7), Twister, Sun Solaris, HP-UX, Ant
|
* Development of a Perl log file parser module for a Swiss Telecoms Company
- Developed a perl module to extract various data from log files for Government compliance project.
|
* Migration of Legal Interception Application to new Architecture:
- Technical design, development and migration of a multi-tier application allowing on-demand interception of emails
- Implementation of a module in C for GPG decryption and base 64 decoding of emails.
- Implementation of SOAP web service clients in Java and Perl.
- Technologies used: C, Perl, Java, J2EE, JSP, SOAP, Eclipse, WebSphere Application Studio Developer, SunOne Application Server, Tomcat, Oracle.
|
* Technical Design and Development of Single Sign-On functionality for a Swiss Telecoms Company
- Technical design, development and testing of a SOAP web service to enable single sign-on to multiple portal sites.
- Design, development and deployment of an emulator servlet to test SOAP web service locally.
- Technologies used: Java, JSP, Servlets, SOAP, Tomcat, Weblogic, Apache, Eclipse.
|
* Ongoing Support of Legal Interception Application for a Swiss Telecom Company
- Testing and production-level support of a multi-tier application allowing on-demand interception of emails.
- Technologies used: C, Perl, Java, J2EE, JSP, Eclipse, SunOne Application Server, Tomcat, Oracle.
|
| |
| Jan. 2003 - May 2004 |
GFI International |
Position : Contract Java Team Lead |
- Technical Team Lead for Customer Service module of large Pension Fund project, delivered on time to tight deadlines.
- Senior Developer on various other modules of this project.
- Analysis, design and refactoring of core modules.
- Technologies used: WebSphere Application Server 5.1, WebSphere Studio Application Developer, J2EE, Struts, XML, XSLT, XSL/FO and Ant on an AS/400, DB/2 backend.
|
| |
| Jan. 2002-Jan. 2003 |
Altamedius Ltd. |
Position : Senior Software Engineer |
- Java, J2EE and Struts development on various payment intermediation projects
- Implemented all projects successfully to tight deadlines.
- Technologies used: Java, J2EE, XML, XSLT, Struts, Weblogic 6.1, Oracle 8i
|
| |
| Nov. 2000 - Nov. 2001 |
KPMG Consulting Ltd. |
Position : Contract Software Engineer |
- Java, J2EE, development on Weblogic Server of a customer care portal for a leading Irish telecoms company.
- Java server-sided development of online trading product for Ireland's leading stockbroking company.
- Technologies used: Java, J2EE, Corba, LDAP, JDBC, WebLogic 6.0, iPlanet, Oracle 8i.
|
| |
| Nov. 1999 - Nov. 2000 |
Factor-e Ltd. |
Position : Software Engineer |
* Developer on various Java projects for an online bank (first-e):
- Component-based J2EE-focused design of new architecture using Catalysis UML methods.
- Designed new development process using UML.
- Java development to extremely tight deadlines for successful German launch of the bank.
- Technologies used: Java, J2EE, JBuilder, Rational Rose, Rational SoDA, UML.
|
| |
| Mar. 1999 - Oct. 1999 |
Openet Telecom Ltd. |
Position : Software Engineer |
| * Project: NETFusion |
- Design and build of Web-based front-end for core mediation product, using Java Swing components.
- Received much positive feedback at trade shows and from investors.
- Porting of mediation system to Solaris. Design and build of collection component for mediation system, using C.
|
| |
| June 1998 - March 1999 |
Lucent Technologies Ltd. |
Position : Software Engineer |
| * Project : Telecel Network Configuration Tool |
- Systems Verification team lead for small project using RAD technique, Dynamic Systems Development Method(DSDM).
- Attended DSDM workshop with client.
- Designed tool to automatically generate bug reports using Perl, Unix shell scripting.
|
| * Project : OMC Release 4.5 |
- Systems Verification of the OMC-2000, GSM Base Station Controller.
- Testing all aspects of the product (especially GUI), regression testing, scripting(TCL/TK).
- Webmaster for systems verification team.
- Mentor for new employees.
|
| |
| |
|
Education |
| |
| 1997-1998 |
Higher Diploma in Computer Science |
University College Dublin, Ireland |
| First Class Honours |
| Subjects: Programming and Data
Structures (C++); Program Design and Verification;
Systems Design and Development; Networks; Operating
Systems (Unix); Information Technology and Applications
(Filemaker Pro, Word, Excel, Access); Interactive
Multimedia Systems; Object-oriented Programming;
Information Systems; Computer Architecture; Artificial
Intelligence. |
| Projects: Web design; Lotto
Simulator (C++); Internet Security essay; Design of a
Client/Server model using C in a Unix environment;
Database Design (MS Access); Shell Programming; Process
Scheduling model; File System Model (all Unix) Major
Project: Car Navigation simulator (C++, OOP). |
| |
| 1989-1993 |
Batchelor of International Commerce and French |
University College Dublin, Ireland |
| Bachelor's Degree - Second Class Honours |
| |
| 1983-1989 |
Clongowes Wood College |
Leaving Certificate, 1989 |
| English (A), French (A), Irish (B),
Maths (B), History (B), Economics (B), Business
Organisation (B). |
| |
| |
| Additional Information |
| |
| Languages: |
Fluent French |
| Courses: |
Catalysis Object Oriented Development Method |
| |
A Component-based development method using UML (1 week full-time, 2001) |
| |
| |
| Personal Information |
| |
| Address: |
604 Rue Guy de Maupassant, Divonne les Bains, 01220 France. |
| Gender: |
Male |
Status: |
Married |
| Date Of Birth: |
21 January 1971 |
Nationality: |
Irish |
| Email: |
gavcos |
Tel: |
+33628707133 |
}
@main("Gavin Costello - Curriculum Vitae") {
@header
@content
}
|


| | |
- builtins.object
-
- jetson.inference.tensorNet
-
- jetson.inference.actionNet
- jetson.inference.backgroundNet
- jetson.inference.depthNet
- jetson.inference.detectNet
- jetson.inference.imageNet
- jetson.inference.poseNet
- jetson.inference.segNet
class actionNet(tensorNet) |
| |
Action Recognition DNN - classifies an image sequence
Examples (jetson-inference/python/examples)
actionnet.py
__init__(...)
Loads an action classification model.
Parameters:
network (string) -- name of a built-in network to use,
see below for available options.
argv (strings) -- command line arguments passed to actionNet,
see below for available options.
Extended parameters for loading custom models:
model (string) -- path to self-trained ONNX model to load.
labels (string) -- path to labels.txt file (optional)
input_blob (string) -- name of the input layer of the model.
output_blob (string) -- name of the output layer of the model.
actionNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* resnet-18 (default)
* resnet-34
--model=MODEL path to custom model to load (.onnx)
--labels=LABELS path to text file containing the labels for each class
--input-blob=INPUT name of the input layer (default is 'input')
--output-blob=OUTPUT name of the output layer (default is 'output')
--threshold=CONF minimum confidence threshold for classification (default is 0.01)
--skip-frames=SKIP how many frames to skip between classifications (default is 1)
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- actionNet
- tensorNet
- builtins.object
Methods defined here:
- Classify(...)
- Append an image to the sequence and classify the action, returning the class and confidence.
Parameters:
image (capsule) -- CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
Returns:
(int, float) -- tuple containing the action's class index and confidence
- GetClassDesc(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassLabel(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetNetworkName(...)
- Return the name of the built-in network used by the model.
Parameters: (none)
Returns:
(string) -- name of the network (e.g. 'googlenet', 'alexnet')
or 'custom' if using a custom-loaded model
- GetNumClasses(...)
- Return the number of object classes that this network model is able to classify.
Parameters: (none)
Returns:
(int) -- number of object classes that the model supports
- GetSkipFrames(...)
- Return the number of frames that are skipped in between classifications.
Parameters: (none)
Returns:
(int) -- the number of frames skipped in between classifications
- GetThreshold(...)
- Return the minimum confidence threshold for classification.
Parameters: (none)
Returns:
(float) -- the confidence threshold for classification
- SetSkipFrames(...)
- Set the number of frames that are skipped in between classifications.
Since actionNet operates on video sequences, it's often helpful to skip frames
to lengthen the window of time the model gets to 'see' an action being performed.
The default setting is 1, where every other frame is skipped.
Setting this to 0 will disable it, and every frame will be processed.
When a frame is skipped, the classification results from the last frame are returned.
Parameters:
(int) -- the number of frames skipped in between classifications
Returns: (none)
- SetThreshold(...)
- Set the minimum confidence threshold for classification.
Parameters:
(float) -- confidence threshold
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
|
class backgroundNet(tensorNet) |
| |
Background removal DNN - performs background subtraction on images.
Examples (jetson-inference/python/examples)
backgroundnet.py
__init__(...)
Loads a background subtraction/removal model.
Parameters:
network (string) -- name of a built-in network to use,
see below for available options.
argv (strings) -- command line arguments passed to backgroundNet,
see below for available options.
Extended parameters for loading custom models:
model (string) -- path to self-trained ONNX model to load.
input_blob (string) -- name of the input layer of the model.
output_blob (string) -- name of the output layer of the model.
backgroundNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* u2net (default)
--model=MODEL path to custom model to load (caffemodel, uff, or onnx)
--input-blob=INPUT name of the input layer (default is 'input_0')
--output-blob=OUTPUT name of the output layer (default is 'output_0')
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- backgroundNet
- tensorNet
- builtins.object
Methods defined here:
- GetNetworkName(...)
- Return the name of the built-in network used by the model.
Parameters: (none)
Returns:
(string) -- name of the network (e.g. 'u2net')
or 'custom' if using a custom-loaded model
- Process(...)
- Perform background subtraction/removal on the image.
If only an input image is provided, the operation will be performed in-place.
If an output image is also provided, the results will be written to the output image.
If the images have an alpha channel (uchar4 or float4) and mask_alpha is true (default),
then the background/foreground mask will be written to the output's alpha channel.
Parameters:
input (capsule) -- CUDA memory capsule (input image)
output (capsule) -- CUDA memory capsule (optional output image)
filter (string) -- filtering used in upscaling the mask, 'point' or 'linear' (default is 'linear')
mask_alpha (bool) -- if true (default), the mask will be applied to the alpha channel as well
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
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class depthNet(tensorNet) |
| |
Mono depth estimation DNN - performs depth mapping on monocular images
Examples (jetson-inference/python/examples)
depthnet.py
__init__(...)
Loads a mono depth estimation model.
Parameters:
network (string) -- name of a built-in network to use,
see below for available options.
argv (strings) -- command line arguments passed to depthNet,
see below for available options.
depthNet arguments:
--network NETWORK pre-trained model to load, one of the following:
* fcn-mobilenet
* fcn-resnet18
* fcn-resnet50
--model MODEL path to custom model to load (onnx)
--input_blob INPUT name of the input layer (default is 'input_0')
--output_blob OUTPUT name of the output layer (default is 'output_0')
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- depthNet
- tensorNet
- builtins.object
Methods defined here:
- GetDepthField(...)
- Return a cudaImage object of the raw depth field.
This is a single-channel float32 image that contains the depth estimates.
Parameters: (none)
Returns:
(cudaImage) -- single-channel float32 depth field
- GetDepthFieldHeight(...)
- Return the height of the depth field, in pixels.
Parameters: (none)
Returns:
(int) -- height of the depth field, in pixels
- GetDepthFieldWidth(...)
- Return the width of the depth field, in pixels.
Parameters: (none)
Returns:
(int) -- width of the depth field, in pixels
- GetNetworkName(...)
- Return the name of the built-in network used by the model.
Parameters: (none)
Returns:
(string) -- name of the network (e.g. 'MonoDepth-Mobilenet', 'MonoDepth-ResNet18')
or 'custom' if using a custom-loaded model
- Process(...)
- Compute the depth field from a monocular RGB/RGBA image.
The results can also be visualized if output image is provided.
Parameters:
input (capsule) -- CUDA memory capsule (input image)
output (capsule) -- CUDA memory capsule (optional output image)
colormap (string) -- colormap name (optional)
filter (string) -- filtering used in upscaling, 'point' or 'linear' (default is 'linear')
Returns: (none)
- Visualize(...)
- Visualize the raw depth field into a colorized RGB/RGBA depth map.
Parameters:
output (capsule) -- output CUDA memory capsule
colormap (string) -- colormap name (optional)
filter (string) -- filtering used in upscaling, 'point' or 'linear' (default is 'linear')
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
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class detectNet(tensorNet) |
| |
Object Detection DNN - locates objects in an image
Examples (jetson-inference/python/examples)
detectnet.py
__init__(...)
Loads an object detection model.
Parameters:
network (string) -- name of a built-in network to use
see below for available options.
argv (strings) -- command line arguments passed to detectNet,
see below for available options.
threshold (float) -- minimum detection threshold.
default value is 0.5
Extended parameters for loading custom models:
model (string) -- path to self-trained ONNX model to load.
labels (string) -- path to labels.txt file (optional)
colors (string) -- path to colors.txt file (optional)
input_blob (string) -- name of the input layer of the model.
output_cvg (string) -- name of the output coverage/confidence layer.
output_bbox (string) -- name of the output bounding boxes layer.
detectNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* ssd-mobilenet-v1
* ssd-mobilenet-v2 (default)
* ssd-inception-v2
* peoplenet
* peoplenet-pruned
* dashcamnet
* trafficcamnet
* facedetect
--model=MODEL path to custom model to load (caffemodel, uff, or onnx)
--prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)
--labels=LABELS path to text file containing the labels for each class
--input-blob=INPUT name of the input layer (default is 'data')
--output-cvg=COVERAGE name of the coverage/confidence output layer (default is 'coverage')
--output-bbox=BOXES name of the bounding output layer (default is 'bboxes')
--mean-pixel=PIXEL mean pixel value to subtract from input (default is 0.0)
--confidence=CONF minimum confidence threshold for detection (default is 0.5)
--clustering=CLUSTER minimum overlapping area threshold for clustering (default is 0.75)
--alpha=ALPHA overlay alpha blending value, range 0-255 (default: 120)
--overlay=OVERLAY detection overlay flags (e.g. --overlay=box,labels,conf)
valid combinations are: 'box', 'lines', 'labels', 'conf', 'none'
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- detectNet
- tensorNet
- builtins.object
Methods defined here:
- Detect(...)
- Detect objects in an RGBA image and return a list of detections.
Parameters:
image (capsule) -- CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
overlay (str) -- combination of box,lines,labels,conf,none flags (default is 'box,labels,conf')
Returns:
[Detections] -- list containing the detected objects (see detectNet.Detection)
- GetClassDesc(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassLabel(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassSynset(...)
- Return the synset data category string for the given class.
The synset generally maps to the class training data folder.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the synset of the class, typically 9 characters long
- GetClusteringThreshold(...)
- Return the overlapping area % threshold for clustering.
Parameters: (none)
Returns:
(float) -- the overlapping area % threshold for merging bounding boxes
- GetConfidenceThreshold(...)
- Return the minimum detection threshold.
Parameters: (none)
Returns:
(float) -- the threshold for detection
- GetLineWidth(...)
- Return the line width used during overlay when 'lines' mode is used.
Parameters: (none)
Returns:
(float) -- line width in pixels
- GetNumClasses(...)
- Return the number of object classes that this network model is able to detect.
Parameters: (none)
Returns:
(int) -- number of object classes that the model supports
- GetOverlayAlpha(...)
- Return the overlay alpha blending value for classes that don't have it explicitly set.
Parameters: (none)
Returns:
(float) -- alpha blending value between [0,255]
- GetThreshold(...)
- Return the minimum detection threshold.
Parameters: (none)
Returns:
(float) -- the threshold for detection
- GetTrackerType(...)
- Returns the type of tracker being used as a string
Parameters: (none)
Returns:
(string) -- 'IOU', 'KLT', or None if no tracking
- GetTrackingParams(...)
- Returns a dict containing various tracking parameters.
Parameters: (none)
Returns: a dict containing the following keys/values (dependent on the type of tracker):
minFrames (int) -- the number of re-identified frames before before establishing a track (IOU tracker only)
dropFrames (int) -- the number of consecutive lost frames after which a track is removed (IOU tracker only)
overlapThreshold (float) -- how much IOU overlap is required for a bounding box to be matched, between [0,1] (IOU tracker only)
- IsTrackingEnabled(...)
- Returns true if tracking is enabled, otherwise false
Parameters: (none)
Returns:
(bool) -- true if tracking is enabled, otherwise false
- Overlay(...)
- Overlay a list of detections in an RGBA image.
Parameters:
image (capsule) -- CUDA memory capsule
[Detections] -- list containing the detected objects (see detectNet.Detection) width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
overlay (str) -- combination of box,lines,labels,conf,none flags (default is 'box,labels,conf')
Returns:
None
- SetClusteringThreshold(...)
- Set the overlapping area % threshold for clustering.
Parameters:
(float) -- the overlapping area % threshold for merging bounding boxes
Returns: (none)
- SetConfidenceThreshold(...)
- Set the minimum detection threshold.
Parameters:
(float) -- detection threshold
Returns: (none)
- SetLineWidth(...)
- Set the line width used during overlay when 'lines' mode is used
Parameters:
width (float) -- desired line width, in pixels
Returns: (none)
- SetOverlayAlpha(...)
- Set the alpha blending value used during overlay visualization for all classes
Parameters:
alpha (float) -- desired alpha value, between 0.0 and 255.0
Returns: (none)
- SetThreshold(...)
- Set the minimum detection threshold.
Parameters:
(float) -- detection threshold
Returns: (none)
- SetTrackerType(...)
- Sets the type of tracker being used
Parameters:
(string) -- 'IOU' or 'KLT' (other strings will disable tracking)
Returns: (none)
- SetTrackingEnabled(...)
- Sets if tracking is enabled or disabled.
When enabling tracking, if the tracker type wasn't previously
set with detectNet.SetTrackerType(), then 'IOU' will be used.
Parameters:
(bool) -- true to enable tracking, false to disable it
Returns: (none)
- SetTrackingParams(...)
- Sets various tracker parameters using keyword arguments.
Parameters:
minFrames (int) -- the number of re-identified frames before before establishing a track (IOU tracker only)
dropFrames (int) -- the number of consecutive lost frames after which a track is removed (IOU tracker only)
overlapThreshold (float) -- how much IOU overlap is required for a bounding box to be matched, between [0,1] (IOU tracker only)
Returns:
None
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Data and other attributes defined here:
- Detection = <class 'jetson.inference.detectNet.Detection'>
- Object Detection Result
----------------------------------------------------------------------
Data descriptors defined here:
Confidence
Confidence value of the detected object
ClassID
Class index of the detected object
TrackID
Unique tracking ID (or -1 if untracked)
TrackStatus
-1 for dropped, 0 for initializing, 1 for active/valid
TrackFrames
The number of frames the object has been re-identified for
TrackLost
The number of consecutive frames tracking has been lost for
Width
Width of bounding box
Height
Height of bounding box
Left
Left bounding box coordinate
Right
Right bounding box coordinate
Top
Top bounding box coordinate
Bottom
Bottom bounding box coordinate
ROI
(Left, Top, Right, Bottom) tuple
Center
Center (x,y) coordinate tuple
Area
Area of bounding box
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
|
class imageNet(tensorNet) |
| |
Image Recognition DNN - classifies an image
Examples (jetson-inference/python/examples)
imagenet.py
my-recognition.py
__init__(...)
Loads an image recognition model.
Parameters:
network (string) -- name of a built-in network to use,
see below for available options.
argv (strings) -- command line arguments passed to imageNet,
see below for available options.
Extended parameters for loading custom models:
model (string) -- path to self-trained ONNX model to load.
labels (string) -- path to labels.txt file (optional)
input_blob (string) -- name of the input layer of the model.
output_blob (string) -- name of the output layer of the model.
imageNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* alexnet
* googlenet (default)
* googlenet-12
* resnet-18
* resnet-50
* resnet-101
* resnet-152
* vgg-16
* vgg-19
* inception-v4
--model=MODEL path to custom model to load (caffemodel, uff, or onnx)
--prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)
--labels=LABELS path to text file containing the labels for each class
--input-blob=INPUT name of the input layer (default is 'data')
--output-blob=OUTPUT name of the output layer (default is 'prob')
--threshold=CONF minimum confidence threshold for classification (default is 0.01)
--smoothing=WEIGHT weight between [0,1] or number of frames (disabled by default)
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- imageNet
- tensorNet
- builtins.object
Methods defined here:
- Classify(...)
- Classify an image and return the object's class and confidence.
Parameters:
image (capsule) -- CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
topK (int) -- the number of predictions to return (sorted by confidence)
if topK is 0, then all valid predictions will be returned
Returns:
(int, float) -- tuple containing the object's class index and confidence
if topK is set, then a list of these tuples will be returned
- GetClassDesc(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassLabel(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassSynset(...)
- Return the synset data category string for the given class.
The synset generally maps to the class training data folder.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the synset of the class, typically 9 characters long
- GetNetworkName(...)
- Return the name of the built-in network used by the model.
Parameters: (none)
Returns:
(string) -- name of the network (e.g. 'googlenet', 'alexnet')
or 'custom' if using a custom-loaded model
- GetNumClasses(...)
- Return the number of object classes that this network model is able to classify.
Parameters: (none)
Returns:
(int) -- number of object classes that the model supports
- GetSmoothing(...)
- Return the temporal smoothing factor applied to the results.
Parameters: (none)
Returns:
(float) -- the weight between [0,1] or the number of frames in the smoothing window
- GetThreshold(...)
- Return the minimum confidence threshold for classification.
Parameters: (none)
Returns:
(float) -- the confidence threshold for classification
- SetSmoothing(...)
- Set the temporal smoothing factor applied to the results.
Parameters:
(float) -- A weight between [0,1] that's placed on the latest confidence values,
or the smoothing window as a number of frames (where the weight will be 1/N)
Setting this to 0 or 1 will disable smoothing and use the unfiltered outputs
Returns: (none)
- SetThreshold(...)
- Set the minimum confidence threshold for classification.
Parameters:
(float) -- confidence threshold
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
|
class poseNet(tensorNet) |
| |
Pose Estimation DNN - detects the poses of objects in an image
Examples (jetson-inference/python/examples)
posenet.py
__init__(...)
Loads an pose estimation model.
Parameters:
network (string) -- name of a built-in network to use
see below for available options.
argv (strings) -- command line arguments passed to poseNet,
see below for available options.
threshold (float) -- minimum detection threshold.
default value is 0.15
poseNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* resnet18-body (default)
* resnet18-hand
* densenet121-body
--model=MODEL path to custom model to load (caffemodel, uff, or onnx)
--prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)
--labels=LABELS path to text file containing the labels for each class
--input-blob=INPUT name of the input layer (default is 'input')
--output-cvg=COVERAGE name of the coverge output layer (default is 'cmap')
--output-bbox=BOXES name of the bounding output layer (default is 'paf')
--mean-pixel=PIXEL mean pixel value to subtract from input (default is 0.0)
--threshold=THRESHOLD minimum threshold for detection (default is 0.5)
--overlay=OVERLAY detection overlay flags (e.g. --overlay=links,keypoints)
valid combinations are: 'box', 'links', 'keypoints', 'none'
--keypoint-scale=X radius scale for keypoints, relative to image (default: 0.0052)
--link-scale=X line width scale for links, relative to image (default: 0.0013)
--profile enable layer profiling in TensorRT |
| |
- Method resolution order:
- poseNet
- tensorNet
- builtins.object
Methods defined here:
- FindKeypointID(...)
- Return the keypoint ID for the given keypoint name.
Parameters:
(str) -- name of the keypoint
Returns:
(int) -- the ID of the keypoint
- GetKeypointName(...)
- Return the keypoint name for the given keypoint ID.
Parameters:
(int) -- index of the keypoint, between [0, GetNumKeypoints()]
Returns:
(string) -- the text description of the keypoint
- GetKeypointScale(...)
- Get the scale used to calculate the radius of keypoints based on image dimensions.
Parameters: (none)
Returns:
(float) -- the scale used to calculate the radius of keypoints based on image dimensions
- GetLinkScale(...)
- Get the scale used to calculate the width of link lines based on image dimensions.
Parameters: (none)
Returns:
(float) -- the scale used to calculate the width of link lines based on image dimensions
- GetNumKeypoints(...)
- Return the number of keypoints in the model's pose topology.
Parameters: (none)
Returns:
(int) -- number of keypoints in the model's pose topology
- GetThreshold(...)
- Return the minimum detection threshold.
Parameters: (none)
Returns:
(float) -- the threshold for detection
- Overlay(...)
- Overlay a list of object poses onto an image.
Parameters:
input (capsule) -- input image (CUDA memory capsule)
[ObjectPoses] -- list containing the detected object poses (see poseNet.ObjectPose) width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
overlay (str) -- combination of box,labels,none flags (default is 'box')
output (capsule) -- output image (CUDA memory capsule)
Returns:
None
- Process(...)
- Perform pose estimation on the given image, returning object poses, and overlay the results..
Parameters:
image (capsule) -- CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
overlay (str) -- combination of box,labels,none flags (default is 'box')
Returns:
[ObjectPoses] -- list containing the detected object poses (see poseNet.ObjectPose)
- SetKeypointAlpha(...)
- Set the alpha blending value used during overlay visualization for one or all keypoint types
Parameters:
alpha (float) -- desired alpha value, between 0.0 and 255.0
keypoint (int) -- optional index of the keypoint to set the alpha (otherwise will apply to all keypoints)
Returns: (none)
- SetKeypointScale(...)
- Set the scale used to calculate the radius of keypoint circles.
This scale will be multiplied by the largest image dimension.
Parameters:
scale (float) -- desired scaling factor
Returns: (none)
- SetLinkScale(...)
- Set the scale used to calculate the width of link lines.
This scale will be multiplied by the largest image dimension.
Parameters:
scale (float) -- desired scaling factor
Returns: (none)
- SetThreshold(...)
- Return the minimum detection threshold.
Parameters:
(float) -- detection threshold
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Data and other attributes defined here:
- ObjectPose = <class 'jetson.inference.poseNet.ObjectPose'>
- Object Pose Estimation Result
----------------------------------------------------------------------
Data descriptors defined here:
Keypoints
List of poseNet.ObjectPose.Keypoint objects
Links
List of (a,b) tuples, where a & b are indexes into the Keypoints list
ID
Object ID from the image frame, starting at 0
Left
Left bounding box coordinate
Right
Right bounding box coordinate
Top
Top bounding box coordinate
Bottom
Bottom bounding box coordinate
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
|
class segNet(tensorNet) |
| |
Semantic Segmentation DNN - segments an image with per-pixel classification
Examples (jetson-inference/python/examples)
segnet-console.py
segnet-camera.py
__init__(...)
Loads an semantic segmentation model.
Parameters:
network (string) -- name of a built-in network to use,
see below for available options.
argv (strings) -- command line arguments passed to segNet,
see below for available options.
Extended parameters for loading custom models:
model (string) -- path to self-trained ONNX model to load.
labels (string) -- path to labels.txt file (optional)
colors (string) -- path to colors.txt file (optional)
input_blob (string) -- name of the input layer of the model.
output_blob (string) -- name of the output layer of the model.
segNet arguments:
--network=NETWORK pre-trained model to load, one of the following:
* fcn-resnet18-cityscapes-512x256
* fcn-resnet18-cityscapes-1024x512
* fcn-resnet18-cityscapes-2048x1024
* fcn-resnet18-deepscene-576x320
* fcn-resnet18-deepscene-864x480
* fcn-resnet18-mhp-512x320
* fcn-resnet18-mhp-640x360
* fcn-resnet18-voc-320x320 (default)
* fcn-resnet18-voc-512x320
* fcn-resnet18-sun-512x400
* fcn-resnet18-sun-640x512
--model=MODEL path to custom model to load (caffemodel, uff, or onnx)
--prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)
--labels=LABELS path to text file containing the labels for each class
--colors=COLORS path to text file containing the colors for each class
--input-blob=INPUT name of the input layer (default: 'input_0')
--output-blob=OUTPUT name of the output layer (default: 'output_0')
--alpha=ALPHA overlay alpha blending value, range 0-255 (default: 150)
--visualize=VISUAL visualization flags (e.g. --visualize=overlay,mask)
valid combinations are: 'overlay', 'mask'
--profile enable layer profiling in TensorRT |
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- Method resolution order:
- segNet
- tensorNet
- builtins.object
Methods defined here:
- GetClassColor(...)
- Return the class color for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(r,g,b,a) tuple -- tuple containing the RGBA color of the object class
- GetClassDesc(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetClassLabel(...)
- Return the class description for the given object class.
Parameters:
(int) -- index of the class, between [0, GetNumClasses()]
Returns:
(string) -- the text description of the object class
- GetGridHeight(...)
- Return the number of rows in the segmentation mask classification grid.
These are the raw dimensions, they are typically smaller than the image size.
In segNet.Mask() the classification grid gets upscaled to match the image size,
but this function returns the original unscaled size of the grid.
Parameters: (none)
Returns:
(int) -- height of the segmentation mask's classification grid
- GetGridSize(...)
- Return a (width, height) tuple with the dimensions of the segmentation mask classification grid.
These are the raw dimensions, they are typically smaller than the image size.
In segNet.Mask() the classification grid gets upscaled to match the image size,
but this function returns the original unscaled size of the grid.
Parameters: (none)
Returns:
(int, int) -- tuple containing the width and height of the segmentation mask's classification grid
- GetGridWidth(...)
- Return the number of columns in the segmentation mask classification grid.
These are the raw dimensions, they are typically smaller than the image size.
In segNet.Mask() the classification grid gets upscaled to match the image size,
but this function returns the original unscaled size of the grid.
Parameters: (none)
Returns:
(int) -- width of the segmentation mask's classification grid
- GetNetworkName(...)
- Return the name of the built-in network used by the model.
Parameters: (none)
Returns:
(string) -- name of the network (e.g. 'FCN_ResNet18', 'FCN_Alexnet')
or 'custom' if using a custom-loaded model
- GetNumClasses(...)
- Return the number of object classes that this network model is able to classify.
Parameters: (none)
Returns:
(int) -- number of object classes that the model supports
- GetOverlayAlpha(...)
- Return the overlay alpha blending value for classes that don't have it explicitly set.
Parameters: (none)
Returns:
(float) -- alpha blending value between [0,255]
- Mask(...)
- Produce a colorized RGBA segmentation mask of the output.
Parameters:
image (capsule) -- output CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
filter_mode (string) -- optional string indicating the filter mode, 'point' or 'linear' (default: 'linear')
Returns: (none)
- Overlay(...)
- Produce the segmentation overlay alpha blended on top of the original image.
Parameters:
image (capsule) -- output CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
filter_mode (string) -- optional string indicating the filter mode, 'point' or 'linear' (default: 'linear')
Returns: (none)
- Process(...)
- Perform the initial inferencing processing of the segmentation.
The results can then be visualized using the Overlay() and Mask() functions.
Parameters:
image (capsule) -- CUDA memory capsule
width (int) -- width of the image (in pixels)
height (int) -- height of the image (in pixels)
ignore_class (string) -- optional label name of class to ignore in the classification (default: 'void')
Returns: (none)
- SetOverlayAlpha(...)
- Set the alpha blending value used during overlay visualization for all classes
Parameters:
alpha (float) -- desired alpha value, between 0.0 and 255.0
explicit_exempt (optional, bool) -- if True, the global alpha doesn't apply to classes that have an alpha value explicitly set in the colors file (default: True)
Returns: (none)
- __init__(self, /, *args, **kwargs)
- Initialize self. See help(type(self)) for accurate signature.
Static methods defined here:
- Usage(...)
- Return the command line parameters accepted by __init__()
Parameters: (none)
Returns:
(string) -- usage string documenting command-line options
Methods inherited from tensorNet:
- EnableDebug(...)
- Enable TensorRT debug messages and device synchronization
- EnableLayerProfiler(...)
- Enable the profiling of network layer execution times
- GetModelPath(...)
- Return the path to the network model file on disk
- GetModelType(...)
- Return the type of model format (caffe, ONNX, UFF, or custom)
- GetNetworkFPS(...)
- Return the runtime of the network (in frames per second)
- GetNetworkTime(...)
- Return the runtime of the network (in milliseconds)
- GetPrototxtPath(...)
- Return the path to the network prototxt file on disk
- PrintProfilerTimes(...)
- Print out performance timing info
Static methods inherited from tensorNet:
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
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Syntax highlighting
To compile:
alectryon syntax_highlighting.v
# Coq → HTML; produces ‘syntax_highlighting.html’
Definition a : nat :=
let '(x, y) := (1 , 2 ) in
let ' (z, t) := (x, y) in
let 'tt := tt in
let fix f (x : nat) {struct x } : nat := x in
(fun ' (f, x, y) => f (x + y)) (f, t, y).
Print a.a =
let
'(x, y) := (1 , 2 ) in
let
'(_, t) := (x, y) in
let
'tt := tt in
let fix f (x0 : nat) : nat := x0 in
(fun '(f0, x0, y1) => f0 (x0 + y1)) (f, t, y)
: nat
Definition test (as' is' with' : nat) :=
match match 1 with
| S is' => 0
| 0 => 1
end with
| S with' => None
| 0 => Some 1
end .
Print test.test =
fun _ _ _ : nat =>
match match 1 with
| 0 => 1
| S _ => 0
end with
| 0 => Some 1
| S _ => None
end
: nat -> nat -> nat -> option nat
Arguments test (as' is' with')%nat_scope
Notation "$0" := (0 , 0 ).
Infix "$+" := (fun '(u, a) '(_, b) => (u, a + b)) (at level 39 ).
Definition b := fun '(x, y) '(pair x' y') => (x + x', y + y').
Print b.b =
fun '(x, y) '(x', y') => (x + x', y + y')
: nat * nat -> nat * nat -> nat * nat
Arguments b pat pat
Notation "` x '' y `" := (x + y).
Check (1 + 2 ).