npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@good-i-deer/node-red-contrib-face-detection

v1.0.1

Published

Face Detection Node for Node-RED

Downloads

20

Readme

@GOOD-I-DEER/node-red-contrib-face-detection

platform npm version GitHub license

This module provides a node that detects faces using AI in Node-RED.

These nodes require node.js version 18.16.1 and Node-RED version 3.1.0.

Description

This module provides a node that detects faces in the picture. you can use it with other services. You can also cut the face images you want from the origin image and download them. If you want, you can easily create a service by uploading images directly, using a webcam, or inputting photos from an IoT camera.

Pre-requisites

The Node-Red-Contrib-Face-Detection requires Node-RED to be installed.

Install

To use this module you need to go to the folder where node-red is installed and use the npm install command.

cd ~/.node-red/
npm install @good-i-deer/node-red-contrib-face-detection

Node

face-detection

This is a node that detects and outputs faces in image.

input

Image Buffer

  • Image represented as binary buffer

property

Name

  • The name of the node displayed on the workspace.

Return Value

  • Type of data to be transmitted as output of the node. Supports Detected Object, Image Buffer, and Image File.
    • Detected Object : Result object of the pre-trained model. A model can contain only the face object. Included values are x, y, w, h, prob.
      • x : zero-indexed offset from left edge of the original image
      • y : zero-indexed offset from top edge of the original image
      • w : the width of cropped image
      • h : the height of cropped image
      • prob : Accuracy of the face detected by the model
    • Image Buffer : Image buffer resulting from a pre-trained model
    • Image File : Image file resulting from a pre-trained model.

Absolute Path Dir

  • Absolute path to save the file to. Show only when you select Image File as Return Value

Confidence Threshold

  • Confidence threshold of the results of the pre-trained model. You must specify a value between 0 and 1. The lower the value, the more faces are detected.

output

Data is output in the output format selected in the 'Return Value' property.

Examples

This is a simple example of this node.

JSON

[
    {
        "id": "bf67e15413744e7a",
        "type": "debug",
        "z": "83078a0b9760cbee",
        "name": "Result",
        "active": true,
        "tosidebar": true,
        "console": false,
        "tostatus": false,
        "complete": "payload",
        "targetType": "msg",
        "statusVal": "",
        "statusType": "auto",
        "x": 910,
        "y": 340,
        "wires": []
    },
    {
        "id": "8619fc0fa7da3fe8",
        "type": "good-face-detection",
        "z": "83078a0b9760cbee",
        "name": "",
        "returnValue": "2",
        "threshold": 0.5,
        "absolutePathDir": "C:\\Users\\SSAFY\\Desktop\\test",
        "x": 720,
        "y": 340,
        "wires": [
            [
                "bf67e15413744e7a"
            ]
        ]
    },
    {
        "id": "0f11aafbbf09699e",
        "type": "file in",
        "z": "83078a0b9760cbee",
        "name": "Image Path",
        "filename": "C:\\Users\\SSAFY\\Desktop\\ssdc\\object\\플로우만들기\\test.png",
        "filenameType": "str",
        "format": "",
        "chunk": false,
        "sendError": false,
        "encoding": "none",
        "allProps": false,
        "x": 510,
        "y": 340,
        "wires": [
            [
                "8619fc0fa7da3fe8"
            ]
        ]
    },
    {
        "id": "b9dc304adfa64f1c",
        "type": "inject",
        "z": "83078a0b9760cbee",
        "name": "Inject",
        "props": [
            {
                "p": "payload"
            },
            {
                "p": "topic",
                "vt": "str"
            }
        ],
        "repeat": "",
        "crontab": "",
        "once": false,
        "onceDelay": "3",
        "topic": "",
        "payload": "",
        "payloadType": "date",
        "x": 350,
        "y": 340,
        "wires": [
            [
                "0f11aafbbf09699e"
            ]
        ]
    }
]

Discussions and suggestions

Use GitHub Issues to ask questions or to discuss new features.

Authors

GOOD-I-DEER in SSAFY(Samsung Software Academy for Youth) 9th

Copyright and license

Copyright Samsung Automation Studio Team under the GNU General Public License v3.0 license.

Reference