1684948140
A Signature Pad Widget That Help Draw Smooth Signatures in Flutter
A Flutter plugin providing Signature Pad for drawing smooth signatures. Library is written in pure Dart/Flutter environment to provide support for all platforms..
Easy to use library with variety of draw and export settings. Also supports SVG files.
Signature pad drawing is based on Cubic Bézier curves.
Offers to choose between performance and beauty mode.
Usage
import 'package:hand_signature/signature.dart';
With HandSignatureControl and HandSignature is possible to tweak some drawing aspects like stroke width, smoothing ratio or velocity weight.
final control = HandSignatureControl(
threshold: 3.0,
smoothRatio: 0.65,
velocityRange: 2.0,
);
final widget = HandSignature(
control: control,
color: Colors.blueGrey,
strokeWidth: 1.0,
maxStrokeWidth: 10.0,
type: SignatureDrawType.shape,
);
HandSignatureControl sets up 'math' to control input touches and handles control points of signature curve.
- threshold: (LP) controls minimal distance between two points - higher distance creates smoother curve, but less precise. Higher distance also creates bigger input draw lag.
- smoothRatio: (0 - 1) controls how smooth curve will be - higher ratio creates smoother curve, but less precise. In most of cases are best results with values between 0.5 - 0.75.
- velocityRange: (LP per millisecond) controls curve size based on distance and duration between two points. Thin line - fast move, thick line - slow move. With higher velocityRange user must swing faster to draw thinner line.
- reverseVelocity: swaps stroke width. Thin line - slow move, thick line - fast move. Simply swaps min/max size based on velocity.
HandSignature sets up visual style of signature curve.
- control: processes input, handles math and stores raw data.
- color: just color of line.
- strokeWidth: minimal width of line. Width at maximum swing speed (clamped by velocityRange).
- maxStrokeWidth: maximum width of line. Width at slowest swing speed.
- type: draw type of curve. Default and main draw type is shape - not so nice as arc, but has better performance. And line is simple path with uniform stroke width.
- line: basic Bezier line with best performance.
- shape: like Ink drawn signature with still pretty good performance.
- arc: beauty mode for Ink styled signature.
Export
Properties, like canvas size, stroke min/max width and color can be modified during export.
There are more ways and more formats how to export signature, most used ones are svg and png formats.
final control = HandSignatureControl();
final svg = control.toSvg();
final png = control.toImage();
final json = control.toMap();
control.importData(json);
Svg: SignatureDrawType shape generates reasonably small file and is read well by all programs. On the other side arc generates really big svg file and some programs can have hard times handling so much objects. Line is simple Bezier Curve.
Image: Export to image supports ImageByteFormat and provides png or raw rgba data.
Json/Map: Exports current state - raw data that can be used later to restore state.
Parsing and drawing saved SVG
Exported svg String is possible to display in another lib like: flutter_svg.
Use this package as a library
Depend on it
Run this command:
With Flutter:
$ flutter pub add hand_signatureThis will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):
dependencies:
hand_signature: ^3.0.1Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.
Import it
Now in your Dart code, you can use:
import 'package:hand_signature/signature.dart';import 'dart:typed_data';
import 'package:flutter/cupertino.dart';
import 'package:flutter/material.dart';
import 'package:flutter_svg/flutter_svg.dart';
import 'package:hand_signature/signature.dart';
import 'scroll_test.dart';
void main() => runApp(MyApp());
HandSignatureControl control = new HandSignatureControl(
threshold: 0.01,
smoothRatio: 0.65,
velocityRange: 2.0,
);
ValueNotifier<String?> svg = ValueNotifier<String?>(null);
ValueNotifier<ByteData?> rawImage = ValueNotifier<ByteData?>(null);
ValueNotifier<ByteData?> rawImageFit = ValueNotifier<ByteData?>(null);
class MyApp extends StatelessWidget {
bool get scrollTest => false;
// This widget is the root of your application.
@override
Widget build(BuildContext context) {
return MaterialApp(
title: 'Signature Demo',
theme: ThemeData(
primarySwatch: Colors.blue,
),
home: Scaffold(
backgroundColor: Colors.orange,
body: scrollTest
? ScrollTest()
: SafeArea(
child: Stack(
children: <Widget>[
Column(
children: <Widget>[
Expanded(
child: Center(
child: AspectRatio(
aspectRatio: 2.0,
child: Stack(
children: <Widget>[
Container(
constraints: BoxConstraints.expand(),
color: Colors.white,
child: HandSignature(
control: control,
type: SignatureDrawType.shape,
),
),
CustomPaint(
painter: DebugSignaturePainterCP(
control: control,
cp: false,
cpStart: false,
cpEnd: false,
),
),
],
),
),
),
),
Row(
children: <Widget>[
CupertinoButton(
onPressed: () {
control.clear();
svg.value = null;
rawImage.value = null;
rawImageFit.value = null;
},
child: Text('clear'),
),
CupertinoButton(
onPressed: () async {
svg.value = control.toSvg(
color: Colors.blueGrey,
type: SignatureDrawType.shape,
fit: true,
);
rawImage.value = await control.toImage(
color: Colors.blueAccent,
background: Colors.greenAccent,
fit: false,
);
rawImageFit.value = await control.toImage(
color: Colors.black,
background: Colors.greenAccent,
fit: true,
);
},
child: Text('export'),
),
],
),
SizedBox(
height: 16.0,
),
],
),
Align(
alignment: Alignment.bottomRight,
child: Column(
mainAxisSize: MainAxisSize.min,
children: <Widget>[
_buildImageView(),
_buildScaledImageView(),
_buildSvgView(),
],
),
),
],
),
),
),
);
}
Widget _buildImageView() => Container(
width: 192.0,
height: 96.0,
decoration: BoxDecoration(
border: Border.all(),
color: Colors.white30,
),
child: ValueListenableBuilder<ByteData?>(
valueListenable: rawImage,
builder: (context, data, child) {
if (data == null) {
return Container(
color: Colors.red,
child: Center(
child: Text('not signed yet (png)\nscaleToFill: false'),
),
);
} else {
return Padding(
padding: EdgeInsets.all(8.0),
child: Image.memory(data.buffer.asUint8List()),
);
}
},
),
);
Widget _buildScaledImageView() => Container(
width: 192.0,
height: 96.0,
decoration: BoxDecoration(
border: Border.all(),
color: Colors.white30,
),
child: ValueListenableBuilder<ByteData?>(
valueListenable: rawImageFit,
builder: (context, data, child) {
if (data == null) {
return Container(
color: Colors.red,
child: Center(
child: Text('not signed yet (png)\nscaleToFill: true'),
),
);
} else {
return Container(
padding: EdgeInsets.all(8.0),
color: Colors.orange,
child: Image.memory(data.buffer.asUint8List()),
);
}
},
),
);
Widget _buildSvgView() => Container(
width: 192.0,
height: 96.0,
decoration: BoxDecoration(
border: Border.all(),
color: Colors.white30,
),
child: ValueListenableBuilder<String?>(
valueListenable: svg,
builder: (context, data, child) {
if (data == null) {
return Container(
color: Colors.red,
child: Center(
child: Text('not signed yet (svg)'),
),
);
}
return Padding(
padding: EdgeInsets.all(8.0),
child: SvgPicture.string(
data,
placeholderBuilder: (_) => Container(
color: Colors.lightBlueAccent,
child: Center(
child: Text('parsing data(svg)'),
),
),
),
);
},
),
);
}Download Details:
Author: basecontrol.dev
Source Code: https://github.com/romanbase/hand_signature
1673727660
Yoha: A Practical Hand Tracking Engine
Yoha
A practical hand tracking engine.
Installation
npm install @handtracking.io/yoha
Please note:
- You need to serve the files from
node_modules/@handtracking.io/yohasince the library needs to download the model files from here. (Webpack Example) - You need to serve your page with https for webcam access. (Webpack Example)
- You should use cross-origin isolation as it improves the engine's performance in certain scenarios. (Webpack Example)
Description
Yoha is a hand tracking engine that is built with the goal of being a versatile solution in practical scenarios where hand tracking is employed to add value to an application. While ultimately the goal is to be a general purpose hand tracking engine supporting any hand pose, the engine evolves around specific hand poses that users/developers find useful. These poses are detected by the engine which allows to build applications with meaningful interactions. See the demo for an example.
Yoha is currently in beta.
About the name: Yoha is short for ("Your Hand Tracking").
Language Support
Yoha is currently available for the web via JavaScript. More languages will be added in the future. If you want to port Yoha to another language and need help feel free reach out.
Technical Details
Yoha was built from scratch. It uses a custom neural network trained using a custom dataset. The backbone for the inference in the browser is currently TensorFlow.js
Features:
- Detection of 21 2D-landmark coordinates (single hand).
- Hand presence detection.
- Hand orientation (left/right hand) detection.
- Inbuilt pose detection.
Supported Hand Poses:
- Pinch (index finger and thumb touch)
- Fist
Your desired pose is not on this list? Feel free to create an issue for it.
Performance
Yoha was built with performance in mind. It is able to provide realtime user experience on a broad range of laptops and desktop devices. The performance on mobile devices is not great which hopefuly will change with the further development of inference frameworks like TensorFlow.js
Please note that native inference speed can not be compared with the web inference speed. Differently put, if you were to run Yoha natively it would be much faster than via the web browser.
Minimal Example
- Source
- Running locally:
git clone https://github.com/handtracking-io/yoha && \
cd yoha/example && \
yarn && \
yarn start
Drawing Demo
- Live Version
- Source
- Running locally:
git clone https://github.com/handtracking-io/yoha && \
cd yoha && \
./download_models.sh && \
yarn && \
yarn start
Quick Links:
Download Details:
Author: Handtracking-io
Source Code: https://github.com/handtracking-io/yoha
License: MIT license
