learning_selfie_segmentation

Description:

learning selfie segmentation

ML Selfie Segmentation #
The easy way to use ML Kit for selfie segmentation in Flutter.
ML Kit's Selfie Segmentation allows developers to easily separate the background from users within a scene and focus on what matters. Adding cool effects to selfies or inserting your users into interesting background environments has never been easier.
It takes an input image and produces an output mask. By default, the mask will be the same size as the input image. Each pixel of the mask is assigned a float number that has a range between [0.0, 1.0]. The closer the number is to 1.0, the higher the confidence that the pixel represents a person, and vice versa.
It works with static images and live video use cases. During live video, it will leverage output from previous frames to return smoother segmentation results.

Getting Started #
Add dependency to your flutter project:
$ flutter pub add learning_selfie_segmentation
copied to clipboard
or
dependencies:
learning_selfie_segmentation: ^0.0.2
copied to clipboard
Then run flutter pub get.
Usage #
import 'package:learning_selfie_segmentation/learning_selfie_segmentation.dart';
copied to clipboard
Input Image #
As in other ML vision plugins, input is fed as an instance of InputImage, which is part of package learning_input_image.
You can use widget InputCameraView from learning_input_image as default implementation for processing image (or image stream) from camera / storage into InputImage format. But feel free to learn the inside of InputCameraView code if you want to create your own custom implementation.
Here is example of using InputCameraView to get InputImage for selfie segmentation.
import 'package:learning_input_image/learning_input_image.dart';

InputCameraView(
title: 'Selfie Segmentation',
onImage: (InputImage image) {
// now we can feed the input image into selfie segmenter
},
)
copied to clipboard
Selfie Segmentation #
After getting the InputImage, we can start doing selfie segmentation by calling method process from an instance of SelfieSegmenter.
SelfieSegmenter segmenter = SelfieSegmenter();
SegmentationMask? mask = await segmenter.process(image);
copied to clipboard
SelfieSegmenter is instantiated with default parameters as following.
SelfieSegmenter segmenter = SelfieSegmenter(
isStream: true,
enableRawSizeMask: false,
)
copied to clipboard
But we can override this by passing other values.


Parameter
Value
Default


isStream
false / true
true


enableRawSizeMask
false / true
false


Output #
The result of selfie segmentation process is a SegmentationMask object that contains the following data.
int width; // width of segmented mask
int height; // height of segmented mask
List confidences // list of values representing the confidence of the pixel in the mask being in the foreground
copied to clipboard
Segmentation Mask Painting #
To make it easy to paint from SegmentationMask to the screen, we provide SegmentationOverlay which you can pass to parameter overlay of InputCameraView. For more detail about how to use this painting, you can see at the working example code here.
SegmentationOverlay(
size: size,
originalSize: originalSize,
rotation: rotation,
mask: segmentationMask,
)
copied to clipboard
Dispose #
segmenter.dispose();
copied to clipboard
Example Project #
You can learn more from example project here.