tribe_lib

Virtual Background Bodypix :computer:

Description :page_facing_up:

Using getusermedia as a video input, we offer the effects blur background, blur bodyParts, virtualBackground, grayScale having bodyPix as a backbone! Use bodypix in 3 lines!, making easy for non developers and developers to use bodypix with multiple effects.

Install

Can install it via npm for use in a TypeScript / ES6 project.

$ npm i tribe_lib

How To Use

After installing we simply use our three magic lines, to get a MediaStream Object that you can use for wherever you want.

//Common Javascript
const virtualBackgroundBodypixLite = require('tribe_lib');

//ES6
import { VideoTracking, Prediction } from 'tribe_lib';

//How to Use
const Tracking = new VideoTracking(
  type_model_architecture,
  effect_config_type,
  type_of_device
  width:number, 
  height:number, 
  device_id_str?:string
);
Tracking.predictionModel.loop_(type_prediciton, configEffect);
const MediaStream = Tracking.predictionModel.canvas_mediaStream();

How To Reuse Object

if you want to reuse the VideoTracking object after initialazing it and starting a Loop do the following.

const Tracking = new VideoTracking(
  type_model_architecture,
  effect_config_type,
  type_of_device
  width:number, 
  height:number, 
  device_id_str?:string
);
Tracking.predictionModel.loop_(type_prediciton, configEffect);

//Stop Actual Loop
Tracking.predicitionModel.stopAnimationLoop();

//start another desired Effect
Tracking.predictionModel.loop_(type_prediction_2, configEffect_2);

Models

We can define 5 categories Ultra low, Low, Medium, High, Ultra for model configuration

Categories	Description	Option
Ultra low	Recommended for low-end mobile devices	0
Low	Recommended for mid-end mobile devices	1
Medium	Recommended for computers with at least one intel pentium processor	2
High	Recommended for computers with at least one intel core i3 processor	3

const model_config_ultra_low = {
  architecture: 'MobileNetV1',
  outputStride: 16,
  multiplier: 0.5,
  quantBytes: 2,
};
const model_config_low = {
  architecture: 'MobileNetV1',
  outputStride: 16,
  multiplier: 0.75,
  quantBytes: 2,
};
const model_config_medium = {
  architecture: 'MobileNetV1',
  outputStride: 16,
  multiplier: 0.75,
  quantBytes: 2,
};
const model_config_high = {
  architecture: 'MobileNetV1',
  outputStride: 8,
  multiplier: 1,
  quantBytes: 2,
};
const model_config_ultra = {
  architecture: 'ResNet50',
  outputStride: 16,
  quantBytes: 2,
};

Level Of Prediction

We can define the level of prediction

Categories	Resolution	Option
Low	Minimum	0
Medium	Average	1
High	High	2
ultra	ultra	3

const effect_config_precission_low = {
  flipHorizontal: false,
  internalResolution: 'low',
  segmentationThreshold: 0.7,
};
const effect_config_precission_mid = {
  flipHorizontal: false,
  internalResolution: 'medium',
  segmentationThreshold: 0.7,
};
const effect_config_precission_high = {
  flipHorizontal: false,
  internalResolution: 'high',
  segmentationThreshold: 0.7,
};
const effect_config_precission_high = {
  flipHorizontal: false,
  internalResolution: 'ultra',
  segmentationThreshold: 0.7,
};

Type Of Devices

We can define the dimensions of the video, in addition to which camera to request in mobile.

const mobile_front_camera = {
  audio: false,
  video: { facingMode: 'user', width: width, height: height },
};
const mobile_rear_camera = {
  audio: false,
  video: { facingMode: { exact: 'environment' }, width: width, height: height },
};
const desktop_selected_camera_device = {
  audio: false,
  video: {
    deviceId: '0faf4c1dc3b35ff09df6a31...',
    width: width,
    height: height,
  },
};
const desktop_select_camera_default = {
  audio: false,
  video: { width: width, height: height },
};

Config Effects

These are the effects, the magic of this library, you can define what effect use:

Grey scale

Tracking.predictionModel.loop_(3);
const MediaStream = Tracking.predictionModel.canvas_mediStream();
Tracking.predictionModel.stopAnimationLoop();/*To Stop Loop*/

Blur

Tracking.predictionModel.loop_(1, config_effect_bokek);
const MediaStream = Tracking.predictionModel.canvas_mediaStream();
Tracking.predictionModel.stopAnimationLoop();/*To Stop Loop*/

Virtual Background

Tracking.predictionModel.loop_(2, config_virtual_background);
const MediaStream = Tracking.predictionModel.canvas_mediaStream();
Tracking.predictionModel.stopAnimationLoop();/*To Stop Loop*/

Blur Body Parts

const parts: partsbody = {
  left_face: 0,
  torso_front: 12,
  right_face: 1,
  torso_back: 13,
  left_upper_arm_front: 2,
  left_upper_leg_front: 14,
  left_upper_arm_back: 3,
  left_upper_leg_back: 15,
  right_upper_arm_front: 4,
  right_upper_leg_front: 16,
  right_upper_arm_back: 5,
  right_upper_leg_back: 17,
  left_lower_arm_front: 8,
  left_lower_leg_front: 18,
  left_lower_arm_back: 7,
  left_lower_leg_back: 19,
  right_lower_arm_front: 8,
  right_lower_leg_front: 20,
  right_lower_arm_back: 9,
  right_lower_leg_back: 21,
  left_hand: 10,
  left_foot: 22,
  right_hand: 11,
  right_foot: 23,
};
const config_blur_body_part = { backgroundBlurAmount: 30, edgeBlurAmount: 2.1, faceBodyPartIdsToBlur: [0, 1] };
Tracking.predictionModel.loop_(4, config_blur_body_part);
const MediaStream = Tracking.predictionModel.canvas_mediaStream();
Tracking.predictionModel.stopAnimationLoop();/*To Stop Loop*/

const config_effect_bokek = { backgroundBlurAmount: 20, edgeBlurAmount: 10 };
const config_virtual_background = {
  backgroundBlurAmount: 1,
  edgeBlurAmount: 2.1,
  URL: 'base64',
};
const config_greyScale = {};
const config_blur_body_part = {
  backgroundBlurAmount: 30,
  edgeBlurAmount: 2.1,
  faceBodyPartIdsToBlur: [0, 1],
};

More information about the model

Repo BodyPix: Github

Authors :black_nib:

Hector Lopez Github

Hugo Fernel Github

Jhonathan Angarita Github

David De La Hoz Github