- NOGO in player, load the hardcoded from a file - clean up the code and demo the case - link the hardcoded one with the image of the multi marker --- # Porting to a-frame - port only multi markers controls - start the port - it will make it easier to do demo and accessible for aframe people - preset='multimarker-localstorage' - put the smoother on option for aframe - use the profile by default - remove all the default from the aframe configuration - so first, init basic of profile, then push specific configuration from configuration - avoid to duplicate all the default --- - posts "Area Learning with Marker-based Augmented Reality" - larger AR - more stable AR - describe workflow: dynamic learning + usage - screenshots of the experience - describe the algorightm you used to learn and to use multi markers - algo definition: ALGORITHM.md here - how to learn - how to use - may become a post on medium --- # new Learning UI - DONE in learner, display all the markers as text - each got a progress in percent - display percent in red - when percent is 100%, display check character in green - find function to get name of marker (worst case scenario - display index) - type + patternUrl basename ? - add a .name() function in the controls ? - progress is directly the result.confidenceFactor clamp [0, 1] - display the origin markers - DONE Global progress for the whole area - sum of all progress divided by the number of sub markers. - It should exclude the origin markers - change compute .computeAverageMatrix() - rename to .computeResult() - put a confidence factor in the Number - userData.markerLearningResult.averageMatrix / .confidenceFactor - what if the result can not be computed ? - .averageMatrix === null && .confidenceFactor === 0 - .computeResult() MUST support unlearned markers - aka we can learn among X markers and get a valid result for less markers - it MUST work in the learner, in the result production, and in the player - result.confidenceFactor is about amount of sample used to compute the averageMatrix. - Say that 200 samples is deemed good enough. - result.confidenceFactor is n-samples / required-n-samples. - notes that it can go above 1 # new learning algo --- - make all markers children of a parent THREEx.ArBaseControls() - GOAL: make it explicit what is expected from a AR controls - emit event - have id - anything which is common - TODO put a smoother on sub-marker while learning ? as a way to remove noise ? - do it and hide it behind a flags - in relation with THREEx.ArBaseControls - add a-frame support - support for learning new area - can i just use the area-learner.html - support of multi-marker description - DONE make a function in controls to compute the center of the multi markers - it can be used as a default position for the origin controls.computer - this is simply the average of all sub-marker matrix - so compute all the sub-markers matrix - make a video for it - recorded on the phab2 - abcf - Multi marker with AR.js - insert: Designed for easy workflow - Step 1: scan all markers with your phone - Step 2: once done, you just use it! - show what happen when you go close to the markers at low altitude - insert: more stable - insert: only one is visible? still works! - what to do with the official multimarker support - make an example for it, and keep supporting it - issue with the last jsartoolkit - park multimarker.html - do an apps which does something with it - a minecraft going from submarkers to submarkers - do a post about multimarker - https://medium.com/p/4bcafc785dfd/edit - DONE merge it all in the README.md