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305 lines (265 loc) · 12.5 KB
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import SimulatorPlugins from "./reusable/SimulatorPlugins.js"
import StatusTable from "./reusable/StatusTable.js"
import MobileNotifications from "./reusable/MobileNotifications.js"
import { PLUGINS_APIKEY } from "./reusable/apikey.js"
import GoogleMapsLocation from "./reusable/GoogleMapsLocation.js"
async function fetchRowsFromSpreadsheet(spreadsheetId, apiKey) {
// Set the range to A1:Z1000
const range = "A1:Z1000";
// Fetch the rows from the Google Spreadsheet API
const response = await fetch(
`https://sheets.googleapis.com/v4/spreadsheets/${spreadsheetId}/values/${range}?key=${encodeURIComponent(apiKey)}`
);
const json = await response.json();
// Get the headers from the first row
const headers = json.values[0];
// Convert the remaining rows to an array of objects
const rows = json.values.slice(1).map(row => {
const rowObject = {};
for (let i = 0; i < row.length; i++) {
rowObject[headers[i]] = row[i];
}
return rowObject;
});
return rows;
}
const plugin = ({widgets, simulator, vehicle}) => {
let API_PATH = 'https://aiotapp.net/walletdetection/image-upload'
let apikey = 'h644blf0bp1g3k4d8ffkazchyfb412e'
let apisecret = 'yswm5qiyg0lhf45fo3pn1epsv5m01li03094wgwf7hgactxlq76kdd55whymfx'
let endpoint_id = '582a8a02-0357-412f-a31d-865549855e43'
let simInterval = null
const loadSpreadSheet = async () => {
let sheetID = "1KopET4hpEUQqswqvBP1Nx2xljYE7Ws-6kRqH1rxGJv4";
fetchRowsFromSpreadsheet(sheetID, PLUGINS_APIKEY)
.then((rows) => {
SimulatorPlugins(rows, simulator)
})
}
widgets.register("Table",
StatusTable({
apis:["Vehicle.Connectivity.IsConnectivityAvailable","Vehicle.IsMoving", "Vehicle.Cabin.Seat.Row1.Pos1.IsOccupied", "Vehicle.CurrentLocation.Latitude", "Vehicle.CurrentLocation.Longitude"],
vehicle: vehicle,
refresh: 800
}))
let setLocationGlobal = null;
widgets.register("Map", (box) => {
const initialLocation = {
"lat": 46.477127,
"lng": 10.367829
}
GoogleMapsLocation(PLUGINS_APIKEY, box, initialLocation).then(({setLocation}) => {
setLocationGlobal = setLocation
})
})
let container = null
let resultImgDiv = null
let resultRecDiv = null
let imgWidth = 0;
let imgHeight = 0;
widgets.register("Result", (box) => {
container = document.createElement('div')
container.innerHTML = `
<div style="width:100%;height:100%;position: relative">
<div id="resultRec" style="position:absolute;border: 2px solid red;top: 0;left:0;width:0;height:0;z-index:2;"></div>
<img id="resultImg" style="width:100%;height:100%;position:absolute;top:0;left:0;right:0;bottom:0;z-index:1;"
src="https://firebasestorage.googleapis.com/v0/b/digital-auto.appspot.com/o/media%2F2023-05-18_17h11_11.png?alt=media&token=81335e79-b793-4c81-ad40-1e6dc4d93f54"/>
</div>
`
resultImgDiv = container.querySelector("#resultImg")
resultRecDiv = container.querySelector("#resultRec")
box.injectNode(container)
})
widgets.register("Video Panel", (box) => {
container = document.createElement('div')
container.innerHTML =
`
<div id="image" style="display:block;z-index:1;">
<img id="output" width="100%" height="100%"
src="https://firebasestorage.googleapis.com/v0/b/digital-auto.appspot.com/o/media%2F0000.JPG?alt=media&token=4e2bb785-846f-4ee1-8774-0a101b473bca"/>
</div>
<!-- <div id="video" style="display:block; width:100%; height:100%"> -->
<!-- <video id="raw-video" width="100%" height="100%" style="object-fit:fill">
<source src="https://firebasestorage.googleapis.com/v0/b/digital-auto.appspot.com/o/media%2Fwallet-detection%2Fwallet-detection-default.mp4?alt=media&token=e7a9ed4e-a463-4bd8-be45-af1a3e498f51" type="video/mp4"></source>
</video> -->
<!-- <div style="width:3em;cursor: pointer;position:absolute;bottom:45%;left:45%"" id="play-btn">
<img src="https://firebasestorage.googleapis.com/v0/b/digital-auto.appspot.com/o/media%2Fplay.svg?alt=media&token=4f68e20d-5c11-4e2c-9ae3-7f44ebdd0416" alt="play" style="filter: invert(100%);">
</div> -->
<!-- </div> -->
<div class="btn btn-color" style="display:flex;z-index:2; position:absolute; width: 100%; bottom: 10px; opacity:85%; align-items:center; align-content:center; flex-direction:row; justify-content:center">
<button id="upload-btn" style="background-color: rgb(104 130 158);padding: 10px 24px;cursor: pointer;float: left;margin:2px;border-radius:5px;font-size:1em;font-family:Lato;color: rgb(255, 255, 227);border:0px">
Upload
</button>
<button id="submit-btn" style="background-color: rgb(104 130 158);padding: 10px 24px;cursor: pointer;float: left;margin:2px;border-radius:5px;font-size:1em;font-family:Lato;color: rgb(255, 255, 227);border:0px">
Submit
</button>
<input id="upload" type="file" accept="image/*" style="display:none">
</div>
`
const upload_btn = container.querySelector("#upload-btn")
const upload = container.querySelector("#upload")
upload_btn.onclick = () => {
// container.querySelector("#upload").click()
if(upload) upload.click()
}
let imageEncoded = null
const img_output = container.querySelector('#output');
const img = container.querySelector("#image")
upload.onchange = (event) => {
img_output.src = URL.createObjectURL(event.target.files[0]);
img.style = "display: block"
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d');
var base_image = new Image();
base_image.src = img_output.src;
base_image.onload = function() {
canvas.width = base_image.width;
canvas.height = base_image.height;
imgWidth = base_image.width;
imgHeight = base_image.height;
ctx.drawImage(base_image, 0, 0);
imageEncoded = canvas.toDataURL('image/jpeg')
canvas.remove();
}
// const video = container.querySelector("#raw-video");
// container.querySelector("#video").style = "display: none"
// video.innerHTML = `<source src=${URL.createObjectURL(event.target.files[0])} type="video/mp4"></source>`
// video.load()
// container.querySelector("#video").style = "display: block"
}
const imageUpload = async (image) => {
image = image.replace('data:image/jpeg;base64,', '')
const res = await fetch(
API_PATH, {
method:'POST',
mode: 'cors',
cache: 'no-cache',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
image,
apikey,
apisecret,
endpoint_id
})
});
// waits until the request completes...
if (!res.ok) {
const message = `An error has occured: ${res.status}`;
throw new Error(message);
}
//conver response to json
const response = await res.json()
let op = response["output"]
op = op.replaceAll('\"', "'").replaceAll('"{', '{').replaceAll('"}','}').replaceAll("'", '"')
return op
}
const videoUpload = async () => {
const data = new FormData()
data.append('file', upload.files[0])
console.log(data)
const res = await fetch(
`https://predict.app.landing.ai/inference/v1/predict?endpoint_id=582a8a02-0357-412f-a31d-865549855e43`, {
method:'POST',
mode: 'cors',
headers: {
'Content-Type': 'multipart/form-data',
'apikey':'h644blf0bp1g3k4d8ffkazchyfb412e',
'apisecret':'yswm5qiyg0lhf45fo3pn1epsv5m01li03094wgwf7hgactxlq76kdd55whymfx'
},
body: data
});
// waits until the request completes...
if (!res.ok) {
const message = `An error has occured: ${res.status}`;
throw new Error(message);
}
//convert response to json
const response = await res.json()
return response
}
const submit_btn = container.querySelector("#submit-btn")
submit_btn.onclick = async () => {
// console.log(setLocationGlobal)
const res = await imageUpload(imageEncoded)
// console.log(resultImgDiv)
// console.log(imageEncoded)
if(res) {
let resData = JSON.parse(res)
// console.log(resData)
if(resData && resData.backbonepredictions) {
for(let key in resData.backbonepredictions) {
let coordinates = resData.backbonepredictions[key].coordinates
// console.log("res.backbonepredictions.coordinates", coordinates)
if(resultImgDiv) {
resultImgDiv.src = imageEncoded;
let imgWidthDiv = resultImgDiv.width
let imgHeightDiv = resultImgDiv.height
let xmax = coordinates.xmax
let xmin = coordinates.xmin
let ymax = coordinates.ymax
let ymin = coordinates.ymin
let leftPercent = (1.0*xmin)/(imgWidth*1.0)
let topPercent = (1.0*ymin)/(imgHeight*1.0)
let widthPercent = (xmax-xmin)/(imgWidth*1.0)
let heightPercent = (ymax-ymin)/(imgHeight*1.0)
resultRecDiv.style.left = `${imgWidthDiv * leftPercent}px`
resultRecDiv.style.top = `${imgHeightDiv * topPercent}px`
resultRecDiv.style.width = `${imgWidthDiv * widthPercent}px`
resultRecDiv.style.height = `${imgHeightDiv * heightPercent}px`
}
break;
}
}
}
}
/* const play_btn = container.querySelector("#play-btn")
play_btn.onclick = () => {
container.querySelector("#raw-video").play();
} */
box.injectNode(container)
return () => {
clearInterval(simInterval)
}
})
let mobileNotificationsGlobal = null;
widgets.register("Mobile",
(box) => {
const {printNotification} = MobileNotifications(
{
box:box,
backgroundColor: "rgb(0 80 114)"
}
)
mobileNotificationsGlobal = printNotification
})
let count = 0;
return {
load_data: async () => {
loadSpreadSheet()
},
mobile_notification: (message) => {
mobileNotificationsGlobal(message)
},
set_api_info: (in_api_path, in_api_key, in_api_secret, in_endpoint_id) => {
API_PATH = in_api_path || 'https://aiotapp.net/walletdetection/image-upload'
apikey = in_api_key || 'h644blf0bp1g3k4d8ffkazchyfb412e'
apisecret = in_api_secret || 'yswm5qiyg0lhf45fo3pn1epsv5m01li03094wgwf7hgactxlq76kdd55whymfx'
endpoint_id = in_endpoint_id || '582a8a02-0357-412f-a31d-865549855e43'
},
start_simulation: (time) => {
simInterval = setInterval(async () => {
const lat = parseFloat(await vehicle.CurrentLocation.Latitude.get())
const lng = parseFloat(await vehicle.CurrentLocation.Longitude.get())
setLocationGlobal({lat, lng})
if(count === 0) {
container.querySelector("#raw-video").play()
count++
}
await vehicle.Next.get()
}, time)
}
}
}
export default plugin;