This EyePop.ai Node Module provides convenient 2d rendering functions for predictions returned by to the EyePop.ai's inference API from applications written in the TypeScript or JavaScript language.
The module requires the EyePop Node SDK
npm install --save @eyepop.ai/eyepop @eyepop.ai/eyepop-render-2d<script src="https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop/dist/eyepop.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop-render-2d/dist/eyepop.render2d.min.js"></script>This EyePop Node Module provides 2d rendering for predictions using canvas.CanvasRenderingContext2D.
import { EyePop } from '@eyepop.ai/eyepop';
import { Render2d } from '@eyepop.ai/eyepop-render-2d';
import {createCanvas, loadImage} from "canvas";
import {open} from 'openurl';
import { mkdtempSync, writeFileSync } from 'node:fs';
import { join } from 'node:path';
import { tmpdir } from 'node:os';
const example_image_path = 'examples/example.jpg';
(async() => {
const image = await loadImage(example_image_path)
const canvas = createCanvas(image.width, image.height)
const context = canvas.getContext("2d")
const renderer = Render2d.renderer(context)
context.drawImage(image, 0, 0)
const endpoint = await EyePop.endpoint().connect()
try {
let results = await endpoint.process({path: example_image_path})
for await (let result of results) {
renderer.draw(result)
}
} finally {
await endpoint.disconnect()
}
const tmp_dir = mkdtempSync(join(tmpdir(), 'ep-demo-'))
const temp_file = join(tmp_dir, 'out.png')
console.log(`creating temp file: ${temp_file}`)
const buffer = canvas.toBuffer('image/png')
writeFileSync(temp_file, buffer)
open(`file://${temp_file}`)
})();<!DOCTYPE html>
<html lang="en">
<head>
<script src="https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop/dist/eyepop.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop-render-2d/dist/eyepop.render2d.min.js"></script>
</head>
<body>
<!-- ... -->
<input type="file" id="my-file-chooser">
<!-- ... -->
<canvas id="my-canvas"></canvas>
<!-- ... -->
<script>
async
uploadFile(event)
{
const fileChooser = document.getElementById('my-file-chooser');
const context = document.getElementById('my-canvas').getContext("2d");
const renderer = Render2d.renderer(context);
const endpoint = await EyePop.workerEndpoint({auth: {oAuth2: true}, popId: '< Pop Id>'}).connect();
endpoint.process({file: fileChooser.files[0]}).then(async (results) => {
for await (let result of results) {
renderer.draw(result);
}
});
await endpoint.disconnect();
}
)
;
</script>
</body>
</html>By default, the 2d renderer renders boxes and class-labels for every top level object in the prediction. Change this rendering behaviour by passing in rendering rule(s), e.g.:
// ...
Render2d.renderer(context,[Render2.renderFace()]).draw(result);
// ...Each rule has a render object and a target attribute. All prebuild render classes accept a JSONPath expression as target parameter to select which elements should be rendered from predictions.
See JSONPath expression
Most prebuild render classes provide a reasonable defaults, as shown below.
Render2d.renderBox({
showClass = true,
showConfidence = false,
showTraceId = false,
showNestedClasses = false,
target = '$..objects.*'
})Render2d.renderPose({
target: '$..objects[?(@.category=="person")]'
})Render2d.renderHand({
target: '$..objects[?(@.classLabel=="hand circumference")]'
})Render2d.renderFace({
showLabels = false,
target = '$..objects[?(@.classLabel=="face")]'
}) Render2d.renderText({
target: '$..objects[?(@.classLabel=="text")]'
})Render2d.renderMask({
target: '$..objects[?(@.mask)]'
}) Render2d.renderContour({
target: '$..objects[?(@.contours)]'
}) Render2d.renderBlur({
target: '$..objects[?(@.classLabel=="face")]'
})Render2d.renderTrail({
target: '$..objects[?(@.traceId)]',
trailLengthSeconds:1,
})By default, this traces the mid-point of the object's bounding box. Instead, one can also draw trails of sub-objects or key points of the traced object. Use the optional parameter traceDetails for this purpose.
E.g. trail the nose of every traced person:
Render2d.renderTrail({
target: '$..objects[?(@.traceId)]',
trailLengthSeconds:1,
traceDetails:'$..keyPoints[?(@.category=="3d-body-points")].points[?(@.classLabel.includes("nose"))]'
})To implement custom rendering rules, create a custom class as follows:
import { PredictedObject, StreamTime } from '@eyepop.ai/eyepop'
export interface Render {
target: string
start(context: CanvasRenderingContext2D, style: Style): void
draw(element: any, xOffset: number, yOffset: number, xScale: number, yScale: number, streamTime: StreamTime): void
}
export class RenderCustomImage implements Render
{
public target: string = "$..objects[?(@.category=='person')]"
private context: CanvasRenderingContext2D | undefined
private style: any
// Optionally, add a constructor here. ie: constructor(...) { }
// The start method sets the context and style properties.
start(context: CanvasRenderingContext2D, style: any)
{
this.context = context
this.style = style
}
// The draw method draws the image and line on the canvas based on the positions of the points in the PredictedObject.
public draw(
element: PredictedObject, // The object containing the keypoints
xOffset: number, // The x offset for the drawing
yOffset: number, // The y offset for the drawing
xScale: number, // The x scale factor for the drawing
yScale: number, // The y scale factor for the drawing
streamTime: StreamTime // The timestamp for the stream of predicted objects
): void
{
// Check if the context and style properties have been set
if (!this.context || !this.style)
{
throw new Error('render() called before start()')
}
// Check if the keypoints are defined
if (!element.keyPoints) return
if (!element.keyPoints.length) return
// Find the points of interest in the keypoints
for (let i = 0; i < element.keyPoints.length; i++)
{
const kp = element.keyPoints[ i ]
for (let j = 0; j < kp.points.length; j++)
{
const point = kp.points[ j ]
if (point.classLabel === 'left eye' || point.classLabel === 'right eye')
{
// Draw with the canvas context here
}
}
}
}
}