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ode/issues"},"author":{"name":"EyePop.ai"},"license":"MIT","homepage":"https://github.com/eyepop-ai/eyepop-sdk-node#readme","keywords":["AI","CV","ML","EyePop"],"repository":{"url":"git+https://github.com/eyepop-ai/eyepop-sdk-node.git","type":"git","directory":"src/eyepop-render-2d"},"description":"The Node.js / Typescript library for 2d rendering of EyePop.ai's inference API","maintainers":[{"name":"andy_eyepop.ai","email":"andy@eyepop.ai"},{"name":"tschulz_ep","email":"torsten@eyepop.ai"}],"readme":"# EyePop.ai Render 2d Node Module\n\nThis EyePop.ai Node Module provides convenient 2d rendering functions for predictions returned by\nto the EyePop.ai's inference API from applications written in the TypeScript or JavaScript language.\n\nThe module requires the [EyePop Node SDK](https://www.npmjs.com/package/@eyepop.ai/eyepop)\n\n## Installation\n\n### Node\n\n```shell\nnpm install --save @eyepop.ai/eyepop @eyepop.ai/eyepop-render-2d\n```\n\n### Browser\n\n```html\n<script src=\"https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop/dist/eyepop.min.js\"></script>\n<script src=\"https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop-render-2d/dist/eyepop.render2d.min.js\"></script>\n```\n\n## Usage example\n\n### Node\n\nThis EyePop Node Module provides 2d rendering for predictions using `canvas.CanvasRenderingContext2D`.\n\n```typescript\nimport { EyePop } from '@eyepop.ai/eyepop'\nimport { Render2d } from '@eyepop.ai/eyepop-render-2d'\n\nimport { createCanvas, loadImage } from 'canvas'\nimport { open } from 'openurl'\nimport { mkdtempSync, writeFileSync } from 'node:fs'\nimport { join } from 'node:path'\nimport { tmpdir } from 'node:os'\n\nconst example_image_path = 'examples/example.jpg'\n\n;(async () => {\n    const image = await loadImage(example_image_path)\n    const canvas = createCanvas(image.width, image.height)\n    const context = canvas.getContext('2d')\n    const renderer = Render2d.renderer(context)\n\n    context.drawImage(image, 0, 0)\n\n    const endpoint = await EyePop.workerEndpoint({\n        pop: {\n            components: [\n                {\n                    type: 'inference',\n                    ability: 'eyepop.person:latest',\n                    categoryName: 'person',\n                },\n            ],\n        },\n    }).connect()\n    try {\n        let results = await endpoint.process({ source: { path: example_image_path } })\n        for await (let result of results) {\n            renderer.draw(result)\n        }\n    } finally {\n        await endpoint.disconnect()\n    }\n\n    const tmp_dir = mkdtempSync(join(tmpdir(), 'ep-demo-'))\n    const temp_file = join(tmp_dir, 'out.png')\n    console.log(`creating temp file: ${temp_file}`)\n\n    const buffer = canvas.toBuffer('image/png')\n    writeFileSync(temp_file, buffer)\n\n    open(`file://${temp_file}`)\n})()\n```\n\n### Browser\n\n```html\n<!doctype html>\n<html lang=\"en\">\n    <head>\n        <script src=\"https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop/dist/eyepop.min.js\"></script>\n        <script src=\"https://cdn.jsdelivr.net/npm/@eyepop.ai/eyepop-render-2d/dist/eyepop.render2d.min.js\"></script>\n    </head>\n    <body>\n        <!-- ... -->\n        <input\n            type=\"file\"\n            id=\"my-file-chooser\"\n        />\n        <!-- ... -->\n        <canvas id=\"my-canvas\"></canvas>\n        <!-- ... -->\n        <script>\n            async uploadFile(event) {\n                const fileChooser = document.getElementById('my-file-chooser');\n                const context = document.getElementById('my-canvas').getContext(\"2d\");\n                const renderer = Render2d.renderer(context);\n\n                const endpoint = await EyePop.workerEndpoint({auth: {oAuth2: true}}).connect();\n                await endpoint.changePop({ components: [{\n                    type: 'inference',\n                    model: 'eyepop.person:latest',\n                    categoryName: 'person'\n                }]})\n                await endpoint.process({ source: {file: fileChooser.files[0]} }).then(async (results) => {\n                    for await (let result of results) {\n                        renderer.draw(result);\n                    }\n                });\n                await endpoint.disconnect();\n            });\n        </script>\n    </body>\n</html>\n```\n\n### Rendering Rules\n\nBy default, the 2d renderer renders boxes and class-labels for every top level object in the prediction.\nChange this rendering behaviour by passing in rendering rule(s), e.g.:\n\n```javascript\n// ...\nRender2d.renderer(context, [Render2d.renderFace()]).draw(result)\n// ...\n```\n\nEach 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.\nSee [JSONPath expression](https://www.npmjs.com/package/jsonpath)\n\nMost prebuild render classes provide a reasonable defaults, as shown below.\n\n#### Rendering Bounding Boxes and Class Labels\n\n```typescript\nRender2d.renderBox({\n    showClass: true,\n    showConfidence: false,\n    showTrackId: false,\n    showNestedClasses: false,\n    target: '$..objects.*',\n})\n```\n\n#### Render Human Body Poses (2d or 3d)\n\n```typescript\nRender2d.renderPose({\n    target: '$..objects[?(@.category==\"person\")]',\n})\n```\n\n#### Render Human Hand Details\n\n```typescript\nRender2d.renderHand({\n    target: '$..objects[?(@.classLabel==\"hand circumference\")]',\n})\n```\n\n#### Render Human Faces\n\n```typescript\nRender2d.renderFace({\n    showLabels: false,\n    target: '$..objects[?(@.classLabel==\"face\")]',\n})\n```\n\n#### Render Text (OCR Overlay)\n\n```typescript\nRender2d.renderText({\n    target: '$..objects[?(@.classLabel==\"text\")]',\n})\n```\n\n#### Render Classification Labels\n\nRenders whole-image classification labels. Defaults shown:\n\n```typescript\nRender2d.renderClasses({\n    showConfidence: false,\n    labelSizePercentage: 0.05,\n    target: '$',\n})\n```\n\n#### Render Key Points\n\nRenders key points for any object that carries them. Defaults shown:\n\n```typescript\nRender2d.renderKeypoints({\n    showLabels: false,\n    target: '$..objects[?(@.keyPoints)]',\n})\n```\n\n#### Render Segmentation Masks\n\n```typescript\nRender2d.renderMask({\n    target: '$..objects[?(@.mask)]',\n})\n```\n\n#### Render Segmentation Contours\n\n```typescript\nRender2d.renderContour({\n    target: '$..objects[?(@.contours)]',\n})\n```\n\n#### Render Depth Maps\n\nRenders the frame-level depth map produced by depth estimation abilities (e.g. `eyepop.depth.*`)\nas a turbo-colormap heatmap blended over the frame: near = warm (red/yellow), far = cool (blue).\nSky pixels (+Infinity) leave the frame untouched unless `renderSky` is set.\n\n```typescript\nRender2d.renderDepth({\n    target: '$',\n    opacity: 0.5, // overlay opacity over the underlying frame\n    renderSky: false, // also paint sky pixels\n})\n```\n\n#### Blur an Object\n\n```typescript\nRender2d.renderBlur({\n    target: '$..objects[?(@.classLabel==\"face\")]',\n})\n```\n\nNote: this currently paints a solid fill over the object rather than applying a true blur.\n\n#### Render a Trail of a traced object over time\n\n```typescript\nRender2d.renderTrail({\n    target: '$..objects[?(@.traceId)]',\n    trailLengthSeconds: 1,\n})\n```\n\nBy 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.\nE.g. trail the nose of every traced person:\n\n```typescript\nRender2d.renderTrail({\n    target: '$..objects[?(@.traceId)]',\n    trailLengthSeconds: 1,\n    traceDetails: '$..keyPoints[?(@.category==\"3d-body-points\")].points[?(@.classLabel.includes(\"nose\"))]',\n})\n```\n\n#### Custom render implementation\n\nTo implement custom rendering rules, create a custom class as follows:\n\n```typescript\nimport { PredictedObject, StreamTime } from '@eyepop.ai/eyepop'\n\nexport interface Render {\n    target: string\n    start(context: CanvasRenderingContext2D, style: Style): void\n    draw(element: any, xOffset: number, yOffset: number, xScale: number, yScale: number, streamTime: StreamTime): void\n}\n\nexport class RenderCustomImage implements Render {\n    public target: string = \"$..objects[?(@.category=='person')]\"\n\n    private context: CanvasRenderingContext2D | undefined\n    private style: any\n\n    // Optionally, add a constructor here. ie: constructor(...) { }\n\n    // The start method sets the context and style properties.\n    start(context: CanvasRenderingContext2D, style: any) {\n        this.context = context\n        this.style = style\n    }\n\n    // The draw method draws the image and line on the canvas based on the positions of the points in the PredictedObject.\n    public draw(\n        element: PredictedObject, // The object containing the keypoints\n        xOffset: number, // The x offset for the drawing\n        yOffset: number, // The y offset for the drawing\n        xScale: number, // The x scale factor for the drawing\n        yScale: number, // The y scale factor for the drawing\n        streamTime: StreamTime, // The timestamp for the stream of predicted objects\n    ): void {\n        // Check if the context and style properties have been set\n        if (!this.context || !this.style) {\n            throw new Error('render() called before start()')\n        }\n\n        // Check if the keypoints are defined\n        if (!element.keyPoints) return\n        if (!element.keyPoints.length) return\n\n        // Find the points of interest in the keypoints\n        for (let i = 0; i < element.keyPoints.length; i++) {\n            const kp = element.keyPoints[i]\n            for (let j = 0; j < kp.points.length; j++) {\n                const point = kp.points[j]\n                if (point.classLabel === 'left eye' || point.classLabel === 'right eye') {\n                    // Draw with the canvas context here\n                }\n            }\n        }\n    }\n}\n```\n","readmeFilename":"README.md"}