yolov8m-pose / README.md

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	---
	library_name: transformers.js
	tags:
	- pose-estimation
	license: agpl-3.0
	---

	YOLOv8m-pose with ONNX weights to be compatible with Transformers.js.

	## Usage (Transformers.js)

	If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
	```bash
	npm i @xenova/transformers
	```

	Example: Perform pose-estimation w/ `Xenova/yolov8m-pose`.

	```js
	import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers';

	// Load model and processor
	const model_id = 'Xenova/yolov8m-pose';
	const model = await AutoModel.from_pretrained(model_id);
	const processor = await AutoProcessor.from_pretrained(model_id);

	// Read image and run processor
	const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg';
	const image = await RawImage.read(url);
	const { pixel_values } = await processor(image);

	// Set thresholds
	const threshold = 0.3; // Remove detections with low confidence
	const iouThreshold = 0.5; // Used to remove duplicates
	const pointThreshold = 0.3; // Hide uncertain points

	// Predict bounding boxes and keypoints
	const { output0 } = await model({ images: pixel_values });

	// Post-process:
	const permuted = output0[0].transpose(1, 0);
	// `permuted` is a Tensor of shape [ 8400, 56 ]:
	// - 8400 potential detections
	// - 56 parameters for each box:
	// - 4 for the bounding box dimensions (x-center, y-center, width, height)
	// - 1 for the confidence score
	// - 17 * 3 = 51 for the pose keypoints: 17 labels, each with (x, y, visibilitiy)

	// Example code to format it nicely:
	const results = [];
	const [scaledHeight, scaledWidth] = pixel_values.dims.slice(-2);
	for (const [xc, yc, w, h, score, ...keypoints] of permuted.tolist()) {
	if (score < threshold) continue;

	// Get pixel values, taking into account the original image size
	const x1 = (xc - w / 2) / scaledWidth * image.width;
	const y1 = (yc - h / 2) / scaledHeight * image.height;
	const x2 = (xc + w / 2) / scaledWidth * image.width;
	const y2 = (yc + h / 2) / scaledHeight * image.height;
	results.push({ x1, x2, y1, y2, score, keypoints })
	}


	// Define helper functions
	function removeDuplicates(detections, iouThreshold) {
	const filteredDetections = [];

	for (const detection of detections) {
	let isDuplicate = false;
	let duplicateIndex = -1;
	let maxIoU = 0;

	for (let i = 0; i < filteredDetections.length; ++i) {
	const filteredDetection = filteredDetections[i];
	const iou = calculateIoU(detection, filteredDetection);
	if (iou > iouThreshold) {
	isDuplicate = true;
	if (iou > maxIoU) {
	maxIoU = iou;
	duplicateIndex = i;
	}
	}
	}

	if (!isDuplicate) {
	filteredDetections.push(detection);
	} else if (duplicateIndex !== -1 && detection.score > filteredDetections[duplicateIndex].score) {
	filteredDetections[duplicateIndex] = detection;
	}
	}

	return filteredDetections;
	}

	function calculateIoU(detection1, detection2) {
	const xOverlap = Math.max(0, Math.min(detection1.x2, detection2.x2) - Math.max(detection1.x1, detection2.x1));
	const yOverlap = Math.max(0, Math.min(detection1.y2, detection2.y2) - Math.max(detection1.y1, detection2.y1));
	const overlapArea = xOverlap * yOverlap;

	const area1 = (detection1.x2 - detection1.x1) * (detection1.y2 - detection1.y1);
	const area2 = (detection2.x2 - detection2.x1) * (detection2.y2 - detection2.y1);
	const unionArea = area1 + area2 - overlapArea;

	return overlapArea / unionArea;
	}

	const filteredResults = removeDuplicates(results, iouThreshold);

	// Display results
	for (const { x1, x2, y1, y2, score, keypoints } of filteredResults) {
	console.log(`Found person at [${x1}, ${y1}, ${x2}, ${y2}] with score ${score.toFixed(3)}`)
	for (let i = 0; i < keypoints.length; i += 3) {
	const label = model.config.id2label[Math.floor(i / 3)];
	const [x, y, point_score] = keypoints.slice(i, i + 3);
	if (point_score < pointThreshold) continue;
	console.log(` - ${label}: (${x.toFixed(2)}, ${y.toFixed(2)}) with score ${point_score.toFixed(3)}`);
	}
	}
	```

	<details>

	<summary>See example output</summary>

	```
	Found person at [535.503101348877, 39.878777217864986, 644.8351860046387, 346.3689248085022] with score 0.655
	- nose: (444.86, 91.25) with score 0.912
	- left_eye: (449.55, 79.71) with score 0.912
	- right_eye: (436.53, 82.54) with score 0.689
	- left_ear: (457.66, 83.08) with score 0.774
	- left_shoulder: (476.25, 126.43) with score 0.984
	- right_shoulder: (419.05, 129.94) with score 0.675
	- left_elbow: (495.99, 180.55) with score 0.960
	- left_wrist: (504.15, 233.96) with score 0.888
	- left_hip: (469.08, 227.61) with score 0.961
	- right_hip: (428.82, 228.95) with score 0.821
	- left_knee: (474.97, 301.15) with score 0.919
	- right_knee: (434.24, 305.24) with score 0.704
	- left_ankle: (467.31, 384.83) with score 0.625
	- right_ankle: (439.09, 379.35) with score 0.378
	Found person at [-0.08985519409179688, 56.876064038276674, 158.62728118896484, 371.25909755229947] with score 0.902
	- nose: (61.15, 102.21) with score 0.979
	- left_eye: (66.59, 91.92) with score 0.939
	- right_eye: (51.35, 95.02) with score 0.905
	- left_ear: (70.82, 97.11) with score 0.778
	- right_ear: (48.08, 97.46) with score 0.655
	- left_shoulder: (84.60, 139.95) with score 0.997
	- right_shoulder: (38.36, 139.32) with score 0.996
	- left_elbow: (98.25, 196.80) with score 0.990
	- right_elbow: (24.83, 188.15) with score 0.981
	- left_wrist: (103.38, 252.91) with score 0.977
	- right_wrist: (9.42, 233.04) with score 0.965
	- left_hip: (82.91, 247.50) with score 0.999
	- right_hip: (51.28, 248.31) with score 0.999
	- left_knee: (85.25, 326.65) with score 0.997
	- right_knee: (49.12, 330.50) with score 0.996
	- left_ankle: (96.84, 419.45) with score 0.964
	- right_ankle: (51.88, 416.89) with score 0.960
	Found person at [109.41852569580077, 13.203005981445314, 505.06954193115234, 532.9905454635621] with score 0.911
	- nose: (126.16, 102.84) with score 0.586
	- left_eye: (125.44, 84.07) with score 0.352
	- left_ear: (137.38, 77.79) with score 0.722
	- left_shoulder: (181.75, 122.32) with score 0.997
	- right_shoulder: (180.20, 152.15) with score 0.998
	- left_elbow: (262.31, 202.36) with score 0.996
	- right_elbow: (194.94, 277.60) with score 0.997
	- left_wrist: (298.87, 269.32) with score 0.987
	- right_wrist: (132.86, 281.44) with score 0.990
	- left_hip: (272.70, 284.47) with score 1.000
	- right_hip: (274.35, 307.48) with score 1.000
	- left_knee: (247.66, 441.74) with score 0.997
	- right_knee: (256.27, 500.82) with score 0.998
	- left_ankle: (340.54, 455.33) with score 0.848
	- right_ankle: (338.54, 543.24) with score 0.882
	Found person at [425.35156250000006, 68.73829221725464, 640.3047943115234, 494.19192361831665] with score 0.901
	- nose: (425.40, 147.53) with score 0.995
	- left_eye: (432.33, 133.12) with score 0.985
	- right_eye: (410.70, 135.98) with score 0.969
	- left_ear: (440.72, 134.14) with score 0.901
	- right_ear: (400.69, 134.89) with score 0.800
	- left_shoulder: (455.11, 201.19) with score 1.000
	- right_shoulder: (368.64, 201.60) with score 0.999
	- left_elbow: (455.25, 292.03) with score 0.998
	- right_elbow: (350.65, 258.24) with score 0.989
	- left_wrist: (475.06, 370.36) with score 0.992
	- right_wrist: (398.78, 263.84) with score 0.975
	- left_hip: (441.94, 359.78) with score 1.000
	- right_hip: (384.06, 368.70) with score 1.000
	- left_knee: (462.74, 452.41) with score 0.998
	- right_knee: (395.50, 488.42) with score 0.997
	- left_ankle: (465.12, 540.38) with score 0.960
	- right_ankle: (433.43, 569.37) with score 0.938
	```
	</details>