--- library_name: transformers.js tags: - pose-estimation license: agpl-3.0 --- YOLOv8n-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/yolov8n-pose`. ```js import { AutoModel, AutoProcessor, RawImage } from '@xenova/transformers'; // Load model and processor const model_id = 'Xenova/yolov8n-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)}`); } } ```
See example output ``` Found person at [536.1322975158691, 37.87850737571716, 645.2879905700684, 286.9420547962189] with score 0.791 - nose: (445.81, 87.11) with score 0.936 - left_eye: (450.90, 80.87) with score 0.976 - right_eye: (439.37, 81.31) with score 0.664 - left_ear: (460.76, 81.94) with score 0.945 - left_shoulder: (478.06, 126.18) with score 0.993 - right_shoulder: (420.69, 125.17) with score 0.469 - left_elbow: (496.96, 178.36) with score 0.976 - left_wrist: (509.41, 232.75) with score 0.892 - left_hip: (469.15, 215.80) with score 0.980 - right_hip: (433.73, 218.39) with score 0.794 - left_knee: (471.45, 278.44) with score 0.969 - right_knee: (439.23, 281.77) with score 0.701 - left_ankle: (474.88, 345.49) with score 0.913 - right_ankle: (441.99, 339.82) with score 0.664 Found person at [-0.15300750732421875, 59.96129276752472, 158.73897552490234, 369.92224643230435] with score 0.863 - nose: (57.30, 95.37) with score 0.960 - left_eye: (63.85, 89.48) with score 0.889 - right_eye: (53.59, 91.60) with score 0.909 - left_ear: (73.54, 92.67) with score 0.626 - right_ear: (50.12, 95.95) with score 0.674 - left_shoulder: (87.62, 132.72) with score 0.965 - right_shoulder: (39.72, 136.82) with score 0.986 - left_elbow: (108.17, 186.58) with score 0.857 - right_elbow: (21.47, 184.66) with score 0.951 - left_wrist: (113.36, 244.21) with score 0.822 - right_wrist: (8.04, 240.50) with score 0.915 - left_hip: (83.47, 234.43) with score 0.990 - right_hip: (47.29, 237.45) with score 0.994 - left_knee: (92.12, 324.78) with score 0.985 - right_knee: (50.70, 325.75) with score 0.991 - left_ankle: (101.13, 410.45) with score 0.933 - right_ankle: (49.62, 410.14) with score 0.954 Found person at [104.13589477539062, 20.16922025680542, 505.84068298339844, 522.6950127601624] with score 0.770 - nose: (132.51, 99.38) with score 0.693 - left_eye: (138.68, 89.00) with score 0.451 - left_ear: (145.60, 85.21) with score 0.766 - left_shoulder: (188.92, 133.25) with score 0.996 - right_shoulder: (163.12, 158.90) with score 0.985 - left_elbow: (263.01, 205.18) with score 0.991 - right_elbow: (181.52, 249.12) with score 0.949 - left_wrist: (315.65, 259.88) with score 0.964 - right_wrist: (125.19, 275.10) with score 0.891 - left_hip: (279.47, 294.29) with score 0.998 - right_hip: (266.84, 309.38) with score 0.997 - left_knee: (261.67, 416.57) with score 0.989 - right_knee: (256.66, 428.75) with score 0.982 - left_ankle: (322.92, 454.74) with score 0.805 - right_ankle: (339.15, 459.64) with score 0.780 Found person at [423.3617973327637, 72.75799512863159, 638.2988166809082, 513.1156357765198] with score 0.903 - nose: (417.19, 137.27) with score 0.992 - left_eye: (429.74, 127.59) with score 0.975 - right_eye: (409.83, 129.06) with score 0.961 - left_ear: (445.81, 133.82) with score 0.847 - right_ear: (399.09, 132.99) with score 0.711 - left_shoulder: (451.43, 195.71) with score 0.997 - right_shoulder: (372.58, 196.25) with score 0.995 - left_elbow: (463.89, 286.56) with score 0.991 - right_elbow: (351.35, 260.40) with score 0.978 - left_wrist: (488.70, 367.36) with score 0.986 - right_wrist: (395.69, 272.20) with score 0.973 - left_hip: (435.84, 345.96) with score 0.999 - right_hip: (380.21, 355.38) with score 0.999 - left_knee: (454.88, 456.63) with score 0.994 - right_knee: (395.82, 478.67) with score 0.992 - left_ankle: (453.75, 556.37) with score 0.889 - right_ankle: (402.35, 582.09) with score 0.872 ```