Update README.md

README.md

@@ -3,4 +3,193 @@ 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)}`);
+    }
+}
+```
+
+<details>
+
+<summary>See example output</summary>
+
+```
+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
+
+```
+</details>