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| /* Decode standard YOLOv8 exports: batch 1, xywh + nine class scores, no NMS. */ | |
| (function (scope) { | |
| 'use strict'; | |
| function iou(a, b) { | |
| const width = Math.max(0, Math.min(a[2], b[2]) - Math.max(a[0], b[0])); | |
| const height = Math.max(0, Math.min(a[3], b[3]) - Math.max(a[1], b[1])); | |
| const overlap = width * height; | |
| const areaA = Math.max(0, a[2] - a[0]) * Math.max(0, a[3] - a[1]); | |
| const areaB = Math.max(0, b[2] - b[0]) * Math.max(0, b[3] - b[1]); | |
| return overlap / Math.max(1e-12, areaA + areaB - overlap); | |
| } | |
| function decode(output, names, transform, confidence, threshold = 0.7) { | |
| const dims = output.dims; | |
| const channels = 4 + names.length; | |
| if (dims.length !== 3 || dims[0] !== 1 || | |
| (dims[1] !== channels && dims[2] !== channels)) { | |
| throw new Error('Unsupported ONNX output. Export YOLOv8 with nine classes and nms=False.'); | |
| } | |
| const channelFirst = dims[1] === channels; | |
| const count = channelFirst ? dims[2] : dims[1]; | |
| const value = (candidate, channel) => output.data[ | |
| channelFirst ? channel * count + candidate : candidate * channels + channel | |
| ]; | |
| const candidates = []; | |
| for (let index = 0; index < count; index += 1) { | |
| let classId = 0; | |
| let score = -Infinity; | |
| for (let cls = 0; cls < names.length; cls += 1) { | |
| const current = value(index, cls + 4); | |
| if (current > score) { score = current; classId = cls; } | |
| } | |
| if (!Number.isFinite(score) || score < confidence) continue; | |
| const cx = value(index, 0), cy = value(index, 1); | |
| const width = value(index, 2), height = value(index, 3); | |
| if (![cx, cy, width, height].every(Number.isFinite) || width <= 0 || height <= 0) continue; | |
| candidates.push({ | |
| raw: [cx - width / 2, cy - height / 2, cx + width / 2, cy + height / 2], | |
| confidence: score, class_id: classId, class_name: names[classId], | |
| }); | |
| } | |
| candidates.sort((a, b) => b.confidence - a.confidence); | |
| // Match Ultralytics' pre-NMS candidate cap; class-aware NMS, max_det=100. | |
| const retained = []; | |
| for (const candidate of candidates.slice(0, 30000)) { | |
| if (retained.some(item => item.class_id === candidate.class_id && iou(item.raw, candidate.raw) > threshold)) continue; | |
| retained.push(candidate); | |
| if (retained.length === 100) break; | |
| } | |
| const clamp = (value, maximum) => Math.max(0, Math.min(maximum, value)); | |
| return retained.map(item => ({ | |
| class_id: item.class_id, | |
| class_name: item.class_name, | |
| confidence: item.confidence, | |
| box: item.raw.map((value, axis) => { | |
| const horizontal = axis % 2 === 0; | |
| const coordinate = (value - (horizontal ? transform.left : transform.top)) / transform.ratio; | |
| return Math.round(clamp(coordinate, horizontal ? transform.width : transform.height) * 100) / 100; | |
| }), | |
| })); | |
| } | |
| scope.PaddyDetectionCore = { decode, iou }; | |
| })(typeof self !== 'undefined' ? self : globalThis); | |