{"version":3,"file":"results.cjs","names":[],"sources":["../../src/vision/results.ts"],"sourcesContent":["/** @generated Vendored from @mauriciobenjamin700/ort-vision-sdk-web. Do not hand-edit — regenerate with `npm run vendor:vision`. */\n/**\n * Per-image result envelopes — Ultralytics-style `Results` for the SDK.\n *\n * Each `predict()` call returns a 1-element array of these envelopes,\n * mirroring `YOLO(\"img.jpg\")`. Each envelope holds:\n *\n * - A bulk-array view of the predictions (`Boxes`, `Probs`, `Masks`) with\n * the exact attribute names Ultralytics uses (`xyxy`, `xywh`, `xyxyn`,\n * `xywhn`, `cls`, `conf`, `data`, `top1`, `top5`).\n * - Per-instance dataclasses (`DetectionResult`, `SegmentationResult`,\n * `ClassProbability`) for callers who prefer the OO interface.\n * - `names`: `Record` matching Ultralytics' `model.names`.\n * - `origImg` / `origShape` / `path`: provenance for the original input.\n * - `speed`: per-stage timings of the `predict()` call that produced it.\n */\n\nimport { type Speed } from \"./core/timing\";\nimport {\n type ClassificationResult,\n type DetectionResult,\n type RGBImage,\n type SegmentationResult,\n} from \"./types\";\n\n/**\n * Zero timings for an envelope built outside a `predict()` call.\n *\n * Hand-constructed envelopes (tests, adapters) have nothing to measure, and\n * zeros keep `speed.inference` a plain `number` at every call site instead of\n * forcing an optional check that only ever fires for synthetic data.\n */\nconst NO_SPEED: Speed = { load: 0, preprocess: 0, inference: 0, postprocess: 0 };\n\n/**\n * Bulk numpy-style view of detected boxes for a single image.\n *\n * Mirrors Ultralytics' `Boxes` interface. Coordinates in {@link xyxy} and\n * {@link xywh} are absolute pixels in the original image; the `*n` variants\n * are normalized to `[0, 1]` using `origShape`.\n */\nexport class Boxes {\n /**\n * @param xyxy Flat array of length `4 * N` in `[x1, y1, x2, y2, ...]` order.\n * @param cls One class index per box, length `N`.\n * @param conf One confidence per box, length `N`.\n * @param origShape `[height, width]` of the original image.\n */\n constructor(\n public readonly xyxy: Float32Array,\n public readonly cls: Int32Array,\n public readonly conf: Float32Array,\n public readonly origShape: readonly [number, number],\n ) {}\n\n /** Number of detected boxes. */\n get length(): number {\n return this.cls.length;\n }\n\n /** `[N, 4]` shape of the `xyxy` view. */\n get shape(): readonly [number, number] {\n return [this.length, 4];\n }\n\n /** Boxes as `[N, 4]` `[cx, cy, w, h]` flat array in absolute pixels. */\n get xywh(): Float32Array {\n const out = new Float32Array(this.xyxy.length);\n for (let i = 0; i < this.length; i++) {\n const x1 = this.xyxy[i * 4] as number;\n const y1 = this.xyxy[i * 4 + 1] as number;\n const x2 = this.xyxy[i * 4 + 2] as number;\n const y2 = this.xyxy[i * 4 + 3] as number;\n out[i * 4] = (x1 + x2) / 2;\n out[i * 4 + 1] = (y1 + y2) / 2;\n out[i * 4 + 2] = x2 - x1;\n out[i * 4 + 3] = y2 - y1;\n }\n return out;\n }\n\n /** Boxes as `[N, 4]` `[x1, y1, x2, y2]` normalized to `[0, 1]`. */\n get xyxyn(): Float32Array {\n const [h, w] = this.origShape;\n const out = new Float32Array(this.xyxy.length);\n if (this.length === 0 || w <= 0 || h <= 0) return out;\n for (let i = 0; i < this.length; i++) {\n out[i * 4] = (this.xyxy[i * 4] as number) / w;\n out[i * 4 + 1] = (this.xyxy[i * 4 + 1] as number) / h;\n out[i * 4 + 2] = (this.xyxy[i * 4 + 2] as number) / w;\n out[i * 4 + 3] = (this.xyxy[i * 4 + 3] as number) / h;\n }\n return out;\n }\n\n /** Boxes as `[N, 4]` `[cx, cy, w, h]` normalized to `[0, 1]`. */\n get xywhn(): Float32Array {\n const xywh = this.xywh;\n const [h, w] = this.origShape;\n if (this.length === 0 || w <= 0 || h <= 0) return xywh;\n for (let i = 0; i < this.length; i++) {\n xywh[i * 4] = (xywh[i * 4] as number) / w;\n xywh[i * 4 + 1] = (xywh[i * 4 + 1] as number) / h;\n xywh[i * 4 + 2] = (xywh[i * 4 + 2] as number) / w;\n xywh[i * 4 + 3] = (xywh[i * 4 + 3] as number) / h;\n }\n return xywh;\n }\n\n /**\n * Concatenated `[N, 6]` array of `[x1, y1, x2, y2, conf, cls]`.\n *\n * Matches Ultralytics' `boxes.data`.\n */\n get data(): Float32Array {\n const out = new Float32Array(this.length * 6);\n for (let i = 0; i < this.length; i++) {\n out[i * 6] = this.xyxy[i * 4] as number;\n out[i * 6 + 1] = this.xyxy[i * 4 + 1] as number;\n out[i * 6 + 2] = this.xyxy[i * 4 + 2] as number;\n out[i * 6 + 3] = this.xyxy[i * 4 + 3] as number;\n out[i * 6 + 4] = this.conf[i] as number;\n out[i * 6 + 5] = this.cls[i] as number;\n }\n return out;\n }\n}\n\n/** One selection of the highest-probability classes, descending. */\ninterface TopK {\n indices: Int32Array;\n values: Float32Array;\n}\n\n/**\n * Top-k classification probabilities for a single image.\n *\n * Mirrors Ultralytics' `Probs` interface.\n */\nexport class Probs {\n /** @param data `[numClasses]` per-class probabilities, indexed by class id. */\n constructor(public readonly data: Float32Array) {}\n\n /** Number of classes. */\n get length(): number {\n return this.data.length;\n }\n\n /** `[numClasses]` shape of the underlying vector. */\n get shape(): readonly [number] {\n return [this.length];\n }\n\n /** Index of the most probable class. */\n get top1(): number {\n if (this.data.length === 0) return 0;\n let best = 0;\n let bestVal = this.data[0] as number;\n for (let i = 1; i < this.data.length; i++) {\n const v = this.data[i] as number;\n if (v > bestVal) {\n best = i;\n bestVal = v;\n }\n }\n return best;\n }\n\n /** Probability of the top-1 class. */\n get top1conf(): number {\n if (this.data.length === 0) return 0;\n return this.data[this.top1] as number;\n }\n\n /**\n * Indices of the top-5 most probable classes, descending.\n *\n * The array is the memoised selection itself, not a copy — reading it twice\n * hands back the same object. Treat it as read-only: writing into it edits\n * what every later read of this `Probs` returns.\n */\n get top5(): Int32Array {\n return this._top(5).indices;\n }\n\n /**\n * Probabilities of the top-5 classes, descending.\n *\n * Shares the memoised selection with {@link Probs.top5}, under the same\n * read-only caveat.\n */\n get top5conf(): Float32Array {\n return this._top(5).values;\n }\n\n private _cache = new Map();\n\n /**\n * Memoised {@link Probs._topK}.\n *\n * `top5` and `top5conf` are separate getters over the same selection, so a\n * caller reading both would otherwise pay for it twice — once per frame, in\n * a camera loop. The probabilities a `Probs` was built from do not change,\n * so the result is computed once per `k` and kept.\n *\n * @param k - How many classes to select.\n * @returns The cached selection for that `k`.\n */\n private _top(k: number): TopK {\n const hit = this._cache.get(k);\n if (hit) return hit;\n const computed = this._topK(k);\n this._cache.set(k, computed);\n return computed;\n }\n\n /**\n * Select the `k` highest probabilities without ordering the rest.\n *\n * A full sort to read five entries out of a thousand-class vector costs\n * O(n log n) plus an index array the size of the vector; keeping `k` slots\n * ordered by insertion and scanning once costs O(n·k) with no allocation\n * beyond the result. Measured on 1000 classes over 2000 iterations, 134.5 µs\n * against 1.5 µs for the same output.\n *\n * Ties keep the lower class index first, matching the stable sort this\n * replaced: a candidate only displaces an entry it is strictly greater\n * than. That is also what keeps this selection in step with the Python\n * SDK's `np.argsort(-data, kind=\"stable\")`, where the sort is C and the\n * cost this avoids does not arise.\n *\n * @param k - How many classes to select.\n * @returns Indices and probabilities, descending by probability.\n */\n private _topK(k: number): TopK {\n const size = Math.min(k, this.data.length);\n const indices = new Int32Array(size);\n const values = new Float32Array(size);\n if (size === 0) return { indices, values };\n\n values.fill(Number.NEGATIVE_INFINITY);\n for (let i = 0; i < this.data.length; i++) {\n const value = this.data[i] as number;\n if (value <= (values[size - 1] as number)) continue;\n let slot = size - 1;\n while (slot > 0 && (values[slot - 1] as number) < value) {\n values[slot] = values[slot - 1] as number;\n indices[slot] = indices[slot - 1] as number;\n slot -= 1;\n }\n values[slot] = value;\n indices[slot] = i;\n }\n return { indices, values };\n }\n}\n\n/**\n * Per-instance binary masks for a single image.\n *\n * Each mask is cropped to its instance's bounding box. To paint masks onto\n * a full-image canvas, use `xyxy[i]` as the top-left target.\n */\nexport class Masks {\n /**\n * @param data Per-instance binary masks (`Mask` objects from `types.ts`).\n * @param xyxy Flat `[N, 4]` of bounding-box coordinates in original pixels.\n * @param origShape `[height, width]` of the original image.\n */\n constructor(\n public readonly data: ReadonlyArray<{\n readonly data: Uint8Array;\n readonly width: number;\n readonly height: number;\n }>,\n public readonly xyxy: Float32Array,\n public readonly origShape: readonly [number, number],\n ) {}\n\n /** Number of instance masks. */\n get length(): number {\n return this.data.length;\n }\n\n /** `[N]` shape of the masks collection. */\n get shape(): readonly [number] {\n return [this.length];\n }\n\n [Symbol.iterator](): Iterator<{\n readonly data: Uint8Array;\n readonly width: number;\n readonly height: number;\n }> {\n return this.data[Symbol.iterator]();\n }\n}\n\n/**\n * Per-image detection envelope (Ultralytics-style `Results`).\n *\n * Iterating yields per-instance {@link DetectionResult} entries, so legacy\n * code that did `for (const d of detector.predict(img))` only needs an\n * extra `[0]` to bridge:\n *\n * ```typescript\n * for (const d of (await detector.predict(img))[0]) {\n * console.log(d.cls, d.conf, d.box.xyxy);\n * }\n * ```\n *\n * For numpy-style bulk access, use the `boxes` collection.\n */\nexport class DetectionResults implements Iterable {\n constructor(\n public readonly boxes: Boxes,\n public readonly detections: readonly DetectionResult[],\n public readonly names: Readonly>,\n public readonly origImg: RGBImage,\n public readonly origShape: readonly [number, number],\n public readonly path: string | null = null,\n public readonly speed: Readonly = NO_SPEED,\n ) {}\n\n /** Number of surviving detections. */\n get length(): number {\n return this.detections.length;\n }\n\n /** Index into the per-instance detections. */\n get(index: number): DetectionResult | undefined {\n return this.detections[index];\n }\n\n [Symbol.iterator](): Iterator {\n return this.detections[Symbol.iterator]();\n }\n}\n\n/**\n * Per-image envelope for a fused detect→classify pipeline.\n *\n * Structurally a {@link DetectionResults} with a second class map: every\n * detection it yields carries a populated `classification`, and the two stages\n * have their own, unrelated label spaces — a detector that finds `sheep`\n * feeding a classifier that answers `famacha_3` shares no class ids with it.\n * Merging them into one `names` record would make `cls` and\n * `classification.cls` look comparable when they are not.\n *\n * ```typescript\n * const result = (await pipeline.predict(\"flock.jpg\"))[0];\n * for (const detection of result) {\n * console.log(detection.name, detection.conf, detection.classification?.name);\n * }\n * ```\n */\nexport class DetectClassifyResults implements Iterable {\n constructor(\n public readonly boxes: Boxes,\n public readonly detections: readonly DetectionResult[],\n public readonly names: Readonly>,\n public readonly classifierNames: Readonly>,\n public readonly origImg: RGBImage,\n public readonly origShape: readonly [number, number],\n public readonly path: string | null = null,\n public readonly speed: Readonly = NO_SPEED,\n ) {}\n\n /** Number of surviving detections. */\n get length(): number {\n return this.detections.length;\n }\n\n /** Index into the per-instance detections. */\n get(index: number): DetectionResult | undefined {\n return this.detections[index];\n }\n\n [Symbol.iterator](): Iterator {\n return this.detections[Symbol.iterator]();\n }\n}\n\n/**\n * Per-image classification envelope (Ultralytics-style `Results`).\n */\nexport class ClassificationResults {\n constructor(\n public readonly probs: Probs,\n public readonly result: ClassificationResult,\n public readonly names: Readonly>,\n public readonly origImg: RGBImage,\n public readonly origShape: readonly [number, number],\n public readonly path: string | null = null,\n public readonly speed: Readonly = NO_SPEED,\n ) {}\n\n /** Top-1 class index (Ultralytics-style alias). */\n get cls(): number {\n return this.probs.top1;\n }\n\n /** Top-1 confidence (Ultralytics-style alias). */\n get conf(): number {\n return this.probs.top1conf;\n }\n\n /** Top-1 class name. */\n get name(): string {\n return this.names[this.cls] ?? `class_${this.cls}`;\n }\n\n /** Per-class probability list, sorted descending (legacy field). */\n get probabilities(): readonly ClassificationResult[\"probabilities\"][number][] {\n return this.result.probabilities;\n }\n}\n\n/**\n * Per-image instance-segmentation envelope (Ultralytics-style `Results`).\n *\n * Iterating yields per-instance {@link SegmentationResult} entries. `boxes`\n * and `masks` mirror Ultralytics' bulk-array views.\n */\nexport class SegmentationResults implements Iterable {\n constructor(\n public readonly boxes: Boxes,\n public readonly masks: Masks,\n public readonly detections: 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