feat: 完善淘宝小程序拍照、报告与登录流程
接入本地实时识别和自动三连拍，调整报告裁切、商品跳转、分享标题及居中页眉；完善游客会话、后台渠道隔离、部署脚本与回归测试。
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Vendored
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# Local tensor face engine
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Runtime adapter: `taobao/utils/native-face.js`.
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The vendor bundle, tiny detector and full 68-point model come from the existing,
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unchanged `h5/vendor/faceapi/` files. The added tiny 68-point model is downloaded
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only from the same official upstream's `weights/` directory; its manifest,
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77224-byte shard and source URLs/hashes are in `reference/native-face-models/`.
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Upstream is
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https://github.com/justadudewhohacks/face-api.js. MIT and TensorFlow Apache 2.0
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licenses are included; original inline Google/Microsoft notices are retained.
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Build or verify from the repository root:
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```
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node taobao/scripts/build-native-face.cjs
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node taobao/scripts/build-native-face.cjs --check
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node --test taobao/test/native-face.test.cjs
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node taobao/scripts/benchmark-native-face.cjs --repeats=2
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node taobao/scripts/benchmark-native-face.cjs --guided --input-sizes=256,160,128 --repeats=2
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```
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`provenance.json` records every source file's SHA-256 and byte length. The
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quantized model bytes total 627385 bytes including the optional full model;
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the default tiny detector plus tiny landmark weights total 270545 bytes.
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Base64 and manifests are packaged in
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one CJS module. Runtime decoding uses typed arrays, with no filesystem or Buffer.
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The generated face-api copy has a private TFJS global registry and shadows Node
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`process`/`Buffer`. Optional Node-only imports (`crypto`, `node-fetch`, `util`,
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`fs`) are replaced with functions that explicitly throw if reached. The
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tfjs-node help-string reference is also replaced. The build fails if an
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unreviewed static `require()` remains, because the IDE resolves unreachable
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imports too. No Node polyfill or native AI plugin is bundled.
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Create an engine with `create({defaultPixelFormat:'RGBA', maxSide:320,
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inputSize:160, landmarkModel:'tiny', scoreThreshold:0.35})`; these are the
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defaults. `landmarkModel:'full'` remains available for controlled comparisons.
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The runtime default backend remains `cpu` for compatibility. Select WebGL
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explicitly with `create({backend:'webgl', platform:my, ...})`; no unsupported
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WebGL request silently falls back to the CPU profile that was too slow on phone.
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`init()` returns a Promise with backend, inputSize, maxSide, landmarkModel and
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cpuOptimization. `detect(frame)` returns a Promise with original width,
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height, total faceCount and at most two complete detections. Each detection has
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`detection.{box:{x,y,width,height},score}` and `landmarks.positions` containing
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68 `{x,y}` points. All coordinates refer to original raw frame pixels; no
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mirroring, rotation or preview crop is applied. Two complete detections suffice
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for the existing multiple-person rejection gate.
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Input must have integer width/height in 1..4096 and tightly packed four-channel
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data in an ArrayBuffer or typed-array view. RGBA, BGRA, ARGB, ABGR and their
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8888 aliases are supported. An explicit format is authoritative: the configured
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default only fills missing metadata. Unknown formats (including NV/YUV), a
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stride/bytesPerRow other than width*4, and a nonzero rotation marker are rejected.
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`detect()` synchronously snapshots only the downsampled RGB before returning,
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so native buffer reuse cannot change a queued inference. All model operations
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use the CPU backend. The lower-level tensor crop/landmark path avoids the
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high-level `withFaceLandmarks()` canvas dependency. Concurrent inference rejects
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with `NATIVE_FACE_BUSY`. `dispose()` is idempotent; pending work rejects with
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`NATIVE_FACE_DISPOSED`, and tensors are released after that work settles. The
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last engine instance releases the shared model tensors. `stats()` returns only
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backend, selected configuration, memory counters and lifecycle booleans.
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The CPU `pad-copy` specialization copies contiguous source blocks into the
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already zero-filled output tensor. It preserves the original pad values exactly;
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unhandled scalar/complex inputs fall back to the bundled implementation. Disable
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it with `cpuOptimization:false` (`cpuOptimization:'none'` in returned diagnostics).
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It does not change model arithmetic or relax quality/freshness gates. The trial
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1x1 convolution specialization showed no stable benefit and was removed.
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`detect()` additionally returns inputSize, maxSide, landmarkModel,
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cpuOptimization and `timings.{snapshotMs,modelMs,tensorMs,detectorMs,landmarkMs,
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totalMs}` in milliseconds. `elapsedMs` remains total elapsed wall time; source
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frame age must still be calculated from the camera's frame arrival time.
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Tests run the real models without document, Image, canvas, fetch, process,
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Buffer or host Node imports. The local face fixture derives from the already
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present reference SDK example `C3_180.jpg`; it is never transmitted. Tests check
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actual positive face detection and 68 points, all supported byte layouts,
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original-coordinate scaling, concurrent/disposed calls, native buffer reuse,
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and zero tensors after final disposal. The pad kernel is tested bit-for-bit
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against original CPU pad across ranks 1–4, dtypes and padding/constants; the
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real full-model detection and all 68 points are identical with it enabled or
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disabled. An aspect-preserving two-face composition must reach the actual
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native-face-quality single-person rejection gate. Tests print timings/counts,
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never the image or point coordinates.
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Desktop no-DOM VM stage measurements on the same 540×960 portrait reference
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(two samples per profile): old full/256/no specialization took about 934 ms
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(detector 730, landmarks 189, snapshot 16). Tiny/160/pad-copy took about 302 ms
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(detector 188, landmarks 99, snapshot 15), about 3.1× faster. Default model
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tensors occupy 1082180 bytes (47 tensors), versus full's 2200644 bytes (68).
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The original small face ratio was 0.208, below the H5 near gate of 0.30. With
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an aspect-preserving crop targeting a valid larger face, tiny/128 can detect
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one face and 68 points in about 254 ms; however, 128 misses one person in the
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two-face regression while 160 detects both. Therefore 160 remains the default.
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Reducing maxSide from 320 to 192 saved only about 24 ms on the eligible portrait;
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320 is retained for landmark detail. These are desktop CPU observations, not
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phone performance guarantees. Benchmark flags `--baseline-cpu`, `--max-side=192`
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and `--input-sizes=160,128` support reproducing the separate comparisons. A zero
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face result is explicitly flagged by `allSamplesDetected:false`, not a speed win.
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## Explicit WebGL candidate
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`utils/native-face-webgl.js` obtains an owned logical-layer Canvas using the
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official synchronous `my.createOffscreenCanvas(32,32)` and
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`getContext('webgl',{enable_flip_y_after_read_pixels:false})`.
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The host app must enable its documented Skia capability. The adapter injects the
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actual GL1 context into the bundled TFJS backend; it never forges HAS_WEBGL or
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float-extension ENV flags. Initialization runs a 16×16 matrix product, a
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four-channel convolution, numerical readback checks and observed real shader
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draw/readPixels counters. A missing or readonly instrumentable native API fails
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explicitly; the adapter does not report GPU success from API names alone.
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The current device candidate is `LOCAL-FACE-20261102-07`. The preceding `03`
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candidate executed four matrix shader draws on the iPhone, then failed the
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strict numerical check with `MATMUL_FAILED`; it did not process camera frames.
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Candidate `04` tried `default`, `packed_byte` (disables float downloads),
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then `unpacked_byte` (also disables packed shaders). Each attempt runs the same
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matrix/convolution/readback checks with the original numerical tolerance. A
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failed attempt releases its backend and tracked GL objects before the next
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attempt. Only numerical/readback failures permit another profile; missing APIs,
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context loss and resource errors stop explicitly. There is no CPU fallback.
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All three profiles failed on the device in `04`. The final unpacked byte
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attempt observed five draws, one byte read, checksum 0 versus expected 2786,
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244 mismatches and maximum absolute error 22. These aggregate values have an
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all-zero signature; they do not by themselves prove every output value is zero
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or identify the cause. Real camera frames were never processed.
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Candidate `05` disables the host's extra readPixels flip using the documented
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[Taobao context option](https://developer.alibaba.com/docs/doc.htm?articleId=121761&docType=1&treeId=829).
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A canvas-height-based read-coordinate conversion with a smaller framebuffer can
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produce zero output in an isolated experiment; that is a testable hypothesis,
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not a confirmed description of the device implementation. Before TFJS setup,
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the adapter checks a 2×2 RGBA8 framebuffer with clear/readback, an asymmetric
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shader pattern, byte-texture sampling and float-texture sampling. Fixed nonzero
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synthetic inputs and a prefilled destination sentinel distinguish an unchanged
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typed array from incorrect GPU output. Any failed preflight stops initialization
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and releases its graphics objects. Passing this preflight does not replace the
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original matrix/convolution numerical checks, compatibility selection or model
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warmup.
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On the device, `05` passed clear/readback but failed the asymmetric shader with
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three mismatches: expected checksum 2244, actual 2040. The actual checksum equals
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the preceding clear color's checksum. This is consistent with no draw coverage,
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but does not uniquely distinguish coverage, coordinates, typed-buffer uploads
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or GL state. Real camera inference still did not start.
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Candidate `06` explicitly initializes drawing state and clears to black before
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each draw. It first checks a constant-color `drawArrays` pass and then an indexed
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pass, followed by the original asymmetric shader and byte/float texture tests.
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It verifies native strategies in order: `standard`, `buffer_copy` (tightly copy
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a view's bytes into an ArrayBuffer for standard bufferData upload),
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`framebuffer_y` (compensate viewport/scissor Y only for a bound framebuffer),
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then `combined`. These are candidates selected by actual numerical checks, not
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assumptions based on phone model. Only `VALUE_MISMATCH` permits another native
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strategy; all other failures stop. All four native strategies share one context
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and release each probe's objects before the next. The TF profiles still create
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at most three contexts. A selected native strategy must pass every native test,
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then the unchanged strict matrix/convolution tests and real-model warmup.
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The source frame freshness limit remains 1500 ms. Device strategy selection,
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compatibility, speed and automatic capture still need phone acceptance.
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Candidate `06` failed on the device after its standard constant-array draw
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passed: `nativeInitialStage=constant_indexed`. The later coordinate candidate's
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black output is not the original failure. Candidate `07` first rebinds the
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element buffer immediately before indexed draws. If the indexed result still
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fails while the array draw passed, it verifies a four-vertex triangle strip as
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an alternative. Strip probes report `constantQuad:passed` and leave
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`constantIndexed:not_run`; `drawStrategy:quad_strip` identifies the physical
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method without claiming the native indexed API passed.
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Only the owned quad with uploaded indices `[0,1,2,2,1,3]` and exact
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`TRIANGLES/6/UNSIGNED_SHORT/offset0` arguments can be converted. Upload bytes
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are checked within typed-view bounds. Unknown buffers, different signatures,
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numeric allocations and buffers modified with bufferSubData are refused;
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deletion invalidates the record. This matches the pinned upstream
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[quad data](https://raw.githubusercontent.com/tensorflow/tfjs/tfjs-core-v1.7.0/tfjs-core/src/backends/webgl/gpgpu_util.ts)
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and retains the original tensor checks and model warmup. Native strategies can
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each add a verified strip attempt, up to eight probes on the same context.
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This is an experimental device compatibility path, not an assertion that the
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Taobao indexed API is generally unsupported. Phone success and speed remain
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unverified.
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After the numerical checks, initialization loads the selected real models and
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warms both the detector on synthetic portrait black pixels and the landmark
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network on a synthetic tensor crop. Warmup results are discarded and never
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count as user face/pose data. Only after the warmup does initialization return
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`backend:'webgl'`, `gpu.selftest:'passed'` and `gpu.stage:'ready'`. The source
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frame age and 1500 ms capture gate remain the camera's responsibility.
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The returned `gpu` record contains fixed stage/code/profile enums, booleans,
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counts and metrics computed only from the fixed synthetic selftest arrays:
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requested, selftest, stage, code, contextLost, profile, attemptCount,
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matmulDraws, convDraws, pixelReads, floatReads, byteReads, selftestMs and
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warmupMs. Each synthetic matrix/convolution record reports actual length,
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nonfinite/mismatch counts, maximum absolute error, first expected/actual values
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and expected/actual checksums. The camera caches those synthetic metrics,
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profile, attempts and read counts only during initialization; later detection
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results cannot replace them in copied diagnostics. Errors use
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`NATIVE_FACE_WEBGL_*` codes and the same safe record. Native error strings,
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renderer strings, image pixels and coordinates do not enter that record. Each
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detect checks actual context-loss state before and after model work; a lost
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backend must be retired and rebuilt.
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The startup record also includes the fixed-input `nativeProbe` stage/result,
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sentinel-change and mismatch counts, expected/actual checksums, read return-kind
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enum, GL error and framebuffer status. The record also preserves
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`nativeInitialStage`/`nativeInitialCode`, selected `nativeStrategy`,
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`constantArrays`/`constantIndexed`, optional vertex/index buffer byte counts and
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`pixel0` through `pixel3` as big-endian RGBA uint32 values from fixed synthetic
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input only. TFJS readback state is collected before
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throwing for a numerical mismatch, so a failing value no longer hides a native
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GL error. These fields are cached only during initialization and cannot be
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replaced by real-camera inference results.
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A shared live engine group admits one backend kind at a time. Initialization,
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model loading, warmup and inference are queued; decoded weights are not reused
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across a CPU/WebGL switch. Disposal waits for pending work, then releases model
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tensors, backend programs/textures, the externally supplied GPGPUContext and all
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remaining tracked GL objects including TFJS 1.7's vertex buffer/shader leftovers.
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Native cleanup exceptions are isolated so they cannot preserve a busy lock or
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replace the original inference error.
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```
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node --test taobao/test/native-face.test.cjs taobao/test/native-face-webgl.test.cjs
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```
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The WebGL tests run packaged modules with DOM/Node environment names hidden,
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explicitly supply real headless WebGL1 and abort all network routes. They cover
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numerical shader/readback, corrupted FLOAT readback followed by packed byte
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readback, two failed contexts followed by unpacked byte readback, and all three
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profiles failing closed. Both compatible paths run the real 68-point model and
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are compared with CPU face boxes, confidence and landmark geometry. They also
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cover context loss, initialization failure, shared engines, deferred disposal
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and rebuilding, with zero final
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tensors and tracked GL objects. Desktop SwiftShader proves the computation and
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lifecycle paths; it does not establish native Taobao iPhone compatibility or
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phone inference speed. Those still require the device's actual diagnostics.
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