spike(vision): rtmpose latency, birefnet matting proxies, body-shape prototype
T-P0-16/12/10 可行性验证：CPU 基准 + 代理质检指标 + 合成图体型剖面法 原型 + REPORT.md 三问三答（Sprint 1 门槛的决策依据）。 偏差说明：模型齐全未触发降级（rtmpose-s/m + birefnet_lite fp32 全部首选 候选成功，hf-mirror 实测 4~12MB/s）；huggingface_hub 不在 spike extra 且 禁新增依赖，改用 stdlib urllib 直连 resolve 端点（同 HTTP 语义）；PINTO model zoo 无法脚本化定位小模型、dhkim0115 仓不存在，姿态模型改用 bukuroo/RTMPose-ONNX；BiRefNet 官方仓无 ONNX，用 onnx-community 导出； GPU 档未测（CPU 版 ORT 无 CUDA EP，按文档不折腾）；BiRefNet 单张>3s 正式 次数自适应降为 10 并在输出注明；RTMPose 在合成剪影上置信度低（域差异）， 体型断言按文档允许走真值定高路径，关键点路径仅统计参考一致率。
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"""T9 SPIKE · RTMPose ONNX CPU 单张耗时基准（T-P0-16 验收口径）。
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- 输入：scripts/spikes/assets/synthetic_person.png（synthetic.py 生成的合成人形，
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不涉及真实人脸/人体数据）；
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- 口径：预热 3 + 正式 20，报「纯模型 session.run」与「预处理+推理+解码」两条链路
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的均值 / P95 / 单次最大（time.perf_counter）；
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- GPU 档：先探测 nvidia-smi 与 ORT providers，CUDA EP 不可用则只报 CPU（不折腾）；
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- 降级：models/ 下无 RTMPose ONNX 时，用 cv2.HOG 人体检测作关键点占位并在输出声明
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（HOG 无关键点，仅给检测框 + 按人体比例推的占位点，报告需注明）；
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- 输出：out/rtmpose_bench.json（耗时）与 out/rtmpose_keypoints.json（关键点）。
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用法：.venv/bin/python scripts/spikes/rtmpose_benchmark.py
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"""
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from __future__ import annotations
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import json
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import sys
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from typing import Any
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import cv2
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import numpy as np
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from synthetic import ASSETS_DIR, ensure_assets
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from vision_common import (
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COCO17_NAMES,
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IDX_HIP,
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IDX_SHOULDER,
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MODELS_DIR,
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OUT_DIR,
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POSE_CHAIN,
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RTMPoseRunner,
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bench,
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dump_json,
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gpu_probe,
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load_session,
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)
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# GT 校验容差：合成图人形位置已知，肩/臀线估计偏差在该范围内视为「关键点可用」
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LINE_TOLERANCE_PX = 15
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CONF_USABLE = 0.30 # 关键点平均置信度低于该值视为「定位不可信」（软指标，仅提示）
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def _print_gpu_verdict(gpu: dict[str, Any]) -> None:
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print("== GPU 探测 ==")
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print(f" nvidia-smi: {gpu['nvidia_smi'] or '不可用'}")
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print(f" ORT providers: {gpu['ort_providers']}")
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if gpu["cuda_ep_available"]:
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print(" 结论：CUDA EP 可用（但本 spike 约定只装 CPU 版 ORT，仅报 CPU 档）")
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else:
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print(" 结论：CUDA EP 不可用 → 只测 CPU 档（任务文档：探测失败不折腾）")
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def _print_keypoints(name: str, kps: np.ndarray) -> None:
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print(f"\n== {name} 关键点（画布坐标 {kps.shape[0]} 点）==")
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print(f" {'idx':>3} {'name':<14} {'x':>6} {'y':>6} {'conf':>6}")
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for i, (x, y, c) in enumerate(kps):
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kp_name = COCO17_NAMES[i] if i < len(COCO17_NAMES) else f"kp{i}"
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print(f" {i:>3} {kp_name:<14} {x:>6.1f} {y:>6.1f} {c:>6.3f}")
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def _check_against_gt(name: str, kps: np.ndarray, gt_std: dict[str, Any]) -> dict[str, Any]:
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"""合成图真值校验：肩线/臀线高度与置信度（RTMPose 是否真的「看懂」了人形）。"""
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gt_lines: dict[str, int] = gt_std["line_y"]
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y_sh = float(np.mean(kps[list(IDX_SHOULDER), 1]))
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y_hip = float(np.mean(kps[list(IDX_HIP), 1]))
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d_sh = y_sh - gt_lines["shoulder"]
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d_hip = y_hip - gt_lines["hip"]
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conf_mean = float(np.mean(kps[:, 2]))
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usable = (
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abs(d_sh) <= LINE_TOLERANCE_PX
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and abs(d_hip) <= LINE_TOLERANCE_PX
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and conf_mean >= CONF_USABLE
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)
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print(
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f" [GT 校验] 肩线偏差 {d_sh:+.1f}px（真值 {gt_lines['shoulder']}），"
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f"臀线偏差 {d_hip:+.1f}px（真值 {gt_lines['hip']}），平均置信度 {conf_mean:.3f}"
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f" → {'可用' if usable else '定位不可信（降级口径见 REPORT）'}"
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)
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return {
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"shoulder_line_delta_px": round(d_sh, 1),
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"hip_line_delta_px": round(d_hip, 1),
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"conf_mean": round(conf_mean, 3),
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"keypoints_usable": usable,
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}
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def _bench_model(runner: RTMPoseRunner, tensor: np.ndarray, img: np.ndarray) -> dict[str, Any]:
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def fn_model() -> None:
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runner.session.run(None, {runner.input_name: tensor})
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def fn_full() -> None:
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runner.infer(img)
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return {
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"model_only": bench(fn_model, warmup=3, runs=20),
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"full_pipeline": bench(fn_full, warmup=3, runs=20),
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}
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def _hog_fallback(img: np.ndarray) -> int:
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"""无 RTMPose 时的降级路径：HOG 人体检测作关键点占位（报告必须注明此口径）。"""
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print("\n== 降级路径：cv2.HOG 人体检测（关键点占位，非 RTMPose）==")
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hog = cv2.HOGDescriptor()
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hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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def fn() -> None:
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hog.detectMultiScale(img, winStride=(8, 8), padding=(4, 4), scale=1.05)
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stats = bench(fn, warmup=2, runs=10)
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found, _weights = hog.detectMultiScale(img, winStride=(8, 8), padding=(4, 4), scale=1.05)
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if len(found):
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x, y, w, h = found[0]
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print(f" 检测到人体框: x={x} y={y} w={w} h={h}；耗时 {stats}")
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# 占位关键点：按人体比例从检测框推（HOG 不输出关键点，仅维持管线可跑通）
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placeholder = {
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"left_shoulder": [x + 0.2 * w, y + 0.25 * h],
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"right_shoulder": [x + 0.8 * w, y + 0.25 * h],
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"left_hip": [x + 0.3 * w, y + 0.55 * h],
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"right_hip": [x + 0.7 * w, y + 0.55 * h],
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}
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else:
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print(" 未检测到人体：占位关键点不可用")
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placeholder = {}
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dump_json(
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OUT_DIR / "rtmpose_keypoints.json",
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{
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"source": "hog-placeholder",
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"detections": [list(map(int, b)) for b in found],
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"placeholder": placeholder,
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},
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)
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dump_json(OUT_DIR / "rtmpose_bench.json", {"source": "hog-placeholder", "latency": stats})
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return 0
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def main() -> int:
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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ensure_assets()
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img = cv2.imread(str(ASSETS_DIR / "synthetic_person.png"))
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gt = json.loads((ASSETS_DIR / "ground_truth.json").read_text(encoding="utf-8"))
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gpu = gpu_probe()
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_print_gpu_verdict(gpu)
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available = [name for name in POSE_CHAIN if (MODELS_DIR / name).exists()]
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if not available:
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print("[降级] models/ 下无 RTMPose ONNX，改用 cv2.HOG 作关键点占位（见 REPORT）")
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return _hog_fallback(img)
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results: dict[str, Any] = {
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"gpu": gpu,
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"input": "synthetic_person.png (384x512 合成人形)",
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"models": {},
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}
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keypoints_dump: dict[str, Any] = {}
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for name in available:
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path = MODELS_DIR / name
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print(f"\n==== {name}（{path.stat().st_size / 1048576:.1f} MB，CPUExecutionProvider）====")
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runner = RTMPoseRunner(load_session(path))
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print(f" 输入: {runner.input_name} {runner.input_hw}(H,W)")
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kps = runner.infer(img)
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_print_keypoints(name, kps)
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gt_check = _check_against_gt(name, kps, gt["variants"]["STD"])
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stats = _bench_model(runner, runner.preprocess(img), img)
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for lane, s in stats.items():
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print(
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f" [{lane}] runs={s['runs']} 均值={s['mean_s'] * 1000:.1f}ms "
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f"P95={s['p95_s'] * 1000:.1f}ms 最大={s['max_s'] * 1000:.1f}ms"
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)
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results["models"][name] = {
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"input_hw": list(runner.input_hw),
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"num_keypoints": int(kps.shape[0]),
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"gt_check": gt_check,
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"latency": stats,
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}
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keypoints_dump[name] = {
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"names": list(COCO17_NAMES)[: kps.shape[0]],
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"keypoints": [
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[round(float(x), 1), round(float(y), 1), round(float(c), 3)] for x, y, c in kps
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],
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}
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dump_json(OUT_DIR / "rtmpose_bench.json", results)
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dump_json(OUT_DIR / "rtmpose_keypoints.json", keypoints_dump)
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print(f"\n结果已写入 {OUT_DIR / 'rtmpose_bench.json'} 与 {OUT_DIR / 'rtmpose_keypoints.json'}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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