feat(edge): real HailoRT yolov8s_pose adapter (host DFL+NMS decode) + POSE_BACKEND switch

Replace the NotImplementedError stub with a working HailoPoseAdapter for Pi5 +
Hailo-8. yolov8s_pose has no on-chip NMS, so the adapter does host-side DFL box
decode + keypoint decode + greedy NMS + letterbox un-map + COCO-17 assembly
(numpy decode ported from Hailo's MIT PoseEstPostProcessing, locked by a synthetic
9-tensor unit test; the HailoRT device wiring is gated + device-validated on bringup).
daemon.main() gains a POSE_BACKEND switch (hailo|movenet).

edge/adapters/hailo_pose.py

@@ -1,16 +1,198 @@
-"""Hailo yolov8s_pose HEF 어댑터 (실장비). PosePort 구현.
+"""Hailo yolov8s_pose HEF adapter (실장비 Pi5 + Hailo-8/8L). Implements PosePort.
-개발 PC에선 인스턴스화 불가(hailort 부재). Pi에서 HailoRT로 실제 추론 배선."""
+
+yolov8s_pose has NO on-chip NMS: the HEF emits 9 raw feature-map tensors (3 strides
+x {box DFL (H,W,64), class score (H,W,1, already sigmoid), keypoints (H,W,51)}). This
+adapter does the host-side DFL box-decode + keypoint-decode + NMS and remaps to
+COCO-17 `list[Person]`.
+
+The pure-numpy decode below is ported from Hailo's MIT-licensed reference
+(Hailo-Application-Code-Examples/runtime/python/pose_estimation/pose_estimation_utils.py,
+class PoseEstPostProcessing) and locked by tests/test_hailo_pose.py via an injected
+fake infer_fn (no hardware needed).
+
+DEVICE VALIDATION (bringup): the HailoRT wiring in `_build_hailo_infer` cannot run off
+Pi/hailort. Validate it on device per docs/hardware-bringup.md, and confirm the model's
+input (640x640 uint8 RGB, on-chip normalization) and the 9-tensor output shapes against
+the actual HEF before trusting inference. The remap/decode math is what the unit tests fix.
+"""
+import cv2
+import numpy as np
 
 from edge.models import Person, BBox, Keypoint
 
+# COCO-17 keypoint order (yolov8_pose): nose, l/r eye, l/r ear, l/r shoulder,
+# l/r elbow, l/r wrist, l/r hip, l/r knee, l/r ankle. Emitted in this order, so the
+# 17 keypoints map positionally to edge.models.Keypoint without reindexing.
+_NUM_KPTS = 17
+
+
+def _sigmoid(x):
+    return 1.0 / (1.0 + np.exp(-x))
+
+
+def _softmax(x):
+    e = np.exp(x - np.max(x, axis=-1, keepdims=True))
+    return e / np.sum(e, axis=-1, keepdims=True)
+
+
+def _letterbox(frame, size):
+    """Aspect-preserving resize to size x size with centered padding; BGR->RGB uint8.
+    Returns (canvas, scale, (pad_x, pad_y)) so results can be mapped back to `frame`."""
+    h, w = frame.shape[:2]
+    scale = min(size / w, size / h)
+    nw, nh = max(1, round(w * scale)), max(1, round(h * scale))
+    resized = cv2.resize(frame, (nw, nh))
+    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+    canvas = np.full((size, size, 3), 114, dtype=np.uint8)
+    pad_x, pad_y = (size - nw) // 2, (size - nh) // 2
+    canvas[pad_y:pad_y + nh, pad_x:pad_x + nw] = rgb
+    return canvas, scale, (pad_x, pad_y)
+
+
+def _pick(outs, size, channels):
+    for t in outs:
+        if t.shape[1] == size and t.shape[2] == size and t.shape[3] == channels:
+            return t
+    raise ValueError(f"missing yolov8s_pose tensor for size={size} channels={channels}")
+
+
+def _iou(box, boxes):
+    x1 = np.maximum(box[0], boxes[:, 0]); y1 = np.maximum(box[1], boxes[:, 1])
+    x2 = np.minimum(box[2], boxes[:, 2]); y2 = np.minimum(box[3], boxes[:, 3])
+    inter = np.clip(x2 - x1, 0, None) * np.clip(y2 - y1, 0, None)
+    area = (box[2] - box[0]) * (box[3] - box[1])
+    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+    union = area + areas - inter
+    return np.where(union > 0, inter / union, 0.0)
+
+
+def _nms(pred, conf_thres, iou_thres, max_det):
+    """pred: (N, 4 + 1 + 51) = [xywh, score, 17*(x,y,score)]. Greedy IoU NMS (1 class)."""
+    keep_mask = pred[:, 4] > conf_thres
+    pred = pred[keep_mask]
+    if pred.shape[0] == 0:
+        return []
+    xywh = pred[:, :4]
+    xyxy = np.empty_like(xywh)
+    xyxy[:, 0] = xywh[:, 0] - xywh[:, 2] / 2
+    xyxy[:, 1] = xywh[:, 1] - xywh[:, 3] / 2
+    xyxy[:, 2] = xywh[:, 0] + xywh[:, 2] / 2
+    xyxy[:, 3] = xywh[:, 1] + xywh[:, 3] / 2
+    scores = pred[:, 4]
+    kpts = pred[:, 5:].reshape(-1, _NUM_KPTS, 3)
+    order = np.argsort(-scores)
+    dets = []
+    while order.size > 0 and len(dets) < max_det:
+        i = order[0]
+        dets.append({"bbox": xyxy[i], "score": float(scores[i]), "kpts": kpts[i]})
+        if order.size == 1:
+            break
+        ious = _iou(xyxy[i], xyxy[order[1:]])
+        order = order[1:][ious <= iou_thres]
+    return dets
+
+
+def decode_yolov8_pose(outs, input_size, strides, reg_max,
+                       conf_thres, iou_thres, max_det):
+    """Host decode of the 9 raw yolov8s_pose tensors -> NMS'd detections (letterbox px).
+    Ported from Hailo's MIT PoseEstPostProcessing (decoder + extract + NMS)."""
+    reg_range = np.arange(reg_max + 1)
+    boxes_all = scores_all = kpts_all = None
+    for stride in strides:
+        size = input_size // stride
+        box = _pick(outs, size, 4 * (reg_max + 1)).astype(np.float32)
+        score = _pick(outs, size, 1).astype(np.float32)
+        kpt = _pick(outs, size, _NUM_KPTS * 3).astype(np.float32).copy()
+
+        gx, gy = np.meshgrid(np.arange(size) + 0.5, np.arange(size) + 0.5)
+        ct_col, ct_row = gx.flatten() * stride, gy.flatten() * stride
+        center = np.stack((ct_col, ct_row, ct_col, ct_row), axis=1)  # (HW, 4)
+
+        bd = box.reshape(1, size * size, 4, reg_max + 1)
+        bd = _softmax(bd) * reg_range.reshape(1, 1, 1, -1)
+        bd = np.sum(bd, axis=-1) * stride                            # (1, HW, 4) distances (px)
+        bd = np.concatenate([bd[:, :, :2] * (-1), bd[:, :, 2:]], axis=-1)
+        decode_box = center[None] + bd                              # xyxy
+        xmin, ymin = decode_box[:, :, 0], decode_box[:, :, 1]
+        xmax, ymax = decode_box[:, :, 2], decode_box[:, :, 3]
+        xywh = np.transpose(np.array([(xmin + xmax) / 2, (ymin + ymax) / 2,
+                                      xmax - xmin, ymax - ymin]), [1, 2, 0])
+
+        sc = score.reshape(1, size * size, 1)
+        kp = kpt.reshape(1, size * size, _NUM_KPTS, 3)
+        kp[..., :2] *= 2
+        kp[..., :2] = stride * (kp[..., :2] - 0.5) + center[:, :2].reshape(size * size, 1, 2)
+        kp = kp.reshape(1, size * size, _NUM_KPTS * 3)
+
+        boxes_all = xywh if boxes_all is None else np.concatenate([boxes_all, xywh], axis=1)
+        scores_all = sc if scores_all is None else np.concatenate([scores_all, sc], axis=1)
+        kpts_all = kp if kpts_all is None else np.concatenate([kpts_all, kp], axis=1)
+
+    pred = np.concatenate([boxes_all, scores_all, kpts_all], axis=2)[0]
+    return _nms(pred, conf_thres, iou_thres, max_det)
+
 
 class HailoPoseAdapter:
-    def __init__(self, hef_path: str):
+    def __init__(self, hef_path, input_size=640, infer_fn=None,
+                 score_threshold=0.5, nms_iou=0.7, reg_max=15, strides=(8, 16, 32),
+                 min_person_score=0.2, max_detections=100):
         self.hef_path = hef_path
-        # 실장비 배선 지점: HailoRT VDevice/InferModel 로드, yolov8s_pose 후처리.
+        self.input_size = input_size
-        # 참고: hailo-rpi5-examples pose 파이프라인 (MIT).
+        self.score_threshold = score_threshold
-        raise NotImplementedError("실장비 전용: HailoRT 배선 필요")
+        self.nms_iou = nms_iou
+        self.reg_max = reg_max
+        self.strides = tuple(strides)
+        self.min_person_score = min_person_score
+        self.max_detections = max_detections
+        self._infer_fn = infer_fn if infer_fn is not None else self._build_hailo_infer(hef_path)
+
+    def _build_hailo_infer(self, hef_path):  # pragma: no cover (device-only: needs hailort + HEF)
+        """Lazily build the HailoRT inference closure. Validate on device (bringup).
+        Returns infer_fn(frame_640_uint8_rgb) -> list of the 9 output tensors."""
+        from hailo_platform import HEF, VDevice, FormatType, HailoSchedulingAlgorithm
+
+        params = VDevice.create_params()
+        params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
+        vdevice = VDevice(params)
+        hef = HEF(hef_path)
+        infer_model = vdevice.create_infer_model(hef_path)
+        infer_model.set_batch_size(1)
+        infer_model.input().set_format_type(FormatType.UINT8)     # on-chip normalization
+        out_infos = hef.get_output_vstream_infos()
+        for info in out_infos:
+            infer_model.output(info.name).set_format_type(FormatType.FLOAT32)  # dequantized
+        configured = infer_model.configure()
+        self._hailo_ctx = (vdevice, configured)  # keep alive for the session
+
+        def infer_fn(frame):
+            buffers = {info.name: np.empty(infer_model.output(info.name).shape, dtype=np.float32)
+                       for info in out_infos}
+            bindings = configured.create_bindings(output_buffers=buffers)
+            bindings.input().set_buffer(np.ascontiguousarray(frame))
+            configured.run([bindings], timeout_ms=10000)
+            return [buffers[info.name][None, ...] if buffers[info.name].ndim == 3
+                    else buffers[info.name] for info in out_infos]
+
+        return infer_fn
 
     def infer(self, frame) -> list:
-        # frame(640x640 RGB) -> yolov8s_pose -> list[Person(bbox, 17 keypoints)]
+        h, w = frame.shape[:2]
-        raise NotImplementedError
+        canvas, scale, (pad_x, pad_y) = _letterbox(frame, self.input_size)
+        outs = self._infer_fn(canvas)
+        dets = decode_yolov8_pose(outs, self.input_size, self.strides, self.reg_max,
+                                  self.score_threshold, self.nms_iou, self.max_detections)
+        persons = []
+        for d in dets:
+            if d["score"] < self.min_person_score:
+                continue
+            x1, y1, x2, y2 = d["bbox"]
+            bbox = BBox((x1 - pad_x) / scale, (y1 - pad_y) / scale,
+                        (x2 - pad_x) / scale, (y2 - pad_y) / scale)
+            keypoints = []
+            for j in range(_NUM_KPTS):
+                kx, ky, ksc = d["kpts"][j]
+                keypoints.append(Keypoint(float((kx - pad_x) / scale),
+                                          float((ky - pad_y) / scale),
+                                          float(_sigmoid(ksc))))
+            persons.append(Person(bbox, keypoints))
+        return persons

edge/daemon.py

@@ -41,7 +41,6 @@ def main() -> None:  # pragma: no cover (integration entrypoint, exercised on de
     from edge.queue import OfflineQueue
     from edge.runtime import PipelineRuntime
     from edge.adapters.uvc_source import UvcSource
-    from edge.adapters.movenet_pose import MoveNetPoseAdapter
 
     cfg = load_config(dict(os.environ))
     nonce = lambda: os.urandom(8).hex()
@@ -51,7 +50,14 @@ def main() -> None:  # pragma: no cover (integration entrypoint, exercised on de
     post = PostClient(base + "/api/events", cfg.device_id, cfg.secret, nonce, now)
     queue = OfflineQueue(os.environ.get("QUEUE_DB", "queue.db"), post)
     source = UvcSource(int(os.environ.get("UVC_DEVICE", "0")))
-    pose = MoveNetPoseAdapter(os.environ["MOVENET_ONNX"])
+    # POSE_BACKEND selects the accelerator path: "hailo" (Pi5 + Hailo-8, needs HAILO_HEF)
+    # or the default CPU "movenet" (needs MOVENET_ONNX).
+    if os.environ.get("POSE_BACKEND", "movenet").lower() == "hailo":
+        from edge.adapters.hailo_pose import HailoPoseAdapter
+        pose = HailoPoseAdapter(os.environ["HAILO_HEF"])
+    else:
+        from edge.adapters.movenet_pose import MoveNetPoseAdapter
+        pose = MoveNetPoseAdapter(os.environ["MOVENET_ONNX"])
     runtime = PipelineRuntime(cfg, source, pose, queue)
     run_loop(runtime, client, queue, clock=now, sleep=time.sleep, iterations=None,
              frame_interval=1.0 / float(os.environ.get("TARGET_FPS", "12")))

tests/test_hailo_pose.py

@@ -0,0 +1,64 @@
+"""TDD for HailoPoseAdapter — locks the yolov8s_pose host decode (DFL box + keypoint
+decode + NMS + COCO-17 assembly + letterbox un-mapping) WITHOUT hardware/hailort,
+by injecting a fake infer_fn that returns synthetic raw output tensors.
+
+yolov8s_pose emits 9 raw tensors (no on-chip NMS): per stride (8/16/32) a box(H,W,64
+DFL), score(H,W,1 already-sigmoid), and kpts(H,W,51 = 17*(x,y,score)).
+"""
+import numpy as np
+
+from edge.adapters.hailo_pose import HailoPoseAdapter
+
+INPUT = 640
+STRIDES = (8, 16, 32)
+
+
+def _blank_outputs():
+    outs = []
+    for s in STRIDES:
+        size = INPUT // s
+        outs.append(np.zeros((1, size, size, 64), np.float32))  # box DFL
+        outs.append(np.zeros((1, size, size, 1), np.float32))   # class score (sigmoid'd)
+        outs.append(np.zeros((1, size, size, 51), np.float32))  # keypoints
+    return outs
+
+
+def _one_person_outputs():
+    outs = _blank_outputs()
+    # activate a single cell on the stride-8 map (size 80), grid cell (40, 40)
+    box, score, kpts = outs[0], outs[1], outs[2]
+    r = c = 40
+    score[0, r, c, 0] = 0.9                       # person prob (already sigmoid on-chip)
+    # DFL: one-hot each of the 4 sides at bin index 2 -> distance = 2 cells * stride8 = 16 px
+    b = box[0, r, c].reshape(4, 16)
+    b[:, 2] = 50.0
+    box[0, r, c] = b.reshape(64)
+    # keypoints: raw x=y=0.5, raw score=3.0 for all 17 joints
+    k = kpts[0, r, c].reshape(17, 3)
+    k[:, 0] = 0.5
+    k[:, 1] = 0.5
+    k[:, 2] = 3.0
+    kpts[0, r, c] = k.reshape(51)
+    return outs
+
+
+def test_infer_maps_yolov8_pose_to_coco17_person():
+    frame = np.zeros((640, 640, 3), np.uint8)   # already 640x640 -> scale 1, no padding
+    outs = _one_person_outputs()
+    a = HailoPoseAdapter("unused.hef", infer_fn=lambda f: outs)
+    persons = a.infer(frame)
+    assert len(persons) == 1
+    p = persons[0]
+    assert len(p.keypoints) == 17
+    # grid center = (40 + 0.5) * 8 = 324 ; box +/- 16 -> [308, 340]
+    assert abs(p.bbox.x1 - 308) < 2 and abs(p.bbox.y1 - 308) < 2
+    assert abs(p.bbox.x2 - 340) < 2 and abs(p.bbox.y2 - 340) < 2
+    # keypoint decode: stride*(2*0.5 - 0.5) + 324 = 8*0.5 + 324 = 328
+    assert abs(p.keypoints[0].x - 328) < 2 and abs(p.keypoints[0].y - 328) < 2
+    # joint score sigmoided: sigmoid(3.0) ~= 0.9526
+    assert abs(p.keypoints[0].conf - 0.9526) < 0.01
+
+
+def test_no_detection_returns_empty():
+    a = HailoPoseAdapter("unused.hef", infer_fn=lambda f: _blank_outputs())
+    assert a.infer(np.zeros((640, 640, 3), np.uint8)) == []