feat(edge): MoveNet ONNX CPU pose adapter (COCO-17 remap)

edge/adapters/movenet_pose.py

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+import cv2
+import numpy as np
+
+from edge.models import Person, BBox, Keypoint
+
+
+class MoveNetPoseAdapter:
+    """MoveNet SinglePose Lightning (COCO-17) via onnxruntime. CPU dev/pilot PosePort.
+    출력 [1,1,17,3] = (y, x, score) normalized 0..1."""
+
+    def __init__(self, onnx_path: str, input_size: int = 192, session=None,
+                 min_person_score: float = 0.2):
+        self.input_size = input_size
+        self.min_person_score = min_person_score
+        if session is None:
+            import onnxruntime  # gated: bundled on build machine
+            session = onnxruntime.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
+        self.session = session
+        self._input_name = session.get_inputs()[0].name
+
+    def infer(self, frame) -> list:
+        h, w = frame.shape[:2]
+        img = cv2.resize(frame, (self.input_size, self.input_size))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.int32)[None, ...]
+        out = self.session.run(None, {self._input_name: img})[0]  # [1,1,17,3]
+        kp = out[0, 0]  # [17,3] (y,x,score)
+        keypoints = [Keypoint(float(kp[j, 1] * w), float(kp[j, 0] * h), float(kp[j, 2]))
+                     for j in range(17)]
+        vis = [k for k in keypoints if k.conf >= self.min_person_score]
+        if not vis:
+            return []
+        xs = [k.x for k in vis]; ys = [k.y for k in vis]
+        bbox = BBox(min(xs), min(ys), max(xs), max(ys))
+        return [Person(bbox, keypoints)]

edge/tools/fetch_movenet.py

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+"""빌드머신 전용: MoveNet SinglePose Lightning ONNX 다운로드(폐쇄망 번들). Apache-2.0."""
+import os
+import urllib.request
+
+# MoveNet Lightning ONNX (예시 소스; 조직 미러로 교체 가능)
+URL = "https://github.com/PINTO0309/PINTO_model_zoo/raw/main/115_MoveNet/resources/movenet_singlepose_lightning_4.onnx"
+DEST = os.path.join(os.path.dirname(__file__), "..", "..", "models", "movenet_lightning.onnx")
+
+def main():
+    os.makedirs(os.path.dirname(DEST), exist_ok=True)
+    print(f"downloading MoveNet ONNX -> {DEST}")
+    urllib.request.urlretrieve(URL, DEST)
+    print("done")
+
+if __name__ == "__main__":
+    main()

tests/test_movenet_pose.py

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+import numpy as np
+from edge.adapters.movenet_pose import MoveNetPoseAdapter
+
+class _FakeSession:
+    """MoveNet 출력 형태 [1,1,17,3] (y,x,score) 정규화."""
+    def get_inputs(self):
+        class I: name = "input"
+        return [I()]
+    def run(self, _out, feed):
+        kp = np.zeros((1, 1, 17, 3), dtype=np.float32)
+        for j in range(17):
+            kp[0, 0, j] = [0.5, 0.25, 0.9]  # y=0.5, x=0.25, score=0.9
+        return [kp]
+
+def test_infer_maps_movenet_to_coco17_persons():
+    frame = np.zeros((400, 800, 3), dtype=np.uint8)
+    a = MoveNetPoseAdapter("unused.onnx", session=_FakeSession())
+    persons = a.infer(frame)
+    assert len(persons) == 1
+    p = persons[0]
+    assert len(p.keypoints) == 17
+    # x = 0.25 * 800 = 200, y = 0.5 * 400 = 200
+    assert abs(p.keypoints[0].x - 200) < 1 and abs(p.keypoints[0].y - 200) < 1
+    assert abs(p.keypoints[0].conf - 0.9) < 1e-6
+    assert p.bbox.x1 <= 200 <= p.bbox.x2
+
+def test_low_score_person_dropped():
+    class Low(_FakeSession):
+        def run(self, _o, feed):
+            return [np.zeros((1, 1, 17, 3), dtype=np.float32)]  # all score 0
+    a = MoveNetPoseAdapter("unused.onnx", session=Low())
+    assert a.infer(np.zeros((10, 10, 3), np.uint8)) == []