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)]