#!/opt/homebrew/bin/python3.11
"""
Pose Processor Wrapper
Calls Swift Vision Framework pose (swift_pose) with fallback to YOLOv8 Pose.
Uses VNDetectHumanBodyPoseRequest with ANE acceleration.
"""

import sys
import json
import os
import subprocess
import argparse

SWIFT_POSE_PATH = os.path.join(
    os.path.dirname(os.path.abspath(__file__)),
    "swift_processors/.build/debug/swift_pose"
)
SWIFT_POSE_ALT = os.path.join(
    os.path.dirname(os.path.abspath(__file__)),
    "swift_processors/.build/arm64-apple-macosx/debug/swift_pose"
)


def process_pose(
    video_path: str,
    output_path: str,
    uuid: str = "",
    sample_interval: int = 30,
) -> dict:
    swift_bin = SWIFT_POSE_PATH
    if not os.path.exists(swift_bin):
        swift_bin = SWIFT_POSE_ALT

    if not os.path.exists(swift_bin):
        print("[Pose] Swift binary not found, using YOLOv8 fallback", file=sys.stderr)
        return _fallback(video_path, output_path, uuid, sample_interval)

    cmd = [swift_bin, video_path, output_path,
           "--sample-interval", str(sample_interval),
           "--uuid", uuid]

    print(f"[Pose] Running Swift Pose (Vision Framework)", file=sys.stderr)
    result = subprocess.run(cmd, capture_output=True, text=True, timeout=7200)

    if result.stdout:
        for line in result.stdout.strip().split("\n"):
            print(f"  {line}", file=sys.stderr)
    if result.stderr:
        for line in result.stderr.strip().split("\n"):
            print(f"  {line}", file=sys.stderr)

    if result.returncode != 0 or not os.path.exists(output_path):
        print(f"[Pose] Swift Pose failed, falling back to YOLOv8", file=sys.stderr)
        return _fallback(video_path, output_path, uuid, sample_interval)

    with open(output_path) as f:
        return json.load(f)


def _fallback(video_path, output_path, uuid, sample_interval):
    """Fallback to YOLOv8 Pose"""
    from ultralytics import YOLO
    import cv2
    model = YOLO("yolov8n-pose.pt")
    cap = cv2.VideoCapture(video_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_count = 0
    frames = []
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        if frame_count % sample_interval == 0:
            ts = frame_count / fps if fps > 0 else 0
            results = model(frame, verbose=False, device="cpu")
            persons = []
            for r in results:
                if r.keypoints is None:
                    continue
                for kp_data in r.keypoints:
                    kps = kp_data.xy[0].cpu().numpy() if hasattr(kp_data, 'xy') else []
                    confs = kp_data.conf[0].cpu().numpy() if hasattr(kp_data, 'conf') else []
                    keypoints = []
                    names = ["nose", "left_eye", "right_eye", "left_ear", "right_ear",
                             "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
                             "left_wrist", "right_wrist", "left_hip", "right_hip",
                             "left_knee", "right_knee", "left_ankle", "right_ankle"]
                    for j, name in enumerate(names):
                        if j < len(kps):
                            x, y = float(kps[j][0]), float(kps[j][1])
                            c = float(confs[j]) if j < len(confs) else 0
                            keypoints.append({"name": name, "x": x, "y": y, "confidence": c})
                    if keypoints:
                        xs = [k["x"] for k in keypoints if k["confidence"] > 0.1]
                        ys = [k["y"] for k in keypoints if k["confidence"] > 0.1]
                        bbox = {"x": int(min(xs)), "y": int(min(ys)), "width": int(max(xs)-min(xs)), "height": int(max(ys)-min(ys))} if xs else {"x": 0, "y": 0, "width": 0, "height": 0}
                        persons.append({"keypoints": keypoints, "bbox": bbox})
            if persons:
                frames.append({"frame": frame_count, "timestamp": ts, "persons": persons})
        frame_count += 1
    cap.release()
    result = {"frame_count": len(frames), "fps": fps, "frames": frames}
    with open(output_path, "w") as f:
        json.dump(result, f, indent=2)
    return result


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Pose Processor (Swift Vision)")
    parser.add_argument("video_path")
    parser.add_argument("output_path")
    parser.add_argument("--uuid", "-u", default="")
    parser.add_argument("--sample-interval", type=int, default=30)
    args = parser.parse_args()

    result = process_pose(args.video_path, args.output_path, args.uuid, args.sample_interval)
    with open(args.output_path, "w") as f:
        json.dump(result, f, indent=2)
    print(f"Pose: {len(result.get('frames', []))} frames with poses")