wsy182
4485cbf702
Split monolithic files into focused modules: - app/core: settings, logging, lifecycle - app/signaling: websocket server, ICE parser, message models - app/webrtc: peer session, video receiver, frame source - app/vision: pose landmarker wrapper, model config, pose types - app/exercises/dead_bug: detector, metrics, rules, state machine, types - app/rendering: skeleton renderer, status overlay, window display - app/audio: rep announcer - app/diagnostics: perf timer, crash handler - configs: environment-based settings - tests: unit tests for rules, state machine, ICE parser - run.py: entry point
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
from __future__ import annotations
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import threading
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import time
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from typing import Callable
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import mediapipe as mp
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from loguru import logger
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from app.vision.pose_models import DEFAULT_MODEL_PATH
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PoseLandmarker = mp.tasks.vision.PoseLandmarker
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PoseLandmarkerOptions = mp.tasks.vision.PoseLandmarkerOptions
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VisionRunningMode = mp.tasks.vision.RunningMode
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BaseOptions = mp.tasks.BaseOptions
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class PoseLandmarkerWrapper:
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def __init__(
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self,
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*,
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model_path: str | None = None,
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prefer_gpu: bool = True,
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result_callback: Callable | None = None,
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) -> None:
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self.model_path = model_path or DEFAULT_MODEL_PATH
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if prefer_gpu:
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try:
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self.delegate = BaseOptions.Delegate.GPU
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self._landmarker = self._create(PoseLandmarker.Delegate.GPU)
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logger.info("MediaPipe PoseLandmarker initialized with GPU delegate")
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return
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except Exception as exc:
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logger.warning("MediaPipe GPU delegate unavailable, falling back to CPU: {}", exc)
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self.delegate = BaseOptions.Delegate.CPU
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self._landmarker = self._create(PoseLandmarker.Delegate.CPU, result_callback)
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logger.info("MediaPipe PoseLandmarker initialized with CPU delegate")
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def _create(self, delegate, result_callback=None):
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options = PoseLandmarkerOptions(
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base_options=BaseOptions(model_asset_path=self.model_path, delegate=delegate),
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running_mode=VisionRunningMode.LIVE_STREAM,
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result_callback=result_callback,
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num_poses=1,
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min_pose_detection_confidence=0.5,
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min_pose_presence_confidence=0.5,
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min_tracking_confidence=0.5,
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)
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return PoseLandmarker.create_from_options(options)
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def detect_async(self, mp_image, timestamp_ms: int) -> None:
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return self._landmarker.detect_async(mp_image, timestamp_ms)
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def close(self) -> None:
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self._landmarker.close()
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