crosstyan
7f073179d7
Align bbox coordinate handling across primary and fallback paths, normalize Both-mode raw mask rendering, and tighten demo result typing to reduce runtime/display inconsistencies.
763 lines
26 KiB
Python
763 lines
26 KiB
Python
from __future__ import annotations
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from collections.abc import Callable
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from contextlib import suppress
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import logging
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from pathlib import Path
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import time
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from typing import TYPE_CHECKING, Protocol, cast
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from beartype import beartype
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import click
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import jaxtyping
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from jaxtyping import Float, UInt8
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import numpy as np
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from numpy import ndarray
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from numpy.typing import NDArray
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from ultralytics.models.yolo.model import YOLO
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from .input import FrameStream, create_source
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from .output import DemoResult, ResultPublisher, create_publisher, create_result
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from .preprocess import BBoxXYXY, frame_to_person_mask, mask_to_silhouette
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from .sconet_demo import ScoNetDemo
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from .window import SilhouetteWindow, select_person
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if TYPE_CHECKING:
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from .visualizer import OpenCVVisualizer
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logger = logging.getLogger(__name__)
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JaxtypedDecorator = Callable[[Callable[..., object]], Callable[..., object]]
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JaxtypedFactory = Callable[..., JaxtypedDecorator]
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jaxtyped = cast(JaxtypedFactory, jaxtyping.jaxtyped)
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class _BoxesLike(Protocol):
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@property
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def xyxy(self) -> NDArray[np.float32] | object: ...
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@property
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def id(self) -> NDArray[np.int64] | object | None: ...
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class _MasksLike(Protocol):
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@property
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def data(self) -> NDArray[np.float32] | object: ...
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class _DetectionResultsLike(Protocol):
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@property
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def boxes(self) -> _BoxesLike: ...
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@property
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def masks(self) -> _MasksLike: ...
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class _TrackCallable(Protocol):
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def __call__(
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self,
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source: object,
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*,
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persist: bool = True,
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verbose: bool = False,
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device: str | None = None,
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classes: list[int] | None = None,
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) -> object: ...
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class ScoliosisPipeline:
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_detector: object
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_source: FrameStream
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_window: SilhouetteWindow
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_publisher: ResultPublisher
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_classifier: ScoNetDemo
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_device: str
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_closed: bool
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_preprocess_only: bool
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_silhouette_export_path: Path | None
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_silhouette_export_format: str
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_silhouette_buffer: list[dict[str, object]]
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_silhouette_visualize_dir: Path | None
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_result_export_path: Path | None
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_result_export_format: str
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_result_buffer: list[DemoResult]
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_visualizer: OpenCVVisualizer | None
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_last_viz_payload: dict[str, object] | None
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def __init__(
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self,
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*,
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source: str,
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checkpoint: str,
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config: str,
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device: str,
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yolo_model: str,
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window: int,
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stride: int,
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nats_url: str | None,
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nats_subject: str,
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max_frames: int | None,
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preprocess_only: bool = False,
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silhouette_export_path: str | None = None,
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silhouette_export_format: str = "pickle",
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silhouette_visualize_dir: str | None = None,
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result_export_path: str | None = None,
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result_export_format: str = "json",
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visualize: bool = False,
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) -> None:
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self._detector = YOLO(yolo_model)
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self._source = create_source(source, max_frames=max_frames)
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self._window = SilhouetteWindow(window_size=window, stride=stride)
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self._publisher = create_publisher(nats_url=nats_url, subject=nats_subject)
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self._classifier = ScoNetDemo(
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cfg_path=config,
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checkpoint_path=checkpoint,
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device=device,
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)
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self._device = device
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self._closed = False
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self._preprocess_only = preprocess_only
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self._silhouette_export_path = (
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Path(silhouette_export_path) if silhouette_export_path else None
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)
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self._silhouette_export_format = silhouette_export_format
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self._silhouette_buffer = []
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self._silhouette_visualize_dir = (
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Path(silhouette_visualize_dir) if silhouette_visualize_dir else None
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)
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self._result_export_path = (
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Path(result_export_path) if result_export_path else None
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)
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self._result_export_format = result_export_format
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self._result_buffer = []
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if visualize:
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from .visualizer import OpenCVVisualizer
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self._visualizer = OpenCVVisualizer()
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else:
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self._visualizer = None
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self._last_viz_payload = None
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@staticmethod
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def _extract_int(meta: dict[str, object], key: str, fallback: int) -> int:
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value = meta.get(key)
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if isinstance(value, int):
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return value
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return fallback
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@staticmethod
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def _extract_timestamp(meta: dict[str, object]) -> int:
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value = meta.get("timestamp_ns")
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if isinstance(value, int):
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return value
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return time.monotonic_ns()
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@staticmethod
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def _to_mask_u8(mask: ndarray) -> UInt8[ndarray, "h w"]:
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binary = np.where(np.asarray(mask) > 0.5, np.uint8(255), np.uint8(0)).astype(
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np.uint8
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)
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return cast(UInt8[ndarray, "h w"], binary)
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def _first_result(self, detections: object) -> _DetectionResultsLike | None:
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if isinstance(detections, list):
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return cast(_DetectionResultsLike, detections[0]) if detections else None
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if isinstance(detections, tuple):
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return cast(_DetectionResultsLike, detections[0]) if detections else None
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return cast(_DetectionResultsLike, detections)
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def _select_silhouette(
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self,
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result: _DetectionResultsLike,
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) -> (
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tuple[
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Float[ndarray, "64 44"],
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UInt8[ndarray, "h w"],
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BBoxXYXY,
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int,
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]
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| None
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):
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selected = select_person(result)
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if selected is not None:
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mask_raw, bbox_mask, bbox_frame, track_id = selected
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silhouette = cast(
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Float[ndarray, "64 44"] | None,
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mask_to_silhouette(self._to_mask_u8(mask_raw), bbox_mask),
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)
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if silhouette is not None:
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return silhouette, mask_raw, bbox_frame, int(track_id)
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fallback = cast(
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tuple[UInt8[ndarray, "h w"], BBoxXYXY] | None,
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frame_to_person_mask(result),
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)
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if fallback is None:
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return None
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mask_u8, bbox_mask = fallback
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silhouette = cast(
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Float[ndarray, "64 44"] | None,
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mask_to_silhouette(mask_u8, bbox_mask),
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)
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if silhouette is None:
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return None
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# Convert mask-space bbox to frame-space for visualization
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# Use result.orig_shape to get frame dimensions safely
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orig_shape = getattr(result, "orig_shape", None)
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if orig_shape is not None and isinstance(orig_shape, (tuple, list)) and len(orig_shape) >= 2:
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frame_h, frame_w = int(orig_shape[0]), int(orig_shape[1])
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mask_h, mask_w = mask_u8.shape[0], mask_u8.shape[1]
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if mask_w > 0 and mask_h > 0 and frame_w > 0 and frame_h > 0:
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scale_x = frame_w / mask_w
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scale_y = frame_h / mask_h
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bbox_frame = (
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int(bbox_mask[0] * scale_x),
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int(bbox_mask[1] * scale_y),
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int(bbox_mask[2] * scale_x),
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int(bbox_mask[3] * scale_y),
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)
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else:
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# Fallback: use mask-space bbox if dimensions invalid
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bbox_frame = bbox_mask
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else:
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# Fallback: use mask-space bbox if orig_shape unavailable
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bbox_frame = bbox_mask
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# For fallback case, mask_raw is the same as mask_u8
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return silhouette, mask_u8, bbox_frame, 0
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@jaxtyped(typechecker=beartype)
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def process_frame(
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self,
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frame: UInt8[ndarray, "h w c"],
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metadata: dict[str, object],
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) -> dict[str, object] | None:
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frame_idx = self._extract_int(metadata, "frame_count", fallback=0)
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timestamp_ns = self._extract_timestamp(metadata)
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track_fn_obj = getattr(self._detector, "track", None)
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if not callable(track_fn_obj):
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raise RuntimeError("YOLO detector does not expose a callable track()")
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track_fn = cast(_TrackCallable, track_fn_obj)
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detections = track_fn(
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frame,
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persist=True,
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verbose=False,
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device=self._device,
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classes=[0],
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)
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first = self._first_result(detections)
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if first is None:
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return None
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selected = self._select_silhouette(first)
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if selected is None:
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return None
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silhouette, mask_raw, bbox, track_id = selected
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# Store silhouette for export if in preprocess-only mode or if export requested
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if self._silhouette_export_path is not None or self._preprocess_only:
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self._silhouette_buffer.append(
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{
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"frame": frame_idx,
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"track_id": track_id,
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"timestamp_ns": timestamp_ns,
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"silhouette": silhouette.copy(),
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}
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)
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# Visualize silhouette if requested
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if self._silhouette_visualize_dir is not None:
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self._visualize_silhouette(silhouette, frame_idx, track_id)
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if self._preprocess_only:
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# Return visualization payload for display even in preprocess-only mode
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return {
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"mask_raw": mask_raw,
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"bbox": bbox,
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"silhouette": silhouette,
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"track_id": track_id,
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"label": None,
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"confidence": None,
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}
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self._window.push(silhouette, frame_idx=frame_idx, track_id=track_id)
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if not self._window.should_classify():
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# Return visualization payload even when not classifying yet
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return {
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"mask_raw": mask_raw,
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"bbox": bbox,
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"silhouette": silhouette,
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"track_id": track_id,
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"label": None,
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"confidence": None,
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}
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window_tensor = self._window.get_tensor(device=self._device)
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label, confidence = cast(
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tuple[str, float],
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self._classifier.predict(window_tensor),
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)
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self._window.mark_classified()
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window_start = frame_idx - self._window.window_size + 1
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result = create_result(
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frame=frame_idx,
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track_id=track_id,
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label=label,
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confidence=float(confidence),
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window=(max(0, window_start), frame_idx),
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timestamp_ns=timestamp_ns,
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)
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# Store result for export if export path specified
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if self._result_export_path is not None:
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self._result_buffer.append(result)
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self._publisher.publish(result)
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# Return result with visualization payload
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return {
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"result": result,
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"mask_raw": mask_raw,
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"bbox": bbox,
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"silhouette": silhouette,
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"track_id": track_id,
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"label": label,
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"confidence": confidence,
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}
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def run(self) -> int:
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frame_count = 0
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start_time = time.perf_counter()
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# EMA FPS state (alpha=0.1 for smoothing)
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ema_fps = 0.0
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alpha = 0.1
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prev_time = start_time
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try:
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for item in self._source:
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frame, metadata = item
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frame_u8 = np.asarray(frame, dtype=np.uint8)
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frame_idx = self._extract_int(metadata, "frame_count", fallback=0)
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frame_count += 1
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# Compute per-frame EMA FPS
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curr_time = time.perf_counter()
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delta = curr_time - prev_time
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prev_time = curr_time
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if delta > 0:
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instant_fps = 1.0 / delta
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if ema_fps == 0.0:
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ema_fps = instant_fps
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else:
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ema_fps = alpha * instant_fps + (1 - alpha) * ema_fps
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viz_payload = None
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try:
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viz_payload = self.process_frame(frame_u8, metadata)
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except Exception as frame_error:
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logger.warning(
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"Skipping frame %d due to processing error: %s",
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frame_idx,
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frame_error,
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)
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# Update visualizer if enabled
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if self._visualizer is not None:
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# Cache valid payload for no-detection frames
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if viz_payload is not None:
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# Cache a copy to prevent mutation of original data
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viz_payload_dict = cast(dict[str, object], viz_payload)
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cached: dict[str, object] = {}
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for k, v in viz_payload_dict.items():
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copy_method = cast(Callable[[], object] | None, getattr(v, "copy", None))
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if copy_method is not None:
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cached[k] = copy_method()
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else:
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cached[k] = v
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self._last_viz_payload = cached
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# Use cached payload if current is None
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viz_data = viz_payload if viz_payload is not None else self._last_viz_payload
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if viz_data is not None:
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# Cast viz_payload to dict for type checking
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viz_dict = cast(dict[str, object], viz_data)
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mask_raw_obj = viz_dict.get("mask_raw")
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bbox_obj = viz_dict.get("bbox")
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silhouette_obj = viz_dict.get("silhouette")
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track_id_val = viz_dict.get("track_id", 0)
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track_id = track_id_val if isinstance(track_id_val, int) else 0
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label_obj = viz_dict.get("label")
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confidence_obj = viz_dict.get("confidence")
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# Cast extracted values to expected types
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mask_raw = cast(NDArray[np.uint8] | None, mask_raw_obj)
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bbox = cast(BBoxXYXY | None, bbox_obj)
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silhouette = cast(NDArray[np.float32] | None, silhouette_obj)
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label = cast(str | None, label_obj)
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confidence = cast(float | None, confidence_obj)
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else:
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# No detection and no cache - use default values
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mask_raw = None
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bbox = None
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track_id = 0
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silhouette = None
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label = None
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confidence = None
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keep_running = self._visualizer.update(
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frame_u8,
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bbox,
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track_id,
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mask_raw,
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silhouette,
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label,
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confidence,
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ema_fps,
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)
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if not keep_running:
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logger.info("Visualization closed by user.")
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break
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if frame_count % 100 == 0:
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elapsed = time.perf_counter() - start_time
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fps = frame_count / elapsed if elapsed > 0 else 0.0
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logger.info("Processed %d frames (%.2f FPS)", frame_count, fps)
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return 0
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except KeyboardInterrupt:
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logger.info("Interrupted by user, shutting down cleanly.")
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return 130
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finally:
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self.close()
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def close(self) -> None:
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if self._closed:
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return
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# Close visualizer if enabled
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if self._visualizer is not None:
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with suppress(Exception):
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self._visualizer.close()
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# Export silhouettes if requested
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if self._silhouette_export_path is not None and self._silhouette_buffer:
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self._export_silhouettes()
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# Export results if requested
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if self._result_export_path is not None and self._result_buffer:
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self._export_results()
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close_fn = getattr(self._publisher, "close", None)
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if callable(close_fn):
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with suppress(Exception):
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_ = close_fn()
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self._closed = True
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def _export_silhouettes(self) -> None:
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"""Export silhouettes to file in specified format."""
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if self._silhouette_export_path is None:
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return
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self._silhouette_export_path.parent.mkdir(parents=True, exist_ok=True)
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if self._silhouette_export_format == "pickle":
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import pickle
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with open(self._silhouette_export_path, "wb") as f:
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pickle.dump(self._silhouette_buffer, f)
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logger.info(
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"Exported %d silhouettes to %s",
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len(self._silhouette_buffer),
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self._silhouette_export_path,
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)
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elif self._silhouette_export_format == "parquet":
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self._export_parquet_silhouettes()
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else:
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raise ValueError(
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f"Unsupported silhouette export format: {self._silhouette_export_format}"
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)
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def _visualize_silhouette(
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self,
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silhouette: Float[ndarray, "64 44"],
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frame_idx: int,
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track_id: int,
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) -> None:
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"""Save silhouette as PNG image."""
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if self._silhouette_visualize_dir is None:
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return
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self._silhouette_visualize_dir.mkdir(parents=True, exist_ok=True)
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# Convert float silhouette to uint8 (0-255)
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silhouette_u8 = (silhouette * 255).astype(np.uint8)
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# Create deterministic filename
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filename = f"silhouette_frame{frame_idx:06d}_track{track_id:04d}.png"
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output_path = self._silhouette_visualize_dir / filename
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# Save using PIL
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from PIL import Image
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Image.fromarray(silhouette_u8).save(output_path)
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def _export_parquet_silhouettes(self) -> None:
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"""Export silhouettes to parquet format."""
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import importlib
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try:
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pa = importlib.import_module("pyarrow")
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pq = importlib.import_module("pyarrow.parquet")
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except ImportError as e:
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raise RuntimeError(
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|
"Parquet export requires pyarrow. Install with: pip install pyarrow"
|
|
) from e
|
|
|
|
# Convert silhouettes to columnar format
|
|
frames = []
|
|
track_ids = []
|
|
timestamps = []
|
|
silhouettes = []
|
|
|
|
for item in self._silhouette_buffer:
|
|
frames.append(item["frame"])
|
|
track_ids.append(item["track_id"])
|
|
timestamps.append(item["timestamp_ns"])
|
|
silhouette_array = cast(ndarray, item["silhouette"])
|
|
silhouettes.append(silhouette_array.flatten().tolist())
|
|
|
|
table = pa.table(
|
|
{
|
|
"frame": pa.array(frames, type=pa.int64()),
|
|
"track_id": pa.array(track_ids, type=pa.int64()),
|
|
"timestamp_ns": pa.array(timestamps, type=pa.int64()),
|
|
"silhouette": pa.array(silhouettes, type=pa.list_(pa.float64())),
|
|
}
|
|
)
|
|
|
|
pq.write_table(table, self._silhouette_export_path)
|
|
logger.info(
|
|
"Exported %d silhouettes to parquet: %s",
|
|
len(self._silhouette_buffer),
|
|
self._silhouette_export_path,
|
|
)
|
|
|
|
def _export_results(self) -> None:
|
|
"""Export results to file in specified format."""
|
|
if self._result_export_path is None:
|
|
return
|
|
|
|
self._result_export_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
if self._result_export_format == "json":
|
|
import json
|
|
|
|
with open(self._result_export_path, "w", encoding="utf-8") as f:
|
|
for result in self._result_buffer:
|
|
f.write(json.dumps(result, ensure_ascii=False, default=str) + "\n")
|
|
logger.info(
|
|
"Exported %d results to JSON: %s",
|
|
len(self._result_buffer),
|
|
self._result_export_path,
|
|
)
|
|
elif self._result_export_format == "pickle":
|
|
import pickle
|
|
|
|
with open(self._result_export_path, "wb") as f:
|
|
pickle.dump(self._result_buffer, f)
|
|
logger.info(
|
|
"Exported %d results to pickle: %s",
|
|
len(self._result_buffer),
|
|
self._result_export_path,
|
|
)
|
|
elif self._result_export_format == "parquet":
|
|
self._export_parquet_results()
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported result export format: {self._result_export_format}"
|
|
)
|
|
|
|
def _export_parquet_results(self) -> None:
|
|
"""Export results to parquet format."""
|
|
import importlib
|
|
|
|
try:
|
|
pa = importlib.import_module("pyarrow")
|
|
pq = importlib.import_module("pyarrow.parquet")
|
|
except ImportError as e:
|
|
raise RuntimeError(
|
|
"Parquet export requires pyarrow. Install with: pip install pyarrow"
|
|
) from e
|
|
|
|
frames = []
|
|
track_ids = []
|
|
labels = []
|
|
confidences = []
|
|
windows = []
|
|
timestamps = []
|
|
|
|
for result in self._result_buffer:
|
|
frames.append(result["frame"])
|
|
track_ids.append(result["track_id"])
|
|
labels.append(result["label"])
|
|
confidences.append(result["confidence"])
|
|
windows.append(result["window"])
|
|
timestamps.append(result["timestamp_ns"])
|
|
|
|
table = pa.table(
|
|
{
|
|
"frame": pa.array(frames, type=pa.int64()),
|
|
"track_id": pa.array(track_ids, type=pa.int64()),
|
|
"label": pa.array(labels, type=pa.string()),
|
|
"confidence": pa.array(confidences, type=pa.float64()),
|
|
"window": pa.array(windows, type=pa.int64()),
|
|
"timestamp_ns": pa.array(timestamps, type=pa.int64()),
|
|
}
|
|
)
|
|
|
|
pq.write_table(table, self._result_export_path)
|
|
logger.info(
|
|
"Exported %d results to parquet: %s",
|
|
len(self._result_buffer),
|
|
self._result_export_path,
|
|
)
|
|
|
|
|
|
def validate_runtime_inputs(source: str, checkpoint: str, config: str) -> None:
|
|
if source.startswith("cvmmap://") or source.isdigit():
|
|
pass
|
|
else:
|
|
source_path = Path(source)
|
|
if not source_path.is_file():
|
|
raise ValueError(f"Video source not found: {source}")
|
|
|
|
checkpoint_path = Path(checkpoint)
|
|
if not checkpoint_path.is_file():
|
|
raise ValueError(f"Checkpoint not found: {checkpoint}")
|
|
|
|
config_path = Path(config)
|
|
if not config_path.is_file():
|
|
raise ValueError(f"Config not found: {config}")
|
|
|
|
|
|
@click.command(context_settings={"help_option_names": ["-h", "--help"]})
|
|
@click.option("--source", type=str, required=True)
|
|
@click.option("--checkpoint", type=str, required=True)
|
|
@click.option(
|
|
"--config",
|
|
type=str,
|
|
default="configs/sconet/sconet_scoliosis1k.yaml",
|
|
show_default=True,
|
|
)
|
|
@click.option("--device", type=str, default="cuda:0", show_default=True)
|
|
@click.option("--yolo-model", type=str, default="ckpt/yolo11n-seg.pt", show_default=True)
|
|
@click.option("--window", type=click.IntRange(min=1), default=30, show_default=True)
|
|
@click.option("--stride", type=click.IntRange(min=1), default=30, show_default=True)
|
|
@click.option("--nats-url", type=str, default=None)
|
|
@click.option(
|
|
"--nats-subject",
|
|
type=str,
|
|
default="scoliosis.result",
|
|
show_default=True,
|
|
)
|
|
@click.option("--max-frames", type=click.IntRange(min=1), default=None)
|
|
@click.option(
|
|
"--preprocess-only",
|
|
is_flag=True,
|
|
default=False,
|
|
help="Only preprocess silhouettes, skip classification.",
|
|
)
|
|
@click.option(
|
|
"--silhouette-export-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to export silhouettes (required for preprocess-only mode).",
|
|
)
|
|
@click.option(
|
|
"--silhouette-export-format",
|
|
type=click.Choice(["pickle", "parquet"]),
|
|
default="pickle",
|
|
show_default=True,
|
|
help="Format for silhouette export.",
|
|
)
|
|
@click.option(
|
|
"--result-export-path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to export inference results.",
|
|
)
|
|
@click.option(
|
|
"--result-export-format",
|
|
type=click.Choice(["json", "pickle", "parquet"]),
|
|
default="json",
|
|
show_default=True,
|
|
help="Format for result export.",
|
|
)
|
|
@click.option(
|
|
"--silhouette-visualize-dir",
|
|
type=str,
|
|
default=None,
|
|
help="Directory to save silhouette PNG visualizations.",
|
|
)
|
|
def main(
|
|
source: str,
|
|
checkpoint: str,
|
|
config: str,
|
|
device: str,
|
|
yolo_model: str,
|
|
window: int,
|
|
stride: int,
|
|
nats_url: str | None,
|
|
nats_subject: str,
|
|
max_frames: int | None,
|
|
preprocess_only: bool,
|
|
silhouette_export_path: str | None,
|
|
silhouette_export_format: str,
|
|
result_export_path: str | None,
|
|
result_export_format: str,
|
|
silhouette_visualize_dir: str | None,
|
|
) -> None:
|
|
logging.basicConfig(
|
|
level=logging.INFO,
|
|
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
|
|
)
|
|
|
|
# Validate preprocess-only mode requirements
|
|
if preprocess_only and not silhouette_export_path:
|
|
raise click.UsageError(
|
|
"--silhouette-export-path is required when using --preprocess-only"
|
|
)
|
|
|
|
try:
|
|
validate_runtime_inputs(source=source, checkpoint=checkpoint, config=config)
|
|
pipeline = ScoliosisPipeline(
|
|
source=source,
|
|
checkpoint=checkpoint,
|
|
config=config,
|
|
device=device,
|
|
yolo_model=yolo_model,
|
|
window=window,
|
|
stride=stride,
|
|
nats_url=nats_url,
|
|
nats_subject=nats_subject,
|
|
max_frames=max_frames,
|
|
preprocess_only=preprocess_only,
|
|
silhouette_export_path=silhouette_export_path,
|
|
silhouette_export_format=silhouette_export_format,
|
|
silhouette_visualize_dir=silhouette_visualize_dir,
|
|
result_export_path=result_export_path,
|
|
result_export_format=result_export_format,
|
|
)
|
|
raise SystemExit(pipeline.run())
|
|
except ValueError as err:
|
|
click.echo(f"Error: {err}", err=True)
|
|
raise SystemExit(2) from err
|
|
except RuntimeError as err:
|
|
click.echo(f"Runtime error: {err}", err=True)
|
|
raise SystemExit(1) from err
|