新增双目yolo实现python代码

utils/stereo_yolo/frontend.py

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+"""StereoYoloFrontend —— 双目检测前端主类（需求说明 §2/§3）。
+
+单帧流程：左右图（已校正、原生分辨率）→ 左右并发检测 → 出口自检 + 类别
+白名单 → 每目封装 DetectionFrame（共用 frame_id，§2.3 同帧契约）。
+
+前端**无跨帧状态**：没有历史就没有外推，结构性满足"只送本帧原生实测框"
+的输入纯净性硬约束（§2.5 第 1 条）。
+"""
+
+import logging
+import math
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Optional, Sequence, Tuple
+
+import numpy as np
+
+from stereo_ranger.types import Detection, DetectionFrame
+
+from .config import StereoYoloConfig
+from .detector import UltralyticsDetector
+from .warn_throttle import WarnThrottle
+
+logger = logging.getLogger("stereo_yolo")
+
+_EYE_LABELS = ("左目", "右目")
+
+
+class StereoYoloFrontend:
+    """图像 → 符合 stereo_ranger 输入契约的检测框（§2 接口契约）。
+
+    ``detector`` 可注入替换：接口为 ``detect(img) -> list[Detection]``
+    （单测用假检测器驱动，§6 输出合法性/失败隔离用例无需真实模型）。
+    """
+
+    def __init__(self, config: StereoYoloConfig, detector: Optional[object] = None) -> None:
+        self._cfg = config
+        self._detector = detector if detector is not None else UltralyticsDetector(config)
+        self._throttle = WarnThrottle()
+        self._executor = (
+            ThreadPoolExecutor(max_workers=2, thread_name_prefix="stereo_yolo")
+            if config.concurrency == "parallel"
+            else None
+        )
+
+    @property
+    def config(self) -> StereoYoloConfig:
+        return self._cfg
+
+    def detect(
+        self,
+        left_img: np.ndarray,
+        right_img: np.ndarray,
+        frame_id: int,
+    ) -> Tuple[DetectionFrame, DetectionFrame]:
+        """左右各输出一个 DetectionFrame；两帧共用同一 frame_id（§2.3）。"""
+        if left_img.shape != right_img.shape:
+            self._warn(
+                "shape_mismatch",
+                f"左右图尺寸不一致（{left_img.shape} vs {right_img.shape}），整帧拒收输出空（§2.2 第 2 条）",
+            )
+            return DetectionFrame(frame_id, ()), DetectionFrame(frame_id, ())
+
+        if self._executor is not None:
+            left_future = self._executor.submit(self._detect_eye, left_img, _EYE_LABELS[0])
+            right_future = self._executor.submit(self._detect_eye, right_img, _EYE_LABELS[1])
+            left, right = left_future.result(), right_future.result()
+        else:
+            left = self._detect_eye(left_img, _EYE_LABELS[0])
+            right = self._detect_eye(right_img, _EYE_LABELS[1])
+
+        return DetectionFrame(frame_id, tuple(left)), DetectionFrame(frame_id, tuple(right))
+
+    def close(self) -> None:
+        """释放并发线程池（前端不再使用时调用）。"""
+        if self._executor is not None:
+            self._executor.shutdown(wait=True)
+
+    # ---------------- 内部 ----------------
+
+    def _detect_eye(self, image: np.ndarray, eye_label: str) -> List[Detection]:
+        try:
+            raw = self._detector.detect(image)
+        except Exception as exc:  # 单目失败隔离：不阻塞整体，该目输出空帧列表（§5 失败处理）
+            self._warn(f"infer_failed:{eye_label}", f"{eye_label}推理失败，该目输出空帧列表：{exc!r}")
+            return []
+        return self._sanitize(raw)
+
+    def _sanitize(self, detections: Sequence[Detection]) -> List[Detection]:
+        """出口自检（§2.2 第 6 条）：丢弃非有限值 / x2<=x1 / y2<=y1 的框；白名单过滤。"""
+        kept: List[Detection] = []
+        dropped = 0
+        whitelist = self._cfg.class_whitelist
+        for detection in detections:
+            if not _is_valid_box(detection):
+                dropped += 1
+                continue
+            if whitelist is not None and detection.class_id not in whitelist:
+                continue
+            kept.append(detection)
+        if dropped:
+            self._warn("degenerate_box", f"丢弃退化检测框 {dropped} 个（非有限值或 x2<=x1/y2<=y1）")
+        return kept
+
+    def _warn(self, category: str, message: str) -> None:
+        if self._throttle.should_emit(category):
+            logger.warning("%s（累计 %d 次）", message, self._throttle.count(category))
+
+
+def _is_valid_box(detection: Detection) -> bool:
+    values = (detection.x1, detection.y1, detection.x2, detection.y2)
+    if not all(math.isfinite(value) for value in values):
+        return False
+    return detection.x2 > detection.x1 and detection.y2 > detection.y1