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feat: 支持按算法独立配置置信度阈值

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通过 ALGO_CONF_{ALGO_CODE} 环境变量为每个算法设置独立的 conf_threshold，
未配置的算法回退到全局 CONF_THRESHOLD。推理过程零改动，仅后处理过滤阶段
按 bind.algo_code 使用对应阈值。

当前配置：离岗=0.4（降低漏检），入侵=0.5（减少误报）。

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
16337
2026-03-08 17:31:12 +08:00
parent 3d88dfc1c6
commit 7a5ddef2f6
3 changed files with 61 additions and 17 deletions
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37
config/settings.py
View File

@@ -5,7 +5,7 @@
import os
from dataclasses import dataclass, field
from typing import List, Optional
from typing import Dict, List, Optional
@dataclass
@@ -117,10 +117,17 @@ class InferenceConfig:
input_width: int = 480
input_height: int = 480
batch_size: int = 1
conf_threshold: float = 0.4
nms_threshold: float = 0.45
conf_threshold: float = 0.45
nms_threshold: float = 0.5
device_id: int = 0
fp16_mode: bool = True
# 按算法类型覆盖置信度阈值，key=algo_code, value=threshold
# 未命中时回退到 conf_threshold
algo_conf_thresholds: Dict[str, float] = field(default_factory=dict)
def get_conf_threshold(self, algo_code: str) -> float:
"""获取指定算法的置信度阈值，未配置则回退全局值"""
return self.algo_conf_thresholds.get(algo_code, self.conf_threshold)
# ===================== Debug / Local Sync =====================
@@ -273,8 +280,9 @@ class Settings:
input_width=int(os.getenv("INPUT_WIDTH", "480")),
input_height=int(os.getenv("INPUT_HEIGHT", "480")),
batch_size=int(os.getenv("BATCH_SIZE", "4")),
conf_threshold=float(os.getenv("CONF_THRESHOLD", "0.4")),
nms_threshold=float(os.getenv("NMS_THRESHOLD", "0.45")),
conf_threshold=float(os.getenv("CONF_THRESHOLD", "0.45")),
nms_threshold=float(os.getenv("NMS_THRESHOLD", "0.5")),
algo_conf_thresholds=self._parse_algo_conf_thresholds(),
)
self.config_sync_mode = os.getenv("CONFIG_SYNC_MODE", "LOCAL").upper()
@@ -297,6 +305,25 @@ class Settings:
# 使用 COCO 类别名称
self.class_names = COCO_CLASS_NAMES
@staticmethod
def _parse_algo_conf_thresholds() -> Dict[str, float]:
"""解析 ALGO_CONF_* 环境变量，返回 {algo_code: threshold} 字典
环境变量命名规则: ALGO_CONF_{ALGO_CODE}，如:
ALGO_CONF_LEAVE_POST=0.35
ALGO_CONF_INTRUSION=0.55
"""
prefix = "ALGO_CONF_"
result = {}
for key, value in os.environ.items():
if key.startswith(prefix) and value:
algo_code = key[len(prefix):].lower()
try:
result[algo_code] = float(value)
except ValueError:
pass
return result
def _parse_working_hours(self) -> List[dict]:
"""解析工作时间配置"""
working_hours_str = os.getenv("WORKING_HOURS", "")

27
core/postprocessor.py
View File

@@ -35,7 +35,7 @@ class NMSProcessor:
可选 GPU 加速 (torchvision.ops.nms)
"""
def __init__(self, nms_threshold: float = 0.45, use_gpu: bool = False):
def __init__(self, nms_threshold: float = 0.5, use_gpu: bool = False):
self.nms_threshold = nms_threshold
self.use_gpu = use_gpu and _HAS_TORCH
self._logger = get_logger("postprocessor")
@@ -162,7 +162,7 @@ class NMSProcessor:
boxes: np.ndarray,
scores: np.ndarray,
class_ids: np.ndarray,
conf_threshold: float = 0.5,
conf_threshold: float = 0.45,
max_output_size: int = 300
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
@@ -579,8 +579,8 @@ class PostProcessor:
"alert_cooldown": 300,
}
self.nms_threshold = config.get("nms_threshold", 0.45)
self.conf_threshold = config.get("conf_threshold", 0.5)
self.nms_threshold = config.get("nms_threshold", 0.5)
self.conf_threshold = config.get("conf_threshold", 0.45)
MAX_DETECTIONS = 8400
self._buffer_xyxy = np.zeros((MAX_DETECTIONS, 4), dtype=np.float32)
@@ -707,22 +707,24 @@ class PostProcessor:
batch_outputs: List[np.ndarray],
batch_size: int,
conf_threshold: Optional[float] = None,
nms_threshold: Optional[float] = None
nms_threshold: Optional[float] = None,
per_item_conf_thresholds: Optional[List[float]] = None,
) -> List[Tuple[np.ndarray, np.ndarray, np.ndarray]]:
"""
批量处理多 ROI 检测结果
Args:
batch_outputs: 模型输出 (可能是 [output] 或单个 batch 数组)
batch_size: batch 大小
conf_threshold: 置信度阈值
nms_threshold: NMS阈值
per_item_conf_thresholds: 每个 batch item 独立的置信度阈值列表，
长度必须等于 batch_size。传入时忽略 conf_threshold 参数。
Returns:
List of (检测框, 置信度, 类别ID) per ROI
"""
if conf_threshold is None:
conf_threshold = self.conf_threshold
default_conf = conf_threshold if conf_threshold is not None else self.conf_threshold
if nms_threshold is None:
nms_threshold = self.nms_threshold
@@ -793,11 +795,16 @@ class PostProcessor:
self._buffer_xyxy[:valid_count, 2] = boxes[:, 0] + boxes[:, 2] / 2
self._buffer_xyxy[:valid_count, 3] = boxes[:, 1] + boxes[:, 3] / 2
item_conf = (
per_item_conf_thresholds[batch_idx]
if per_item_conf_thresholds is not None
else default_conf
)
keep_boxes, keep_scores, keep_classes = nms_processor.process_with_confidence_filter(
self._buffer_xyxy[:valid_count],
scores_coarse.astype(np.float32),
class_ids,
conf_threshold
item_conf
)
results.append((keep_boxes, keep_scores, keep_classes))

14
main.py
View File

@@ -598,14 +598,24 @@ class EdgeInferenceService:
self._logger.debug(f"[推理诊断] batch_data shape={batch_data.shape}, outputs={shapes}, 耗时={inference_time_ms:.1f}ms")
batch_size = len(chunk)
# 按算法类型获取每个 item 的独立置信度阈值
per_item_conf = [
self._settings.inference.get_conf_threshold(item[2].algo_code)
for item in chunk
]
batch_results = self._postprocessor.batch_process_detections(
outputs,
batch_size,
conf_threshold=self._settings.inference.conf_threshold
per_item_conf_thresholds=per_item_conf,
)
total_detections = sum(len(r[0]) for r in batch_results)
self._logger.debug(f"[推理] batch_size={batch_size}, 总检测数={total_detections}, conf_thresh={self._settings.inference.conf_threshold}")
self._logger.debug(
f"[推理] batch_size={batch_size}, 总检测数={total_detections}, "
f"conf_thresholds={per_item_conf}"
)
for idx, (camera_id, roi, bind, frame, _, scale_info) in enumerate(chunk):
boxes, scores, class_ids = batch_results[idx]