fix: 修复10个关键bug提升系统稳定性和性能
1. YOLO11输出解析错误: 移除不存在的objectness行，正确使用class_scores.max() 2. CPU NMS逻辑错误: keep_mask同时标记保留和抑制框导致NMS失效，改用独立suppressed集合 3. 坐标映射缺失: _build_tracks中scale_info未使用，添加revert_boxes还原到ROI裁剪空间 4. batch=1限制: 恢复真正的动态batch推理(1~8)，BatchPreprocessor支持多图stack 5. 帧率控制缺失: _read_frame添加time.monotonic()间隔控制，按target_fps跳帧 6. 拉流推理耦合: 新增独立推理线程(InferenceWorker)，生产者-消费者模式解耦 7. 攒批形同虚设: 添加50ms攒批窗口+max_batch阈值，替代>=1立即处理 8. LeavePost双重等待: LEAVING确认后直接触发告警，不再进入OFF_DUTY二次等待 9. register_algorithm每帧调用: 添加_registered_keys缓存，O(1)快速路径跳过 10. GPU context线程安全: TensorRT infer()内部加锁，防止多线程CUDA context竞争 附带修复: - reset_algorithm中未定义algorithm_type变量(NameError) - update_roi_params中循环变量key覆盖外层key - AlertInfo缺少bind_id字段(TypeError) - _logger.log_alert在标准logger上不存在(AttributeError) - AlarmStateMachine死锁(Lock改为RLock) - ROICropper.create_mask坐标解析错误 - 更新测试用例适配新API Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -104,55 +104,55 @@ class NMSProcessor:
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""CPU 向量化 NMS"""
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order = np.argsort(scores)[::-1]
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keep_mask = np.zeros(len(boxes), dtype=bool)
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i = 0
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while i < len(order) and i < max_output_size:
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keep = []
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suppressed = np.zeros(len(boxes), dtype=bool)
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for i in range(len(order)):
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if len(keep) >= max_output_size:
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break
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idx = order[i]
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if keep_mask[idx]:
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i += 1
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if suppressed[idx]:
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continue
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keep_mask[idx] = True
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keep.append(idx)
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remaining = order[i + 1:]
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if len(remaining) == 0:
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break
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remaining_mask = ~keep_mask[remaining]
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if not np.any(remaining_mask):
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remaining = remaining[~suppressed[remaining]]
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if len(remaining) == 0:
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break
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remaining = remaining[remaining_mask]
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xx1 = np.maximum(boxes[idx, 0], boxes[remaining, 0])
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yy1 = np.maximum(boxes[idx, 1], boxes[remaining, 1])
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xx2 = np.minimum(boxes[idx, 2], boxes[remaining, 2])
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yy2 = np.minimum(boxes[idx, 3], boxes[remaining, 3])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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areas = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
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ovr = inter / (areas[idx] + areas[remaining] - inter + 1e-6)
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suppress_mask = ovr > self.nms_threshold
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for j in np.where(suppress_mask)[0]:
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keep_mask[remaining[j]] = True
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i += 1
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keep_indices = np.where(keep_mask)[0]
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if len(keep_indices) > max_output_size:
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top_k = np.argsort(scores[keep_indices])[::-1][:max_output_size]
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keep_indices = keep_indices[top_k]
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suppressed[remaining[suppress_mask]] = True
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keep_indices = np.array(keep, dtype=np.int32) if keep else np.array([], dtype=np.int32)
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if len(keep_indices) == 0:
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return (
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np.array([], dtype=np.int32),
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np.array([]),
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np.array([])
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)
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return (
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keep_indices.astype(np.int32),
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keep_indices,
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scores[keep_indices],
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class_ids[keep_indices] if class_ids is not None else np.array([])
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)
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@@ -448,7 +448,7 @@ class AlarmStateMachine:
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self.alert_cooldown = alert_cooldown
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self._states: Dict[str, AlarmState] = {}
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self._lock = threading.Lock()
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self._lock = threading.RLock()
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self._logger = get_logger("postprocessor")
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@@ -513,9 +513,9 @@ class AlarmStateMachine:
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self._logger.log_alert(
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"detection_threshold_reached",
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roi_id=roi_id,
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camera_id="",
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confidence=None
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"",
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roi_id,
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None
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)
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return {
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@@ -669,32 +669,33 @@ class PostProcessor:
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return np.array([]), np.array([]), np.array([])
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boxes_xywh = output[0:4, :].T
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obj_conf = output[4, :]
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person_scores = output[5, :]
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scores = obj_conf * person_scores
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# YOLO11 输出格式: [4+num_classes, 8400]
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# 前4行是 xywh，后80行是各类别分数，没有单独的 objectness 行
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class_scores = output[4:, :] # [num_classes, 8400]
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scores = class_scores.max(axis=0) # 取各类别最大分数
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class_ids = class_scores.argmax(axis=0) # 对应类别ID
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coarse_mask = scores > prefilter_threshold
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if not np.any(coarse_mask):
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return np.array([]), np.array([]), np.array([])
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boxes = boxes_xywh[coarse_mask]
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scores_coarse = scores[coarse_mask]
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class_ids_filtered = class_ids[coarse_mask]
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valid_count = len(boxes)
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np.copyto(self._buffer_boxes_xywh[:valid_count], boxes)
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self._buffer_xyxy[:valid_count, 0] = boxes[:, 0] - boxes[:, 2] / 2
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self._buffer_xyxy[:valid_count, 1] = boxes[:, 1] - boxes[:, 3] / 2
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self._buffer_xyxy[:valid_count, 2] = boxes[:, 0] + boxes[:, 2] / 2
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self._buffer_xyxy[:valid_count, 3] = boxes[:, 1] + boxes[:, 3] / 2
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self._buffer_class_ids[:valid_count] = 0
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self._buffer_class_ids[:valid_count] = class_ids_filtered
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return (
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self._buffer_xyxy[:valid_count].copy(),
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scores_coarse.astype(np.float32),
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@@ -762,9 +763,10 @@ class PostProcessor:
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continue
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boxes_xywh = output[0:4, :].T
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obj_conf = output[4, :]
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person_scores = output[5, :]
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scores = obj_conf * person_scores
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# YOLO11: 无 objectness，直接取各类别最大分数
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class_scores = output[4:, :]
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scores = class_scores.max(axis=0)
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class_ids_raw = class_scores.argmax(axis=0)
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coarse_mask = scores > 0.3
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if not np.any(coarse_mask):
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@@ -773,7 +775,7 @@ class PostProcessor:
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boxes = boxes_xywh[coarse_mask]
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scores_coarse = scores[coarse_mask]
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class_ids = np.zeros(len(boxes), dtype=np.int32)
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class_ids = class_ids_raw[coarse_mask].astype(np.int32)
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valid_count = len(boxes)
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