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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@@ -127,8 +127,8 @@ class ROICropper:
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if roi.roi_type == ROIType.RECTANGLE:
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if len(roi.coordinates) >= 2:
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x1, y1 = int(roi.coordinates[0])
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x2, y2 = int(roi.coordinates[1])
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x1, y1 = int(roi.coordinates[0][0]), int(roi.coordinates[0][1])
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x2, y2 = int(roi.coordinates[1][0]), int(roi.coordinates[1][1])
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x1, x2 = sorted([x1, x2])
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y1, y2 = sorted([y1, y2])
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mask[y1:y2, x1:x2] = 255
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@@ -225,10 +225,10 @@ class LetterboxPreprocessor:
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class BatchPreprocessor:
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"""Batch预处理器类 (batch=1)"""
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BATCH_SIZE = 1
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"""Batch预处理器类 (支持动态 batch 1~8)"""
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MAX_BATCH_SIZE = 8
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def __init__(
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self,
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target_size: Tuple[int, int] = (480, 480),
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@@ -236,12 +236,12 @@ class BatchPreprocessor:
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):
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self.target_size = target_size
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self.fp16_mode = fp16_mode
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self.batch_size = self.BATCH_SIZE
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self.max_batch_size = self.MAX_BATCH_SIZE
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self._logger = get_logger("preprocessor")
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self._logger.info(
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f"Batch预处理器: batch={self.batch_size}, "
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f"Batch预处理器: max_batch={self.max_batch_size}, "
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f"target_size={target_size}, fp16={fp16_mode}"
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)
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@@ -272,23 +272,39 @@ class BatchPreprocessor:
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images: List[np.ndarray]
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) -> Tuple[np.ndarray, List[Tuple[float, float, float, float]]]:
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"""
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预处理批次图像 (batch=1)
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预处理批次图像 (支持动态 batch)
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Args:
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images: 图像列表 (只处理第一帧)
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images: 已经过 letterbox 的图像列表
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Returns:
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tuple: (批次数据 [1, 3, H, W], 缩放信息列表)
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tuple: (批次数据 [N, 3, H, W], 缩放信息列表)
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"""
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if not images:
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raise ValueError("Empty images list")
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letterbox = LetterboxPreprocessor(self.target_size)
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processed, scale_info = letterbox.preprocess(images[0])
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batch_data = self.preprocess_single(processed)
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return batch_data, [scale_info]
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processed_list = []
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scale_infos = []
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for img in images:
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processed, scale_info = letterbox.preprocess(img)
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processed_list.append(processed)
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scale_infos.append(scale_info)
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# 逐帧 normalize + transpose，然后 stack 成 [N, 3, H, W]
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batch_frames = []
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for processed in processed_list:
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normalized = processed.astype(np.float32) / 255.0
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transposed = np.transpose(normalized, (2, 0, 1))
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batch_frames.append(transposed)
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batch_data = np.stack(batch_frames)
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if self.fp16_mode:
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batch_data = batch_data.astype(np.float16)
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return batch_data, scale_infos
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class ImagePreprocessor:
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@@ -323,7 +339,7 @@ class ImagePreprocessor:
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self._logger.info(
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f"图像预处理器初始化完成: "
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f"输入尺寸 {config.input_width}x{config.input_height}, "
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f"Batch大小 {self._batch_preprocessor.batch_size}, "
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f"最大Batch {self._batch_preprocessor.max_batch_size}, "
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f"FP16模式 {config.fp16_mode}"
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)
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@@ -359,31 +375,36 @@ class ImagePreprocessor:
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rois: Optional[List[Optional[ROIInfo]]] = None
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) -> Tuple[np.ndarray, List[Tuple[float, float, float, float]]]:
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"""
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预处理批次图像，自动 padding 到 batch=4
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预处理批次图像
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Args:
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images: 原始图像列表
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rois: 可选的ROI配置列表
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Returns:
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tuple: (批次数据 [4, 3, H, W], 缩放信息列表)
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tuple: (批次数据 [N, 3, H, W], 缩放信息列表)
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"""
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from core.tensorrt_engine import pad_to_batch4
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if rois is None:
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rois = [None] * len(images)
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processed_images = []
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scale_info_list = []
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for image, roi in zip(images, rois):
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processed, scale_info = self.preprocess_single(image, roi)
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processed_images.append(processed)
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scale_info_list.append(scale_info)
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batch_data = self._batch_preprocessor.preprocess_batch(processed_images)
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return batch_data, scale_info_list
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if roi is not None:
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cropped = self._cropper.crop(image, roi)
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if cropped is None:
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cropped = image
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else:
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cropped = image
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processed_images.append(cropped)
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# BatchPreprocessor 处理 letterbox + normalize + stack
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batch_data, batch_scale_infos = self._batch_preprocessor.preprocess_batch(
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processed_images
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)
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return batch_data, batch_scale_infos
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def revert_boxes(
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self,
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@@ -408,7 +429,7 @@ class ImagePreprocessor:
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"config": {
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"input_width": self.config.input_width,
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"input_height": self.config.input_height,
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"batch_size": self._batch_preprocessor.batch_size,
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"batch_size": self._batch_preprocessor.max_batch_size,
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"fp16_mode": self.config.fp16_mode,
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},
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}
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